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Cambridge, Massachusetts 02139

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Avoided Energy Supply Costs in

New England: 2013 Report

Prepared for the Avoided‐Energy‐Supply‐Component

(AESC) Study Group

July 12, 2013

AUTHORS

Rick Hornby

Paul Chernick

David White, PhD

John Rosenkranz

Ron Denhardt

Elizabeth A. Stanton, PhD

Jason Gifford

Bob Grace   

Max Chang

Patrick Luckow

Thomas Vitolo, PhD

Patrick Knight

Ben Griffiths

Bruce Biewald

   
Table of Contents

Chapter 1: Executive Summary........................................................................... 1‐1

1.1 Background to Study ......................................................................................................... 1‐2

1.2 Avoided Costs of Electricity ............................................................................................... 1‐3

1.3 Avoided Natural Gas Costs............................................................................................... 1‐13

1.4 Demand Reduction Induced Price Effects (DRIPE) ............................................................ 1‐18

1.5 Avoided Cost of Fuel Oil and Other Fuels......................................................................... 1‐22

Chapter 2: Avoided Natural Gas Costs................................................................ 2‐1

2.1 Overview of New England Gas Market............................................................................... 2‐1

2.2 Henry Hub Gas Price Forecast............................................................................................ 2‐5

2.3 Wholesale Natural Gas Prices in New England ................................................................. 2‐36

2.4 Avoided Natural Gas Costs by End Use............................................................................. 2‐41

Chapter 3: Avoided Fuel Oil Costs and Avoided Costs of Other Fuels by Sector .. 3‐1

3.1 Introduction ...................................................................................................................... 3‐1

3.2 Forecast of Crude Oil Prices............................................................................................... 3‐1

3.3 Forecast of Electric‐Generation Fuel Prices in New England ............................................... 3‐2

3.4 Forecast of Petroleum Prices in the Residential, Commercial, and  Industrial Sectors......... 3‐3

3.5 Avoided Costs of Other Residential Fuels........................................................................... 3‐5

Chapter 4: Embedded and Non‐Embedded Environmental Costs ....................... 4‐1

4.1 Introduction ...................................................................................................................... 4‐1

4.2 Environmental Regulations: Embedded Costs.................................................................... 4‐2

4.3 Non‐Embedded Environmental Costs............................................................................... 4‐12

4.4 Value of Mitigating Significant Pollutants........................................................................ 4‐25

4.5 Discussion of Non‐Embedded NOx Costs.......................................................................... 4‐38

4.6 Emissions from Hydraulic Fracking................................................................................... 4‐40

4.7 Compliance with State‐Specific Climate Plans.................................................................. 4‐46

Chapter 5: Avoided Electric Capacity Costs......................................................... 5‐1

5.1 Introduction ...................................................................................................................... 5‐1

5.2 Basic Assumptions and Methodologies Shared with Electric Energy Forecast..................... 5‐2
5.3 Additional Assumptions Specific to Electric Capacity........................................................ 5‐23

5.4 Wholesale Market Prices for Electric Capacity ................................................................. 5‐25

5.5 Wholesale Electric Capacity Market Simulation Model and Inputs................................... 5‐31

5.6 Computation of Avoided Capacity Price........................................................................... 5‐37

5.7 Adjustment of Capacity Costs for Losses on ISO‐Administered Pooled Transmission Facilities5‐41

Chapter 6: Avoided Electric Energy Costs ........................................................... 6‐1

6.1 Introduction ...................................................................................................................... 6‐1

6.2 Wholesale Energy Markets................................................................................................ 6‐1

6.3 Wholesale Electric Energy Market Simulation Model and Inputs........................................ 6‐3

6.4 Avoided Electric Energy Costs.......................................................................................... 6‐12

6.5 Avoided Cost of Compliance with RPS.............................................................................. 6‐29

Chapter 7: Demand Reduction Induced Price Effects.......................................... 7‐1

7.1 Introduction ...................................................................................................................... 7‐1

7.2 Electricity DRIPE ................................................................................................................ 7‐2

7.3 Natural Gas DRIPE ........................................................................................................... 7‐17

7.4 Cross‐Fuel Market Price Effects........................................................................................ 7‐27

Chapter 8: Sensitivity Analyses .......................................................................... 8‐1

8.1 Introduction ...................................................................................................................... 8‐1

Bibliography

Appendix A: Usage Instructions

Appendix B: Avoided Electricity Cost Results

Appendix C: Avoided Natural Gas Cost Results

Appendix D: Avoided Costs of Other Fuels

Appendix E: Common Financial Parameters for AESC 2013

Appendix F: Selected Input Assumptions

Appendix G: Survey of Transmission and Distribution Capacity Values

Appendix H: Pooled Transmission Losses Methodology
Table of Exhibits

Exhibit 1‐1. Relationship of chapters to forecast components and subtasks............................................................ 1‐3

Exhibit 1‐2. Illustration of Avoided Electricity Cost Components, AESC 2013 vs. AESC 2011 (WCMA Zone, Summer

On‐Peak, 15‐Year Levelized Results, 2013$)..................................................................................................... 1‐6

Exhibit 1‐3. AESC 2013 Capacity Requirements vs. Resources (Base Case), MW...................................................... 1‐7

Exhibit 1‐4. New England Generation Mix from AESC 2013 (GWh)........................................................................... 1‐8

Exhibit 1‐5. AESC 2013 vs. AESC 2011 – All‐Hours Prices for West‐Central Massachusetts (2013$/kWh)................ 1‐9

Exhibit 1‐6. Avoided Electric Energy Costs, AESC 2013 vs. AESC 2011 (15‐year levelized, 2013$).......................... 1‐10

Exhibit 1‐7. Avoided Electric Energy Costs for 2013: Change from AESC 2011 (expressed in 2013$/kWh and

percentage values).......................................................................................................................................... 1‐11

Exhibit 1‐8. Actual and Projected Henry Hub Prices (2013$/MMBtu)..................................................................... 1‐15

Exhibit 1‐9. Comparison of Avoided Gas Costs by End Use Assuming No Avoidable Retail Margin, AESC 2013 vs.

AESC 2011 (15 year‐levelized, 2013$/MMBtu except where indicated as 2011$/MMBtu)........................... 1‐16

Exhibit 1‐10. Comparison of Avoided Gas Costs by End Use Assuming Some Avoidable Retail Margin, AESC 2013 vs.

AESC 2011 (15‐year levelized, 2013$/MMBtu except where indicated as 2011$/MMBtu)........................... 1‐17

Exhibit 1‐11. DRIPE Overview .................................................................................................................................. 1‐18

Exhibit 1‐12. State Supply DRIPE Benefit (2013$ per MMBtu for installation in year 2014)................................... 1‐19

Exhibit 1‐13. 15‐year Levelized Natural Gas Supply DRIPE by State (2013$/MMBtu)............................................. 1‐19

Exhibit 1‐14. 15‐year Levelized Gas‐Cross DRIPE for Heating Load 2014 Installation ($/MMBtu).......................... 1‐20

Exhibit 1‐15. Annual Gas Price Benefit per MWh Saved.......................................................................................... 1‐21

Exhibit 1‐16. Electric Cross‐Fuel DRIPE ($/MWh for 2014 Installation)................................................................... 1‐21

Exhibit 1‐17. Comparison of Avoided Costs of Other Retail Fuels (15‐year levelized, 2013$) ................................ 1‐22

Exhibit 2‐1. Annual Gas Use (Tcf) in New England, Actual and AEO 2013 Reference Case projection (Tcf).............. 2‐2

Exhibit 2‐2. New England Natural Gas Delivered Monthly to Customer by Class (MMcf/day)................................. 2‐3

Exhibit 2‐3. Henry Hub Prices ‐ Actual and Projected (2013 $/MMBtu).................................................................... 2‐9

Exhibit 2‐4. Natural Gas Wellhead Prices (2013$/MMcf)........................................................................................ 2‐12

Exhibit 2‐5. Henry Hub and Gas Rig Count .............................................................................................................. 2‐13

Exhibit 2‐6. Oil and Gas Rig Count ........................................................................................................................... 2‐14

Exhibit 2‐7. Breakeven 10% IRR Henry Hub Prices (2013$/MMBtu)....................................................................... 2‐19

Exhibit 2‐8. Costs of Selected Plays in 2020 (2013$/MMBtu) ................................................................................. 2‐20

Exhibit 2‐9. Breakeven Prices 10% ATROR ($/MMBtu) ........................................................................................... 2‐22

Exhibit 2‐10. Potential Impact of Fracturing Regulation on the Breakeven Price of Natural Gas (2013$).............. 2‐25

Exhibit 2‐11. Comparison of Price Projectons (2013$/MMBtu).............................................................................. 2‐27

Exhibit 2‐12. United States Gas Consumption and Supply: 2012 vs. AEO 2012 Projections for 2020 (Tcf/year).... 2‐28

Exhibit 2‐13. Comparison of Henry Hub Natural Gas Price Forecasts (2013$/MMBtu).......................................... 2‐29

Exhibit 2‐14. Forecasts of AESC 2013 Henry Hub Natural Gas Prices: Base, High and Low (2013$/MMBtu).......... 2‐30

Exhibit 2‐15. Dry Shale Gas Production: Actual and Projected (Tcf/year)............................................................... 2‐32

Exhibit 2‐16. Monthly Henry Hub Prices, Historical (EIA) and Projected (2013 Dollars per MMBtu)...................... 2‐33

Exhibit 2‐17. Henry Hub Average Weekly Natural‐Gas Prices, Actual and Futures, Jan 2000 – Dec 2015.............. 2‐34

Exhibit 2‐18. Range of Potential Weekly Price Volatility versus the Forecast Base Case Annual Average Henry Hub

Natural Gas Price(2013$/MMBtu).................................................................................................................. 2‐36
Exhibit 2‐19. AESC 2013 New England Wholesale Gas Price Forecast (2013$/MMBtu) ......................................... 2‐39

Exhibit 2‐20. Natural Gas Wholesale Price Forecasts, 2013$/MMBtu .................................................................... 2‐40

Exhibit 2‐21. Comparison of Algonquin Citygate and Tennessee Zone 6 Indexes ($/MMBtu)................................ 2‐40

Exhibit 2‐22. Comparison of Avoided Gas Costs by End Use Assuming No Avoidable Retail Margin, AESC 2013 vs.

AESC 2011 (15‐year levelized, 2013$/MMBtu except where indicated as 2011$/MMBtu)........................... 2‐41

Exhibit 2‐23. Comparison of Avoided Gas Costs by End Use Assuming Some Avoidable Retail Margin, AESC 2013 vs.

AESC 2011 (15‐year levelized, 2013$/MMBtu except where indicated as 2011$/MMBtu)........................... 2‐42

Exhibit 2‐24. Representative Segmented Load Duration Curve (MMcf/day).......................................................... 2‐44

Exhibit 2‐25. Distribution of Gas Use by Load Segment .......................................................................................... 2‐45

Exhibit 2‐26. New England LDC Gas Supply Capability for the 2012‐13 Planning Year, MMcf/day ........................ 2‐48

Exhibit 2‐27. New England LDC Firm Requirements by Load Segment for 2013, MMcf/day.................................. 2‐50

Exhibit 2‐28. Gas Supply Resources Included in Avoided Cost Analysis.................................................................. 2‐51

Exhibit 2‐29. Marginal Resource Costs for Southern New England Region, 2016................................................... 2‐52

Exhibit 2‐30. Sample Calculation of 2016 Residential Heating for Southern New England Region......................... 2‐52

Exhibit 2‐31. Estimated Avoidable LDC Margins (2013$/MMBtu) .......................................................................... 2‐54

Exhibit 2‐32. Avoided Cost of Gas Delivered to an End‐Use Load, Assuming Some Retail Margin is Avoidable;

Southern New England (2013$/MMBtu)........................................................................................................ 2‐55

Exhibit 2‐33. Avoided Cost of Gas Delivered to an End‐Use Load, Assuming Some Retail Margin is Avoidable;

Northern New England (2013$/MMBtu)........................................................................................................ 2‐56

Exhibit 2‐34. Avoided Cost of Gas Delivered to an End‐Use Load, Assuming some Retail Margin is Avoidable;

Vermont (2013$/MMBtu)............................................................................................................................... 2‐56

Exhibit 2‐35. Avoided Cost of Gas Delivered to LDCs by End‐Use Load Type Assuming No Avoidable Retail Margin,

Southern New England(2013$/MMBtu)......................................................................................................... 2‐57

Exhibit 2‐36. Avoided Cost of Gas Delivered to LDCs by End‐Use Load Type Assuming No Retail Margin, Northern

New England (2013$/MMBtu)........................................................................................................................ 2‐58

Exhibit 2‐37. Avoided Cost of Gas Delivered to LDCs by End‐Use Load Type Assuming No Retail Margin, Vermont

(2013$/MMBtu).............................................................................................................................................. 2‐58

Exhibit 2‐38. Comparison of AESC 2013 and AESC 2011 Avoided Cost of Gas Delivered to Retail Customers by End

Use Assuming Some Retail Margin Avoidable (2013$/MMBtu, unless noted) .............................................. 2‐60

Exhibit 2‐39. Comparison of AESC 2013 and AESC 2011 Avoided Cost of Gas Delivered to Retail Customers by End

Use Assuming No Retail Margin Avoidable (2013$/MMBtu, unless noted)................................................... 2‐61

Exhibit 2‐40. Comparison between AESC 2011 and AESC 2013 of the Components of the Avoided Cost to a

Residential Heating Customer on Vermont Gas Systems in 2015 (2013$/MMBtu)....................................... 2‐62

Exhibit 3‐1. Low‐Sulfur‐Crude Prices, EIA vs. NYMEX as of March 15, 2013 (2013$ per bbl).................................... 3‐2

Exhibit 4‐1. Emission Allowance Prices per Short Ton (Constant 2013$ and Nominal Dollars) ................................ 4‐4

Exhibit 4‐2. U.S. Interagency Working Group Social Cost of Carbon (2013 dollars per short ton CO2)................... 4‐16

Exhibit 4‐3. Alternate Estimates of the Working Group's Social Cost of Carbon (2013 dollars per short ton CO2). 4‐18

Exhibit 4‐4. Marginal Abatement Technologies and Associated Costs for the Year 2030....................................... 4‐20

Exhibit 4‐5. Summary Chart of Marginal Abatement Cost Studies.......................................................................... 4‐21

Exhibit 4‐6. Projected Costs of CCS (in 2013 dollars per short ton of CO2 avoided)................................................ 4‐22

Exhibit 4‐7. AESC 2013 Non‐Embedded CO2 Costs (2013 dollars per short ton CO2).............................................. 4‐24

Exhibit 4‐8. Determination of the Additional Cost of CO2 Emissions(2013$/short ton of CO2 equivalent)............. 4‐25

Exhibit 4‐9. 2013 New England Marginal Heat Rate by Pricing Period (Btu per kWh) ............................................ 4‐27

Exhibit 4‐10. 2013 New England Marginal Fuel by Percentage............................................................................... 4‐27
Exhibit 4‐11. 2013 New England Avoided CO2 Emissions by Modeling Zone and Pricing Period (lbs/MWh).......... 4‐28

Exhibit 4‐12. 2013 New England Avoided NOx Emissions by Modeling Zone and Pricing Period (lbs/MWh)......... 4‐28

Exhibit 4‐13. Emission Rates of Significant Pollutants............................................................................................. 4‐30

Exhibit 4‐14. Pollutant Emissions in New England from Natural Gas...................................................................... 4‐30

Exhibit 4‐15. Annual Pollutant Emission Values (2013$/MMBtu)........................................................................... 4‐32

Exhibit 4‐16. Emission Rates of Significant Pollutants from Fuel Oil ....................................................................... 4‐34

Exhibit 4‐17. Distillate Consumption, 2011 (Trillion BTU) ....................................................................................... 4‐35

Exhibit 4‐18. Pollutant Emissions in New England by Major Source ....................................................................... 4‐36

Exhibit 4‐19. Value of Pollutant Emissions from Fuel Oil in 2013............................................................................ 4‐37

Exhibit 4‐20. Annual NOx Emissions Rate in New England (lb/MWh)...................................................................... 4‐40

Exhibit 4‐21. Estimates of Methane Losses, Shown as a Percent of Lifetime Production....................................... 4‐43

Exhibit 4‐22. GWSA Required Electricity‐Sector Emission Reductions.................................................................... 4‐50

Exhibit 4‐23. Illustrative supply curve of CECP policies........................................................................................... 4‐51

Exhibit 4‐24. Illustration of Current Massachusetts Inventory Method.................................................................. 4‐53

Exhibit 4‐25. Illustration of “Synapse alternative example” Inventory Method...................................................... 4‐54

Exhibit 4‐26. Projected Massachusetts Retail Sales (GWh)..................................................................................... 4‐56

Exhibit 4‐27. GWSA Compliance Emissions Results................................................................................................. 4‐57

Exhibit 5‐1. ISO‐NE Summer Peak Load..................................................................................................................... 5‐4

Exhibit 5‐2. AESC 2013 New England Summer Peak Forecast................................................................................... 5‐5

Exhibit 5‐3. Existing Transmission Paths and Future Upgrades................................................................................. 5‐7

Exhibit 5‐4. Fossil Unit Retirements for Energy Modeling....................................................................................... 5‐10

Exhibit 5‐5. Recent Performance by New England Coal‐Fired Plants...................................................................... 5‐11

Exhibit 5‐6. Recent Performance by Gas‐ and Oil‐Fired Steam Plants..................................................................... 5‐16

Exhibit 5‐7. Summary of ISO‐NE Capacity Requirement and Supply (MW)............................................................. 5‐26

Exhibit 5‐8. FCA Price Results ($/kW‐month) .......................................................................................................... 5‐27

Exhibit 5‐9. Required Reserve Margins by Capacity Year and Filing Date ............................................................... 5‐34

Exhibit 5‐10. Derivation of Net Installed Capacity Requirement (MW)................................................................... 5‐35

Exhibit 5‐11. Supply Curves, FCA #2 to #7 ............................................................................................................... 5‐36

Exhibit 5‐12. FCA 7 Supply Curve, Extrapolated to Zero Price................................................................................. 5‐36

Exhibit 5‐13. FCA Price Extrapolation ...................................................................................................................... 5‐38

Exhibit 5‐14. FCA Price and Avoided Capacity Cost (2013 dollars).......................................................................... 5‐40

Exhibit 6‐1. Assumptions shared with other AESC 2013 analyses............................................................................. 6‐5

Exhibit 6‐2. Load Areas Used to Model New England ............................................................................................... 6‐7

Exhibit 6‐3. Summer Peak Forecast by Model Load Area.......................................................................................... 6‐8

Exhibit 6‐4. Energy Forecast by Model Load Area ..................................................................................................... 6‐9

Exhibit 6‐5. Characteristics of Market Analytics Generic Unit Additions................................................................. 6‐10

Exhibit 6‐6. New England Nuclear Unit Capacity and License Expirations .............................................................. 6‐10

Exhibit 6‐7. Base Case Capacity by Source (MW) .................................................................................................... 6‐13

Exhibit 6‐8. Base Case Generation by Source (GWh)............................................................................................... 6‐14

Exhibit 6‐9. Wholesale Energy Price Forecast for Central Massachusetts............................................................... 6‐15

Exhibit 6‐10. Wholesale Energy Price Forecast for Central Massachusetts (2013$/MWh)..................................... 6‐15

Exhibit 6‐11. Historical and Forecast Annual Wholesale Price Comparisons (2013$/MWh) .................................. 6‐16

Exhibit 6‐12. Forecast Comparison with AEO 2013 (2013$/MWh) ......................................................................... 6‐17
Exhibit 6‐13. ISO‐NE Control Area Monthly Real‐Time Prices ($/MWh) ................................................................. 6‐18

Exhibit 6‐14. AESC vs. NYMEX New England Futures............................................................................................... 6‐19

Exhibit 6‐15. Comparison of Futures and Base Case Annual Prices......................................................................... 6‐20

Exhibit 6‐16. 15‐Year Levelized Cost Comparisonfor Central Massachusetts (2013$/MWh) ................................. 6‐20

Exhibit 6‐17. Historic Monthly New England Electricity and Natural Gas Prices..................................................... 6‐22

Exhibit 6‐18. AESC 2013 vs. AESC 2011 Annual Gas Price Forecast Comparison..................................................... 6‐23

Exhibit 6‐19. AESC 2013 & AESC 2011 CO2 Price Forecasts..................................................................................... 6‐24

Exhibit 6‐20. AESC 2013 vs. AESC 2011 Summer On‐Peak Levelized Cost Comparisons......................................... 6‐25

Exhibit 6‐21. AESC 2013 vs. AESC 2011 Summer On‐Peak Prices for WCMA .......................................................... 6‐26

Exhibit 6‐22. New England Annual Peak Locational Price Forecast......................................................................... 6‐27

Exhibit 6‐23. AESC 2013 Modeling Zone and State Transmission Losses................................................................ 6‐28

Exhibit 6‐24. AESC 2013 Generation within Each Zone as a Percentage of Zonal Load .......................................... 6‐29

Exhibit 6‐25. Comparison of Avoided RPS Costs...................................................................................................... 6‐30

Exhibit 6‐26. REC and APS Prices: 2012 Annual Average and 2013 Year‐to‐Date (2013$/MWh) ........................... 6‐34

Exhibit 6‐27. Summary of New England RPS Demand............................................................................................. 6‐37

Exhibit 6‐28. Cumulative Incremental Supply of Class 1 Renewable Energy Resources in New England, by Fuel

Type(excludes resources already in the 2012 CELT Report)........................................................................... 6‐38

Exhibit 6‐29. Expected Distribution of New Renewable Energy between ISO‐NE and Adjacent Control Areas...... 6‐39

Exhibit 6‐30. REC Premium for Market Entry ($/MWh)........................................................................................... 6‐40

Exhibit 6‐31. Avoided RPS Cost by Class 2013$/MWh of Load Levelized Price Impact 2014 ‐ 2028 (2013$).......... 6‐41

Exhibit 7‐1. DRIPE Overview ...................................................................................................................................... 7‐1

Exhibit 7‐2. Hedged Capacity Requirement by State (MW)....................................................................................... 7‐5

Exhibit 7‐3. Load Subject to Market Capacity Prices by State (MW)......................................................................... 7‐5

Exhibit 7‐4. In‐State Capacity DRIPE by State (MW).................................................................................................. 7‐6

Exhibit 7‐5. Energy DRIPE Coefficients by State ........................................................................................................ 7‐8

Exhibit 7‐6. Hedged Energy Requirement by State (GWh)........................................................................................ 7‐9

Exhibit 7‐7. Load Subject to Market Energy Prices, by State, 2014 installations, GWh........................................... 7‐10

Exhibit 7‐8. Load Subject to Market Energy Prices, by State, 2015 installations..................................................... 7‐11

Exhibit 7‐9. Energy DRIPE Decay, 2014 installations ............................................................................................... 7‐12

Exhibit 7‐10. Energy DRIPE Decay, 2015 installations ............................................................................................. 7‐13

Exhibit 7‐11. Energy DRIPE Values for 2014 Installation On Peak Periods ($/MWh) .............................................. 7‐14

Exhibit 7‐12. Energy DRIPE Values for 2014 Installation Off Peak Periods ($/MWh).............................................. 7‐15

Exhibit 7‐13. Energy DRIPE Values for 2015 Installations On Peak Periods ($/MWh)............................................. 7‐16

Exhibit 7‐14. Energy DRIPE Values for 2015 Installations Off Peak Periods ($/MWh) ............................................ 7‐17

Exhibit 7‐15. Estimates of Gas Price Suppression from Reduced Usage for 2020................................................... 7‐18

Exhibit 7‐16. Gas‐Supply Elasticities........................................................................................................................ 7‐19

Exhibit 7‐17. AEO 2012 Gas‐Demand Sensitivity Cases for 2020............................................................................. 7‐20

Exhibit 7‐18. Gas Demand and Price Changes, AEO 2012, 2010$ ........................................................................... 7‐21

Exhibit 7‐19. Supply DRIPE Benefit in Annual MMBtu Load Reduction, by State.................................................... 7‐22

Exhibit 7‐20. State Supply DRIPE Benefit (2013$ per MMBtu for installation in year 2014)................................... 7‐23

Exhibit 7‐21. Basis from TETCo M‐3 to Algonquin Citygates as function of net TGP and Algonquin load, Dec 2011 to

Feb 2012 ......................................................................................................................................................... 7‐24
Exhibit 7‐22. Basis from TETCo M‐3 to Algonquin Citygates as function of net TGP and Algonquin load, Dec 2012 to

Feb 2013 ......................................................................................................................................................... 7‐25

Exhibit 7‐23. Gas Basis DRIPE Coefficients by Year, $/MMBtu reduction per Mcf/day saved ................................ 7‐26

Exhibit 7‐24. Gas Basis DRIPE Coefficients by Time‐Period by Year, $/MMBtu reduction per quad saved ............ 7‐26

Exhibit 7‐25. Summary of Gas‐Related DRIPE Effects.............................................................................................. 7‐28

Exhibit 7‐26. Decay of Electric DRIPE Caused by Reduction in Gas Use, 2014 Installation...................................... 7‐30

Exhibit 7‐27. Cross‐fuel DRIPE ($/TWh per MMBtu Gas Saved).............................................................................. 7‐30

Exhibit 7‐28. Gas‐to‐Electric Cross‐Fuel Heating DRIPE, $/MMBtu, 2014 gas efficiency installations.................... 7‐31

Exhibit 7‐29. Gas‐to‐Electric Cross‐Fuel Heating DRIPE, $/MMBtu, 2015 gas efficiency installations.................... 7‐31

Exhibit 7‐30. Annual Gas Price benefit per MWh saved.......................................................................................... 7‐32

Exhibit 7‐31. Annual Electric‐Gas‐Electric Price benefit per MWh saved, 2014 installations.................................. 7‐32

Exhibit 7‐32. Annual Electric‐Gas‐Electric Price benefit per MWh saved, 2015 installations.................................. 7‐33

Exhibit 8‐1. Henry Hub AESC 2013 Base Case and High Gas Case Prices (2013$/MMBtu)........................................ 8‐2

Exhibit 8‐2. Henry Hub AESC 2013 Base Case and Low Gas Case Prices (2013$/MMBtu) ........................................ 8‐2

Exhibit 8‐3. Seasonal and Time Period Impacts of Henry Hub Price Changes........................................................... 8‐3

Exhibit 8‐4. Carbon Dioxide Reference and RGGI Case Prices................................................................................... 8‐4

Exhibit 8‐5. Energy Price Impacts of CO2 Price Changes(2013$) ............................................................................... 8‐5

Exhibit 8‐6. Energy Price Impacts of Northern Pass(2013$/MWh) ........................................................................... 8‐6

   
LIST OF ABBREVIATIONS

AEO Annual Energy Outlook  

AGT Algonquin Gas Transmission  

AIM Algonquin Incremental Market  

API American Petroleum Institute

BACT Best Available Control Technology

BART Best Available Retrofit Technology

BDAT Best Demonstrated Available Technology

CAGR Compound Annual Growth Rate

Bcfd Billion Cubic Feet per Day

CCP Central Connecticut Project

CCR Coal Combustion Residuals

CCS Carbon Capture and Storage

CECP Clean Energy Climate Plan (Massachusetts)

CSAPR Cross State Air Pollution Rule

DOE Department of Energy

DOER Massachusetts Department of Energy Resources

DRIPE Demand Reduction Induced Price Effects

EEFWG ISO‐New England Energy‐Efficiency Forecast Working Group

EIA Energy Information Administration

EMF Energy Modeling Forum

EUR Expected Ultimate Recovery

FCAs Forward Capacity Auctions

FCM Forward Capacity Market

FTA Free Trade Agreement

FT Firm Transportation

FTR Financial Transmission Rights  

GAO Government Accountability Office  

GSRP Greater Springfield Reliability Project

GWSA Massachusetts Global Warming Solutions Act

HDD Heating Degree Day

IEA International Energy Agency
IGCC Integrated Gasification Combined‐Cycle

IGTS Iroquois Gas Transmission System

IPCC Intergovernmental Panel on Climate Change

IRP Interstate Reliability Project

LAER Lowest Achievable Emissions Reductions

LDCs Local Distribution Companies

LNG Liquefied Natural Gas

LSEs Load‐Serving Entities

M&N Maritimes & Northeast Pipeline

MACT Maximum Achievable Control Technology

MMcf Million Cubic Feet  

MPRP Maine Power Reliability Plan

NAAQS National Ambient Air Quality Standards

NEEWS New England East‐West Solution

NGLs Natural Gas Liquids

NGPA Natural Gas Policy Act

NRC Nuclear Regulatory Commission

PDR Passive Demand Resources

PNGTS Portland Natural Gas Transmission System

RACT Reasonably Available Control Technology

REC Renewable Energy Certificate

RGGI Regional Greenhouse Gas Initiative

RIRP Rhode Island Reliability Project

RPS Renewable Portfolio Standard

SEDS State Energy Data System

TCPL TransCanada PipeLines

TETCO Texas Eastern Transmission

TGP Tennessee Gas Pipeline

TPH Tudor Pickering Holt

VOM Variable Operating and Maintenance Costs

WTI West Texas Intermediate
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐1

Chapter 1: Executive Summary

This 2013 Avoided‐Energy‐Supply‐Component Study (“AESC 2013,” or “the Study”) provides projections

of marginal energy supply costs that will be avoided due to reductions in the use of electricity, natural

gas, and other fuels resulting from energy efficiency programs offered to customers throughout New

England. All reductions in use referred to in the Study are measured at the customer meter, unless

noted otherwise.

AESC 2013 provides estimates of avoided costs for program administrators throughout New England to

support their internal decision‐making and regulatory filings for energy efficiency program cost‐

effectiveness analyses. The AESC 2013 project team understands that, ultimately, the relevant

regulatory agencies in each state specify the categories of avoided costs that program administrators in

their states are expected to use in their regulatory filings, and approve the values used for each category

of avoided cost.  

In order to determine the value of efficiency programs, AESC 2013 provides projections of avoided costs

of electricity and natural gas in each New England state for a hypothetical future, the “Base Case,” in

which no new energy efficiency programs are implemented in New England from 2014 onward. AESC

2013 avoided costs should not be interpreted as projections of, or proxies for, the market prices of

natural gas, electricity, or other fuels in New England at any future point in time, for the following two

reasons. First, the projections are for a hypothetical future and thus do not reflect the actual market

conditions and prices likely to prevail in New England in an actual future with significant amounts of new

efficiency measures. Second, the Study is providing projections of the avoided costs of these fuels in the

long term. The actual market prices of those fuels at any future point in time will vary above and below

their long‐run avoided costs due to the various factors that affect short‐term market prices.  

AESC 2013 updates the 2011 AESC study (“AESC 2011”) to reflect changes in observed facts and in

expectations regarding future market conditions and future costs. Specific changes in expectations that

contribute to changes from the AESC 2011 avoided costs are:

 Increases in the quantity of shale gas production available at marginal production costs less than

$5/MMBtu, resulting in lower projections of avoided gas supply costs;

 Constraints on pipeline capacity into New England through 2016, resulting in wholesale market

costs of gas in New England higher than the rest of the Northeast during that period, particularly

in winter months;

 Retirements of existing generating units with a total capacity of 7,400 MW, leading to higher

estimates of avoided costs for electric capacity;
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐2

 Higher RGGI allowance prices, and a delay in the start of federal regulation of carbon emissions

from 2018 to 2020; and

 Estimates of demand reduction induced price effects (“DRIPE”) for reductions in gas

consumption resulting from gas efficiency programs and from electric efficiency programs.  

The Study provides detailed projections of avoided costs by year for an initial 15‐year period, 2014

through 2028, and extrapolates values for another 15 years, from 2029 through 2043.1 All values are

reported in 2013 dollars (“2013$”) unless noted otherwise. For ease of reporting and comparison with

AESC 2011, many results are expressed as levelized values over 15 years.

2 The AESC 2013 levelized

results are calculated using the real discount rate of 1.36 percent, solely for illustrative purposes.

3

  

1.1 Background to Study

AESC 2013 was sponsored by a group of electric utilities, gas utilities, and other efficiency program

administrators (collectively, “program administrators” or “PAs”). The sponsors, along with non‐utility

parties and their consultants, formed an AESC 2013 Study Group to oversee the design and execution of

the report.  

The Study sponsors include: Berkshire Gas Company; Cape Light Compact; Liberty Utilities; National Grid

USA; New England Gas Company; New Hampshire Electric Co‐Op; Columbia Gas of Massachusetts;

Northeast Utilities (Connecticut Light and Power, NSTAR Electric & Gas Company, Western

Massachusetts Electric Company, Public Service Company of New Hampshire, and Yankee Gas); Unitil

(Fitchburg Gas and Electric Light Company, Unitil Energy Systems, Inc., and Northern Utilities); United

Illuminating; Southern Connecticut Gas and Connecticut Natural Gas; Efficiency Maine; and the State of

Vermont. The non‐sponsoring parties represented in the Study Group include: Connecticut Energy

Conservation Management Board; Conservation Law Foundation; Massachusetts Department of Public

Utilities; Massachusetts Department of Energy Resources; Massachusetts Department of Environmental

Protection; Massachusetts Attorney General; Massachusetts Low‐Income Energy Affordability Network

(“LEAN”); Massachusetts Energy Efficiency Advisory Council; New Hampshire Public Utilities

Commission; Vermont Gas Systems, Inc.; and Rhode Island Division of Public Utilities and Carriers.

                                                            

1 Escalation rates for extrapolation are based on compound annual growth rates specific to the value stream and are noted

throughout the report.

2 15‐year levelization periods of 2012‐2026 for AESC 2011 and 2014‐2028 for AESC 2013. AESC 2011 used a real discount rate of

2.46 percent.

3 The AESC 2013 real discount rate reflects 30‐year United States Treasury yields as of February 2013. Thirty‐year U.S. Treasury

yields are much lower for AESC 2013 compared to AESC 2011, which explains the difference in the levelization rate, as

detailed in Appendix E.
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐3

The AESC 2013 Study Group specified the scope of services, selected the Synapse Energy Economics

(“Synapse”) project team, and monitored progress of the study. As instructed by the Study Group, the

Synapse team developed seven distinct forecast components, which correspond to Chapters 2 through 7

of this report (See Exhibit 1‐1). Two of the components—avoided fuel oil costs and avoided costs of

other fuels—were combined into one chapter.

For each component, the Synapse project team presented its methodologies, assumptions, and

analytical results in draft deliverables for each of the subtasks specified by the Study Group. The Synapse

team reviewed each draft deliverable with the Study Group in conference calls. The relationships

between the sections of this report, the forecast components, and the subtask deliverables are

presented in Exhibit 1‐1.

Exhibit 1‐1. Relationship of chapters to forecast components and subtasks

Chapter/Appendix Forecast

Component

Subtasks

Chapter 2 – Avoided Natural Gas Costs 1 2A, 3A

Chapter 3 – Avoided Costs of Fuel Oil and Other Fuels 2, 5 2B, 3B, 2E, 3E

Chapter 4 – Embedded and Non‐Embedded Environmental Costs 6 2F, 3F

Chapter 5 – Avoided Electric Capacity Costs 4 2D, 3D

Chapter 6 – Avoided Electric Energy Costs 3 2C, 3C

Chapter 7 – Demand Reduction Induced Price Effects 7 2G, 3G

Chapter 8 – Sensitivity Analyses N/A 4B

Appendix A – Usage Instructions N/A 4C

Appendix G – Survey of Transmission and Distribution Capacity Values N/A 4A

Appendix E – Common Financial Parameters N/A 1

This report was prepared by a project team assembled and led by Synapse. Synapse’s Rick Hornby and

Max Chang managed the project. Ron Denhardt of Strategic Energy and Economic Research, John

Rosenkranz of North Side Energy, and Dr. Thomas Vitolo of Synapse developed the avoided natural gas

cost projections. Dr. David White and Patrick Luckow of Synapse developed projections of avoided costs

of fuel oil and other fuels. Dr. Elizabeth A. Stanton of Synapse led the analysis of non‐embedded

environmental costs avoided due to reductions in electricity and fuel use. Paul Chernick of Resource

Insight led the analysis of wholesale electric capacity costs and demand reduction induced price effects,

with assistance from Ben Griffiths. Dr. David White and Patrick Luckow of Synapse developed the

projections of wholesale electric energy prices. Bob Grace and Jason Gifford of Sustainable Energy

Advantage (“SEA”) provided estimates of renewable energy credit (“REC”) demand, supply, and price.  

1.2 Avoided Costs of Electricity

Initiatives that enable retail customers to reduce their peak electricity use (“demand”) and/or their

annual electricity use (“energy”) have a number of key monetary and environmental benefits. Major

categories of benefits include:
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐4

 Avoided costs due to reductions in quantities of resources required to meet electric demand and

annual energy. Electric capacity costs are avoided due to a reduction in the annual quantity of

electric capacity that load serving entities (“LSEs”) will have to acquire from the Forward

Capacity Market (“FCM”) to ensure an adequate quantity of generation during hours of peak

demand. Electric energy costs are avoided due to a reduction in the annual quantity of electric

energy that LSEs will have to acquire. These avoided costs include a reduction in the cost of

renewable energy incurred to comply with the applicable Renewable Portfolio Standards

(“RPS”).4 Non‐embedded environmental costs are avoided due to a reduction in the quantity of

electric energy generated. (A non‐embedded environmental cost is the cost of an environmental

impact associated with the use of a product or service, such as electricity, that is not reflected in

the price of that product.) AESC 2013 uses the long‐term abatement cost of carbon dioxide

emissions as a proxy for this value.  

 Local transmission and distribution (“T&D”) infrastructure costs are avoided due to delays in the

timing and/or reductions in the size of new projects that have to be built, resulting from the

reduction in electric energy that has to be delivered. AESC 2013 surveys participating sponsors

for recent values.

 Avoided costs due to reductions in wholesale market prices of capacity and energy occur as the

lower requirements for electric demand and annual energy are met by lower‐cost marginal

resources. Reductions in the quantities of capacity and energy being acquired from those

markets will cause prices in those markets to decline relative to Base Case levels for a certain

period of time, after which responses by market participants will lead to a shift in the supply

curve and cause prices to rise back toward the Base Case levels. AESC 2013 refers to the

reduction or mitigation of market prices due to reductions in demand for electric capacity and

electric energy as “capacity DRIPE” and “energy DRIPE,” respectively. In addition, reductions in

annual electricity use will cause a reduction in gas consumption for electric generation, which

will have a price suppression effect on gas supply prices, which we refer to as electric cross‐fuel

DRIPE.  

AESC 2013 develops estimates of each category of avoided costs, and (as noted above) surveys Study

Group members for avoided T&D costs, which are utility‐specific. The forecast components that feed

into the AESC 2013 projections of avoided electricity costs consist of the following:

 Avoided capacity. Avoided capacity costs for the AESC 2013 Base Case consist of revenue from

demand reductions bid into the FCM and the value of generating capacity avoided by demand

reductions that are not bid into the FCM. Levelized annual FCM prices are approximately 61

percent higher than in AESC 2011. This increase is primarily due changes in the Forward Capacity

                                                            

4 Electric energy is measured in kilowatt hours (kWh) or megawatt hours (MWh); electricity capacity is measured in kilowatts

(kW) or megawatts (MW).
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐5

Auction (“FCA”) market rules post‐FCA 8, and earlier need for new capacity additions due to the

increased quantity of existing capacity projected to retire.

 Avoided energy. This is the largest component. It consists of the wholesale electric energy price,

the REC cost, and a wholesale risk premium. Levelized annual avoided energy costs under the

AESC 2013 Base Case range between 4 and 9 percent lower than those in AESC 2011, depending

on the pricing zone. The levelized annual wholesale electric energy costs are lower primarily due

to projections of lower natural gas prices and a delay in the anticipated implementation of

federal regulation of carbon emissions. The decline in this component is offset somewhat in

winter periods by New England natural gas pipeline constraints through 2016, which affect the

winter basis prices, and higher RGGI allowance prices from 2014 through 2019.

 Capacity DRIPE. This is the value of the reduction in capacity market prices due to reductions in

demand. The AESC 2013 15‐year levelized annual capacity DRIPE value is approximately 45

percent lower on average than AESC 2011 due to the extension of the FCA floor price, projection

of new generation in 2020, and a shorter dissipation period.  

 Energy DRIPE. This is the value of the reduction in energy market prices due to reductions in

electric energy use. Levelized annual intrastate energy DRIPE values are approximately 19

percent lower on an annual load‐weighted average than AESC 2011, primarily due to changes in

our assumptions of dissipation of DRIPE effects, lower wholesale energy prices compared to

AESC 2011, and our AESC 2013 assumptions regarding the need for new generation capacity.

 Electricity cross‐fuel DRIPE. This value represents the impact of the reduction in natural gas

used for electric generation upon natural gas prices. This value is new for AESC 2013.

 Avoided non‐embedded CO2 costs. This is the cost of controlling CO2 emissions not reflected in

wholesale energy market prices. The AESC 2013 15‐year levelized annual value is approximately

14 percent higher than AESC 2011 due to our projection of a higher long‐term marginal

abatement cost of carbon.

The relative magnitude of each component for the Summer On‐Peak costing period is illustrated in

Exhibit 1‐2 for an efficiency measure with a 55‐percent load factor implemented in the West Central

Massachusetts zone (“WCMA”).  
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐6

Exhibit 1‐2. Illustration of Avoided Electricity Cost Components, AESC 2013 vs. AESC 2011 (WCMA Zone, Summer

On‐Peak, 15‐Year Levelized Results, 2013$)  

  

For this costing location and period, AESC 2013 is projecting total avoided costs from direct reductions in

energy and capacity of 9.65 cents per kWh. This amount is approximately 7.9 percent lower than the

corresponding AESC 2011 total.  

The total of all components—i.e., the avoided cost of energy and capacity reductions (9.65 cents per

kWh), plus energy and capacity DRIPE, plus non‐embedded CO2 costs—is 17.42 cents per kWh. This total

is 6.1 percent lower than the corresponding AESC 2011 total. Note this illustrative avoided cost

component does not include the electricity cross‐fuel DRIPE effect described in detail in Chapter 7.

1.2.1 Avoided Electric Capacity Costs

Avoided electric capacity costs are an estimate of the value of a load reduction by retail customers

during hours of system peak demand.5 The major input to this calculation is the wholesale forward

capacity price to load (in dollars per kilowatt‐month), which is set for a capacity year (June–May) roughly

three years before the start of the capacity year. To develop an avoided cost at the meter, the wholesale

                                                            

5 The benefit arises from two sources: the reduction of load at the system annual peak hour, and the capacity credit attributed

to energy‐efficiency programs (called “passive demand response” in the ISO‐NE forward capacity mechanism), measured as

the average load reduction of the on‐peak hours in high‐load months or the hours with loads over 95 percent of forecast peak.  

 AESC 2011 AESC 2013

cents/kWh cents/kWh cents/kWh % Difference

Avoided Capacity Costs1,2 1.11 2.01 0.90 80.7%

Avoided Energy Costs 9.36 7.64 -1.73 -18.4%

Capacity and Energy Subtotal 10.47 9.65 -0.83 -7.9%

DRIPE

Capacity2 1.27 0.69 -0.57 -45.2%

Intrastate Energy3 3.29 2.74 -0.54 -16.5%

DRIPE Subtotal 4.55 3.44 -1.11 -24.5%

Subtotal: Avoided Capacity 

and Energy + Intrastate DRIPE 15.03 13.09 -1.94 -12.9%

CO2 Non-Embedded4 3.52 4.33 0.81 23.0%

Total 18.55 17.42 -1.13 -6.1%

Notes

-Values may not sum due to rounding

-AESC 2011 values levelized (2012-2026) escalated to 2013$

1) Avoided capacity costs assumes 100% selling into Forward Capacity Markets

2) Assuming a 55% load factor

3) Values are for Intrastate energy DRIPE 

4) For AESC 2013, 2013 CO2 prices and physical emission rates

-Avoided energy costs for Summer On-Peak incorporate avoided REC costs 

Difference Relative to 

AESC 2011
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐7

electric capacity price is first increased by the reserve margin requirements forecasted for the year, then

increased by eight percent to reflect ISO‐New England’s (ISO‐NE’s) estimate of distribution losses.

The major drivers of the avoided wholesale capacity price are system peak demand, capacity resources,

and the detailed ISO‐NE rules governing the auction. ISO‐NE rules specify which resources are allowed to

bid in the auction, how the resources’ capacity values are computed, and what range of prices each

resource category is allowed to bid. The load‐resource balance is determined by load growth,

retirements of existing capacity, addition of new capacity from resources to comply with RPS

requirements, imports, exports, and new, non‐RPS capacity additions.  

As indicated in Exhibit 1‐3, AESC 2013 projects that new capacity, other than RPS‐related renewable

resources, will have to be added starting in 2020. This change, which results largely from the projected

retirements of existing fossil units, is somewhat reduced by the lower CELT load forecast.  

Exhibit 1‐3. AESC 2013 Capacity Requirements vs. Resources (Base Case), MW

The AESC 2013 Base Case estimate of levelized capacity prices is approximately 61 percent higher than

the estimate from AESC 2011. The 15‐year levelized projection of capacity prices from AESC 2011 was

$49.67/kW‐year in each pricing zone (in 2013 dollars), while the corresponding levelized value from

AESC 2013 is $79.89/kW‐year. The higher values are primarily due to the projected cost of new capacity

additions to replace the existing capacity that AESC 2013 projects will retire due to the cost of complying

with tighter environmental requirements and to changes in the FCM.  

 ‐

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

MW

New Renewables

Existing

Renewables

DR/Pumped

Storage

PetProd

New Generic

Natural Gas

Existing NG

Coal

Nuclear

Hydro
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐8

The actual amount of wholesale avoided electric capacity costs that a reduction in demand will avoid

depends on the approach that the program administrator (PA) responsible for that reduction takes

towards bidding it into the FCM. PAs will achieve the maximum avoided cost by bidding the entire

anticipated kW reduction from measures in a given year into the FCA for that power year. However, PAs

have to submit those bids when the FCA is held, which is approximately three years in advance of the

applicable power year. Some expected load reductions may not be bid into the first FCA for which the

reduction would be effective, due to uncertainty about future program funding and energy savings.6

  

1.2.2 Avoided Electric Energy Costs

Avoided electric energy costs at the customer meter consist of the wholesale electric energy price plus

the REC cost plus a wholesale risk premium. Exhibit 1‐4 presents the projected mix of generation

underlying our projection of electric energy prices.  

Exhibit 1‐4. New England Generation Mix from AESC 2013 (GWh)

                                                            

6 PAs also avoid capacity costs from kW reductions that are not bid into FCAs, since those kW reductions lower actual demand,

and ISO‐NE eventually reflects those lower demands when setting the maximum demand to be met in future FCAs and the

allocation of capacity requirements to load. However, the total amount of avoided capacity costs is lower because of the time

lag—up to four years—between the year in which the kW reduction first causes a lower actual peak demand and the year in

which ISO‐NE translates that kW reduction into a reduction in the total demand for which capacity has to be acquired in an

FCA. Since the load reduction in one year will affect the allocation of capacity responsibility in the next year, the PA’s

customers experience a one‐year delay in realized savings that are not bid into the auctions at all.

 ‐

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

GWh

New Renewables

Existing

Renewables

DR/Pumped Stor.

PetProd

New Generic

Natural Gas

Existing NG

Coal

Nuclear

Hydro
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐9

Exhibit 1‐5 presents the AESC 2013 electric energy prices for the West Central Massachusetts zone for all

hours compared to energy prices from AESC 2011. This WCMA price also represents the ISO‐NE Control

Area price, which is within this zone. On a levelized basis, the AESC 2013 annual all‐hours price for the

period 2014 through 2028 is $59.86/MWh, compared to the equivalent value of $64.68/MWh from

AESC 2011, representing a reduction of 7.4 percent. The lower estimate for AESC 2013 is primarily due

to a lower estimate of wholesale natural gas prices in New England, and delayed introduction of federal

CO2 prices.  

Exhibit 1‐5. AESC 2013 vs. AESC 2011 – All‐Hours Prices for West‐Central Massachusetts (2013$/kWh)

   

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.080

0.090

0.100

2014 2016 2018 2020 2022 2024 2026 2028

2013$/kWh

Annual Energy Price

AESC 2011

AESC 2013
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐10

Exhibit 1‐6 presents the resulting 15‐year levelized avoided electric energy costs for AESC 2013 by zone,

after adding in the relevant REC costs and wholesale risk premiums. This exhibit also provides the

corresponding estimates from AESC 2011 by zone.

Exhibit 1‐6. Avoided Electric Energy Costs, AESC 2013 vs. AESC 2011 (15‐year levelized, 2013$)

   

Winter On 

Peak Energy

Winter OffPeak 

Energy

Summer On 

Peak Energy

Summer OffPeak Energy

Annual 

Weighted 

Average

AESC 2013 $/kWh $/kWh $/kWh $/kWh $/kWh

1 Maine (ME) 0.064 0.058 0.062 0.052 0.059

2 Vermont (VT) 0.072 0.063 0.071 0.057 0.066

3 New Hampshire (NH) 0.073 0.066 0.071 0.060 0.068

4 Connecticut (statewide) 0.076 0.067 0.075 0.062 0.070

5 Massachusetts (statewide) 0.076 0.068 0.075 0.062 0.071

6 Rhode Island (RI) 0.064 0.058 0.062 0.051 0.059

7 SEMA 0.074 0.067 0.074 0.061 0.069

8 Central & Western Massachusetts (WCMA) 0.077 0.069 0.076 0.063 0.072

9 NEMA 0.075 0.068 0.075 0.062 0.070

10 Rest of Massachusetts (non-NEMA) 0.076 0.068 0.075 0.062 0.071

11 Norwalk / Stamford (NS) 0.077 0.068 0.076 0.062 0.071

12 Southwest Connecticut (SWCT) including Norwalk/Stamford 0.077 0.068 0.076 0.062 0.071

13 Southwest Connecticut (SWCT) excluding Norwalk/Stamford 0.077 0.068 0.076 0.062 0.071

14 Rest of Connecticut (non-SWCT) 0.075 0.067 0.075 0.061 0.070 Simple Average 0 074 0 066 0 073 0 060 0 068

AESC 2011 $/kWh $/kWh $/kWh $/kWh $/kWh

1 Maine (ME) 0.069 0.061 0.074 0.060 0.066 

2 Vermont (VT) 0.076 0.066 0.090 0.065 0.073 

3 New Hampshire (NH) 0.075 0.066 0.081 0.064 0.071 

4 Connecticut (statewide) 0.077 0.067 0.092 0.066 0.074 

5 Massachusetts (statewide) 0.079 0.070 0.093 0.068 0.076 

6 Rhode Island (RI) 0.067 0.057 0.079 0.057 0.064 

7 SEMA 0.079 0.070 0.092 0.068 0.076 

8 Central & Western Massachusetts (WCMA) 0.080 0.070 0.094 0.069 0.077 

9 NEMA 0.079 0.069 0.093 0.067 0.075 

10 Rest of Massachusetts (non-NEMA) 0.079 0.070 0.094 0.068 0.076 

11 Norwalk / Stamford (NS) 0.078 0.068 0.093 0.067 0.075 

12 Southwest Connecticut (SWCT) including Norwalk/Stamford 0.078 0.068 0.093 0.067 0.075 

13 Southwest Connecticut (SWCT) excluding Norwalk/Stamford 0.078 0.068 0.092 0.067 0.075 

14 Rest of Connecticut (non-SWCT) 0.076 0.067 0.091 0.065 0.073 
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐11

Exhibit 1‐7 shows the change between AESC 2013 and AESC 2011 values, expressed as a percentage and

in terms of 2013$ per kWh.

Exhibit 1‐7. Avoided Electric Energy Costs for 2013: Change from AESC 2011 (expressed in 2013$/kWh and

percentage values)

1.2.3 Embedded and Non‐Embedded Environmental Costs

Some environmental costs associated with electricity use are “embedded” in our estimates of avoided

energy costs, and others are not. The costs that are embedded are incorporated in the Market Analytics

model used to generate wholesale energy prices for AESC 2013.  

For AESC 2013, we anticipate that the “non‐embedded carbon costs” will continue to be the dominant

non‐embedded environmental cost associated with marginal electricity generation in New England.

Based on our review of the most current research on marginal abatement and carbon capture and

sequestration (“CCS”) costs, and our experience and judgment on the topic, we believe that it is

reasonable to use a CO2 marginal abatement cost of $100 per short ton in 2013 dollars. The AESC 2013

CO2 marginal abatement cost of $100/ton is 20 percent higher than the AESC 2011 value of $83/ton

(2013 dollars). This change results from three major factors:

1) AESC 2013 incorporates new studies with different estimates than two years ago.

Winter On 

Peak Energy

Winter OffPeak 

Energy

Summer On 

Peak Energy

Summer OffPeak Energy

Annual 

Weighted 

Average

Change from AESC 2011 $/kWh $/kWh $/kWh $/kWh $/kWh

1 Maine (ME) (0.006) (0.004) (0.012) (0.008) (0.006) 

2 Vermont (VT) (0.004) (0.003) (0.019) (0.007) (0.007) 

3 New Hampshire (NH) (0.002) 0.000 (0.010) (0.004) (0.003) 

4 Connecticut (statewide) (0.001) 0.000 (0.016) (0.004) (0.004) 

5 Massachusetts (statewide) (0.003) (0.002) (0.018) (0.006) (0.006) 

6 Rhode Island (RI) (0.003) 0.001 (0.017) (0.006) (0.004) 

7 SEMA (0.004) (0.003) (0.019) (0.007) (0.007) 

8 Central & Western Massachusetts (WCMA) (0.003) (0.001) (0.017) (0.006) (0.005) 

9 Boston (NEMA) (0.003) (0.001) (0.018) (0.006) (0.005) 

10 Rest of Massachusetts (non-NEMA) (0.003) (0.002) (0.018) (0.007) (0.006) 

11 Norwalk / Stamford (NS) (0.001) 0.000 (0.016) (0.004) (0.004) 

12 Southwest Connecticut (SWCT) including Norwalk/Stamford (0.001) 0.000 (0.016) (0.004) (0.004) 

13 Southwest Connecticut (SWCT) excluding Norwalk/Stamford (0.001) 0.000 (0.016) (0.004) (0.004) 

14 Rest of Connecticut (non-SWCT) (0.001) 0.000 (0.016) (0.004) (0.004) Simple Average -0.003 -0.001 -0.016 -0.006

% Change from AESC 2011 % % % % %

1 Maine (ME) -8.2% -5.9% -16.2% -12.8% -9.6%

2 Vermont (VT) -5.9% -4.4% -21.4% -11.6% -9.4%

3 New Hampshire (NH) -2.7% 0.5% -11.8% -6.3% -3.9%

4 Connecticut (statewide) -1.5% 0.3% -17.7% -6.6% -4.9%

5 Massachusetts (statewide) -4.2% -2.5% -19.4% -9.5% -7.4%

6 Rhode Island (RI) -4.3% 1.9% -21.4% -9.8% -6.6%

7 SEMA -5.3% -3.9% -20.4% -10.9% -8.7%

8 Central & Western Massachusetts (WCMA) -3.3% -1.6% -18.4% -8.2% -6.5%

9 Boston (NEMA) -3.9% -1.9% -19.3% -8.6% -7.0%

10 Rest of Massachusetts (non-NEMA) -4.2% -2.5% -19.4% -9.6% -7.5%

11 Norwalk / Stamford (NS) -1.6% 0.3% -17.7% -6.7% -4.9%

12 Southwest Connecticut (SWCT) including Norwalk/Stamford -1.6% 0.3% -17.7% -6.7% -4.9%

13 Southwest Connecticut (SWCT) excluding Norwalk/Stamford -1.6% 0.3% -17.7% -6.7% -4.9%

14 Rest of Connecticut (non-SWCT) -1.5% 0.4% -17.7% -6.6% -4.9%
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐12

2) AESC 2011 used values from multiple vintages of the same studies, whereas AESC 2013

only uses values from the most up‐to‐date versions of studies.

3) AESC 2013 incorporates an analysis of CCS technologies that are expected to be the

marginal technology.  

Massachusetts Global Warming Solutions Act (GWSA)

The AESC 2013 scope of work required the Synapse project team to determine if there was some

component of compliance with state‐specific climate plans that would directly impact generators and

that the project team could quantify and credibly support. GWSA was the only state‐specific climate

plan to be reviewed. The key findings from that review are as follows:

 The current Massachusetts Greenhouse Gas Emissions Inventory method does not

provide an accurate accounting of electricity sector emission reductions for GWSA

compliance. Synapse presents an example alternate inventory method that would

provide an accurate accounting.

 The Massachusetts Clean Energy Climate Plan (CECP) assumes the electricity sector will

achieve significant reductions in emissions by 2020 under its Business as Usual Forecast.

The CECP then identifies six policy measures the electricity sector could use to comply

with GWSA targets in 2020 and beyond, as well the quantity of reductions and cost per

ton of reduction from each. The AESC 2013 Base Case reflects the compliance measures

that are currently enforced for the Massachusetts electricity sector except for energy

efficiency, which are RPS, RGGI, and EPA Power Plant Rules. The remaining compliance

measures are all cost‐effective energy efficiency, the Clean Energy Import Strategy (CEI)

and a Clean Energy Performance Standard (CEPS).

 The Massachusetts electricity sector will require reductions from a CEPS or other

additional component in order to comply with the GWSA at some point from 2020

onward. However, there are unresolved policy questions regarding the CECP targets for

the electricity sector beyond 2020 and the inventory method for accounting for

reductions in that sector. As a result, the project team could not determine the size of

reductions that would be required in the electricity sector each year and therefore could

not quantify and credibly support an estimate of the cost of the marginal resource

required to achieve those reductions.

 In the absence of detailed modeling, the project team identified additional renewable

generation, incremental to RPS quantities, as the marginal resource for electric‐sector

compliance with the GWSA. If the quantity of additional renewable generation required

for GWSA compliance in a given year is comparable to the AESC 2013 projected quantity

of renewable generation added to meet RPS requirements in that year, it is reasonable

to expect the cost of that additional renewable generation in that year to be

comparable to the REC prices estimated for Massachusetts for that year (e.g.,

$18.40/MWh in 2020, per Exhibit 6‐30) plus the AESC 2013 estimate of electric energy

costs for Massachusetts in that year. If the quantity of additional renewables required

for GWSA compliance is significantly larger than those added to meet RPS requirements,
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐13

the cost of the marginal resource required to achieve those larger reductions would

have to be determined through new modeling.  

1.3 Avoided Natural Gas Costs

Initiatives that enable retail customers to reduce their natural gas use also have a number of benefits.

The benefits from those reductions include some or all of the following avoided costs:

 Avoided gas supply costs due to a reduction in the annual quantity of gas that has to be

produced;

 Avoided pipeline costs due to a reduction in the quantity of gas that has to be delivered;

and

 Avoided local distribution infrastructure costs due to delays in the timing and/or

reductions in the size of new projects that have to be built resulting from the reduction

in gas that has to be delivered.

Detailed results of our analysis are presented in Appendix C, Avoided Natural Gas Cost Results. A

summary of results is presented below.

1.3.1 Wholesale Natural Gas Supply Costs  

The forecast of wholesale natural gas commodity prices in New England begins with a forecast of the

price of gas at the Henry Hub, Louisiana. Henry Hub is used because: 1) it is a major trading point whose

prices serve as a reference point against which prices at other locations are indexed, 2) most

comparative forecasts provide prices for this location, and 3) the Gulf Coast is a major source of U.S.

supply.  

The AESC 2013 Base Case estimate of Henry Hub prices is $5.37/MMBtu (2013$) on a 15‐year levelized

basis for the period 2014 to 2028. This is approximately 17 percent lower than the 15‐year levelized

price from the AESC 2011 Base Case for the same time period.7

  

The AESC 2013 Base Case Henry Hub estimate is composed of NYMEX futures prices (as of March 15,

2013) through March 2016, and on a forecast derived from the Reference Case forecast from the Energy

Information Administration’s (“EIA’s”) Annual Energy Outlook (“AEO”) 2012 from April 2016 through

2035. The near‐term forecast is based on NYMEX futures because they are an indication of the market’s

estimate of prices for the future months for which trading volumes are significant.8 For the remaining

period, the forecast is based on an AEO long‐term forecast because a long‐term forecast captures the

                                                            

7 The 15‐year levelized (2014‐2028) AESC 2011 Base Case in 2013$ is $6.47/ MMBtu.

8 The NYMEX futures used to prepare prior AESC studies have proven to be higher than actual Henry Hub prices, indicating that

price expectations of the gas industry are not always accurate.  
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐14

market fundamentals that will drive those prices (i.e., demand, supply, competition between fuels) and

because the inputs and model algorithms underlying AEO forecasts are public. The difficulty the project

team faced was to select an appropriate AEO forecast as a starting point and then determine what, if

any, adjustments to make to that AEO forecast.

The Synapse project team chose the AEO 2012 Reference Case as its starting point, and made three

adjustments to that forecast, based primarily upon its review of physical and economic data on the

country’s major shale gas plays. This review included an assessment of the time it would take before

production from dry gas plays would begin setting the market price, i.e., the point in time when growth

in gas production associated with oil plays and gas plays rich in natural gas liquids (“NGL”) would no

longer be sufficient to offset the decline in production from dry gas plays and the growth in natural gas

demand. The project team also reviewed the potential for exports in the form of liquefied natural gas

(“LNG”) to increase Henry Hub prices above those forecast in the AEO 2012 Reference Case. Each of

those assessments is subject to some degree of uncertainty, and different analysts have different views

regarding those trends. The Synapse project team did consider the AEO 2013 Reference Case forecast

but found it to be well below forward market prices in the near term, well below estimates of the

marginal cost of production from the marginal dry gas plays needed to balance demand and supply in

2020 and beyond, and below a range of public forecasts of other entities presented in AEO 2012.  

The Synapse project team derived its Base Case forecast for 2016 onward from the AEO 2012 Reference

Case by making the following three adjustments:  

 EIA Henry Hub methodology downward adjustment: The EIA has made a major change

in the methodology it uses to calculate Henry Hub prices. We incorporate that change

in our estimate, which represents a downward adjustment of approximately $0.50 per

MMBtu (2013$) over the study horizon.

 Marginal well economics upward adjustment: AESC 2013 makes an upward

adjustment of $0.33 per MMBtu that phases in beginning in 2016 and reaches full value

in 2020. We make this adjustment based on our analysis of the economics of marginal

gas plays in 2020.

 Fracturing best practices upward adjustment: We make an upward adjustment starting

in 2017 that reaches $0.54 per MMBtu by 2021 based upon our assessment that

producers are likely to incur costs to reduce the adverse impacts of fracturing through

some combination of industry self‐enforcement of best practices and further

regulations on fracturing.  

The following exhibit illustrates the difference between the AESC 2013 and AESC 2011 Henry Hub prices.
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐15

Exhibit 1‐8. Actual and Projected Henry Hub Prices (2013$/MMBtu)

1.3.2 Avoided Wholesale Gas Costs in New England

AESC 2013 addresses several important developments that have occurred since the release of AESC

2011, and which have changed the historical relationship between wholesale natural gas prices in New

England and the Henry Hub, as described in section 2.3. These developments include the following:

 Rapid growth in Marcellus shale gas production in the Appalachian9 producing area

has reduced gas prices in the Northeast relative to Henry Hub. This trend is expected

to continue as Appalachian gas production expands.

 Lower gas imports from Canada and fewer LNG shipments to New England import

terminals have reduced east‐to‐west gas flows into the New England market.  

 The reduction in east‐side gas receipts has caused the pipelines delivering gas into

New England from the west to operate at or near capacity much more frequently.

                                                            

9 The principal gas‐producing states in the Appalachian area are Pennsylvania and West Virginia.  

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

2011 2016 2021 2026 2031

2013$/MMBtu

AESC 2011 Actual 2011 & 2012, NYMEX Forwards as of 3/15/2013 Actuals & AESC 2013 Base Case
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐16

These gas transmission constraints have caused New England gas prices to diverge

from prices in other Northeast markets. Significant expansion of natural gas pipeline

capacity into New England is not expected to occur before 2016.

The wholesale price forecast methodology for AESC 2013 accounts for the above changes affecting the

relationship between wholesale prices in New England and Henry Hub prices in three major ways. First,

supply from the Appalachian area is expected to replace supply from the Gulf Coast as the primary

driver of Northeast‐region gas prices. Second, Appalachian prices are expected to decline relative to

Henry Hub prices. Third, the expected constraints on gas transmission capacity into New England

through at least 2016 are expected to cause New England prices to diverge from prices elsewhere in the

Northeast.  

1.3.3 Avoided Natural Gas Costs by End Use

The avoided cost of gas at a retail customer’s meter has two components: (1) the avoided cost of gas

delivered to the LDC; and (2) the avoided cost of delivering gas on the LDC system (the “retail margin”).

AESC 2013 presents these avoided gas costs without an avoided retail margin and with an avoided retail

margin, as the ability to avoid the retail margin varies by distribution company.

The AESC 2013 avoided cost estimates are summarized in Exhibit 1‐9 and Exhibit 1‐10. These exhibits

also compare the AESC 2013 results to the corresponding values from AESC 2011.

Exhibit 1‐9. Comparison of Avoided Gas Costs by End Use Assuming No Avoidable Retail Margin, AESC 2013 vs.

AESC 2011 (15 year‐levelized, 2013$/MMBtu except where indicated as 2011$/MMBtu)  

ALL

Non Hot Non RETAIL

Heating Water Heating All Heating Heating All END USES

Southern New England (a)

AESC 2011 (2011$/MMBtu) 7.04 7.04 7.81 7.57 7.04 7.81 7.57 7.57

AESC 2011 (b) 7.27 7.27 8.06 7.83 7.27 8.06 7.83 7.83

AESC 2013 6.08 6.57 6.73 6.60 6.26 6.58 6.44 6.53

 2011 to 2013 change -16.41% -9.61% -16.54% -15.66% -13.88% -18.46% -17.74% -16.61%

Northern New England (a)

AESC 2011 (2011$/MMBtu) 6.94 6.94 7.58 7.39 6.94 7.58 7.39 7.39

AESC 2011 (b) 7.17 7.17 7.83 7.63 7.17 7.83 7.63 7.63

AESC 2013 6.03 7.53 8.02 7.62 6.58 7.54 7.12 7.39

 2011 to 2013 change -15.98% 5.01% 2.41% -0.15% -8.18% -3.67% -6.68% -3.17%

Vermont

AESC 2011 (2011$/MMBtu) 7.06 7.06 8.63 8.16 7.06 8.63 8.16 8.16

AESC 2011 (b) 7.29 7.29 8.91 8.43 7.29 8.91 8.43 8.43

AESC 2013 6.32 6.91 7.11 6.95 6.54 6.92 6.75 6.86

 2011 to 2013 change -13.39% -5.22% -20.28% -17.54% -10.36% -22.41% -19.91% -18.63%

(a) Massachusetts was included with Northern New England in AESC 2011,

 but is included with Southern New England in AESC 2013.

(b) Factor to convert 2011$ to 2013$ 1.0331

Note: AESC 2011 levelized costs for 15 years 2012 - 2026 at a discount rate of 2.465%.

 AESC 2013 levelized costs for 15 years 2014 - 2028 at a discount rate of 1.36%.

RESIDENTIAL COMMERCIAL & INDUSTRIAL
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐17

Exhibit 1‐10. Comparison of Avoided Gas Costs by End Use Assuming Some Avoidable Retail Margin, AESC 2013

vs. AESC 2011 (15‐year levelized, 2013$/MMBtu except where indicated as 2011$/MMBtu)

The avoided natural gas cost estimates for AESC 2013 are generally lower than the AESC 2011 estimates.

The main reason for this is the lower projected gas price at Henry Hub. The AESC 2013 avoided natural

gas cost estimates are also lower than the AESC 2011 estimates because LDCs in Southern New England

and Northern New England are expected to purchase more gas in the Appalachian region, at market

prices that are projected to be below the Henry Hub benchmark price.

The difference between avoidable natural gas costs for heating and non‐heating loads in the Northern

New England region is greater than for AESC 2011. This is mainly the result of the change in region

definitions. Since Massachusetts is now included in Southern New England, the Northern New England

region is composed solely of Maine and New Hampshire. These markets have less access to the Gulf

Coast and Appalachian supply areas, and are more dependent on higher‐cost supply, transportation, and

storage services from Canada. The cost of delivering this gas to Northern New England is greater

because of higher transportation costs on TransCanada PipeLines (“TCPL”) and the additional cost of

pipeline transportation service from the Canadian border to the LDC citygate. Because Northern New

England (Maine and New Hampshire) takes a lot of gas supply from Canada, and the Canadian

transportation services have high fixed costs (and little or no variable cost), the cost of supplying low‐

load‐factor customers is relatively high, especially after 2018 when existing long‐term contracts need to

be extended or replaced.

ALL

Non Hot Non RETAIL

Heating Water Heating All Heating Heating All END USES

Southern New England (a)

AESC 2011 (2011$/MMBtu) 7.64 7.64 9.39 9.11 7.58 8.82 8.44 8.75

AESC 2011 (b) 7.89 7.89 9.70 9.41 7.83 9.11 8.72 9.04

AESC 2013 6.67 7.17 8.30 8.12 6.88 7.74 7.44 7.80

 2011 to 2013 change -15.43% -9.17% -14.43% -13.70% -12.06% -15.02% -14.74% -13.77%

Northern New England (a)

AESC 2011 (2011$/MMBtu) 7.47 7.47 8.96 8.73 7.59 8.79 8.43 8.58

AESC 2011 (b) 7.71 7.71 9.26 9.02 7.84 9.08 8.71 8.86

AESC 2013 6.53 8.04 9.35 8.91 7.04 7.43 7.17 7.31

 2011 to 2013 change -15.34% 4.17% 0.97% -1.19% -10.21% -18.21% -17.67% -17.56%

Vermont

AESC 2011 (2011$/MMBtu) 7.54 7.54 9.88 9.37 7.30 9.08 8.54 8.86

AESC 2011 (b) 7.79 7.79 10.21 9.68 7.54 9.38 8.82 9.15

AESC 2013 6.94 7.53 8.74 8.54 6.68 7.19 6.98 7.61

 2011 to 2013 change -10.88% -3.22% -14.37% -11.85% -11.37% -23.33% -20.86% -16.83%

(a) Massachusetts was included with Northern New England in AESC 2011,

 but is included with Southern New England in AESC 2013.

(b) Factor to convert 2011$ to 2013$ 1.0331

Note: AESC 2011 levelized costs for 15 years 2012 - 2026 at a discount rate of 2.465%.

 AESC 2013 levelized costs for 15 years 2014 - 2028 at a discount rate of 1.36%.

RESIDENTIAL COMMERCIAL & INDUSTRIAL
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐18

Another change from AESC 2011 to AESC 2013 is that the load shape used for residential hot water

customers for AESC 2013 includes a temperature‐sensitive component, while the load shape used for

AESC 2011 did not.

1.4 Demand Reduction Induced Price Effects (DRIPE)

Demand reduction induced price effects refer to the changes in prices in the wholesale markets for

capacity and energy, relative to the prices estimated in the Base Case, resulting from the reduction in

quantities of capacity and energy required from those markets due to the impact of efficiency and/or

demand response programs. Thus, DRIPE is a measure of the value of efficiency in terms of the changes

in wholesale prices seen by all retail customers in a given period.  

DRIPE effects are very small when expressed in terms of an impact on market prices, i.e., reductions of a

fraction of a percent. However, the DRIPE impacts are significant when expressed in absolute dollar

terms. Very small impacts on market prices, when applied to all energy and capacity being purchased in

the market, translate into large absolute dollar amounts. AESC 2005, 2007, 2009, and 2011 each

provided estimates of electricity DRIPE (energy and capacity). AESC 2013 provides an estimate of those

two components of electricity DRIPE and introduces a new component which estimates the effect of

reduced electric energy usage on gas supply prices paid by retail gas customers. This component is

referred to as electricity cross‐fuel DRIPE. In addition, AESC 2013 also introduces estimates of natural

gas DRIPE. The following exhibit provides a high level overview of these estimates of electricity and

natural gas DRIPE.

Exhibit 1‐11. DRIPE Overview

Reduction in Retail Load Affected Cost Categories Affected Cost Component

Natural Gas

Own‐price (retail gas prices) Gas Supply

Cross‐fuel (electric energy prices) Gas Supply

Basis to New England

Electricity Own‐price (electric prices) Electric EnergyElectric Capacity

Cross‐fuel (natural gas supply prices) Gas Supply

  

Our estimates of natural gas “own” DRIPE, natural gas cross‐fuel DRIPE and electricity cross‐fuel DRIPE

begin with a decomposition of the wholesale cost of gas acquired to supply end‐use gas consumers (the

customers of LDCs) and gas‐fired power plants into a gas supply component and a gas transportation

component. The gas supply component reflects the cost of acquiring gas supply in producing regions,

which is determined by demand and supply conditions in the North American market. The gas

transportation component reflects the cost of transporting the gas supply from the point of production

to New England.  

A reduction in the quantity of gas used in New England, whether by retail gas customers or by gas‐fired

power plants, will reduce the demand for gas in producing regions and therefore reduce the market

price for gas supply in those regions. In contrast, a reduction in the quantity of gas used in New England
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐19

has a different impact on the transportation cost component of gas delivered to retail customers’ supply

than on the transportation cost component of gas delivered to gas‐fired power plants. A reduction in gas

use does not generally reduce retail gas customer transportation costs, because LDCs pay regulated

fixed rates to hold pipeline capacity between the point of production and New England. In contrast, a

reduction in gas use by retail gas customers does tend to reduce the transportation cost component of

gas costs to electric generators, because gas‐fired power plants tend to purchase their gas delivered to

New England, and the transportation component of that delivered cost, or basis, is determined by

demand and supply in the New England wholesale market.

1.4.1 Natural Gas DRIPE

Natural Gas “Own” DRIPE

A reduction in the quantity of gas used by retail gas customers reduces the demand for gas in producing

regions and therefore reduces the market price for that gas supply.  

Exhibit 1‐12 presents the natural gas supply DRIPE for each state and the annual benefit for New

England gas consumers based on our analysis of AEO 2012 demand scenarios.  

Exhibit 1‐12. State Supply DRIPE Benefit (2013$ per MMBtu for installation in year 2014)

CT MA ME NH RI VT New England

2014 $0.039 $0.085 $0.012 $0.007 $0.012 $0.003 $0.157

2015 $0.054 $0.119 $0.016 $0.010 $0.016 $0.004 $0.220

2016+ $0.077 $0.171 $0.023 $0.015 $0.024 $0.006 $0.315

  

Notes:    

Supply DRIPE benefit stream extends for gas efficiency measure life  

Based on Exhibit 7‐19

LDC gas supply hedge estimated at 50% Year 1, 30% Year 2, 0% Year 3

As discussed in detail in Chapter 7, we do not expect to see any significant decay in these natural gas

supply DRIPE values. For illustration purposes, a 15‐year natural gas program would have the following

levelized natural gas supply benefits:

Exhibit 1‐13. 15‐year Levelized Natural Gas Supply DRIPE by State (2013$/MMBtu)

   CT MA ME NH RI VT

New

England

15‐year

levelized $0.073   $0.161   $0.022 $0.014 $0.022 $0.005 $0.296

Notes: 15‐year levelized (2014‐2028) at 1.36% discount rate
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐20

Natural Gas Cross‐Fuel DRIPE

A reduction in gas use by retail gas customers reduces the gas supply and gas transportation

components of the wholesale gas costs incurred by electric generators, and hence the bid prices

submitted by the generators into the wholesale electricity market and the resulting market prices of

electric energy.  

Exhibit 1‐14 summarizes the own‐state and ISO‐wide gas cross‐fuel DRIPE values for 2014 gas efficiency.

These values vary based on electric usage and are benefits accrued to gas programs for reducing natural

gas prices for electric generation as a result of natural gas efficiency. For illustrative purposes, we have

included the 15‐year levelized values in the exhibit.  

Exhibit 1‐14. 15‐year Levelized Gas‐Cross DRIPE for Heating Load 2014 Installation ($/MMBtu)

   State Winter Heating DRIPE     

   CT MA ME NH RI VT ISO

2014 $3.04 $5.31 $1.14 $1.13 $0.73 $0.20 $11.55

2015 $8.92 $15.58 $3.34 $3.29 $2.11 $0.75 $33.99

2016 $8.94 $15.60 $3.36 $3.30 $2.12 $0.79 $34.11

2017 $3.49 $6.13 $1.31 $1.29 $0.83 $0.31 $13.36

2018 $2.41 $4.24 $0.90 $0.89 $0.57 $0.22 $9.24

2019 $1.19 $2.10 $0.45 $0.44 $0.28 $0.11 $4.56

2020 $0.76 $1.34 $0.28 $0.28 $0.18 $0.07 $2.92

2021 $0.57 $1.01 $0.21 $0.21 $0.14 $0.05 $2.19

2022 $0.38 $0.67 $0.14 $0.14 $0.09 $0.04 $1.46

Levelized $2.11   $3.69   $0.79   $0.78 $0.50 $0.18 $8.06

1.4.2 Electricity DRIPE

Capacity DRIPE

On a 15‐year levelized basis, the AESC 2013 estimates of capacity DRIPE are approximately 45.3 percent

lower than those from AESC 2011.10 This decrease is primarily due to: 1) the extension of the floor price

through 2016; 2) the projection of generic new generation in 2020; and 3) a change in the assumed

duration of DRIPE. AESC 2013 assumes the phase‐out, or dissipation, of capacity DRIPE will last up to 8

years, versus 11 years assumed in AESC 2011. The shorter projected dissipation of capacity DRIPE is

based on an analysis of the various factors that tend to offset the reduction in capacity prices. Those

factors include timing of new capacity additions, timing of retirements of existing capacity, elasticity of

customer demand, and the portion of capacity that LSEs acquire from the FCM.

                                                            

10 AESC 2011 values for 2012 installations levelized from 2012‐2026.  
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐21

Energy DRIPE

The AESC 2013 estimates of intrastate energy DRIPE are approximately 19 percent lower on a levelized

annual load‐weighted basis than those from AESC 2011. This is primarily due to our change in

assumptions regarding: 1) the dissipation of energy DRIPE effects, which is shorter than our AESC 2011

projection; and 2) lower annual wholesale energy prices than AESC 2011.  

Electric Cross‐Fuel DRIPE

A reduction in the quantity of gas used for electric generation reduces the demand for gas in producing

regions and therefore reduces the market price for gas supply purchased by LDCs for retail gas

customers.  

The electric cross‐DRIPE effect of electric energy efficiency on end‐use gas prices is shown in Exhibit 1‐15

for each state and the region.  

Exhibit 1‐15. Annual Gas Price Benefit per MWh Saved

Coefficient   CT    MA    ME    NH    RI    VT   New

England

Gas End Use (quads)   0.1155 0.2559 0.0347 0.0222 0.0353 0.0085 0.4722

Electric‐Gas DRIPE $/MWh saved 5.103 $0.589 $1.306 $0.177 $0.113 $0.180 $0.043 $2.410

These values would continue for the life of the measure, but would change based on state natural gas

usage.

The effect of electric energy efficiency on electric prices through gas supply prices is shown below in

Exhibit 1‐16, which adjusts by the cross‐fuel DRIPE decay factors to estimate the electric‐gas‐electric

DRIPE effect for 2014 installations.

Exhibit 1‐16. Electric Cross‐Fuel DRIPE ($/MWh for 2014 Installation)

CT MA ME NH RI VT ISO

2014 $0.24 $0.42 $0.09 $0.09 $0.06 $0.02 $0.91

2015 $0.96 $1.68 $0.35 $0.35 $0.23 $0.08 $3.63

2016 $1.07 $1.87 $0.39 $0.39 $0.26 $0.09 $4.06

2017 $1.09 $1.92 $0.40 $0.39 $0.26 $0.09 $4.16

2018 $1.00 $1.76 $0.36 $0.36 $0.24 $0.09 $3.81

2019 $0.73 $1.29 $0.27 $0.27 $0.18 $0.06 $2.79

2020 $0.47 $0.83 $0.17 $0.17 $0.11 $0.04 $1.78

2021 $0.35 $0.62 $0.13 $0.13 $0.08 $0.03 $1.34

2022 $0.23 $0.41 $0.08 $0.08 $0.06 $0.02 $0.89

Levelized

(2014‐2028) $0.43 $0.75 $0.16 $0.16 $0.10 $0.04 $1.63
Synapse Energy Economics, Inc. – AESC 2013 Page 1‐22

These results start in 2014 and dissipate by 2022.

1.5 Avoided Cost of Fuel Oil and Other Fuels

Some electric and gas efficiency programs enable retail customers to reduce their use of energy sources

other than electricity or natural gas. The benefits associated with reducing the use of “other fuels”—

such as fuel oil, propane, kerosene, biofuel, and wood—include avoided fuel supply costs. For

petroleum‐related fuels, the major driver of these avoided costs are forecast crude oil prices.

The avoided costs of fuel oil and other fuels are used primarily by administrators of electric energy

efficiency programs. Detailed results are presented in Appendix D, Avoided Costs of Other Fuels.

Exhibit 1‐17 summarizes the estimated avoided costs of “other fuels” by sector and region for AESC

2013. We report values for wood pellets, in addition to cordwood, based on annual average premiums.  

Exhibit 1‐17. Comparison of Avoided Costs of Other Retail Fuels (15‐year levelized, 2013$)

The projected AESC 2013 avoided costs for these fuels are generally higher than those from AESC 2011,

primarily due to a higher forecast of underlying crude oil prices. On a 15‐year levelized basis, the AESC

2013 values are between 16 percent higher and 24 percent lower than 2011, depending on the fuel and

sector.  

As shown in Exhibit 1‐17, there are two fuels whose avoided cost values are lower in AESC 2013 than in

AESC 2011: propane and No. 6 residual fuel oil (low sulfur). Propane prices are down 24.3 percent due to

lower costs for natural gas liquids associated with the rapid rise in shale gas production. Low‐sulfur fuel

oil prices are down 9.0 percent due to changes to the Energy Information Administration (EIA) Annual

Energy Outlook (AEO) forecast, reflecting future regulations and production expectations.  

No. 2 

Distillate

No. 2 

Distillate

No. 6 

Residual 

(low 

sulfur)

Propane Kerosene BioFuel BioFuel Cord 

Wood Pellets

Sector Res Com Com Res

Res & 

Com B5 Blend

B20 

Blend Res Res

AESC 2013 Levelized Values 

(2013$/MMBtu)

2014-2028 $27.91 $26.84 $16.23 $28.17 $30.66 $28.26 $29.33 $10.11 $16.86

AESC 2011 Levelized Values 

(2013$/MMBtu)

2012-2026 $26.21 $24.31 $17.83 $37.19 $26.34 $26.21 $26.21 $9.78 -

Percent Difference from AESC 2011

6.5% 10.4% -9.0% -24.3% 16.4% 7.8% 11.9% 3.4% -

Notes

Res = Residential Sector

Com = Commercial Sector
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐1  

Chapter 2: Avoided Natural Gas Costs

This chapter presents our estimate of avoided natural gas costs, the first of the seven distinct

components required under the scope of work. The projections include estimates of wholesale natural

gas commodity prices—starting at the Henry Hub and then moving to New England—which are major

inputs to the energy model described in Chapter 6. The projections also include estimates of the

marginal costs of gas supply and distribution by retail end use for various time periods within the year,

which represent the avoided costs for commercial, industrial, and residential gas uses. The chapter

describes the assumptions and methodology used to develop each of these projections.

2.1 Overview of New England Gas Market

In order to place the projections of avoided natural gas costs in context, we begin with an overview of

demand for natural gas in New England by major consuming sector and by month, as well as the physical

supply of gas to the region.

2.1.1 Demand for Wholesale Gas in New England

In 2011, the six New England states consumed 925,183 million cubic feet (MMcf) of natural gas, or

approximately 2,500 MMcf per day. That annual gas consumption can be grouped into two broad

categories. The first group is natural gas use by very large end users. These are primarily merchant

power plants that generate electricity but also include large users in the industrial, commercial, and

institutional sectors. The second group are retail customers in the residential, commercial, and industrial

(RC&I) sectors. Currently, gas use for electric generation accounts for roughly half of the annual gas

consumption in New England.  

Total consumption of natural gas by all sectors in New England grew 40 percent between 1998 and

2011, as indicated in Exhibit 2‐1. Increased use of gas for electric generation accounted for essentially all

of that increase; there was no material cumulative increase in total gas use in the end‐use sectors (i.e.,

residential, commercial, industrial, or “RC&I”). The increases in use of gas for electric generation

occurred primarily between 1998 and 2004 and again between 2009 and 2011.  

In its 2013 Annual Energy Outlook (AEO 2013) Reference Case, the Energy Information Administration

(EIA) forecasts annual gas use for electric generation in New England to remain constant between 2014

and 2028. That Reference Case forecasts the annual gas use by the RC&I group to grow at about 0.5%

per year between 2014 and 2028.  

Actual and projected levels of annual natural gas use in these two categories are presented in Exhibit

2‐1. (The projections are drawn from the AEO 2013 Reference Case.)
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐2  

Exhibit 2‐1. Annual Gas Use (Tcf) in New England, Actual and AEO 2013 Reference Case projection (Tcf)

Source: EIA data; Natural Gas Consumption by End Use. See: http://www.eia.gov/dnav/ng/ng_cons_sum_dcu_nus_a.htm.

Accessed July 10, 2013.

The pattern of gas use in these two groups varies substantially by season, as well as month to month

within each season. As shown in Exhibit 2‐2, natural gas use by the RC&I sector is highest in December,

January, and February, when heating demand is at its peak, and lowest in May and June, when heating

demand is low. In contrast, gas use for electric generation is higher in the summer than in the winter.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1998 2003 2008 2013 2018 2023 2028

Tcf

New England RCI&T New England Electric

Actual
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐3  

Exhibit 2‐2. New England Natural Gas Delivered Monthly to Customer by Class (MMcf/day)

Source: EIA AEO 2013

The gas procurement strategy of customers also differs between these two groups. Customers in the

first group (electric generation), primarily merchant power plants, typically have a direct physical

connection to a major pipeline and buy all of their supply from third‐party marketers at prices tied to

wholesale gas market prices in New England. In contrast, RC&I customers in the second group have

direct physical connections to their local distribution company (LDCs) and either buy their supply from

their LDC or from third‐party marketers and have that gas delivered by the LDC.  

The gas procurement strategies of New England LDCs typically consist of a portfolio drawing from three

categories of gas supply resources: 1) flowing gas, 2) off‐system storage, and 3) on‐system peaking.

1. Flowing Gas

Flowing gas is primarily natural gas that an LDC purchases in a producing area, or some other

upstream location, and has transported to its citygate under long‐term transportation contracts

with interstate pipelines. Flowing gas also includes gas that LDCs buy from third‐party marketers

at prices tied to wholesale gas market prices in New England.

2. Off‐System Storage

LDCs acquire gas from off‐system storage services to provide a flexible source of firm gas supply

during the peak winter months. Most of the gas storage services purchased by New England

LDCs utilize depleted gas fields located in Pennsylvania, New York, and Michigan. LDCs generally

enter long‐term contracts for the gas storage service as well as for firm pipeline transportation

services from the storage facilities to the LDC citygate.

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

MMcf per day

Month

Residential & Commercial Industrial Electric Generation
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐4  

3. On‐System Peaking

New England LDCs own peaking facilities which use either liquefied natural gas (LNG) or propane

to supplement pipeline deliveries during periods of peak demand. These facilities, which

connect directly to their gas distribution systems, have high daily delivery capability, but

typically have only enough LNG and/or propane storage capacity to operate a few days per year.  

2.1.2 Natural Gas Delivery Capacity into New England

New England depends entirely on pipeline deliveries of domestic and Canadian gas, and LNG imports

delivered by ship. Five interstate pipeline companies, described below, deliver natural gas into the New

England market. Tennessee Gas Pipeline and Algonquin Gas Transmission were the first gas pipeline

companies to enter the region, and still operate the majority of the high‐pressure transmission pipelines

in Connecticut, Massachusetts, and Rhode Island. Three additional pipelines extending into or through

New England were completed between 1992 and 2000. New England also has one active LNG import

terminal and two inactive offshore LNG receiving facilities.  

Pipelines

Tennessee Gas Pipeline (TGP)

The TGP system begins in the Gulf Coast producing areas and extends into New Hampshire. Two TGP

pipelines supply New England. The 200 Line enters western Massachusetts from upstate New York and

extends to the Boston area. The 300 Line enters southwestern Connecticut at Greenwich and connects

to the 200 Line near Springfield, MA. In addition to the two mainlines, TGP operates lateral lines that

extend into Rhode Island and New Hampshire.  

Algonquin Gas Transmission (AGT)

AGT begins in Lambertville, NJ, where it connects Texas Eastern Transmission (TETCO) and delivers gas in

Connecticut, Rhode Island, and eastern Massachusetts. In 2003, AGT built a 25‐mile undersea pipeline

extension (the HubLine pipeline) from Weymouth, MA to Salem, MA.  

Iroquois Gas Transmission System (IGTS)

IGTS connects with TransCanada PipeLines (TCPL) at Waddington, NY, and crosses southwestern

Connecticut before terminating in Long Island and New York City. IGTS interconnects with TGP at

Wright, NY (near Albany) and with AGT at Brookfield, CT. IGTS has delivery meters to two LDCs and four

gas‐fired generating plants in Connecticut.  

Portland Natural Gas Transmission System (PNGTS)

PNGTS connects with the TCPL in northern New Hampshire and delivers gas in Maine and New

Hampshire before terminating at an interconnection with TGP at Dracut, MA. Completed in 1999, PNGTS

replaced a smaller converted oil pipeline that had been the primary source of natural gas for Maine

markets since the 1980s. PNGTS can receive up to 250 MMcf per day from TCPL at the Canadian border.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐5  

Maritimes & Northeast Pipeline (M&N)

M&N was built in 1999 to access new gas‐producing fields in offshore Nova Scotia. The U.S. portion of

the M&N system extends from the Maine‐New Brunswick border to Northeastern Massachusetts. M&N

connects with TGP at Dracut, MA and with AGT at Salem, MA. In 2009, M&N began receiving gas from

the Canaport LNG import terminal in St. John, New Brunswick.

LNG Terminals

Distrigas LNG

The Distrigas LNG import terminal, located in Everett, MA, has operated since 1971. Distrigas expanded

its activities in 1999, with increased vaporization capacity and new LNG supplies from Trinidad. The

Distrigas terminal connects to TGP, AGT, and National Grid, and is the sole source of fuel for 1,500 MW

of gas‐fired generating capacity at Mystic units 8 and 9. Distrigas has sustained delivery capacity of 700

MMcf per day and supplies additional LNG to LDC peaking facilities by truck.

Northeast Gateway

Northeast Gateway is an offshore LNG receiving terminal in Massachusetts Bay. Northeast Gateway can

deliver up to 800 MMcf per day into the AGT HubLine pipeline, but has not operated since 2010.  

Neptune LNG

Neptune LNG is a second offshore LNG receiving terminal connected to the AGT HubLine system.

Neptune LNG has a peak sendout capacity of 700 MMcf per day, but has not operated since it was

completed in 2010.  

2.2 Henry Hub Gas Price Forecast

2.2.1 Summary

This section begins with a summary of the AESC 2011 Base Case forecast of Henry Hub prices and its

performance to date. Next it discusses the methodology and assumptions we used to develop a forecast

of Henry Hub prices for AESC 2013. Finally, it presents our Base Case forecast of Henry Hub prices and

discusses various issues relevant to that forecast.  

AESC 2011. The AESC 2011 Base Case forecast of Henry Hub prices was based upon New York Mercantile

Exchange (NYMEX) futures for 2011 through 2014 and on the EIA AEO 2010 High Shale case forecast

from 2015 onward. The choice of the AEO 2010 High Shale case forecast was informed by several

factors, including an estimate of the “full‐cycle” costs of finding and producing shale gas over the life of

a given project and uncertainty regarding projections of shale gas production costs based upon the

possibility of tighter regulations on fracking and on the production characteristics of future wells. The

AESC 2011 Base Case forecast of Henry Hub prices is $6.47/MMBtu (2013$) on a 15‐year levelized basis.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐6  

Actual Henry Hub prices in 2011 and 2012 were well below the AESC 2011 Base Case forecast, and the

NYMEX forward market prices as of March 15, 2013 were well below AESC 2011 Base Case forecasts

through 2020.  

The fact that actual prices in 2011 and 2012 and current NYMEX futures have proven to be lower than

the AESC 2011 Base Case prices for those years simply indicates that the price expectations of the entire

gas industry as of March 2011 have proven to be incorrect, since the AESC 2011 forecast prices through

2015 were based on NYMEX futures as of March 2011. One major factor which has contributed to the

lower actual prices since 2011, and the lower current NYMEX prices, has been the quantity of

production from plays with “rich” or “wet” gas, which has a high content of natural gas liquids (NGLs).11

Sale of the NGLs provides those producers additional revenue to augment the revenue from sale of gas

from those plays, and can often be enough to allow them to continue producing gas at very low market

prices. A second contributing factor has been “hold by production agreements,” under which producers

must drill even when gas prices were below the level necessary to earn an adequate return.  

Methodology. We developed our forecast of annual Henry Hub natural gas prices for the short term and

for the mid to long term using the same basic methodology used in AESC 2007, 2009, and 2011. Under

that methodology, we base the forecast on futures prices from NYMEX for the first few years of the

study period, then on an appropriate long‐term forecast from the EIA for the bulk of the study period,

and finally on an extrapolation for the remaining years not covered by the EIA forecast. Thus, the AESC

2013 Base Case forecast is developed as follows:

 January – March 2013, Henry Hub actuals monthly prices

 April 2013 – March 2016, NYMEX futures as of March 15, 2013

 April 2016 – December 2035, forecasts of monthly prices derived from our Base Case forecast of

annual Henry Hub prices  

 January 2036 – December 2043 extrapolation using the average compound annual growth rate

(CAGR) from the prior ten years (2026 to 2035).  

Our forecast is based on NYMEX futures in the near term because they provide the market’s estimate of

prices for the future months for which trading volumes are significant. Our forecast is based on an AEO

long‐term forecast for the remaining period because a long‐term forecast captures the market

fundamentals that will drive those prices (i.e., demand, supply, competition between fuels) and because

the inputs and model algorithms underlying AEO forecasts are public.  

Assumptions. The major difficulty in developing a Base Case forecast has been, and continues to be, the

selection of an appropriate AEO forecast as a starting point and then determining what, if any,

adjustments to make to that AEO forecast.

                                                            

11 Natural gas liquids are propane, butane, iso‐butane, and natural gasoline.  
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐7  

We developed the AESC 2013 Base Case forecast by starting from the AEO 2012 Reference Case forecast

and making three adjustments to it. The three adjustments are a downward adjustment to incorporate a

change in EIA’s methodology for forecasting Henry Hub prices (“EIA HH methodology”), an upward

adjustment to reflect the economics of developing marginal wells (“marginal well economics”), and an

upward adjustment to reflect the costs of reducing the adverse environmental impacts of fracturing

(“fracturing best practices”). Our basis for this approach is summarized below and discussed in detail in

each sub‐section.

 Analysis of Gas Market Fundamentals: We began our analysis by reviewing the economics of

United States gas production, which included an analysis of physical and economic data on the

country’s major shale gas plays. This analysis is more comprehensive than the AESC 2011 estimate

of full‐cycle costs of producing shale gas in that it estimates the marginal production cost by play

as well as which plays will set the market price in which time period. This review included an

assessment of the time it would take before production from dry gas plays would begin setting the

market price, i.e. the point in time when natural gas production associated with oil plays and gas

plays rich in natural gas liquids (NGL) would no longer be sufficient to offset the decline in

production from dry gas plays and the growth in natural gas demand. Our review also evaluated

the potential for exports of U.S. gas in the form of LNG to increase Henry Hub prices above those

forecast in the AEO 2012 Reference Case. We concluded that potential was small because LNG

export quantities are not likely to be materially greater than those assumed in the AEO 2012

Reference Case.  

 AEO 2012 Reference Case forecast: Our forecast starts from the AEO 2012 Reference Case

because it compares well with NYMEX forwards as of March 2013 and with the published

forecasts from Energy Ventures Analysis, Deloitte, the International Energy Agency and IHS Global

Insight as of 2012. In contrast, the AEO 2013 ER prices are well below March 2013 NYMEX12 prices

and the average prices from the forecasts we reviewed.  

 EIA HH methodology downward adjustment: The AEO 2013 ER Reference Case Henry Hub

forecast reflects a major change in the methodology that EIA uses to calculate Henry Hub prices.

The AESC 2013 Base Case forecast incorporates that change in methodology, which represents a

downward adjustment of approximately $0.50 per MMBtu (2013$) to the AEO 2012 Reference

case forecast over the study horizon.

 Marginal well economics upward adjustment: Our analysis of the economics of marginal wells

indicates that by 2020 marginal plays would need market prices $0.33 per MMBtu higher than the

AEO 2012 Reference Case projection. The AESC 2013 Base Case forecast phases this adjustment in

gradually beginning in 2016 and reaches the full level in 2020.  

                                                            

12 The volume of trading on NYMEX is very low after three years. Consequently, we do not view prices beyond that period as a

meaningful guide to natural gas price. In June 2008, NYMEX prices were trading at $12 to $13 per MMBtu through 2025.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐8  

 Fracturing best practices upward adjustment: The EIA is prohibited from reflecting the impacts of

potential new or tighter federal and/or state regulation in its AEO forecasts, thus the AEO 2012

Reference Case forecast reflects no increases in costs due to tighter regulations on fracturing or

voluntary adoption of best practices. The AESC 2013 Base Case forecast includes such an

adjustment based on our assessment that producers are likely to incur costs to reduce the adverse

impacts of fracturing through some combination of industry self‐enforcement of best practices

and further regulations on fracturing. Although we have not found rigorous estimates of these

costs, we consider an upward adjustment of approximately $0.54 per MMBtu by 2021 to be a

reasonable order of magnitude estimate. The AESC 2013 Base Case Forecast phases this

adjustment in gradually beginning in 2017 and reaches the full level in 2021.

The AESC 2013 Base Case forecast of Henry Hub prices that results from this methodology and set of

assumptions is $5.37/MMBtu (2013$) on a 15‐year levelized basis. The AESC 2013 Base Case forecast of

Henry Hub prices is approximately 17 percent lower than the corresponding 15‐year levelized AESC 2011

Base Case (2013$) forecast. The prices under this AESC 2013 Base Forecast compare well to NYMEX in

2015 and 2016 and the average of other published forecasts in 2025 and 2035.

2.2.2 Henry Hub as a Starting Point

The forecast of wholesale natural gas commodity prices in New England begins with a forecast of the

price of gas at the Henry Hub, Louisiana. Henry Hub is used because 1) it is a major trading point whose

prices serves as a reference point against which prices at other locations are indexed, 2) most

comparative forecasts provide prices for this location, and 3) the Gulf Coast is a major source of U.S.

supply. In 2011, the Gulf Coast accounted for approximately 31 percent of U.S. supply and 37 percent of

lower 48 U.S. production.  

As a result of the growing U.S. natural gas production from shale, the sources of U.S. supply are

changing significantly. Strong growth in the Appalachian region (primarily Marcellus shale) and the Utica

in Ohio will displace gas flowing to New England from the Gulf Coast and Canada. Our forecast of

wholesale gas prices in New England will reflect the impact of these changes in supply dynamics.

2.2.3 Review of AESC 2011 Projections

The AESC 2011 Base Case forecast is drawn directly from the AEO 2010 High Shale case forecast from

2015 onward. The choice of that forecast was informed by several factors, including an estimate of the

“full‐cycle” costs of finding and producing shale gas over the life of a given project developed from 2010

financial data filed by gas producers, and uncertainty regarding projections of shale gas production costs

based upon the possibility of tighter regulations on fracturing and on the production characteristics of

future wells.  

As indicated in Exhibit 2‐3, the AESC 2011 Base Case forecast has proven to be high relative to actual

prices to date and to more recent AEO forecasts. However, the fact that actual prices in 2011 and 2012,

and current NYMEX futures for 2013 and 2014, were lower than the AESC 2011 prices for those years

simply indicates that the price expectations of the entire gas industry as of March 2011 have proven to
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐9  

be incorrect, since the AESC 2011 forecast prices through 2015 were based on NYMEX futures as of

March 2011.

Exhibit 2‐3. Henry Hub Prices ‐ Actual and Projected (2013 $/MMBtu)

Source: EIA AEO 2011, 2012, AESC 2011, NYMEX

Exhibit 2‐3 also shows that the AESC 2011 Base Case forecast is substantially higher than the Reference

Case forecasts from AEO 2013 and what we have labeled as AESC 2013 Base Case. Those two AEO

forecasts reflect a major change in the method the EIA uses to calculate the Henry Hub price (discussed

below) plus EIA’s estimate of the impacts of improvements in gas production technology and, in the

near term, of production from NGL rich shale resources.

EIA HH Methodology Downward Adjustment

AEO 2013 reflects a significant change in the method EIA uses to forecast Henry Hub prices. The EIA

made this change because it will no longer collect data on wellhead prices, which have been a key input

to its forecasting method until now. Before AEO 2013, the EIA regressed actual Henry Hub prices against

actual wellhead prices and used the regression results to forecast Henry Hub prices as a function of its

forecast of the average national wellhead price. Under its new method, EIA forecasts Henry Hub prices

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

2011 2016 2021 2026 2031

2013$/MMBtu

AESC 2011 AEO 2011

AESC 2013 Base Case AEO 2013

 Actual 2011 & 2012, NYMEX Forwards as of 3/15/2013

AESC 2011 based 

on NYMEX 3/8/11 

through 2014
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐10  

by adding an estimated gathering charge to its forecast of the average Gulf Coast wellhead price. The

EIA’s new method results in substantially lower Henry Hub prices than its previous method. This result

implies that AEO forecasts prior to AEO 2013 have probably overstated the Henry Hub price.

Our review of gas wellhead price data indicates that the new EIA methodology is appropriate. As

discussed later, the AESC 2013 Base Case forecast incorporates an EIA HH methodology adjustment as a

downward adjustment of approximately $0.50 per MMBtu (2013$) to the AEO 2012 Reference Case

forecast over the forecast horizon.

The EIA provided us with an adjusted AEO 2012 forecast of Henry Hub prices that reflect this

methodology adjustment. In the balance of this section we refer to that adjusted AEO 2012 forecasts as

“AEO 2012 Adj.” Our remaining analyses start from AEO 2012 Adj.  

2.2.4 U.S. Natural Gas Price Dynamics

The following brief review of U.S. natural gas price dynamics is intended to provide an understanding of

the inherent uncertainty in the natural gas industry, why analysts have misread the data, and the

current state of the market.  

Like many capital intensive industries the natural gas industry has had periods of boom and bust. These

boom and bust periods often lead to misperceptions of the full‐cycle cost of production and to poor

forecasts of future prices. With few exceptions, industry analysts have gone through periods of

projecting low prices for a long time periods (approximately 1985 to 1999), projecting high prices and

the need for a high level of imports (approximately 2000 to 2009) and projecting low prices and exports

of natural gas (today). The industry is now going through a period in which low prices for natural gas

have caused drilling to drop off sharply. Given the track record of past forecasts of gas prices, it is

understandable that there is substantial disagreement about the current outlook for natural gas prices.

A brief review of gas price dynamics over the past few decades provides some insight into the inherent

uncertainty in natural gas price projections and the factors that have biased forecasts. This

understanding is useful in assessing how recent dynamics might bias the outlook for future natural gas

prices.

Exhibit 2‐4 below shows the variation in annual average natural gas wellhead prices over time under

various regulatory frameworks and market conditions, with prices expressed in 2013$ per MMcf.13

Regulatory decisions have had a major impact on the natural gas industry.  

 1951 to 1978. In 1951, the Phillips Act regulated natural gas prices on the interstate

market. These regulations caused natural gas shortages on the interstate market after

the Arab oil embargo in 1973.  

                                                            

13 One MMcf is one million cubic feet.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐11  

 1978 to 1984. In 1978, the Natural Gas Policy Act (NGPA) created a complex

classification of natural gas production. Old gas prices were regulated at low prices and

“new gas” had a high price ceiling. This price structure along with pipelines’ concern

about meeting requirements to serve customers created an incentive to increase

natural gas productive capacity and proved reserves. During this period, interstate

natural gas pipelines bundled the sale of transportation and gas supply. In part because

they were obligated to serve, pipelines contracted for supplies at prices higher than

would clear the market. Take‐or‐pay obligations in the form of a minimum bill forced

gas distribution companies to buy gas prices above the level that would clear the

market.  

 1984 to 1999. In 1984, FERC Order 380 outlawed including fixed costs in the minimum

bill to LDCs. This change allowed LDCs to buy gas from the lowest cost pipeline. This put

downward pressure on gas prices and caused financial problems for pipelines that had

contracted to take gas at prices that were not competitive. In 1986, FERC Order 436

required all pipelines to provide open access transportation. This allowed interstate

buyers to negotiate directly with producers and transport their own gas. As a result,

natural gas prices dropped sharply and some pipelines had to file for bankruptcy

because they had taken‐or‐pay contracts with producers. Because prices had been well

above the level necessary to clear the market for many years, excess productive

capacity and reserves were developed. In 1986, wells were operating at about 75

percent of capacity and producers didn’t have to develop new reserves. Producers could

drill existing proved reserves and production grew by simply using excess productive

capacity. Companies laid off geologists and crews and this lowered short run costs. Also,

the cost of rigs was low because fewer rigs were needed to grow production. As analysts

looked at the price levels needed to grow production, they concluded that the cost of

developing natural gas supply was very low and projected low natural gas prices for

decades, but they were not taking into account factors that kept costs below the level

necessary to sustain supply over the long term.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐12  

Exhibit 2‐4. Natural Gas Wellhead Prices (2013$/MMcf)

Source: EIA

 2000 to 2008. By the year 2000, productive capacity was being used at a 96 percent

level. As a result of high capacity utilization, wellhead prices increased 68 percent from

1999 to 2000. Because exploration efforts had been at a low level and there was a

shortage of crews and geologists, production grew very slowly. By 2008, wellhead prices

averaged $8.13/MMcf in 2008 a 264 percent increase from 1999. In June 2008, Henry

Hub prices exceeded $12 per MMBtu and forward prices out to 2025 were trading close

to that level. Most analysts concluded that prices would stay at high level for the

foreseeable future and that the U.S. would have import large amounts of liquefied

natural gas.  

 2009 to present. However, in 2008 natural gas production from shale was ramping up.

Most analysts did not recognize the impact this production was going to have on the

market until late in the summer. Henry Hub prices declined from $12.59 per MMBtu in

June 2008 to $5.82 in December 2008 and only averaged $3.89 in 2009. The high prices

in 2008 and the attractive economics of shale caused a great land grab. Producers

wanted to lock in land with attractive shale plays. The lease provisions between the

producers and land owners contained “hold by production” clauses which required

producers to develop wells or lose their lease. Consequently, despite prices that were

below the level necessary to earn an adequate return, producers continued to bring on

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

1951 1956 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011

2013$/MMcf

Shale Boom

Land Grab

1984 Order 380

Eliminates Minimum 

Price Regulation

- Shortages

NGPA

1986 Open Access

Transportation
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐13  

new wells. Production increased 19 percent (a 4.5 percent annual rate) from 2008 to

2012 and by 2012 Henry Hub prices averaged $2.77 per MMBtu.

As a result of low prices, the natural gas rig count has declined from 1,215 in January 2009 to 425 as of

February 2013, as shown in Exhibit 2‐5. Despite the decline in the gas rig count, estimated production

growth in 2012 increased 4.5 percent. Production growth has remained strong despite a low rig count

because uncompleted wells are still being connected, rigs are much more productive, and some supply

additions are coming from gas associated with drilling for oil in shale plays. Also, doubts have been

raised about the accuracy of the rig count. Natural gas production is not economic, so producers have

been claiming they are drilling for oil.  

Exhibit 2‐5. Henry Hub and Gas Rig Count

Exhibit 2‐6 shows the sharp shift that has occurred since 2009 between the number of rigs drilling for

gas and the number drilling for oil. A major question is whether this shift will result in a slowdown or

decline in natural gas production. In a report entitled U.S. Natural Gas Production2013‐2014 Outlook:

Believe the Shale Boom, Bentek estimated associated gas

14 will add 5.7 Bcf per day of production

between 2012 and 2014. This is equal to 8.6 percent of estimated 2012 production. However, there is

                                                            

14 Bentek defines associated gas that comes from plays that are rich in NGLs or oil plays.

400

500

600

700

800

900

1000

1100

1200

1300

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.50

6.00

6.50

2009 2010 2011 2012 2013

Gas Rig Count

Henry Hub $/MMBtu

Henry Hub (L Axis) Gas Rigs (R Axis)
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐14  

very little data on the amount of associated gas because these plays are nascent and the content varies

greatly within a play. Production of “rich” or “wet” gas is economic at relatively low natural gas prices.15  

Exhibit 2‐6. Oil and Gas Rig Count

Source: Baker Hughes North America Rotary Rig Count

This review of natural gas price dynamics suggests the following:

 Forecasting gas supply prices based upon analyses of current finding and

development costs are often inaccurate because the costs of inputs to current

production costs are often either overpriced, or underpriced compared to the long

run equilibrium production cost. During the 1985 to 1999 period, long run prices

were underestimated. From 2000 to 2008, prices were overestimated. The errors in

forecasts were so large that the outlook for natural gas supply changed dramatically

over these periods.

 There is a similar risk in projecting prices based on what is happening in the current

gas market. The land grab in 2008 resulted in land costs that are probably greater

than would have been paid had shale production grown at a more gradual price. The

rapid growth in shale production has caused a sharp rise in the cost of rigs and crews.

                                                            

15 Rich or wet natural gas contains large amounts of NGLs and or Condensate.

0

200

400

600

800

1000

1200

1400

1600

2009 2010 2011 2012 2013

Rig Count

Oil Gas
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐15  

The “hold by production” requirements have resulted in the development of many

areas at prices that are below the level for a producer to earn an adequate return on

investment. However, much of the cost is sunk. Consequently, for a substantial time

period it could be economic for producers to produce at lower prices than would

earn them an adequate return on all of their costs.  

2.2.5 AESC 2013 Henry Hub Forecast Methodology

Consistent with the approach used to develop the gas price forecast in AESC 2011, the AESC 2013 Henry

Hub natural gas price forecast is based upon data from two sources: 1) futures prices from NYMEX

through 2015 and 2) a forecast from an appropriate AEO forecast for the long term. Using this

methodology, we developed a Base Case forecast of Henry Hub gas prices that is a “blend” of NYMEX

and AEO projections. This methodology is used by many forecasters, including various electric utility

IRPs, and is consistent with reports by the National Regulatory Research Institute and Lawrence Berkeley

National Laboratory. It reflects the fact that futures prices are generally considered to provide the most

accurate forecast of near‐term Henry Hub natural gas prices while forecasts from a model that simulates

market fundamentals of physical demand, physical supply and long‐run marginal costs of supply provide

a better estimate of long‐term prices.  

For the long term, we rely upon forecasts from an appropriate AEO case because the inputs and model

algorithms underlying the AEO projections are public, transparent, and incorporate the long‐term

feedback mechanisms of energy prices upon supply, demand, and competition among fuels. Our

selection of which specific AEO forecast to rely upon was informed by our analysis of the full cycle cost

of finding, developing and producing shale gas, adjustments for sunk costs, publically available forecasts

from credible sources, and the NYMEX forward market prices. We focused upon shale gas because,

consistent with most analysts, we expect shale gas to be the dominant marginal source of supply, and

market price setter, in the long‐term.  

We considered Reference Cases from two AEO forecasts as possible starting points for AESC 2013, i.e.,

AEO 2012 and AEO 2013 ER. We chose the AEO 2012 Reference Case because its forecast compared well

with NYMEX forwards as of March 2013 and with independent forecasts as of 2012 from Energy

Ventures Analysis, Deloitte, the International Energy Agency and IHS Global Insights. In contrast, the

AEO 2013 ER forecast prices were well below March 2013 NYMEX prices as well as the independent

forecasts we reviewed.  

We made three adjustments to the AEO 2012 Reference Case forecast in order to develop the AESC

2013 Base Case forecast. The first is the EIA HH methodology adjustment discussed earlier. This is a

downward adjustment to reflect the change in EIA methodology for forecasting Henry Hub prices. The

second and third are upward adjustments, which are discussed below. These two adjustments reflect

the economics of developing marginal wells (“marginal well economics”), and the costs of reducing the

adverse environmental impacts of fracturing (“fracturing best practices”).

In the process of determining which adjustments to make, and the size of those adjustments, we

considered various factors that might affect the AESC 2013 Base Case forecast of Henry Hub prices.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐16  

Members of the Study Group expressed concern about the potential impact of two specific factors, gas

production from NGL rich plays and exports of gas in the form of LNG. We address those two factors

before describing our marginal well economics adjustment and our fracturing best practices adjustment.

2.2.6 Impact of Gas Production from Plays Rich in NGL

As noted earlier, gas prices in 2011 and 2012 proved to be much lower than industry expectations as of

March 2011, yet gas production levels remained high. One of the major factors driving those market

conditions was the ability of producers to maximize the quantity of gas they obtained from gas shale

plays with a high content of NGLs and from natural gas associated with oil shale plays. In this discussion

we refer to that gas supply as “associated gas.” Those producers were able to augment the revenue

from their sale of associated gas with revenue from the sale of the NGLs or oil. In some plays it was

economic for the producers to produce associated gas solely for the revenues from NGLs or oil.  

The question we faced when developing the AESC 2013 Base Case forecast was to estimate the time

period over which the growth in associated gas, would no longer offset the decline in production for dry

gas plays and the growth in natural gas demand. Our analyses indicated that NGL prices would be weak

through 2016 but would rebound and increase in the mid‐ to long‐term due to increases in demand for

NGLs as feedstocks and fuels in both U.S. and export markets.16,17Our review of the costs of plays with

rich gas production indicates that most rich and gas production associated with oil shale plays will be

economic within the likely range of future prices for oil and natural gas liquids. As a result, the primary

factors that will limit the production of rich gas will not be oil and NGL prices but instead will be the pace

of field development, permitting, processing plant construction, and pipeline capacity. Before the gas

and oil industry will make the capital expenditures required for new processing plants and pipeline

capacity, there needs to be assurance that there will be sufficient reserves to support these facilities

over their economic life. Thus, rich gas production is more likely to be limited by processing and pipeline

capacity limitations than by low oil or NGL prices.  

The rapid growth in production of rich gas and associated gas has been a major factor influencing

natural gas prices. In the short run it has made much of the dry gas production uneconomic. In the

longer term, the level of that rich production will influence the timing of when additional dry gas

production will be economic. In the short term (over a few years) changes in rich gas production could

have a major impact on prices, however our review of long term supply elasticities (see below), indicates

                                                            

16 Lyondell Basell, a global petrochemical company, sees U.S. ethane production exceeding demand through 2016. Available at:

http://www.lyondellbasell.com/NR/rdonlyres/257B0667‐D5FC‐4D57‐9924‐

FD8ECD9A41F1/0/GoldmanMay2013Slides05142013FINALNOLinks.pdf (slide 10)  

17 Recent reports from the EIA and Platts have reported that the cracking capacity to process associated gas liquid may increase

significantly by 2020. Platts and the EIA both report an estimated 10.1 million tons of cracking capacity is being planned

through 2020, which would represent approximately 35 percent of the current U.S. cracking capacity. Available at:

http://www.platts.com/IM.Platts.Content/InsightAnalysis/IndustrySolutionPapers/SR_Ethylene_AFPM_2013.pdf and

http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf (page 50)
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐17  

that changes in rich gas production are unlikely to have a major impact on Henry Hub prices in the long

term.

It would take an extremely large change in annual production of rich and associated gas to have a

significant impact on Henry Hub prices in the long term.  

 Henry Hub Price elasticity. Our analysis of the long run supply curve indicates that a 10

percent change in 2020 gas production (6.9 Bcfd) would cause Henry Hub prices to

change about $0.20 per MMBtu or 4.4 percent. This relatively small price impact is

consistent with an analysis by Deloitte that estimated the impact of additional gas

demand of 6 Bcfd of LNG exports would increase their relative projections of Henry Hub

prices by $0.15/MMBtu (2012$), approximately 2.5 percent between 2016 and 2030.18  

 Associated gas portion of total annual gas production. The AEO 2012 projects that

shale gas production in 2020 will represent 38 percent of total natural gas production.

We estimate that approximately 20 percent of total production would come from

associated gas from shale plays. A 10 percent change in that associated gas production

would represent a less than 1 percent change in total gas production and thus would

change Henry Hub prices by less than about 0.44 percent or $0.02 per MMBtu.  

2.2.7 Potential Impact of LNG Exports

The AEO 2012 and AEO 2013 Reference Cases each project LNG exports of 0.7 Bcfd by 2020. Given the

currently low prices, there is a reasonable chance that LNG exports will exceed those forecast quantities

and drive natural gas prices above the corresponding Reference Case forecast prices. However, there is

considerable uncertainty regarding the potential incremental quantities of LNG exports. For example, a

recent study by Wood Mackenzie indicated that U.S. exports of LNG will face strong price competition

after 2018. There is also uncertainty regarding the potential impact of incremental LNG exports on

Henry Hub prices.  

First, the EIA does not have a rigorous model of LNG trade. The AEO 2012 forecasts were based on

exogenous assumptions about LNG and the AEO 2013 forecasts of LNG exports are based on EIA

projections of international natural gas prices.  

Second, the Department of Energy (DOE) must evaluate applications to export LNG. The Natural Gas Act

(NGA) requires DOE to grant a permit unless it finds that such action is not consistent with the public

interest. As a practical matter, the need for DOE to make a public interest judgment is only relevant for

applications to export LNG to countries that have not entered into a free trade agreement (FTA) with

the United States.( The NGA provides that applications involving imports from or exports to an FTA

country are deemed to be in the public interest and shall be granted without modification or delay.)

However, with the exception of South Korea, very few countries that would potentially import LNG from

                                                            

18 Deloitte’s World Gas Model average Henry Hub in 2016 is approximately $6.09/MMBtu (2012$) from Made in America: The

economic impact of LNG exports from the United States.  
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐18  

the United States have FTAs. In April 2013, President Obama made a statement of support for LNG

exports, which some parties believe could result in faster approval of LNG exports by the DOE. However,

the AEO 2012 and AEO 2013 export cases do not cite the need for export approval as a factor that will

slow LNG exports. To date, only the Sabine Pass and Freeport LNG terminals have received LNG export

approval. The Sabine Pass terminal still needs approval from FERC. If approved, the planned in‐service

date for the terminal in the fourth quarter of 2015. The Sabine Pass terminal will have an initial capacity

of approximately 1 Bcfd with expansion to 2 Bcfd. In May 2013, the DOE issued an approval for the

Freeport LNG terminal to export to non‐FTA nations. It is targeted for in‐service date of 2017 with

capacity of 2 Bcfd.

Third, estimates of the impact of incremental LNG exports on Henry Hub prices vary widely. The EIA has

estimated that incremental LNG exports of 3.3 Bcfd, i.e., to a total of 4 Bcfd by 2020, could cause prices

to increase by an average of about $0.42 per MMBtu over the 2016 to 2020 period. However, the EIA

prepared that estimate using their AEO 2011 model, which had a less optimistic gas supply outlook, and

used a Henry Hub methodology that overstates the impact of a change in wellhead prices on Henry Hub

prices. In 2013, Deloitte estimated the impact of increased LNG exports upon Henry Hub prices using

their World Gas Model. Their analysis found that an annual export of 6 Bcfd of LNG caused an

$0.15/MMBtu increase in their projections of Henry Hub prices from 2016 through 2030. Another recent

report issued by the Bipartisan Policy Center also found that LNG exports had a modest impact on

natural gas prices in the future.  

2.2.8 Marginal Well Economic Adjustment

We developed estimates of natural gas production costs for shale plays from our review of state data,

10‐K filings, producer presentations, and analyses of well production data from Lippman Consulting

Incorporated. Exhibit 2‐7 and Exhibit 2‐8 show the prices that various shale plays require to earn a 10

percent internal rate of return (“IRR”). These exhibits present the prices required to earn a 10 percent

IRR with “sunk” costs and without sunk costs. (Sunk costs include land, gathering costs, and overhead.)

The 10 percent IRR is calculated after taxes, and is sometimes referred to as an after‐tax rate of return

(“ATROR”).
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐19  

Exhibit 2‐7. Breakeven 10% IRR Henry Hub Prices (2013$/MMBtu)

In order to estimate the market price necessary to balance the market and provide producers an

adequate return requires one to estimate the marginal supply source. The marginal supply source

changes over time, as higher cost plays replace lower cost plays as the marginal source due to a

combination of declines in production from the lower cost plays and increases in annual gas

consumption. Exhibit 2‐7 and Exhibit 2‐8 provide an indication of the production costs of the plays that

are likely to be adequate to balance the market through 2020 and possibly longer. As production from a

given play increases, its production could increase because of increased labor and rig costs, regulations

that increase costs or it could decrease because of advances in technology and decreases in factors such

as land costs, rigs, and labor. Thus the estimates presented in Exhibit 2‐7 and Exhibit 2‐8 provide an

indication of the marginal pricing of each play assuming the factors that could drive production costs up

are fully offset by the factors that could them down.

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$/MMBtu

Full Cost Without Sunk Cost
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐20  

Exhibit 2‐8. Costs of Selected Plays in 2020 (2013$/MMBtu)

Shale Gas Play 

Required 

Henry Hub 

price with 

Sunk Costs 

(Breakeven 

10%) 

Adjustments for Sunk Costs Henry 

Hub 

without 

Sunk 

Costs 

Land 

Gathering 

and 

Processing 

Cost 

Overhead 

 2013$/MMBtu 

BAKKEN - ND 

BAKKEN - MT 

EAGLE FORD EAST 

CORE 

 

UTICA - OH 

NIOBARA - CO 

MARCELLUS $3.11 $0.20 $0.50 $0.45 $1.96

EAGLE FORD NON 

CORE 

$3.58 $0.55 $0.50 $0.45 $2.08

GRANITE WASH - TX $4.66 $0.25 $0.50 $0.45 $3.46

EAGLE FORD CORE $5.00 $0.55 $0.50 $0.45 $3.49

GRANITE WASH - OK $4.86 $0.30 $0.50 $0.45 $3.61

WOODFORD - CANA $5.01 $0.35 $0.50 $0.45 $3.71

BARNETT - CORE $4.86 $0.35 $0.25 $0.45 $3.81

FAYETTEVILLE $5.22 $0.35 $0.50 $0.45 $3.91

HAY - LA - CORE $5.15 $0.20 $0.50 $0.45 $4.00

WOODFORD - 

ANADARKO 

$5.27 $0.25 $0.50 $0.45 $4.06

BARNETT - NORTH $5.17 $0.35 $0.25 $0.45 $4.11

HAY - LA - WEST CORE $5.40 $0.20 $0.50 $0.45 $4.25

WOODFORD - 

ARKOMA 

$5.77 $0.20 $0.50 $0.45 $4.62

BARNETT - SOUTH $5.82 $0.40 $0.25 $0.45 $4.72

HAY - LA - NON CORE $6.26 $0.50 $0.50 $0.45 $4.80

HAYNESVILLE - TX $6.95 $0.30 $0.50 $0.45 $5.70

Our review of forecast annual gas consumption and production from these plays indicates that by 2020

the marginal sources of production will be plays with a cost structure as high as Barnett‐South and

possibly Hayesville, Louisiana, Non‐Core. Considering the full cost of production this would require a

market price between $5.82 and $6.26 per MMBtu. However, the model the EIA uses to develop its AEO

price forecasts does not include sunk costs in the economic analysis. If one excludes sunk costs, the

variable cost of gas from those marginal plays ranges from $4.72 to $4.80 per MMBtu. Those marginal

production costs are higher than the AEO 2012 Adj. forecast price of $4.39 in 2020.

The AESC 2013 Base Case forecast includes a “marginal well economic adjustment” to reflect the

difference between our estimate of the marginal cost of gas in 2020 and the AEO 2012 Adj. forecast
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐21  

price for that year. That marginal well economic adjustment is $0.3319per MMBtu added to the AEO

2012 Adj. forecast price in 2020. The AESC 2013 Base Case forecast prices reflect a phase‐in of the $0.33

per MMBtu from 2016 to 2020 using a compound annual growth rate of 2.4 percent.  

We consider this marginal well economic adjustment to be conservative. For example, a study by Tudor,

Pickering, & Holt & Co. (“TPH”) estimated that the market price in 2016 required for producers to break

even was $5.65 per MMBtu (2013$). That market price is slightly below our estimate of the full

production cost of Barnett South of $5.82 per MMBtu in 2020. We are assuming prices will stay lower

than the full cycle cost estimate because producers have built gathering systems and purchased land,

and those costs are fixed or sunk. Also, much of the overhead cost is probably fixed. However, one could

argue that the marginal well economic adjustment should be higher, at $143 per MMBtu, which is the

full marginal cost of $5.82 less the AEO 2012 Adj. price of $4.39, rather than $0.33 per MMBtu.

However, the $0.33 per MMBtu adjustment produces forecast prices that are consistent with the

forward market and that we consider to be conservative.  

                                                            

19 The $0.33 estimate rather than the $0.41 because our estimate of the marginal supply source does not allow for natural gas

production associated with oil shale development. This development is likely to lower the marginal source of supply.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐22  

Exhibit 2‐9. Breakeven Prices 10% ATROR ($/MMBtu)

Source: Tudor, Pickering, & Holt & Co.

2.2.9 Fracturing Best Practices Adjustment

Hydraulic fracturing is a technique used in "unconventional" gas production. "Unconventional"

reservoirs can cost‐effectively produce gas only by using a special stimulation technique, like hydraulic

fracturing, or other special recovery process and technology. This is often because the gas is highly

dispersed in the rock, rather than occurring in a concentrated underground location. Hydraulic

fracturing produces fractures in the rock formation that stimulate the flow of natural gas or oil,

increasing the volumes that can be recovered. Wells may be drilled vertically hundreds to thousands of

feet below the land surface and may include horizontal or directional sections extending thousands of

feet.

Horizontal drilling and multi‐stage fracturing can be disruptive to communities, and accidents have

increased as drilling has increased. New regulations and better enforcement will be required to assure

public safety and acceptance of natural gas production from shale. These regulations will come both at a

state and federal level. In 2011, EPA began research under its Plan to Study the Potential Impacts of

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

$/MMBtu

2016 $6 Nominal ($5.65 2013$)
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐23  

Hydraulic Fracturing on Drinking Water Resources20. The study will not be completed until at least 2014

and it is likely that it will be several years before new regulations are implemented.

However, numerous states are considering tighter regulation of fracturing. According to a report by the

National Conference of State Legislatures21

:

 At least 119 bills in 19 states have been introduced this session that address

hydraulic fracturing. At least nine states—Indiana, Maryland, New Jersey, North

Carolina, Pennsylvania, South Dakota, Tennessee, Utah, and Vermont—have enacted

legislation.

 At least 11 states have proposed legislation to impose new or amend existing

severance taxes.

 At least nine states have proposed chemical disclosure requirements.

 At least eight states have proposed casing, well spacing, setback, water withdrawal,

flow back, waste regulation requirements, or other measures to protect water

resources.

 At least 11 states have proposed legislation to impose new or amend existing

severance taxes.

 Legislators in at least eight states have proposed hydraulic fracturing suspensions,

moratoria, or studies to investigate fracking impacts.

 At least seven states have proposed resolutions addressing hydraulic fracturing.  

 At least 13 bills have been introduced in Pennsylvania with a range of proposed rates

and structures. S.B. 352, for example, would impose a natural gas severance tax of 5

percent on the gross value of gas extracted at the wellhead, plus 4.6 cents per 1,000

cubic feet of natural gas extracted. H.B. 1705 would impose a natural gas severance

tax of 1.5 percent of the gross value of gas severed at the wellhead for the first 60

months of production and 5 percent thereafter. This is shown in Exhibit 2‐10.

States also have the ability to impose impact fees on fracturing operations. For example, Pennsylvania

enacted H.B. 1950 (February 2012) to implement an impact fee based on the average price of natural

gas in the preceding year. It is capped at $355,000 per well during a 15‐year period. The new law aims to

benefit local communities that are affected by drilling.

                                                            

20 See http://epa.gov/hfstudy/. Accessed June 8, 2013.

21 Natural Gas Development and Hydraulic Fracturing, A Policy Makers’ Guide, Revised June 2012, Jacquelyn Press, National

Council of State Legislatures.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐24  

Our review of literature in the public domain has identified only three studies that attempt to quantify

regulations that might impact of the production of natural gas. A study done by Advanced Resources

International22 evaluated proposals that had been suggested without regard to whether they are likely

to be implemented. This study concluded that 22 percent of natural gas production from shale would be

lost at prices of $6 per MMcf (2007$) and 10 percent at prices of $9 per MMcf. However, it is our view

that it is unlikely that regulations as stringent as in this study would be implemented.  

A study was done on the impact of New Source Performance Standards23 regulating volatile compounds.

The study concluded that if wellhead prices were above $3.95 per MMcf (2013$) in 2015 there would be

no impact on the cost of natural gas. This is because capturing the volatile compounds has value. Our

forecast wellhead price in 2015 is approximately $4.03 per MMcf.

The Government Accountability Office (GAO)24 found that shale oil and gas development is likely to pose

threats to public health and the environment, but it's unclear how significant those risks will be in the

long run because of gaps in knowledge. There were no cost estimates in the study.

Also, based on a study of TPH, we estimate that regulations fracturing and voluntary producer

adjustments would add about $0.54 per MMBtu to the Haynesville, Louisiana non‐core play (See the

following section for the analysis.) This is our fracturing best practices adjustment. Since this play

probably won’t be economic until about 2021, we phased the additional cost in from 2016 using a

compound annual growth rate.

In 2010, TPH estimated the potential impact of fracturing regulations and voluntary changes by

producers on the cost of production per well. The size and costs of wells vary substantially. We

estimated the required increase in gas prices by running an economic well model of Haynesville non‐

core production in Louisiana and Marcellus shale. The results for Haynesville analysis are shown below.

Since the average Haynesville well will not be economic until about 2021, we phased in the cost of

development beginning in 2016. The required price increase for Marcellus was only about $0.20 per

MMBtu.

                                                            

22 Potential Economic And Energy Supply Impacts Of Proposals To Modify Federal Environmental Laws Applicable To The U.S.

Oil And Gas Exploration And Production Industry, Prepared for U.S. Department of Energy Office of Fossil Energy, Advanced

Resources International, 2007.

23 Regulatory Impact Analysis, Final New Source Performance Standards and Amendments to the National Emissions Standards

for Hazardous Air Pollutants for the Oil and Natural Gas Industry, U.S. Environmental Protection Agency, Office of Air and

Radiation, Office of Air Quality Planning and Standards, April 2012.    

24 Unconventional Oil And Gas Development, Key Environmental and Public Health Requirements Report to Congressional

Requesters, September 2012, Government Accountability Office and Information on Shale Resources, Development, and

Environmental and Public Health Risks, GAO‐12‐732, Sep 5, 2012.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐25  

Exhibit 2‐10. Potential Impact of Fracturing Regulation on the Breakeven Price of Natural Gas (2013$)

2013$

Low High Average

Added cost per well without federal regulation $211,025 $527,564 $369,294  

Potential EPA cost compliance $131,891 $263,782 $197,836  

Total $342,916 $791,345 $567,131  

Cost per MMBtu Haynesville Non‐Core

Added cost per well without federal regulation $0.21 $0.49 $0.35  

Potential EPA cost compliance $0.12 $0.26 $0.19  

Total $0.33 $0.75 $0.54  

Source: Frac Attack, Tudor, Pickering, & Holt & Co. 2010

Some may argue that these estimates are not sufficiently rigorous to use in this study. There are two

possible responses to that position, ignore the costs because we don’t know what the regulations will be

or make the best estimate possible of the likely impact. While the estimated cost of fracturing is based

on limited information, all natural gas forecasts make projections with limited data. In some cases there

is only six months of data on production in a play. One choice would be to ignore the play and the other

is to do the best one can with it. The EIA forecast is full of these assumptions that are based on very

limited data. A few examples are how much liquids are in a play, how much associated gas will come

from oil shale plays, technology advances, etc.  

Another question is the extent to which improvements in fracturing practices and/or tighter regulations

will increase gas production costs or decrease those costs. Our analyses indicate the impact is most

likely to be a modest increase in production costs. It is clear that various potential changes in regulations

and state taxes could increase the cost of natural gas production. For example, with a $4.00 per MMcf

wellhead price, the proposed Pennsylvania severance tax of 4.6 cents per MMcf plus 5 percent of the

gross value would equal 24.6 cents per MMcf ($0.24 per MMBtu). The impact fees of $355,000 per well

would be equal to about 12.5 cent per MMBtu. The total of these costs would be 36.5 cents per MMBtu.

This compares to our estimated fracturing cost of about $0.20 per MMBtu in Pennsylvania and $0.54 per

MMBtu for the marginal well. The 36.5 cent cost would be in addition to the fracturing costs. Also, it is

highly likely that states will prohibit production in certain areas. New York is a prime example of this.

This will add to production cost as well. Since AEO Reference Case forecasts are prohibited by law from

reflecting any potential changes in regulations, such as changes in fracturing regulations, those forecasts

will probably understate future natural gas prices, other things being equal.

Earlier we suggested that an argument could be made that the economic adjustment should be $1.43

per MMBtu versus the $0.33 per MMBtu adjustment actually used (marginal well economics

adjustment). Our total adjustment for marginal well economics and fracturing operations improvements

of $0.87 per MMBtu is a compromise with arguments about these issues. Looking at the entire picture,

we believe it is reasonable. This is supported by the fact that the resulting forecast is close to the

average of other publically available forecasts.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐26  

2.2.10 Comparison to other Forecasts of Annual Henry Hub Prices

Exhibit 2‐11 presents the AEO 2012 Reference Case, the AEO 2012 with the EIA HH methodology

adjustment, and the AESC 2013 Base Case. The table compares those three forecasts with Henry Hub

prices, average prices, and average prices less IHSGI. The comparison shows the following:

 AEO 2013 prices are well below the NYMEX25 prices as of March 15, 2013 and the

average of public forecasts as of 2012. The AEO 2013 ER projected prices in 2016

prices are $0.86 per MMBtu below the NYMEX prices in 2015 and $0.47 per MMBtu

(2013$) below the NYMEX in 2016. The large discrepancy between AEO 2013 forecast

prices and the near term forward market raises questions about the economic

assumptions in AEO 2013. In addition, the comparison of AEO 2013 with other

forecasts and our analysis of the economics of production suggest that the AEO 2013

prices are likely too low.  

 AEO 2012 projections with the EIA HH methodology adjustment are also well below

the average of other forecasts in 2025.

 AESC 2013 Base Case prices are close to NYMEX in 2015 and 2016 and the average of

all forecasts in 2025. Also, after taking out the IHSGI forecast, the AESC 2013 Base

Case is close to the average of forecasts in 2035.

In addition to the above considerations, the full documentation of AEO 2013 was not available at the

time the gas forecasts were being prepared. For these reasons, we chose to use AEO 2012 as our

starting point.

                                                            

25 The volume of trading on NYMEX is very low after three years. Consequently, we do not view prices beyond that period as a

meaningful guide to natural gas price. In June 2008, NYMEX prices were trading at $12 to $13 per MMBtu through 2025.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐27  

Exhibit 2‐11.26 Comparison of Price Projectons (2013$/MMBtu)

 Henry Hub 2013$/MMBtu 

 2015 2025 2035 

NYMEX NYMEX 4.15 5.59 NA 

 

Non-AEO 

Forecasts 

IHSGI 5.01 5.09 5.41 

EVA 4.29 6.83 7.66 

Deloitte 4.48 6.12 7.00 

SEER 4.52 6.64 8.12 

IEA 2012 4.75 6.61 8.26 

Average Non-AEO Forecast 4.61 6.26 7.29 

 

AEO 

Forecasts 

AEO 2013 3.22 5.03 6.53 

AEO 2012 4.53 5.94 7.77 

AEO 2012 EIA HH Meth. 4.20 5.39 7.08 

 

AESC 2013 AESC 2013 Base Case 4.15 6.26 7.95 

 

NYMEX Delta 

AEO 2013 -0.93 -0.56 

AEO 2012 EIA HH Meth. 0.04 -0.20 

AESC 2013 Base Case 0.00 0.67 

 

NON-AEO 

Forecast 

Delta 

AEO 2013 less Average -1.39 -1.22 -0.76 

AEO 2012 EIA HH Meth. -0.42 -0.86 -0.21 

AESC 2013 Base Case -0.46 0.01 0.66 

AESC 2013 Base Case (1) -0.36 -0.29 0.19 

 

(1) Excludes IHSGI from 

average 

Exhibit 2‐12 below shows estimates of total natural gas consumption and supply in the United States in

2012, and AEO 2012 projections for 2020. Projected national consumption is expected to be about the

same in 2020 as in 2012. The 2012 data is strongly influenced by weather. Heating Degree Days were 15

percent below normal in 2012. Much of the projected growth in the Residential and Commercial sectors

is because the forecast assumes normal weather. Also, Cooling Degree Days were almost 15 percent

above normal, this caused high electricity and natural gas demand for power generation. In addition,

                                                            

26 AEO 2013 Early Release Table 13 (http://www.eia.gov/forecasts/aeo/er/tables_ref.cfm). NYMEX prices are closing prices

2/27/2012 deflated using AEO 2013 inflation assumptions. The source of AEO 2012 is

http://www.eia.gov/forecasts/aeo/pdf/0383 (2012).pdf page 113. AEO 2012 (Reference case): AEO 2012 National Energy

Modeling System, run AEO 2012.REF2012.D020112C. IHSGI: IHS Global Insight, 30‐year U.S. and Regional Economic Forecast

(Lexington, MA, November 2011), website www.ihs.com/products/global‐insight/index.aspx (subscription site). EVA: Energy

Ventures Analysis, Inc., e‐mail from Anthony Petruzzo (January 26, 2012). Deloitte: Deloitte LLP, e‐mail from Tom Choi

(January 26, 2012). SEER: Strategic Energy and Economic Research, Inc., e‐mail from Ron Denhardt (February 21, 2012). IEA

(International Energy Agency) World Energy Outlook 2012.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐28  

because of low natural gas prices, on a national basis natural gas displaced a significant amount of coal

generation. As natural gas prices increase, gas is expected to lose market share back to coal throughout

the United States. Despite essentially flat natural gas consumption, U.S. production is expected to

increase to offset declines in Canadian production and for the export of LNG. Shale production will grow,

while other sources of supply are expected to decline.

Exhibit 2‐12. United States Gas Consumption and Supply: 2012 vs. AEO 2012 Projections for 2020 (Tcf/year)

      2012 2020

     (a) (b)

Consumption of Natural Gas

   Residential and Commercial 7.06 8.26

   Industrial and Transportation 7.11 7.16

   Electric Power Generation 9.11 7.87

   Pipeline and Lease & Plant Fuel 2.10 2.17

    

   Total 25.39 25.47

    

Supply of Natural Gas

   Shale Gas Production (b) 8.13 9.69

   Other 15.85 15.40

   Total Production 23.98 25.09

    

   Pipeline Net Imports 1.34 1.01

   LNG Net Imports 0.18 ‐0.66

   Total Net Imports 1.51 0.35

    

   Total Supply 25.53 25.5

Source: (a) Actual 2012 is estimated based on EIA Short Term Energy Outlook, April 9th 2013. Supply does not add to total

because of inventory change.. (b) Data from AEO 2012, Table A14 Oil and Gas Supply. Available at

http://www.eia.gov/forecasts/aeo/pdf/0383(2012).pdf

The AESC 2013 Base Case forecast of Henry Hub prices that results from this methodology and set of

assumptions is $5.37/MMBtu (2013$) on a 15‐year levelized basis. This AESC 2013 Base Case forecast of

Henry Hub prices would be approximately 17 percent less than the AESC 2011 Base Case forecast over

that period.

Comparison to AESC 2011 Base Case

Exhibit 2‐13 compares the AESC 2013 Base Case forecast with the AESC 2011 Base Case forecast. This

comparison shows the AESC 2013 Base Case annual Henry Hub natural gas price forecast and the actual

Henry Hub gas prices since 2011 through 2012.  
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐29  

Exhibit 2‐13. Comparison of Henry Hub Natural Gas Price Forecasts (2013$/MMBtu)

2.2.11 High and Low Case Forecasts of Henry Hub Prices

As discussed earlier in this chapter, there is a great deal of uncertainty about the natural gas price

outlook. Among the factors driving these uncertainties are the size of the resource base, the expected

ultimate recovery (EUR) of these resources, proposed LNG exports, technology changes, fracturing

regulations, state taxes, oil prices, potential for increased use in the residential, commercial and

industrial sectors, carbon regulations, and economic growth. To reflect this uncertainty, we have

developed high and low gas price forecasts. These forecasts reflect high and low trends and do not

account for short term variability caused by factors such as weather or supply disruptions. The high and

low price forecasts are intended to reflect plausible outcomes rather than extreme possible values. The

AESC 2013 Base Case forecast could be viewed as an expected value of the possible outcomes.

The forecasts of the AESC 2013 Base Case, High Price Case, and Low Price Case are shown in Exhibit

2‐14. The average percentage deviations from the Base Case are approximately 23 percent for both the

High Price and Low Price Cases. In the High Price Case, the Alaska gas pipeline comes online in 2031

because of the high prices. This causes prices to fall for a few years.  

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

2010 2015 2020 2025 2030 2035

2013$/MMBtu

AESC 2011 Base Case AESC 2013 Base Case Actual
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐30  

Exhibit 2‐14. Forecasts of AESC 2013 Henry Hub Natural Gas Prices: Base, High and Low (2013$/MMBtu)  

Both the high and low price cases are based on alternative assumptions about the EUR from the AESC

2013 Base Case. Disagreement about the EUR is one of the most contentious areas of the natural gas

outlook. Since many plays are nascent, the data is very limited. The High Price case assumes the EUR is

50 percent lower than the EUR in the AESC 2013 Base Case and the Low Price case assumes the EUR is

50 percent higher. In addition, the Low Price projections do not include any adjustments for the

economics of fracturing regulations. Also, AEO 2013 prices were used through 2016 because they were

lower than the High EUR case. The High Price forecast adds an economic adjustment of $0.88 per

MMBtu versus $0.33 per MMBtu in the AESC 2012 Base Case to the low EUR price. The higher economic

adjustment is based on the assumption that a return is earned on some of the sunk costs.  

Any estimate of the probability of these cases is highly subjective. Our estimate is that there is a 20

percent probability that the levelized value exceeds the high value and a 20 percent probability of it

exceeding the low value; alternatively, there is a 60 percent probability of the levelized prices being

within the range of the high and low price scenarios. The Base Case is the expected value.

2.2.12 Uncertainty Regarding Shale Gas Production

There is considerable uncertainty regarding projections of shale gas production quantities and costs, as

described in previous sections and below. Given the uncertainty associated with projections of shale gas

resource availability, production quantities, regulations, and costs, there is certainly a possibility that

material changes in the long‐term outlook for shale gas production and cost may occur after the

completion of AESC 2013 and before the initiation of AESC 2015. Those material changes might be

driven by public developments such as significant revisions to public geological analyses; a legislative

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

$10.00

2013 2018 2023 2028 2033

2013$/MMBtu

Low Case 2013 AESC Base High Case
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐31  

body, policy agency, or regulatory agency identifying specific changes in the regulation of hydraulic

fracturing; published estimates of the costs associated with regulatory changes; or changes in natural

gas market prices. In the event of such public developments, members of the Study Group may choose

to determine if the AESC 2013 Base Case and High Gas Price Case projections of natural gas prices are

still suitable for use in energy efficiency cost‐effectiveness analyses. If they determine that neither of

those projections is within a range of reasonableness in light of the public developments, the members

of the Study Group should consider revising the natural gas price forecast and the avoided costs.

Technical Uncertainty  

The first area of uncertainty relates to the estimates of technically recoverable quantities of shale gas

and the costs of recovering those volumes. AEO 2013 acknowledges this uncertainty and identifies

several factors that could tend to result in less production or higher costs under some scenarios, or

more production and lower costs under other scenarios.27 These factors include limited reliable data on

long‐term production profiles and ultimate gas recovery rates, use of production rates from portions of

certain formations to infer the productive potential of the entire formation, and the possibility that

technical advances could reduce drilling and completion costs.  

Exhibit 2‐15 presents actual levels of annual shale gas production from 2008 through 2011 as well as the

projected production underlying the various cases we examined.

                                                            

27 AEO 2013 report page 77.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐32  

Exhibit 2‐15. Dry Shale Gas Production: Actual and Projected (Tcf/year)

2.2.13 Volatility in Henry Hub Prices

Volatility is a measure of the randomness of variations in prices over time as affected by short‐term

factors such as extreme temperatures, hurricanes, supply systems disruptions, etc. It is not a measure of

the underlying trend in the price over the long‐term. Our forecasts of Henry Hub prices under the Base,

high, and low cases provide projections of expected average natural‐gas price in any year. Actual gas

prices are volatile and in any future month, week, or day will vary around the expected annual average

prices forecast in each of those three cases. We have not attempted to forecast the actual monthly or

weekly prices that would reflect historical price volatility primarily because we are forecasting prices

used to evaluate avoided costs in the long term. Our analyses indicate that the levelized price of gas

over the long term would not be materially different if one estimated increases from an occasional one‐

to‐three‐day price spike during a cold snap or even the type of several month gas price increases

following Hurricane Katrina in the fall of 2005. For example, monthly Henry Hub prices were very volatile

from 2000 through 2010, ranging from less than $4.00/MMBtu to over $14.00/MMBtu. See Exhibit 2‐16.

However, the levelized average annual cost over that period was $5.80/MMBtu. If one substitutes

annual average prices for certain months with very high prices, such as the four months affected by

Hurricanes Katrina and Rita, and the three month price spike in mid‐2008, the levelized price over the

entire eleven year period remains very similar at $5.65/MMBtu.

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

2008200920102011201220132014201520162017201820192020202120222023202420252026202720282029203020312032203320342035

Trillion Cubic Feet

Actual AESC 2013 Base Case AESC 2013 Low Case AESC 2013 High Case
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐33  

Exhibit 2‐16. Monthly Henry Hub Prices, Historical (EIA) and Projected (2013 Dollars per MMBtu)

The range of volatility in weekly Henry Hub gas prices is even higher. Exhibit 2‐17 shows the weekly

average of the daily spot price of natural gas at the Henry Hub from 2000 through May of 2013 and then

monthly NYMEX gas futures prices through December 2013. These prices are in nominal dollars; they

have not been adjusted for inflation because this discussion of volatility does not require prices in real

terms.

$0.00

$2.00

$4.00

$6.00

$8.00

$10.00

$12.00

$14.00

$16.00

2013$/MMBtu

Actual Projected

California 

energy crisis

Hurricanes 

Katrina & Rita

Colder than normal winter, more gas for elec 

generation, fewer imports, high oil prices
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐34  

Exhibit 2‐17. Henry Hub Average Weekly Natural‐Gas Prices, Actual and Futures, Jan 2000 – Dec 2015

Price spikes and dips show price volatility. In New England and in other gas consuming areas, there have

been daily price spikes during very cold weather, including recent price spikes in November 2012 and

February 2013 in New England. In addition, natural‐gas prices have increased for longer periods. The

recent example of Hurricane Katrina in 2005 is illustrative, as follows.

 July 29, 2005: the NYMEX gas futures contract for September 2005 delivery was

priced at $7.89 per MMBtu;

 August 29, 2005: Katrina hit the Gulf Coast;

 December 13, 2005: the NYMEX January 2006 gas futures contract settlement price

was $15.38 per MMBtu;

 March 1, 2006: six months after Katrina struck the Gulf Coast, the April 2006 gas‐

futures contract was priced at $6.73 per MMBtu;

 Subsequently, 2006 experienced few hurricanes and on September 27, 2006 the

October 2006 gas futures contract closed at $4.21 per MMBtu.

0

2

4

6

8

10

12

14

16

7‐Jan‐00

7‐Jul‐00

7‐Jan‐01

7‐Jul‐01

7‐Jan‐02

7‐Jul‐02

7‐Jan‐03

7‐Jul‐03

7‐Jan‐04

7‐Jul‐04

7‐Jan‐05

7‐Jul‐05

7‐Jan‐06

7‐Jul‐06

7‐Jan‐07

7‐Jul‐07

7‐Jan‐08

7‐Jul‐08

7‐Jan‐09

7‐Jul‐09

7‐Jan‐10

7‐Jul‐10

7‐Jan‐11

7‐Jul‐11

7‐Jan‐12

7‐Jul‐12

7‐Jan‐13

7‐Jul‐13

7‐Jan‐14

7‐Jul‐14

7‐Jan‐15

7‐Jul‐15

Nominal $ per MMBtu

Actual Weekly Average Price NYMEX Future Prices 105 Week Moving Average
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐35  

In this example, a shock that removed 5 billion cubic feet per day of natural‐gas supply produced a

strong increase in prices. However, prices quickly reversed to more‐typical levels and in less than a year

gas futures price fell (temporarily) to a level less than one‐third of the peak of December 2005. We

expect such shocks and gas price volatility to continue periodically in the future. Nonetheless, the AESC

2013 Base Case gas price provides a reasonable estimate of average or expected Henry Hub gas prices

for the purposes of this study.

We quantify Henry Hub–price volatility as follows. First, we find a 105‐week moving average of the

weekly prices centered on the current week. This 105‐week moving average is the average of the 52

previous weeks of prices, the price of the instant week, and the prices from the 52 weeks following.

Then, for each week we calculate the ratio of the current price to the 105 week average price. There

have been four peak prices during this period of 2000 to March 2011 and the average ratio of the peak

price to the 105‐week moving average price as of that week is 2.17. Similarly, there were four downside

bottoms in price and the average ratio of the four bottom prices is 0.56 of the 105‐week moving average

price. These results indicate that the actual average of daily prices in any week could range between

0.59 and 2.17 of the long‐term average of Henry Hub daily prices. Exhibit 2‐18 depicts this range. The

range of price volatility is large, especially compared with the upper and lower range of forecast average

prices.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐36  

Exhibit 2‐18. Range of Potential Weekly Price Volatility versus the Forecast Base Case Annual Average Henry Hub

Natural Gas Price (2013$/MMBtu)

2.3 Wholesale Natural Gas Prices in New England

The wholesale natural gas price in New England is the market price paid by merchant power plants,

large direct use customers, and the LDCs for gas purchased at interstate pipeline delivery points in New

England. The wholesale natural gas price in New England is our estimate of the avoided costs for electric

generation.

For the New England market, the principal measures of wholesale gas prices are the Algonquin Citygates

price index and the Tennessee Zone 6 price index. These are published indices of prices for monthly

sales and daily spot sales for gas delivered from the two largest transporters of natural gas into the New

England market. These price indices are also used as reference prices for long‐term, firm sales

transactions.

2.3.1 Changes in Gas Industry Affecting Wholesale Prices in New England

The following summarizes the difference between the AESC 2011 and AESC 2013 methodology.

0.00

2.00

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8.00

10.00

12.00

14.00

16.00

18.00

20132014201520162017201820192020202120222023202420252026202720282029203020312032203320342035

2013$ per MMBtu

AESC 2013 Base Case High Price Case Low Price Case Upper Volatility Range Lower Volatility Range
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐37  

AESC 2011

AESC 2011 developed its forecast of New England wholesale gas prices directly from its forecast of the

Henry Hub prices. It began by calculating the historical average ratio of the New England wholesale

market prices to Henry Hub prices for each calendar month. It then applied that ratio to the monthly

Henry Hub forecast to develop the monthly New England wholesale market price forecast. AESC 2011

calculated separate ratios for the Tennessee Zone 6 price index—which was used to develop the

wholesale price forecast for Massachusetts, New Hampshire, and Maine—and for the Algonquin

Citygates price index—which was used to develop the wholesale price forecast for Connecticut and

Rhode Island. AESC 2011 also developed a combined forecast for the New England region (excluding

Vermont) using an average of the two ratios.

AESC 2013

Several important developments have occurred since the release of AESC 2011 which have changed the

historical relationship between wholesale natural gas prices in New England and natural gas prices at

the Henry Hub:

 Rapid growth in Marcellus shale gas production in the Appalachian28 producing area

has reduced gas prices in the Northeast relative to Henry Hub. This trend is expected

to continue as Appalachian gas production expands.

 Lower gas imports from Canada and fewer LNG shipments to New England import

terminals have reduced east‐to‐west gas flows into the New England market.

Between 2011 and 2012, Maritimes & Northeast Pipeline’s average receipts at the

New Brunswick border declined from just over 400 MMcf per day (approximately 15

percent of total New England natural gas consumption) to less than 190 MMcf per

day. Receipts at the Distrigas LNG terminal in Everett, Massachusetts dropped from

371 MMcf per day in 2011 to 236 MMcf per day in 2012.

 The reduction in east‐side gas receipts has caused the pipelines delivering gas into

New England from the west to operate at, or near, capacity much more frequently.

These gas transmission constraints have caused New England gas prices to diverge

from prices in other Northeast markets. Significant expansion of natural gas pipeline

capacity into New England is not expected to occur before 2016.

The wholesale price forecast methodology for AESC 2013 accounts for the above changes affecting the

relationship between wholesale prices in New England and Henry Hub prices. First, supply from the

Appalachian area is expected to replace supply from the Gulf Coast as the primary driver of Northeast

region gas prices. Second, Appalachian prices are expected to decline relative to Henry Hub prices.

Third, the expected constraints on gas transmission capacity into New England through 2016 are

                                                            

28 The principal gas‐producing states in the Appalachian area are Pennsylvania and West Virginia.  
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐38  

expected to cause New England prices to diverge from prices elsewhere in the Northeast. Based upon

those factors, we develop the AESC 2013 New England wholesale gas price forecast in two steps.

The first step is to create a price forecast for Appalachian area gas supplies from the forecast of Henry

Hub prices. We do this by applying the ratio of forecast Northeast region wellhead prices to forecast

Henry Hub prices from the AEO 2012 Reference Case to the Henry Hub price. This ratio reflects the EIA’s

projected reduction in annual Appalachian gas prices relative to annual Henry Hub prices over the

forecast period. The EIA projects that the Appalachian supply area price, which has historically been

higher than the Henry Hub price, will be 5 percent below Henry Hub in 2015, and 9 percent below Henry

Hub in 2035. We add an adjustment of $0.10 per MMBtu (2013$) to the resulting Appalachian supply

area wellhead price forecast to arrive at a forecast Appalachian “into‐pipeline” price.

The second step is to develop the New England wholesale price forecast. We develop separate forecasts

for two time periods: (1) January 2013 through March 2016, and (2) April 2016 onward. For the period

January 2013 through March 2016 we calculate the wholesale market gas price by adding the exchange‐

traded basis futures prices for the Algonquin Citygates price index to our forecast Henry Hub price.29 This

is consistent with the methodology for the AESC 2013 Henry Hub gas price forecast, which also uses

NYMEX natural gas futures prices for the first three years of the forecast period.  

From April 2016 onward, the New England wholesale price forecast is based on the Appalachian “into‐

pipeline” price forecast described above. We assume the relationship between the New England

wholesale market gas price and the Appalachian supply area price will return to the conditions that

existed before gas transmission constraints began to cause extreme, localized price spikes in the New

England market, i.e. prior to 2012. The methodology used here is similar to methodology used to

forecast New England wholesale prices for AESC 2011, in that the wholesale price is calculated by

applying historical ratios of monthly New England prices to monthly Appalachian area prices to the

Appalachian area price forecast. The historical New England market prices used are the average of the

Tennessee Zone 6 index price and the Algonquin Citygates index price. The Appalachian area price is

measured by the Dominion South Point index price. The historical ratios for each calendar month are

calculated by the averaging the individual‐year ratios for the years 2007 through 2011.

The graph below compares the AESC 2013 New England wholesale natural gas price forecast to the AESC

2013 Henry Hub and Appalachian price forecasts and the New England price forecast from AESC 2011.

After the first three years, when the New England price forecast is based on future prices, the ratio

between the New England price and the Appalachian price remains constant. Because the Appalachian

price is expected to decline relative to the Henry Hub price, the difference between the New England

price and the Henry Hub price narrows over time. Except for 2013, the AESC 2013 New England gas price

forecast is substantially lower than the AESC 2011 forecast.

                                                            

29The basis price futures product is the CME ClearPort “Algonquin Citygates Natural Gas (Platts IFERC) Basis Futures.” We use

the settlement prices for the March 15, 2013 trading day.  
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐39  

The New England price forecast for the years 2013 through 2016 reflects the supply reductions and gas

transmission constraints that are currently affecting gas deliveries into the New England market. We

assume that these extreme supply conditions will phase out by 2017, and that the relationships between

New England natural gas prices and prices in other Northeast markets will return to the relationships

that existed prior to 2012. This assumption is largely supported by the fact that several significant

projects to expand pipeline capacity into New England have been proposed that could be completed by

2016. In addition, other near‐term developments are expected to narrow the gap between New England

prices and prices in other markets. One of these is the start‐up of production from the Deep Panuke

field in offshore Nova Scotia. A second is the construction of new pipeline capacity to deliver Marcellus

shale gas into New York City and Long Island, which should allow some of the existing Canadian and

domestic gas supplies flowing on IGTS to be diverted to New England markets. This suggests that the

New England gas price basis will gradually decline between now and 2017, even if one or more of the

proposed New England pipeline expansion projects is delayed.  

Exhibit 2‐19. AESC 2013 New England Wholesale Gas Price Forecast (2013$/MMBtu)

Natural Gas Price Forecasts for Other Market Hubs

As noted above, AESC 2013 estimates the avoided cost of gas supply to LDCs based upon an analysis of

their various gas supply resources. These estimates require price forecasts for two supply areas other

than Appalachia: the Mid‐Atlantic region (defined as Eastern Pennsylvania, New Jersey, and portions of

downstate New York), and the Dawn Hub in southeastern Ontario. Natural gas prices in the Mid‐Atlantic

market region are represented by the index price for gas delivered to points within the Texas Eastern

Transmission market zone 3 (TETCO M3).

The TETCO M3 price forecast for AESC 2013 is developed by applying historical monthly price ratios for

the years 2008 to 2012 to the Appalachian price forecast. The Dawn price forecast is developed by

adding the average monthly basis over the 2008 to 2012 period, in 2013$, to the Henry Hub forecast.

Forecasts for years 2013 through 2028 can be found in Exhibit 2‐20.  

$0

$1

$2

$3

$4

$5

$6

$7

$8

$9

2011201220132014201520162017201820192020202120222023202420252026202720282029203020312032203320342035

2013$/MMBtu

Appalachia Henry Hub New England (AESC 2013) New England (AESC 2011)
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐40  

Exhibit 2‐20. Natural Gas Wholesale Price Forecasts, 2013$/MMBtu

Year Henry Hub Appalachia TETCO M3 Dawn 

New 

England 

2013 3.84 3.75 4.13 4.19 6.35 

2014 4.12 4.01 4.42 4.47 5.98 

2015 4.15 4.03 4.44 4.50 5.75 

2016 4.18 4.03 4.44 4.53 5.14 

2017 4.50 4.30 4.74 4.85 4.91 

2018 4.77 4.59 5.05 5.11 5.24 

2019 5.01 4.84 5.33 5.36 5.53 

2020 5.34 5.16 5.68 5.69 5.90 

2021 5.48 5.27 5.80 5.83 6.02 

2022 5.77 5.48 6.03 6.11 6.26 

2023 5.95 5.61 6.18 6.30 6.41 

2024 6.07 5.71 6.29 6.41 6.52 

2025 6.26 5.92 6.52 6.61 6.76 

2026 6.41 6.03 6.64 6.76 6.89 

2027 6.58 6.16 6.78 6.93 7.04 

2028 6.69 6.26 6.89 7.03 7.15 

  

2.3.2 Natural Gas Price Forecast by State

AESC 2011 developed separate New England wholesale natural gas price forecasts by region. For the

states of Massachusetts, New Hampshire, and Maine, the wholesale price was based on the historical

relationship between Henry Hub and the Tennessee Zone 6 price index. For Connecticut and Rhode

Island, the wholesale was based on the historical relationship between Henry Hub and the Algonquin

Citygates price index. AESC 2011 did not include a wholesale natural gas price forecast for Vermont

because there is not a liquid spot market for gas delivered to locations within the state.

In preparing the New England wholesale natural gas price forecast for AESC 2013, we examined the

relationship between the Tennessee Zone 6 and Algonquin Citygates price indexes using daily data for

the years 2009 through 2012. The results, which are summarized in Exhibit 2‐21, show little or no

difference between the two indexes when day‐to‐day differences are averaged over the course of a

year. In addition, the three Southern New England states receive gas from both major pipelines. For

forecast purposes, therefore, we use a single natural gas price to represent the wholesale market in the

five states included in the Southern New England and Northern New England regions. This price

represents an average of the Algonquin Citygates price and the Tennessee Zone 6 price.

Exhibit 2‐21. Comparison of Algonquin Citygate and Tennessee Zone 6 Indexes ($/MMBtu)

2009 2010 2011 2012

AGT Citygates $4.80 $5.29 $4.99 $3.94

TGP Zone 6 (200 Leg) $4.80 $5.23 $5.00 $3.92

Difference $0.00 $0.06  ‐$0.01 $0.02

Source: IntercontinentalExchange
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐41  

2.4 Avoided Natural Gas Costs by End Use

2.4.1 Introduction and Summary

The avoided cost of gas at a retail customer’s meter has two components: (1) the avoided cost of gas

delivered to the LDC; and (2) the avoided cost of delivering gas on the LDC system (the “retail margin”).

Natural gas avoided costs are presented with and without the retail margin.

Avoided natural gas cost estimates are developed for three regions: Southern New England

(Connecticut, Rhode Island, and Massachusetts), Northern New England (New Hampshire and Maine),

and Vermont. These region definitions are different from AESC 2011, which grouped Massachusetts with

New Hampshire and Maine. New Hampshire and Maine are separated from Southern New England

because these markets are much more dependent on gas deliveries from the Maritimes & Northeast

Pipeline and the Portland Natural Gas Transmission System than the other New England states. Avoided

gas costs for Vermont, which receives natural gas directly from TransCanada PipeLines and has no

connections to the rest of the New England gas market, are estimated independently.

The AESC 2013 avoided cost estimates are summarized in Exhibit 2‐22 and Exhibit 2‐23. These exhibits

also compare the AESC 2013 results to the corresponding values from AESC 2011.

Exhibit 2‐22. Comparison of Avoided Gas Costs by End Use Assuming No Avoidable Retail Margin, AESC 2013 vs.

AESC 2011 (15‐year levelized, 2013$/MMBtu except where indicated as 2011$/MMBtu)  

ALL

Non Hot Non RETAIL

Heating Water Heating All Heating Heating All END USES

Southern New England (a)

AESC 2011 (2011$/MMBtu) 7.04 7.04 7.81 7.57 7.04 7.81 7.57 7.57

AESC 2011 (b) 7.27 7.27 8.06 7.83 7.27 8.06 7.83 7.83

AESC 2013 6.08 6.57 6.73 6.60 6.26 6.58 6.44 6.53

 2011 to 2013 change -16.41% -9.61% -16.54% -15.66% -13.88% -18.46% -17.74% -16.61%

Northern New England (a)

AESC 2011 (2011$/MMBtu) 6.94 6.94 7.58 7.39 6.94 7.58 7.39 7.39

AESC 2011 (b) 7.17 7.17 7.83 7.63 7.17 7.83 7.63 7.63

AESC 2013 6.03 7.53 8.02 7.62 6.58 7.54 7.12 7.39

 2011 to 2013 change -15.98% 5.01% 2.41% -0.15% -8.18% -3.67% -6.68% -3.17%

Vermont

AESC 2011 (2011$/MMBtu) 7.06 7.06 8.63 8.16 7.06 8.63 8.16 8.16

AESC 2011 (b) 7.29 7.29 8.91 8.43 7.29 8.91 8.43 8.43

AESC 2013 6.32 6.91 7.11 6.95 6.54 6.92 6.75 6.86

 2011 to 2013 change -13.39% -5.22% -20.28% -17.54% -10.36% -22.41% -19.91% -18.63%

(a) Massachusetts was included with Northern New England in AESC 2011,

 but is included with Southern New England in AESC 2013.

(b) Factor to convert 2011$ to 2013$ 1.0331

Note: AESC 2011 levelized costs for 15 years 2012 - 2026 at a discount rate of 2.465%.

 AESC 2013 levelized costs for 15 years 2014 - 2028 at a discount rate of 1.36%.

RESIDENTIAL COMMERCIAL & INDUSTRIAL
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐42  

Exhibit 2‐23. Comparison of Avoided Gas Costs by End Use Assuming Some Avoidable Retail Margin, AESC 2013

vs. AESC 2011 (15‐year levelized, 2013$/MMBtu except where indicated as 2011$/MMBtu)

The avoided natural gas cost estimates for AESC 2013 are generally lower than the AESC 2011 estimates.

The main reason for this is the lower projected gas price at Henry Hub. The AESC 2013 avoided natural

gas cost estimates are also lower than the AESC 2011 estimates because LDCs in Southern New England

and Northern New England are expected to purchase more gas in the Appalachian region, at market

prices that are projected to be below the Henry Hub benchmark price.

The difference between avoidable natural gas costs for heating and non‐heating loads in the Northern

New England region is greater than for AESC 2011. This is mainly the result of the change in region

definitions. Since Massachusetts is now included in Southern New England, the Northern New England

region is composed solely of Maine and New Hampshire. These markets have less access to the Gulf

Coast and Appalachian supply areas, and are more dependent on higher‐cost supply, transportation, and

storage services from Canada. While the Vermont market is even more dependent on Canadian

resources than Northern New England, the cost of delivering this gas to Northern New England is greater

because of higher transportation costs on TCPL and the additional cost of pipeline transportation service

from the Canadian border to the LDC citygate. Because Northern New England (Maine and New

Hampshire) takes a lot of gas supply from Canada, and the Canadian transportation services have high

fixed costs (and little or no variable cost), the cost of supplying low‐load‐factor customers is therefore

relatively high, especially after 2018 when existing long term contracts will need to be extended or

replaced.

ALL

Non Hot Non RETAIL

Heating Water Heating All Heating Heating All END USES

Southern New England (a)

AESC 2011 (2011$/MMBtu) 7.64 7.64 9.39 9.11 7.58 8.82 8.44 8.75

AESC 2011 (b) 7.89 7.89 9.70 9.41 7.83 9.11 8.72 9.04

AESC 2013 6.67 7.17 8.30 8.12 6.88 7.74 7.44 7.80

 2011 to 2013 change -15.43% -9.17% -14.43% -13.70% -12.06% -15.02% -14.74% -13.77%

Northern New England (a)

AESC 2011 (2011$/MMBtu) 7.47 7.47 8.96 8.73 7.59 8.79 8.43 8.58

AESC 2011 (b) 7.71 7.71 9.26 9.02 7.84 9.08 8.71 8.86

AESC 2013 6.53 8.04 9.35 8.91 7.04 7.43 7.17 7.31

 2011 to 2013 change -15.34% 4.17% 0.97% -1.19% -10.21% -18.21% -17.67% -17.56%

Vermont

AESC 2011 (2011$/MMBtu) 7.54 7.54 9.88 9.37 7.30 9.08 8.54 8.86

AESC 2011 (b) 7.79 7.79 10.21 9.68 7.54 9.38 8.82 9.15

AESC 2013 6.94 7.53 8.74 8.54 6.68 7.19 6.98 7.61

 2011 to 2013 change -10.88% -3.22% -14.37% -11.85% -11.37% -23.33% -20.86% -16.83%

(a) Massachusetts was included with Northern New England in AESC 2011,

 but is included with Southern New England in AESC 2013.

(b) Factor to convert 2011$ to 2013$ 1.0331

Note: AESC 2011 levelized costs for 15 years 2012 - 2026 at a discount rate of 2.465%.

 AESC 2013 levelized costs for 15 years 2014 - 2028 at a discount rate of 1.36%.

RESIDENTIAL COMMERCIAL & INDUSTRIAL
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐43  

Another change from AESC 2011 to AESC 2013 is that the load shape used for residential hot water

customers for AESC 2013 includes a temperature‐sensitive component, while the load shape used for

AESC 2011 did not.

2.4.2 Retail Customer Load Shapes

In broad terms, the shape of the retail gas load has a major impact on the cost of natural gas supplied

and thus the avoided natural gas costs. End uses of natural gas at the retail level are distinguished by

three types of end use: heating (low load factor), non‐heating (high load factor), and all. The costs

associated with these end uses also vary by the type of customer or sector, i.e., residential, commercial,

and industrial.30

Seasonal variations in natural gas use have a large impact on delivered gas costs. LDCs typically contract

for firm pipeline transportation services from supply areas to the citygate to meet their average daily

requirement over the year. Off‐system storage services and on‐system peaking resources are used to

meet the higher winter season requirements of temperature‐sensitive customers.  

The variation in daily gas requirements over the course of a year can be described by a load duration

curve. For this study, we represent the load shape of each retail customer type by dividing the annual

gas requirement into six load segments, as represented by the Representative Segmented Load Duration

Curve in Exhibit 2‐24. These are:

(1) Annual base load (365 days per year)

(2) Winter/shoulder load (280 days per year)

(3) Winter base load (151 days per year)

(4) 90‐day load

(5) 30‐day load

(6) 10‐day load

                                                            

30The electric power sector is not addressed here.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐44  

Exhibit 2‐24. Representative Segmented Load Duration Curve (MMcf/day)

Moving up the load curve, each load segment represents the incremental daily gas requirement

compared with the previous segment. These load segments were chosen because they provide a

reasonable representation of the shape of the load duration curve. These segment definitions also

correspond to the types of gas supply resources that New England LDCs use to meet retail customer

requirement. Non‐heating load is the base load responsible for the lowest tier in the load duration

curve, and is constant year round. Heating load, which varies as a function of the heating degree day

(HDD), generally occurs October through May with a peak in January. The heating load represents the

largest end use and determines the shape of the other tiers within the load duration curve.  

On the supply side, long‐haul pipeline capacity is generally used to meet annual baseload and a portion

of the additional requirement in the winter and shoulder months. Off‐system storage is used to meet

winter baseload, 90‐day, and 30‐day requirements. On‐system peaking resources are typically reserved

to meet the incremental requirements on the few coldest days each year.

The distribution of the annual gas demand for each of the five types of end use is shown in Exhibit 2‐25.  

These numbers are derived from base use per day and use per heating degree day (HDD) estimates

provided by National Grid (MA). The shape of the HDD distribution is based on information provided by

Northeast Utilities. These retail end use load shapes were considered representative for all three New

England regions and were used for our analyses of each region.

0

20

40

60

80

100

30 60 90 120 150 180 210 240 270 300 330 360

MMcf per Day

Day

365‐day Load 280‐day Load 151‐day Load 90‐day Load 30‐day Load 10‐day Load
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐45  

Exhibit 2‐25. Distribution of Gas Use by Load Segment

Residential Commercial & Industrial

Load Segment Non‐Heating

Water

Heating Heating Non‐Heating Heating

Annual Baseload 100.00% 24.46% 0% 71.95% 23.80%

Winter/Shoulder 0% 49.67% 65.75% 18.44% 50.10%

Winter Baseload 0% 14.72% 19.49% 5.47% 14.85%

90‐Day 0% 9.33% 12.35% 3.47% 9.41%

30‐Day 0% 1.68% 2.23% 0.62% 1.70%

10‐Day 0% 0.14% 0.18% 0.05% 0.14%

Total 100.00% 100.00% 100.00% 100.00% 100.00%

Source: Synapse calculations and LDC survey results

2.4.3 Avoidable Gas Supply Resources  

Each LDC develops a portfolio of gas supply resources to meet the requirements of its firm retail

customers. As a regulated utility company, the LDC is obligated to provide a reliable supply of natural

gas at the lowest reasonable cost.

The gas supply costs that are avoided by reducing retail customer gas consumption will depend on

which gas supply resources are on the margin. The marginal gas supply resources are determined by the

characteristics of an LDC’s existing gas supply portfolio, and the opportunities to add or eliminate

resources in response to changes in projected requirements. Since LDCs practice least‐cost planning, the

marginal gas supply resource will generally be the resource with the highest delivered cost for serving a

given type of load.

While all gas supply resources are avoidable over the long run, in the near term LDCs often hold multi‐

year contracts that commit the LDC to pay for a firm pipeline transportation service or off‐system

storage service for a minimum period of time. In these situations, the fixed cost of the resource cannot

be avoided until the end of the contract term, when the LDC typically has an option to renew or

terminate the service. Until the “renew or terminate” decision is faced, however, the avoided cost of the

resource is the variable cost of utilizing the resource to supply end use customers. Similarly, we assume

that LDCs will continue to operate and maintain the on‐system peaking facilities that are currently in

service. The avoided cost for an LNG peaking facility is therefore assumed to be the cost of purchased

gas, and the fuel used for liquefaction and vaporization.31 For a propane‐based peaking facility, the

avoided cost is assumed to be the delivered cost of propane.

                                                            

31 Because many New England LDCs depend on winter refill of LNG storage, the commodity price is the peak‐month New

England wholesale price. The LNG fuel use factors are 17 percent for liquefaction and 3 percent for vaporization. These fuel

use factors are the same as those used for AESC 2011.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐46  

To identify the fixed and variable costs associated with the gas supply resources currently held by New

England LDCs, we reviewed the most recent integrated resource plans of thirteen LDCs. While these

LDCs have a variety of gas supply arrangements of different types and vintages, we were able to classify

all of the existing gas supply resources of the New England LDCs into nine basic resource categories

based on the source of the gas and the transportation infrastructure used to deliver it to New England.

1. Flowing Gas Supply

a. Supply via Gulf Coast Transportation

This category consists of natural gas purchased in Texas and Louisiana and transported to Southern New

England and Northern New England on long‐haul pipeline capacity. Most of this gas is transported using

firm transportation (FT) services that LDCs acquired when interstate pipelines unbundled sales services

from transportation services in the 1980s. These “legacy” FT services are priced at standard tariff rates

that reflect the costs of older, depreciated pipeline assets. For Southern New England the transportation

cost is an average of (a) the TGP Zone 1 to Zone 6 tariff rates, and (b) the TETCO and AGT rates for FT

service from TETCO WLA to New England citygates. For Northern New England, the transportation cost

is the TGP Zone 1 to Zone 6 rates. We also assume that LDCs can use long‐haul Gulf Coast capacity to

deliver Appalachian area purchases when it is economic to do so.

b. Supply via Dawn/Niagara Transportation

This category consists of natural gas that is either purchased at the Dawn, Ontario (ON) storage and

transportation hub, or purchased at points upstream of Dawn—such as Michigan or Chicago—and

delivered through Dawn. For LDCs in Southern New England, the transportation path includes Union Gas

and TCPL services in Canada, and IGTS capacity in the U.S. LDCs that cannot receive gas directly from

IGTS hold additional FT service on TGP or AGT. For LDCs in Northern New England and Vermont, the

transportation path also includes Union Gas and TCPL. Vermont Gas takes gas directly from TCPL at

Phillipsburg, while LDCs in Northern New England transport gas from the Canadian border on PNGTS.

This category also includes gas transported from the Niagara import point to Southern New England and

Northern New England on TGP, because of the close similarity between gas prices at Niagara and Dawn.  

c. Supply via Appalachia Transportation

This category consists of natural gas delivered via short‐haul transportation capacity from points in

western Pennsylvania and Southeastern Ohio to Southern New England. Although most of the

transportation that LDCs hold on this path is used to deliver gas from storage, LDCs also hold stand‐

alone capacity from the Appalachian region.  

d. Supply via Wright, NY Transportation

This category consists of natural gas delivered from the interconnection between IGTS and TGP at

Wright, NY to Southern New England. Wright, NY is expected to become more significant as a natural

gas market center as Appalachian production increases, and more pipeline capacity is built to move gas
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐47  

to New England markets. A prime example is the proposed Constitution Pipeline, which would deliver

650 MMcf per day from gas producing areas in Pennsylvania to Wright beginning November 1, 2015. 32

e. Supply via Mid‐Atlantic Transportation

This category consists of gas delivered on AGT from interconnections with TETCO and other interstate

pipelines in New Jersey to Southern New England.  

f. New England Wholesale Purchases

This category is natural gas purchased at locations within the New England market area or firm gas sales

at LDC citygates. The principal locations for local purchases are major pipeline interconnection points

such as Dracut, MA (M&N, PNGTS and TGP); Beverly/Salem, MA (M&N and AGT); and Mendon, MA (AGT

and TGP).

2. Off‐System Storage

a. Supply from New York/Pennsylvania Storage

This category is natural gas delivered from legacy pipeline storage services and some storage services

with independent storage operators in the Appalachian region.  

b. Supply from Michigan/Dawn Storage

This category is natural gas storage services at the Union Gas storage and transmission hub at Dawn,

ON, and services from Michigan storage fields located just west of Dawn. These storage services are

newer than the NY/PA storage services, and generally priced at higher market rates.

c. On‐System Peaking

This category is LNG and propane‐based peaking facilities connected to the LDC distribution systems.

Exhibit 2‐26 shows, on a consolidated basis, the gas supply resources that were available to the LDCs in

each region to meet firm peak day requirements during the 2012‐13 planning year.  

                                                            

32 Federal Energy Regulatory Commission Docket CP13‐499.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐48  

Exhibit 2‐26. New England LDC Gas Supply Capability for the 2012‐13 Planning Year, MMcf/day

Resource Type Resource Southern New

England

Northern New

England Vermont

Flowing Gas

Gulf Coast 936.2 34.7 0

Dawn/Niagara 169.4 15.9 39.5

Appalachia 89.2 0 0

Wright, NY 41.5 0 0

Mid‐Atlantic 78.7 0 0

Local Purchases 318.8 99.7 0

Sub‐Total Flowing Gas 1,633.8 150.3 39.5

Off‐System Storage

NY/PA Storage 662.3 30.8 0

MI/Dawn Storage 127.3 32.9 19.1

Sub‐Total Off‐System

Storage 789.6 63.7 19.1

On‐System Peaking Sub‐Total On‐System

Peaking

1,334.2 57.2 7.7

All Resource Types Total (Flowing,

Storage, Peaking)

3,757.6 271.2 66.3

Source: LDC resource plans

2.4.4 New Transportation Resources

Several projects are currently in development that would increase gas transportation capacity into the

New England market.

Algonquin Incremental Market (AIM) Expansion

AGT proposes to expand its existing system to provide up to 450 MMcf per day of additional

transportation service from interconnects with upstream pipelines near the NJ/NY border to delivery

points in CT, RI, and MA. This service would be priced at an incremental rate tied to the size of the

expansion and the actual cost of facilities. The planned in‐service date is November 1, 2016. AGT held an

open season for the AIM project in late 2012.

TGP Connecticut Expansion

TGP is offering 72 MMcf per day additional firm transportation service from its interconnection with

IGTS at Wright, NY to delivery points in CT. The planned in‐service date is November 1, 2016. TGP

estimates the cost of the Connecticut Expansion Project to be $81.2 million.33  

TGP Northeast Expansion

In addition to the Connecticut Expansion Project, TGP proposes to expand its system from Wright, NY to

other points in CT and MA by constructing a new pipeline across northern Massachusetts. The Northeast

                                                            

33 Kinder Morgan Investor Presentation dated January 30, 2013.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐49  

Expansion Project could range from 500 to 1,200 MMcf per day. Service would be available in November

2017, or later.34

PNGTS Continent to Coast

PNGTS is offering additional firm transportation service from the Canadian border to its interconnection

with M&N at Westbrook, ME. Service would be available as soon as November 1, 2016, but would

require an upstream expansion by TCPL.

TCPL 2013 Eastern Market Expansion

TCPL has received National Energy Board approval for a project that would expand capacity on the

Dawn‐to‐New England transportation path. This project will allow TCPL to provide 3.3 MMcf per day of

additional firm transportation service for Vermont Gas starting November 1, 2013. Further expansions

of the Union Gas and TCPL systems to increase gas deliveries of Dawn and Niagara are planned for 2015.  

The cost of building new, incrementally priced gas transmission capacity into the New England market

has been factored into the AESC 2013 avoided cost analysis by including three new gas supply resources

in the list of options available to LDCs. The first new resource corresponds to the TGP Connecticut

Expansion Project, and provides for up to 72 MMcf per day of firm transportation service beginning in

2016. Based on TGP’s capital cost estimate of $18.2 million, we assume that this capacity has a fixed cost

of $0.52 per MMBtu (2013$). This cost is approximately 60 percent higher than TGP’s standard tariff

rate for transportation service from New York to New England.

The second new resource represents capacity on either the AGT AIM Project or the TGP Northeast

Expansion to delivery points in the Southern New England. The fixed cost of this capacity is assumed to

be $1.00 per MMBtu (2013$) for service starting 2016 or later. This cost is based on the estimated cost

of a generic short‐haul expansion on TGP or AGT that was developed by the three Connecticut LDCs and

filed with the Public Utilities Regulatory Authority with the LDCs’ most recent five‐year supply‐demand

forecasts.35  

The third new resource represents a TGP expansion that would increase gas deliveries into Northern

New England sometime after 2016. The fixed cost for this resource is assumed to be the same as the

cost of a generic short‐haul expansion into Southern New England.  

2.4.5 Avoided Gas Supply Cost by Load Segment

We calculate the cost of using each resource to supply each of the six load segments defined earlier in

section 2.4.2. The gas commodity cost is calculated for each load segment to match the corresponding

time period during the year. For example, the price of gas for the 90‐day load segment is assumed to be

                                                            

34 TGP Presentation at Northeast Gas Association’s Regional Market Trends Forum, April 30, 2013.

35 See Southern Connecticut Gas, “Forecast of Natural Gas Demand and Supply, 2013‐2017”, Docket No. 12‐10‐06, October 1,

2012, Exhibit S‐8.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐50  

the average price for the months of December through February. The gas price for the 30‐day load

segment is the highest monthly price for the same three‐month period. The transportation fixed costs

will also vary depending on the number of days per year that the capacity is utilized. For example, if the

fixed cost of using a transportation service for annual baseload deliveries (100 percent load factor) is

$1.00 per MMBtu, the cost of using that transportation capacity to supply incremental requirements

during the 151‐day winter baseload period is $2.42 per MMBtu ($1.00 per MMBtu x 365 days / 151

days).

Exhibit 2‐27 shows the total LDC firm requirements by load segment for the 2013 base year. These

requirements are assumed to escalate by 0.5 percent per year for the Southern New England Region,

and by 1.4 percent per year for the Northern New England and Vermont regions. These growth rates are

weighted averages of the individual base case load growth forecasts from the LDC resource plans.  

Exhibit 2‐27. New England LDC Firm Requirements by Load Segment for 2013, MMcf/day

Load Segment

Southern New

England

Northern New

England Vermont

Annual Baseload 225.1 12.3 3.0

Winter/Shoulder 463.1 36.4 8.8

Winter Baseload 447.1 36.5 9.0

90‐Day 547.0 42.2 10.4

30‐Day 443.6 34.0 8.4

10‐Day 333.8 25.6 6.3

Total 2,489.7 186.0 45.9

Our analysis considers each of the nine existing supply categories plus the three new resource options to

represent the new delivery capacity that could be constructed in the three New England regions

identified in section 2.5.5. The gas supply resources included in the avoided cost analysis are listed in

Exhibit 2‐28.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐51  

Exhibit 2‐28. Gas Supply Resources Included in Avoided Cost Analysis

Supply Resource Type of Facilities Gas Price Forecast Regions

Gulf Coast Production Existing Henry Hub SNE, NNE

Appalachia Purchases Existing Appalachia SNE

Purchases at Wright, NY Existing TETCO M3 SNE

Purchases at Wright, NY New New TETCO M3 SNE,NNE

Dawn Purchases Existing Dawn SNE, NNE, VT

Dawn Purchases New New Dawn NNE, VT

Mid‐Atlantic Purchases Existing TETCO M3 SNE

Mid‐Atlantic Purchases New New TETCO M3 SNE

New York/Pennsylvania Storage Existing Appalachia SNE, NNE

Michigan/Dawn Storage Existing Dawn SNE, NNE, VT

Local Purchases Not Applicable New England SNE, NNE

On‐System LNG or LPG Existing New England SNE, NNE, VT

SNE = Southern New England, NNE = Northern New England, VT = Vermont

For each gas supply resource we identify the costs of acquiring the resource and the cost of delivering

that resource to the LDC.

 For flowing gas resources, the cost components are: (a) gas purchase costs, (b) the FT service

demand rate, and (c) the variable transportation cost. The variable transportation cost includes

the variable transportation commodity rate charged by the pipeline, and the cost of gas retained

by the pipeline for compressor fuel use and “lost and unaccounted for” gas.

 For off‐system storage resources, which include firm transportation service from the storage to

the LDC, the cost components are: (a) the cost of gas purchased for injection, (b) the fixed

storage and transportation service charges, and (c) the variable storage and transportation

service charges, which includes the storage and transportation fuel costs.

 For on‐system peaking resources, we assume there is only a variable cost component. In the

case of LNG peaking, which is the predominant type of on‐system peaking for LDCs in Southern

New England and Northern New England, the variable cost is the purchased gas cost and the

cost of gas consumed for liquefaction and vaporization. For propane‐based peaking, which is the

only type of on‐system peaking in Vermont, the variable cost is assumed to be the propane

price.

The marginal gas supply resource for each load segment is determined by matching the available gas

supply resources to the LDC firm requirements to minimize the total avoidable gas supply cost. This

optimization is done by year for each of the three regions through 2020 using a linear programming

spreadsheet model developed for this purpose. Since the gas supply resources included in the avoided

cost analysis do not change after 2019, the marginal supply resources are assumed to be the same over

remaining years of the forecast.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐52  

To illustrate, Exhibit 2‐29 shows the delivered cost of the marginal gas supply resources identified for

each load segment for the Southern New England region for the year 2016. Using the Residential

Heating load category as an example, Exhibit 2‐30 shows how the avoided cost for each end use is

calculated as a weighted average of these costs, using the weighting factors presented in Exhibit 2‐25.

The resulting avoided cost of $5.64 per MMBtu for Residential Heating for 2016 appears in Exhibit 2‐35.

Exhibit 2‐29. Marginal Resource Costs for Southern New England Region, 2016

Load Segment Marginal Gas Supply Resource

Delivered Cost

(2013$/MMBtu)

Annual Baseload Gulf Coast Transportation $5.01

Winter/Shoulder Gulf Coast Transportation $5.18

Winter Baseload Gulf Coast Transportation $6.23

90‐Day New York/Pennsylvania Storage $6.57

30‐Day New York/Pennsylvania Storage $8.60

10‐Day On‐System Peaking $8.62

Exhibit 2‐30. Sample Calculation of 2016 Residential Heating for Southern New England Region

Load Segment

Marginal Gas Supply

Resource

Delivered Cost

(2013$/MMBtu) Load Shape

Cost

times

Shape

Annual Baseload Gulf Coast Transportation $5.01 0% $0.00

Winter/Shoulder Gulf Coast Transportation $5.18 65.75% $3.41

Winter Baseload Gulf Coast Transportation $6.23 19.49% $1.21

90‐Day New York/Pennsylvania

Storage

$6.57 12.35% $0.81

30‐Day New York/Pennsylvania

Storage

$8.60 2.23% $0.19

10‐Day On‐System Peaking $8.62 0.18% $0.02

Avoided Cost of Gas Assuming No Retail Margin is Avoidable $5.64

2.4.6 Avoided Distribution Cost by Sector

The avoided cost for each end use by sector is the sum of the avoided cost of the gas sent out by the

LDC and the avoidable distribution cost, called the avoidable LDC margin, applicable from the citygate to

the burner tip.

Estimates of the portion or amount of distribution cost that is avoidable due to reductions in gas use

from efficiency measures vary by LDC. Some LDCs have estimated this amount as their incremental or

marginal cost of distribution; that is, the change in cost of distribution incurred as demand for gas

increases or decreases. The conclusion was that the incremental cost of distribution depends upon the

load type and the customer sector. For low load factor or heating loads, more of the embedded cost for

each sector is incremental or avoidable than for high load factor or non‐heating loads. The incremental
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐53  

or avoidable cost is measured as a percent of the embedded costs. For AESC 2013, we measure the

embedded cost as the difference between the city‐gate price of gas in a state and the price charged

each of the different retail customer types: residential, commercial ‐ industrial, and all retail

customers.36 The embedded distribution cost for each of the two regions, Southern New England and

Northern New England, were the weighted average distribution costs among the relevant states where

the weighting is the volume of gas delivered to each sector in each state.

Exhibit 2‐31 shows the estimated avoidable LDC margin percentage and avoidable costs, measured as

2013 dollars per MMBtu, by each of the end‐use types and customer sectors for each region in New

England.

                                                            

36The citygate gas prices and the prices charged to each retail customer sector are reported by the EIA for each state each year.

In AESC 2013 the cost used is the average for the five years 2007‐2011, which is the most recent data available.  
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐54  

Exhibit 2‐31. Estimated Avoidable LDC Margins (2013$/MMBtu)

Source: EIA website data sources

Some LDCs assume they will not avoid any distribution costs due to reductions in gas use from efficiency

measures. The avoided cost of gas by end use for an LDC with no avoided distribution cost is their

avoided cost of gas delivered to their citygate.

2.4.7 Total Avoided Gas Costs by End Use

Exhibit 2‐32 through Exhibit 2‐37 show the total avoided costs per year per MMBtu for the retail end

uses categorized by the end‐use type and customer sector for Southern New England, Northern New

England, and Vermont. The avoided cost of the gas sent out by the LDCs by load type is the weighted

sum of the avoided cost per MMBtu across all six portions of the load duration curve delivered to the

Type of End Use

Non-heating 

(High Load 

Factor)

Heating (Low 

Load Factor) All

Avoidable Margin (percent) (b)

Residential 8.0% 21.0% 20.4%

Commercial & Industrial 15.0% 28.0% 24.0%

All Retail 22.0%

Southern New England (c)

Average City Gate Price 8.706

Residential 7.466 0.60 1.57 1.52

Commercial & Industrial (e) 4.164 0.62 1.17 1.00

All Retail (f) 5.775 1.27

Northern New England (d)

Average City Gate Price 9.977

Residential 6.324 0.51 1.33 1.29

Commercial & Industrial (e) 3.051 0.46 0.85 0.73

All Retail (f) 3.549 0.78

Vermont

Average City Gate Price 9.616

Residential 7.782 0.62 1.63 1.59

Commercial & Industrial (e) 0.970 0.15 0.27 0.23

All Retail (f) 3.427 0.75

(a) Average of Margins among states for 2007 - 2011 weighted by the delivered volumes in each state.

(b) Based on LDC marginal cost studies from National Grid (MA).

(c) Southern New England is Massachusetts, Connecticut, and Rhode Island.

(d) Northern New England is New Hampshire and Maine.

(e) An average of the margins weighted by the commercial and industrial use delivered volumes.

(f) An average of residential, commercial and industrial margins weighted by associated volumes.

Total LDC Retail Avoidable LDC Margin (a) (2013$/MMBtu)

Margin & CG 

Price (a) 

(2013$/MMBtu)
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐55  

citygate, multiplied by the percent used each load duration curve portion for each load type (heating,

non‐heating, or all) plus the avoided retail margin for each retail customer sector. The levelized avoided

cost is the cost for which the present value at the real rate of return of 1.36 percent has the same

present value as the estimated avoided costs for the 15‐year period 2014 through 2028 at the same rate

of return.

Exhibit 2‐32, Exhibit 2‐33, and Exhibit 2‐34 provide projections of avoidable cost by end use for utilities

in Southern New England, Northern New England, and Vermont, for which some LDC retail margin is

avoidable.  

Exhibit 2‐32. Avoided Cost of Gas Delivered to an End‐Use Load, Assuming Some Retail Margin is Avoidable;

Southern New England (2013$/MMBtu)

ALL

Non Hot Non RETAIL

Year Heating Water Heating All Heating Heating All END USES

2013 5.32 5.78 6.90 6.73 5.51 6.35 6.05 6.41

2014 5.53 5.98 7.10 6.93 5.72 6.56 6.26 6.61

2015 5.54 5.98 7.09 6.93 5.72 6.56 6.26 6.61

2016 5.61 6.08 7.21 7.03 5.81 6.66 6.36 6.71

2017 5.71 6.34 7.51 7.30 5.96 6.91 6.57 6.95

2018 6.02 6.62 7.79 7.58 6.26 7.20 6.86 7.24

2019 6.46 6.95 8.08 7.90 6.66 7.53 7.22 7.58

2020 6.73 7.20 8.33 8.15 6.93 7.78 7.48 7.83

2021 6.87 7.36 8.48 8.30 7.07 7.93 7.62 7.98

2022 7.07 7.55 8.67 8.50 7.27 8.12 7.82 8.18

2023 7.20 7.67 8.80 8.62 7.40 8.25 7.95 8.30

2024 7.33 7.82 8.95 8.77 7.53 8.39 8.08 8.44

2025 7.52 7.99 9.12 8.94 7.72 8.57 8.27 8.62

2026 7.64 8.13 9.25 9.07 7.84 8.70 8.39 8.75

2027 7.76 8.25 9.37 9.19 7.96 8.82 8.51 8.87

2028 7.86 8.34 9.46 9.29 8.06 8.91 8.61 8.97

Levelized (a) 6.67 7.17 8.30 8.12 6.88 7.74 7.44 7.80

Simple Average 6.72 7.21 8.35 8.17 6.93 7.79 7.48 7.84

(a) Years 2014-2028 (15 years); Real (constant $) riskless annual rate of return: 1.360%

RESIDENTIAL COMMERCIAL & INDUSTRIAL
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐56  

Exhibit 2‐33. Avoided Cost of Gas Delivered to an End‐Use Load, Assuming Some Retail Margin is Avoidable;

Northern New England (2013$/MMBtu)

Exhibit 2‐34. Avoided Cost of Gas Delivered to an End‐Use Load, Assuming some Retail Margin is Avoidable;

Vermont (2013$/MMBtu)

ALL

Non Hot Non RETAIL

Year Heating Water Heating All Heating Heating All END USES

2013 5.21 5.81 6.83 6.63 5.38 6.03 5.78 5.92

2014 5.07 5.79 6.84 6.61 5.29 6.24 5.99 6.12

2015 5.14 6.49 7.75 7.35 5.59 6.24 5.99 6.12

2016 5.50 6.12 7.15 6.94 5.68 6.34 6.09 6.22

2017 5.94 6.38 7.34 7.19 6.05 6.59 6.30 6.46

2018 6.07 6.81 7.87 7.64 6.30 6.88 6.59 6.75

2019 6.34 8.19 9.61 9.08 6.98 7.21 6.95 7.09

2020 6.60 8.44 9.86 9.33 7.23 7.46 7.21 7.34

2021 6.73 8.61 10.04 9.50 7.38 7.61 7.35 7.49

2022 6.94 8.83 10.26 9.72 7.59 7.80 7.55 7.69

2023 7.06 8.96 10.39 9.85 7.71 7.93 7.68 7.81

2024 7.19 9.12 10.57 10.02 7.86 8.07 7.81 7.95

2025 7.37 9.31 10.75 10.20 8.04 8.25 8.00 8.13

2026 7.49 9.44 10.90 10.34 8.16 8.38 8.12 8.26

2027 7.62 9.58 11.03 10.48 8.30 8.50 8.24 8.38

2028 7.71 9.67 11.13 10.57 8.39 8.59 8.34 8.48

Levelized (a) 6.53 8.04 9.35 8.91 7.04 7.43 7.17 7.31

Simple Average 6.58 8.11 9.43 8.99 7.10 7.48 7.22 7.35

(a) Years 2014-2028 (15 years); Real (constant $) riskless annual rate of return: 1.360%

RESIDENTIAL COMMERCIAL & INDUSTRIAL

ALL

Non Hot Non RETAIL

Year Heating Water Heating All Heating Heating All END USES

2013 5.59 6.10 7.28 7.10 5.31 5.76 5.58 6.19

2014 5.68 6.19 7.36 7.19 5.40 5.84 5.66 6.27

2015 5.75 6.34 7.55 7.35 5.50 6.00 5.79 6.42

2016 5.81 6.41 7.62 7.42 5.56 6.07 5.86 6.49

2017 6.11 6.71 7.92 7.72 5.86 6.37 6.16 6.79

2018 5.85 6.39 7.58 7.40 5.58 6.05 5.86 6.48

2019 6.65 7.27 8.48 8.28 6.41 6.92 6.71 7.34

2020 6.93 7.53 8.73 8.53 6.68 7.18 6.97 7.60

2021 7.11 7.73 8.95 8.74 6.87 7.39 7.18 7.81

2022 7.37 7.98 9.19 8.99 7.13 7.63 7.42 8.05

2023 7.54 8.14 9.34 9.14 7.29 7.79 7.58 8.21

2024 7.68 8.30 9.51 9.31 7.44 7.95 7.74 8.37

2025 7.86 8.48 9.68 9.48 7.62 8.13 7.92 8.55

2026 8.03 8.64 9.85 9.65 7.79 8.30 8.09 8.72

2027 8.18 8.79 10.00 9.80 7.94 8.44 8.23 8.86

2028 8.28 8.89 10.09 9.89 8.03 8.54 8.33 8.96

Levelized (a) 6.94 7.53 8.74 8.54 6.68 7.19 6.98 7.61

Simple Average 6.99 7.59 8.79 8.59 6.74 7.24 7.04 7.67

(a) Years 2014-2028 (15 years); Real (constant $) riskless annual rate of return in %: 1.360%

RESIDENTIAL COMMERCIAL & INDUSTRIAL
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐57  

Exhibit 2‐35, Exhibit 2‐36, and Exhibit 2‐37 demonstrate the avoided cost by end use for utilities at which

it is assumed that no LDC retail margin is avoidable.

Exhibit 2‐35. Avoided Cost of Gas Delivered to LDCs by End‐Use Load Type Assuming No Avoidable Retail Margin,

Southern New England (2013$/MMBtu)

ALL

Non Hot Non RETAIL

Year Heating Water Heating All Heating Heating All END USES

2013 4.72 5.18 5.33 5.21 4.89 5.18 5.05 5.14

2014 4.93 5.38 5.53 5.41 5.10 5.39 5.26 5.34

2015 4.94 5.38 5.52 5.41 5.10 5.39 5.26 5.34

2016 5.01 5.48 5.64 5.51 5.19 5.49 5.36 5.44

2017 5.11 5.74 5.94 5.78 5.34 5.74 5.57 5.68

2018 5.42 6.02 6.22 6.06 5.64 6.03 5.86 5.97

2019 5.86 6.35 6.51 6.38 6.04 6.36 6.22 6.31

2020 6.13 6.60 6.76 6.63 6.31 6.61 6.48 6.56

2021 6.27 6.76 6.91 6.78 6.45 6.76 6.62 6.71

2022 6.47 6.95 7.10 6.98 6.65 6.95 6.82 6.91

2023 6.60 7.07 7.23 7.10 6.78 7.08 6.95 7.03

2024 6.73 7.22 7.38 7.25 6.91 7.22 7.08 7.17

2025 6.92 7.39 7.55 7.42 7.10 7.40 7.27 7.35

2026 7.04 7.53 7.68 7.55 7.22 7.53 7.39 7.48

2027 7.16 7.65 7.80 7.67 7.34 7.65 7.51 7.60

2028 7.26 7.74 7.89 7.77 7.44 7.74 7.61 7.70

Levelized (a) 6.08 6.57 6.73 6.60 6.26 6.58 6.44 6.53

Simple Average 6.12 6.62 6.78 6.65 6.31 6.62 6.48 6.57

(a) Years 2014-2028 (15 years); Real (constant $) riskless annual rate of return: 1.360%

RESIDENTIAL COMMERCIAL & INDUSTRIAL
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐58  

Exhibit 2‐36. Avoided Cost of Gas Delivered to LDCs by End‐Use Load Type Assuming No Retail Margin, Northern

New England (2013$/MMBtu)

Exhibit 2‐37. Avoided Cost of Gas Delivered to LDCs by End‐Use Load Type Assuming No Retail Margin, Vermont

(2013$/MMBtu)

ALL

Non Hot Non RETAIL

Year Heating Water Heating All Heating Heating All END USES

2013 4.70 5.30 5.50 5.34 4.92 5.31 5.14 5.25

2014 4.56 5.28 5.51 5.32 4.83 5.28 5.08 5.21

2015 4.63 5.98 6.42 6.06 5.13 5.99 5.61 5.85

2016 4.99 5.61 5.82 5.65 5.22 5.62 5.45 5.56

2017 5.43 5.87 6.01 5.90 5.59 5.87 5.75 5.83

2018 5.56 6.30 6.54 6.35 5.84 6.31 6.10 6.23

2019 5.83 7.68 8.28 7.79 6.52 7.70 7.18 7.51

2020 6.09 7.93 8.53 8.04 6.77 7.95 7.43 7.76

2021 6.22 8.10 8.71 8.21 6.92 8.12 7.60 7.93

2022 6.43 8.32 8.93 8.43 7.13 8.33 7.81 8.14

2023 6.55 8.45 9.06 8.56 7.25 8.46 7.93 8.27

2024 6.68 8.61 9.24 8.73 7.40 8.63 8.09 8.43

2025 6.86 8.80 9.42 8.91 7.58 8.81 8.27 8.61

2026 6.98 8.93 9.57 9.05 7.70 8.95 8.40 8.75

2027 7.11 9.07 9.70 9.19 7.84 9.08 8.54 8.89

2028 7.20 9.16 9.80 9.28 7.93 9.18 8.63 8.98

Levelized (a) 6.03 7.53 8.02 7.62 6.58 7.54 7.12 7.39

Simple Average 6.07 7.61 8.10 7.70 6.64 7.62 7.19 7.46

(a) Years 2014-2028 (15 years); Real (constant $) riskless annual rate of return: 1.360%

RESIDENTIAL COMMERCIAL & INDUSTRIAL

ALL

Non Hot Non RETAIL

Year Heating Water Heating All Heating Heating All END USES

2013 4.97 5.48 5.65 5.51 5.16 5.49 5.35 5.44

2014 5.06 5.57 5.73 5.60 5.25 5.57 5.43 5.52

2015 5.13 5.72 5.92 5.76 5.35 5.73 5.56 5.67

2016 5.19 5.79 5.99 5.83 5.41 5.80 5.63 5.74

2017 5.49 6.09 6.29 6.13 5.71 6.10 5.93 6.04

2018 5.23 5.77 5.95 5.81 5.43 5.78 5.63 5.73

2019 6.03 6.65 6.85 6.69 6.26 6.65 6.48 6.59

2020 6.31 6.91 7.10 6.94 6.53 6.91 6.74 6.85

2021 6.49 7.11 7.32 7.15 6.72 7.12 6.95 7.06

2022 6.75 7.36 7.56 7.40 6.98 7.36 7.19 7.30

2023 6.92 7.52 7.71 7.55 7.14 7.52 7.35 7.46

2024 7.06 7.68 7.88 7.72 7.29 7.68 7.51 7.62

2025 7.24 7.86 8.05 7.89 7.47 7.86 7.69 7.80

2026 7.41 8.02 8.22 8.06 7.64 8.03 7.86 7.97

2027 7.56 8.17 8.37 8.21 7.79 8.17 8.00 8.11

2028 7.66 8.27 8.46 8.30 7.88 8.27 8.10 8.21

Levelized (a) 6.32 6.91 7.11 6.95 6.54 6.92 6.75 6.86

Simple Average 6.37 6.97 7.16 7.00 6.59 6.97 6.80 6.91

(a) Years 2014-2028 (15 years); Real (constant $) riskless annual rate of return in %: 1.360%

RESIDENTIAL COMMERCIAL & INDUSTRIAL
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐59  

2.4.8 Comparison of Avoided Retail Gas Costs with AESC 2011

Exhibit 2‐38 and Exhibit 2‐39 show the end use avoided costs of gas use in AESC 2013 as compared to

AESC 2011. Care must be taken when comparing the two study results for both Southern New England

and Northern New England, because while Massachusetts was included in Northern New England in

AESC 2011 (referred to as Northern and Central New England), the AESC 2013 report includes

Massachusetts with Southern New England. Furthermore, the avoided cost of gas delivered to

residential customers for non‐heating and hot water loads are no longer coupled in the model.

The end use avoided costs of gas use in AESC 2013 are generally less than estimated in AESC 2011 for all

three regions in New England.37 As a result of the two fundamental modeling changes detailed above,

the avoided cost of gas reductions aren’t distributed evenly, and in fact there are a few slight increases

for end uses in Northern New England. Southern New England saw significant decreases, because the

decline in the costs of gas delivered to the citygate outpaced slight increases in the avoidable LDC

margin. The changes for Northern New England’s levelized avoided costs from AESC 2011 to AESC 2013

are mixed. The avoidable LDC margin declined considerably due in part to Massachusetts being shifted

to Southern New England in the model, with the bulk of that decline on the commercial ‐ industrial side.

Much of Maine and New Hampshire’s natural gas is supplied by Canada, and has a high fixed‐cost

transportation, resulting in especially high cost in servicing low load factor customers. Similarly, without

Massachusetts’s lower citygate prices, the average citygate price of gas was nearly unchanged for

residential customers in Northern New England, with some decline for commercial and industrial

customers. As a result, residential customers in Northern New England have very similar levelized

avoided costs of gas delivered in AESC 2013 as in AESC 2011, whereas commercial and industrial

customers saw a reduction in AESC 2013 as compared to AESC 2011. Finally, Vermont saw double digit

percentage declines in the levelized avoided costs of gas delivered to retail customers in nearly all end

uses. The only end use that saw a decline less than 10 percent was residential hot water, the result of a

methodological change. Exhibit 2‐38 shows the end use avoided costs of gas use if one assumes some

retail margin is avoidable in AESC 2013.

                                                            

37 Exhibit 2‐38 is the same as Exhibit 2‐22, and Exhibit 2‐39 is the same as Exhibit 2‐23.
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐60  

Exhibit 2‐38. Comparison of AESC 2013 and AESC 2011 Avoided Cost of Gas Delivered to Retail Customers by End

Use Assuming Some Retail Margin Avoidable (2013$/MMBtu, unless noted)

   

ALL

Non Hot Non RETAIL

Heating Water Heating All Heating Heating All END USES

Southern New England (a)

AESC 2011 (2011$/MMBtu) 7.64 7.64 9.39 9.11 7.58 8.82 8.44 8.75

AESC 2011 (b) 7.89 7.89 9.70 9.41 7.83 9.11 8.72 9.04

AESC 2013 6.67 7.17 8.30 8.12 6.88 7.74 7.44 7.80

 2011 to 2013 change -15.43% -9.17% -14.43% -13.70% -12.06% -15.02% -14.74% -13.77%

Northern New England (a)

AESC 2011 (2011$/MMBtu) 7.47 7.47 8.96 8.73 7.59 8.79 8.43 8.58

AESC 2011 (b) 7.71 7.71 9.26 9.02 7.84 9.08 8.71 8.86

AESC 2013 6.53 8.04 9.35 8.91 7.04 7.43 7.17 7.31

 2011 to 2013 change -15.34% 4.17% 0.97% -1.19% -10.21% -18.21% -17.67% -17.56%

Vermont

AESC 2011 (2011$/MMBtu) 7.54 7.54 9.88 9.37 7.30 9.08 8.54 8.86

AESC 2011 (b) 7.79 7.79 10.21 9.68 7.54 9.38 8.82 9.15

AESC 2013 6.94 7.53 8.74 8.54 6.68 7.19 6.98 7.61

 2011 to 2013 change -10.88% -3.22% -14.37% -11.85% -11.37% -23.33% -20.86% -16.83%

(a) Massachusetts was included with Northern New England in AESC 2011,

 but is included with Southern New England in AESC 2013.

(b) Factor to convert 2011$ to 2013$ 1.0331

Note: AESC 2011 levelized costs for 15 years 2012 - 2026 at a discount rate of 2.465%.

 AESC 2013 levelized costs for 15 years 2014 - 2028 at a discount rate of 1.36%.

RESIDENTIAL COMMERCIAL & INDUSTRIAL
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐61  

Exhibit 2‐39 shows the end use avoided costs of gas use if one assumes that no retail margin is avoidable

in AESC 2013.

Exhibit 2‐39. Comparison of AESC 2013 and AESC 2011 Avoided Cost of Gas Delivered to Retail Customers by End

Use Assuming No Retail Margin Avoidable (2013$/MMBtu, unless noted)

   

ALL

Non Hot Non RETAIL

Heating Water Heating All Heating Heating All END USES

Southern New England (a)

AESC 2011 (2011$/MMBtu) 7.04 7.04 7.81 7.57 7.04 7.81 7.57 7.57

AESC 2011 (b) 7.27 7.27 8.06 7.83 7.27 8.06 7.83 7.83

AESC 2013 6.08 6.57 6.73 6.60 6.26 6.58 6.44 6.53

 2011 to 2013 change -16.41% -9.61% -16.54% -15.66% -13.88% -18.46% -17.74% -16.61%

Northern New England (a)

AESC 2011 (2011$/MMBtu) 6.94 6.94 7.58 7.39 6.94 7.58 7.39 7.39

AESC 2011 (b) 7.17 7.17 7.83 7.63 7.17 7.83 7.63 7.63

AESC 2013 6.03 7.53 8.02 7.62 6.58 7.54 7.12 7.39

 2011 to 2013 change -15.98% 5.01% 2.41% -0.15% -8.18% -3.67% -6.68% -3.17%

Vermont

AESC 2011 (2011$/MMBtu) 7.06 7.06 8.63 8.16 7.06 8.63 8.16 8.16

AESC 2011 (b) 7.29 7.29 8.91 8.43 7.29 8.91 8.43 8.43

AESC 2013 6.32 6.91 7.11 6.95 6.54 6.92 6.75 6.86

 2011 to 2013 change -13.39% -5.22% -20.28% -17.54% -10.36% -22.41% -19.91% -18.63%

(a) Massachusetts was included with Northern New England in AESC 2011,

 but is included with Southern New England in AESC 2013.

(b) Factor to convert 2011$ to 2013$ 1.0331

Note: AESC 2011 levelized costs for 15 years 2012 - 2026 at a discount rate of 2.465%.

 AESC 2013 levelized costs for 15 years 2014 - 2028 at a discount rate of 1.36%.

RESIDENTIAL COMMERCIAL & INDUSTRIAL
Synapse Energy Economics, Inc. – AESC 2013 Page 2‐62  

Exhibit 2‐40 shows the contribution to overall avoided cost to a heating customer by each of the

components: cost of gas delivered to VGS; commodity cost of storing, delivering, and transporting the

gas; and the avoidable retail margin. This picture shows more clearly the substantially lower cost of both

delivered gas and pipeline and storage costs in AESC 2013 as compared to AESC 2011, offset slightly by

an increased residential heating retail margin.

Exhibit 2‐40. Comparison between AESC 2011 and AESC 2013 of the Components of the Avoided Cost to a

Residential Heating Customer on Vermont Gas Systems in 2015 (2013$/MMBtu)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

2011 HH Price AESC 2011 Avoided Gas Cost 2013 HH Price AESC 2013 Avoided Gas Cost

2013$ per MMBtu

Henry Hub Price Delivered Gas Cost Pipeline & Storage Costs Res.Heating Retail Margin
Synapse Energy Economics, Inc. – AESC 2013 Page 3‐1  

Chapter 3: Avoided Fuel Oil Costs and Avoided Costs of

Other Fuels by Sector

3.1 Introduction

This chapter details the development of a forecast of prices for petroleum products used in electric

generation as well as in the residential, commercial, and industrial sectors in New England. For AESC

2013, we develop forecast prices for three fuel oil grades (No. 2, No. 4, and No. 6), two biofuel blends

(B5 and B20), and coal prices for the electric sector. In addition, we develop a forecast of unit fuel oil

costs that would be avoided by the installation of oil‐saving energy efficiency measures in the

commercial, industrial, and residential sectors.

This chapter also details the development of avoided costs by state, if supported by research, for other

fuels used in residential heating applications. For AESC 2013, these other fuels are wood, wood chips or

pellets, kerosene, and propane.

Our AESC 2013 forecasts for crude oil and fuels by sector and region are presented in detail in Appendix

D.  

3.2 Forecast of Crude Oil Prices

Our general approach to developing the forecasts of crude‐oil prices and of Henry Hub natural‐gas prices

is to use a set of relevant NYMEX futures prices in the near term, e.g. the first three years, and the

relevant EIA AEO forecast in the long term. Similar to the approach for natural gas described in Chapter

2, this approach is based upon our view that futures market prices are the most accurate estimates in

the near term, while projections from a forecasting model that reflects long‐term demand and supply

fundamentals, such as the EIA’s National Energy Modeling System, are the most accurate estimates in

the long term. As in AESC 2009 and AESC 2011, we developed our forecast of petroleum product prices

based on NYMEX futures for West Texas Intermediate (WTI) in the near term, and EIA’s Reference Case

forecast prices in the following years. Our analyses use prices of WTI for this comparison because it is

actively traded and its price in the past has been very close to that of the low‐sulfur light crude used in

EIA’s Reference Case. We note, however, that there is considerable uncertainty regarding the future

price of crude oil.
Synapse Energy Economics, Inc. – AESC 2013 Page 3‐2  

Based on this general approach, our first step in developing a forecast of crude oil prices was to review

the EIA’s AEO 2013 Reference Case forecast. We then compared the AEO 2013 Reference Case forecasts

of WTI prices through 2017 with NYMEX futures prices for WTI as of March 15, 2013.38  

This comparison revealed a significant difference between NYMEX futures for WTI in the medium to long

term, and the AEO 2013 Reference Case forecast prices. That disparity is presented in Exhibit 3‐1, which

plots, in 2013 dollars per bbl, (1) actual oil prices since 2000, (2) WTI futures through 2021, and (3) AEO

2013 Reference Case forecast prices through 2030. For comparison, it also plots the AEO 2012 forecast.

Exhibit 3‐1. Low‐Sulfur‐Crude Prices, EIA vs. NYMEX as of March 15, 2013 (2013$ per bbl)

  

The exhibit shows that the AEO 2012 projections of crude oil prices differ dramatically from NYMEX

futures from March 15, 2013. Unlike our analysis of market dynamics affecting natural gas prices, we do

not conduct such detailed market analysis of the crude oil market. As such, we rely on a relevant EIA

analysis, which for AESC 2013 is the AEO 2013 Reference Case for crude oil.  

For AESC 2013, we use a combination of NYMEX prices in the first five years, transitioning to AEO 2013

values for the remainder of the forecast. This forecast projects a slight dip in prices in 2013 through

2015 followed by a gradual rise.  

3.3 Forecast of Electric‐Generation Fuel Prices in New England

AEO 2013 provides forecasts of regional prices for distillate, residual, and coal for electricity generation

in New England. This section discusses how those prices are used in AESC 2013.

                                                            

38 AESC 2013 projections using NYMEX all use NYMEX prices as of March 15, 2013.

0

20

40

60

80

100

120

140

160

2000 2005 2010 2015 2020 2025 2030

Crude Oil Price (2013$/bbl)

WTI

AESC 2011

AEO 2012

AEO 2013

NYMEX 3/15/2013

AESC 2013
Synapse Energy Economics, Inc. – AESC 2013 Page 3‐3  

3.3.1 Forecast Prices of Distillate and Residual

We compared historical state ratios of crude oil to distillate prices to develop our forecast of distillate

prices. Our analysis did not identify material differences by state in the historical prices for these fuels in

the electric sector. Therefore, we developed a forecast of these prices by multiplying the corresponding

AEO 2013 forecast price by the average ratio of crude oil prices to delivered distillate prices.

3.3.2 Forecast Prices of Coal

The AEO 2013 (Table 78) Reference Case forecasts slightly rising prices for coal in New England. We

consider this reasonable. The U.S. has substantial coal resources and coal prices have been relatively

stable over a long time period without the volatility seen in oil and natural gas prices. While coal at the

mine mouth is relatively cheap on an energy basis, it is expensive to transport and to burn. Coal is more

expensive in New England because of the transportation costs, and represents a smaller fraction of

annual electric generation in New England than most other parts of the U.S.  

Coal demand is also unlikely to increase significantly because of the age of existing coal‐fired generation

plants and various environmental concerns; in AESC 2013, we assume the retirement of most of the

coal‐fired generation in New England over the study period. We use the AEO 2013 coal prices for AESC

2013. For the modeling, we also make appropriate adjustments based on the source of coal used by the

specific generating plants in New England.

3.4 Forecast of Petroleum Prices in the Residential, Commercial, and  

Industrial Sectors

AEO 2013 provides forecasts of regional prices for distillate and residual fuel oil in the residential,

commercial, and industrial sectors in New England. The retail price of each fuel in each sector of a given

state can be separated into two major components. The first component is the price of the underlying

resource, crude oil. The second component is a margin, or the difference between the price of each fuel

at the retail level and the crude oil price. The margin represents the aggregate unit costs of the refining

process, distribution, and taxes attributed to the particular fuel by sector and state. We developed our

forecast of prices for fuels in each sector in the following three steps:

 First, we calculated the price margin implicit in the AEO 2013 forecast of the New

England regional price for each fuel, expressed as a ratio to the crude oil price, and

compared it to the historical price margin, calculated from historical price data. We

developed a modified New England price margin for any fuel with an AEO 2013

forecast margin that we found unreasonable based on historical patterns.

 Second, we derived regional forecasts of New England prices for each fuel by

multiplying our forecast of the crude oil price by the above product price ratios.

 Finally, we developed our forecast of prices for each fuel by New England state from

the regional forecast to the extent that historical prices for that fuel have differed

materially by state.
Synapse Energy Economics, Inc. – AESC 2013 Page 3‐4  

These steps are detailed in the following sections (3.4.1, 3.4.2, and 3.4.3).

Our analysis found material differences by state in the historical prices for some fuels in these sectors.

Therefore, we adjusted the corresponding AEO 2013 regional forecasts to reflect those differences. We

then developed a forecast of prices for each fuel type by New England state from the regional forecast.

3.4.1 New England Regional Prices by Sector

The forecast of regional prices by fuel and sector in New England is presented in Appendix D.

We derived forecasts of regional petroleum product prices by adjusting the corresponding AEO 2013

forecasts of petroleum product prices in proportion to the ratio of our crude oil forecast to the AEO

2013 crude oil forecast. This approach is based on our conclusion that crude oil is the dominant

component of petroleum product prices, and that preparing a forecast of future absolute margins by

product based upon historical absolute margins is beyond the scope of this project.

In summary, our AESC 2013 forecast of regional prices of petroleum and related products by sector is

based on the following approaches:

 No. 2 and 6 Fuel Oil: The AEO 2013 forecast of the regional product prices were

adjusted by the ratio of AESC 2013 crude oil forecast to AEO 2013 crude oil forecast.

 No. 4 Oil: No projection. No. 4 is a blend of distillate and residual and we had no data

on the relative proportions of that blend.

 B5 and B20: The AEO 2013 forecast of corresponding petroleum products was adjusted

for recent price premium, assuming the phase‐out of the current $1.00 per gallon tax

credit by 2016.

A forecast for B100 (100 percent biodiesel) based on recent premiums over petroleum diesel was

developed for use in forecasts of biofuel blends. This included the phase‐out of the $1/gallon Biodiesel

Excise Tax Credit by 2016. Per ASTM D396, fuel oils for home heating and boiler applications may be

blended with up to 5 percent biodiesel below the rack.39,40 Marketers are not required to disclose

information on biodiesel content below these levels. While the AEO forecast for fuel oil does not reflect

any inherent biodiesel content, the price premium for biodiesel peaks at $1.20 per gallon. At a 5 percent

blend, the maximum blending ratio without requiring additional reporting, this represents a premium of

6 cents per gallon. We did not attempt to include any below‐the‐rack blending in our forecast.

AEO 2013 does not provide a forecast of New England regional prices for biofuels B5 and B20, as these

blends continue to represent a small portion of the New England market. Our research indicated that B5

                                                            

39ASTM International. “ASTM Sets the Standard for Biodiesel.” Jan 2009. Available at:

http://www.astm.org/SNEWS/JF_2009/nelson_jf09.html

40“Below the rack” refers to blending at the refinery, before fuel is sold to wholesalers.  
Synapse Energy Economics, Inc. – AESC 2013 Page 3‐5  

and B20 are each a mix of a petroleum product, such as distillate oil or diesel, and an oil‐like product

derived from an agricultural source (e.g., soy beans). The number in their name is the percent of

agricultural‐derived component. Thus “B5” and “B20” represent products with a 5 percent and a 20

percent agricultural‐derived component, respectively. They are both similar to No. 2 fuel oil and are

used primarily for heating. Each of these fuels has advantages and disadvantages relative to No. 2 fuel

oil. Their advantages include lower greenhouse‐gas emissions per MMBtu of fuel consumed, more

efficient operation of furnaces, and less reliance on imported crude oil. Their disadvantages include

somewhat lower heat contents and concerns about the long‐term supply of agricultural source

feedstocks. Forecasts for B5 and B20 were developed by combining the B100 and petroleum diesel

forecasts.

Since oil prices did not show meaningful variations by month or season, we did not develop monthly or

seasonal price variations for petroleum products. Storage for petroleum products is relatively

inexpensive and this also tends to smooth out variations in costs relative to market prices. For these

reasons, and those presented in the Chapter 2 discussion of volatility in natural gas prices, our forecast

does not address volatility in the prices of these fuels.

3.4.2 Weighted Average Avoided Costs by Sector Based on Regional Prices

We developed weighted average costs of avoided petroleum‐related fuels by sector by multiplying our

projected regional prices for each fuel and sector by the relative quantities of each petroleum‐related

fuel that AEO 2013 projects will be used in that sector. The relative quantity of each petroleum‐related

fuel that AEO 2013 projects for each sector, expressed as percentages, are presented in Appendix D. The

resulting weighted average costs of avoided petroleum‐related fuels by sector are also presented in

Appendix D.

We estimate that the crude oil price component of these projected prices is the portion that can be

avoided.

3.4.3 Prices by State by Sector

To determine if there were material differences by state in the historical prices for any of these fuels in

these sectors, we analyzed the actual prices by sector in each state from 1999 through 2011 using data

from the EIA State Energy Data System (SEDS). This is the most complete and consistent source of state‐

level energy prices.

Given the uncertainty associated with future quantities of fuel use by state by sector, future policies on

fuel taxes by state by sector, and other uncertainties, we concluded that no further precision would be

obtained from an estimate of avoided petroleum‐related fuel prices by sector by state.

3.5 Avoided Costs of Other Residential Fuels

For wood and kerosene, we determined the historical average ratio between the price of each fuel and

the price of distillate in the residential sector from EIA SEDS data. These resulting ratios were 0.36 for
Synapse Energy Economics, Inc. – AESC 2013 Page 3‐6  

wood and 1.1 for kerosene.41 Then we derived the forecast of regional prices for each of those fuels by

multiplying our AESC 2013 forecast price of distillate in the residential sector each year by the historical

ratio. Although wood is not based on oil, they compete in the fuel market and thus such a price

relationship seems reasonable.

The wood values are for both cordwood and pellets. EIA data shows a national average ratio of the price

of pellets to cordwood of 1.7.42,43 Vermont44, Maine45, and New Hampshire46

are the only New England

states to publish prices for both cordwood and pellets; other New England states do not, relying instead

upon prices reported by EIA. Based on these factors, we used the EIA SEDS data to develop prices for

cordwood in New England. Discussions with representatives in Vermont and New Hampshire indicated

that local wood prices can vary widely within New England.

For propane, we drew on the AEO 2013 forecast of New England regional prices. The AESC 2013 forecast

was derived from the AEO 2013 regional forecast using the AESC 2013 crude oil forecast.

Our detailed forecasts of prices for each fuel are presented in Appendix D. All prices are reported in

constant 2013 dollars per MMBtu except where noted.

                                                            

41EIA State Energy Data System, http://www.eia.gov/state/seds/ (accessed 4/1/2013).

42EIA National Average for Heat Cost Calculator, March 13 http://www.eia.gov/tools/faqs/faq.cfm?id=8&t=7 (accessed

4/1/2013)

43Residential customers can purchase either cord wood or wood pellets. We use EIA national average values for pellet price

premiums, due to the small sample size in New England, although recent data has shown pellet premiums ranging from 15

percent to 41 percent for Vermont, Maine, and New Hampshire.

44The Vermont Department of Public Service publishes prices for cordwood and wood pellets collected by the Vermont

Department of Forests through an informal survey each month. http://publicservice.vermont.gov/pub/vt‐fuel‐price‐

report.html

45Maine Current Heating Fuel Prices, April 3, 2013. Available at: http://maine.gov/energy/fuel_prices/index.shtml

46New Hampshire Office of Energy and Planning Fuel price data. April 2, 2013. Available at:

http://www.nh.gov/oep/programs/energy/fuelprices.htm
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐1  

Chapter 4: Embedded and Non‐Embedded

Environmental Costs

4.1 Introduction

This chapter discusses the value associated with mitigating the most significant airborne pollutants

created by: 1) the combustion of natural gas, fuel oil, coal, and biomass for the purpose of electricity

generation; and 2) the combustion of natural gas, fuel oil, wood, and kerosene for use in commercial,

industrial, and residential sectors.  

Some of the environmental costs associated with the combustion of fuels have been “embedded” in

electricity market prices over time, as regulations limiting emission levels have forced suppliers and

buyers to consider at least a portion of those costs in their production and use decisions.47 These costs

are embedded in our analysis for AESC 2013 in the form of pollutant allowance prices. The remainder of

the environmental costs associated with the combustion of fuels, which are not reflected in the price of

that good or service, are “non‐embedded” costs.48

This chapter discusses both embedded and non‐embedded environmental costs, and includes the

following major sections:

 Environmental Regulations: Embedded Costs: This section identifies avoided costs

associated with expected and existing NOx, SOx, and CO2 regulations. These costs are

embedded in the assumptions used by our electric market simulation model (Market

Analytics) to calculate avoided electric energy costs for AESC 2013.  

 Non‐Embedded Environmental Costs: Non‐embedded costs are above and beyond the

costs imposed by NOx, SOx, and CO2 regulations. For AESC 2013, we anticipate that the

“non‐embedded CO2 cost” will continue to be the dominant non‐embedded

environmental cost associated with marginal electricity generation in New England. This

                                                            

47 For example, the Clean Air Transport Rule, while currently in draft form, is expected to adjust the SO2 and NOx emissions

caps downward with an ultimate effect of reducing SO2 emissions approximately 73 percent from 2003 levels. Under the

draft rule, annual emissions of SO2 are required to decline from 4.7 million tons in 2009 to 3.9 million tons by 2012, and then

to 2.5 million tons by 2014, for a cumulative reduction of 47 percent over the five‐year compliance period. Annual NOx

emissions are capped at 1.4 million tons. As a result, while there will be some “external costs” associated with the residual

SO2 and NOx pollution, these externalities are now relatively small. The EPA’s proposed Air Toxics Rule governing electric

utilities under section 112(d) of the Clean Air Act would do the same for emissions of mercury and other air toxics, while the

proposed rule under section 316(b) of the Clean Water Act would minimize the externalities associated with the

impingement and entrainment of aquatic organisms from power plant cooling water intake systems.

48 In economics, a non‐embedded impact can be positive (a non‐embedded benefit) or negative (a non‐embedded cost); in this

discussion we are focusing on negative costs.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐2  

cost is not included in AESC 2013 avoided cost calculations for electric energy or other

fuels. We provide recommendations for PAs to apply avoided non‐embedded CO2 costs

in their evaluations of EE programs.

 Value of Mitigating Significant Pollutants: This section identifies and describes the most

significant pollutants associated with electricity generation, end‐use natural gas, and

end‐use fuel oil and other fuels. The section then provides the value associated with

mitigating those pollutants for end‐use natural gas, fuel oil, and other fuels based on

AESC 2013 NOx, SO2, and CO2 emissions allowance prices per short ton (embedded

costs), and the AESC 2013 recommended CO2 abatement cost (non‐embedded cost). For

end‐use natural gas, fuel oil, and other fuels, the value of mitigating significant

pollutants is not‐embedded.

 Discussion of Non‐Embedded NOx Costs: This section addresses non‐embedded NOx

costs, at the request of the Study Group, in order to increase awareness. Please note

that we are not recommending that PAs use an additional non‐embedded NOx value

beyond the embedded allowance prices discussed in this chapter. Instead, we

recommend a methodology consistent with AESC 2011.

 Emissions from Hydraulic Fracturing: Although calculating upstream avoided

externalities associated with fracking is outside the scope of work for AESC 2013,

discussion of “front end” emissions for gas fracking is important and is included here

because of the large amount of greenhouse gas emissions associated with this fuel

extraction process.  

 Compliance with State‐Specific Climate Plans: The AESC scope of work required the

Synapse project team to determine if there was some component of compliance with

state‐specific regulations or climate plans that would directly impact generators, and

that the project team could credibly support in its estimate of avoided electric energy

costs. This section describes the methodology and policy questions raised by our

analysis.  

4.2 Environmental Regulations: Embedded Costs

For all fuels, we estimate the value associated with the mitigation of NOx, SOx, and CO2 based on the

allowance prices per short ton of emissions described and presented in this section. In addition, future

environmental regulations will impact generator expenses, outages, and retirement decisions that are

inputs into our simulation model.  

4.2.1 Cost of Complying with Existing and Expected SO2, NOx, and CO2 Regulations

For AESC 2013, we used Market Analytics to model and apply the unit costs of complying with

regulations governing the emissions of SO2, NOx and CO2. Market Analytics includes the unit costs

associated with each of these emissions when calculating bid prices, and when making commitment and
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐3  

dispatch decisions.49 In this way, AESC 2013 projects market prices which reflect, or “embed,” the unit‐

compliance costs for each type of emission, excluding mercury. The unit compliance costs assumed for

each pollutant are presented in Exhibit 4‐1.  

The NOx and SO2 allowance prices are based on the Market Analytics default assumptions.50 Since there

is still considerable uncertainty about the longer term, we have kept NOx and SO2 prices level at constant

2013 dollar (2013$) values. For mercury, we assume no trading, and hence no allowance price. The

Ventyx Database Release Notes of February 2013 explains the Market Analytics NOx and SO2 emission

price forecasts as follows:51  

In August 2012, the DC Circuit Court vacated EPA’s Cross State Air Pollution Rule

(CSAPR), sending the rule back to EPA for revision and re‐instating the Clean Air

Interstate Rule (CAIR). The court stated that CSAPR required states to make emissions

reductions beyond what was mandated by the law. EPA has appealed that ruling, but in

the meantime states are required to comply with CAIR. CAIR’s requirements are much

less stringent than those of CSAPR, and the clearing prices of the emissions markets

indicate that those requirements have effectively been met: the value of an SO2 and NOx

allowances are much lower than had been projected—$0/ton for SO2 and around

$27/ton for NOx.  

We note that even though allowance prices are low, these emission standards require investment in

new emission control equipment that may cause the retirement of some older plants, as discussed in

detail in Chapter 5, Electric Capacity.  

Embedded CO2 allowance prices are based on Regional Greenhouse Gas Initiative (RGGI) allowance

prices through 2019. In 2020 and beyond, our estimate of embedded CO2 costs is based on allowance

prices estimated by the 2012 Carbon Dioxide Price Forecast (Wilson et al. 2012) for our Base Case, in

which a national cap‐and‐trade program for GHG is enacted.52

As requested by the Study Group, we have also estimated CO2 allowance prices for a special case that

assumes no new federal regulatory framework and thus continuation of RGGI indefinitely (RGGI‐only).

Under the RGGI‐only scenario, as required by the state of Rhode Island, we assume that RGGI prices will

                                                            

49 These are the carbon allowance prices that are embedded in the cost of electricity. For a discussion of the overall cost of

carbon, including its non‐embedded/climate plan compliance cost and overall value, see the remaining sections of this

chapter.  

50 NOx allowance prices in AESC 2011 fell considerably since the previous AESC report in 2009, and have fallen considerably

once again in 2013. AESC 2011 NOx prices started at $230 and fell to $132 per ton, compared to approximately $27 per ton in

2013. At $0 per ton, the 2013 SO2 prices are also lower than AESC 2011 prices, which started at $3.75 and fell to $1.62 per

ton. Compared to AESC 2011, 2013 CO2 prices are only slightly lower in most years, resulting from the 2012 Synapse CO2

Price Forecast’s policy start date of 2020, compared to 2018 in AESC 2011.

51 Further discussion of EPA regulations is provided in Chapter 4, Environmental Regulations.

52 Wilson et al., “2012 Carbon Dioxide Price Forecast,” October 2012. http://www.synapse‐

energy.com/Downloads/SynapseReport.2012‐10.0.2012‐CO2‐Forecast.A0035.pdf
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐4  

remain relatively stable due to electricity imports. Thus, we assume allowance prices in that RGGI‐only

case will rise at the rate of inflation. This scenario is shown in the last column of Exhibit 4‐1.

When RGGI was established in 2007, the expectation was that the CO2 cap would generate allowance

price revenues for consumer benefit programs such as energy efficiency, renewable energy and clean

energy technologies.53 While RGGI has provided revenues for consumer benefit, its allowance prices

have generally remained near the floor. External influences, including changes to fuel prices, caused a

shift from coal and oil to natural gas generation. Compared to those external factors, the effect of the

RGGI cap requirements were relatively minor in meeting the goals of reducing carbon dioxide emissions

in the power sector.

In 2012 and 2013, the RGGI states evaluated a number of plans for tighter emissions caps that would

result in higher allowance prices. In February of 2012, the RGGI states agreed to lower the CO2 cap from

165 million to 91 million tons by 2020. Their analysis indicated that this would result in the allowance

price rising to about $4 per short ton (2010$) in 2014 and increasing to about $10 per ton (2010$) in

2020.54

Exhibit 4‐1. Emission Allowance Prices per Short Ton (Constant 2013$ and Nominal Dollars)

                                                            

53 http://www.rggi.org/

54 RGGI Press Release 2/7/13 http://www.rggi.org/docs/PressReleases/PR130207_ModelRule.pdf  

Year 2013$ Nominal 2013$ Nominal 2013$ Nominal 2013$ Nominal

2013 27.41 27.41 0.00 0.00 2.80 2.80 2.80 2.80

2014 27.41 27.95 0.00 0.00 4.22 4.30 4.22 4.30

2015 27.41 28.51 0.00 0.00 5.28 5.49 5.28 5.49

2016 27.41 29.08 0.00 0.00 6.33 6.72 6.33 6.72

2017 27.41 29.66 0.00 0.00 7.39 7.99 7.39 7.99

2018 27.41 30.26 0.00 0.00 8.44 9.32 8.44 9.32

2019 27.41 30.86 0.00 0.00 9.50 10.69 9.50 10.69

2020 27.41 31.48 0.00 0.00 20.30 23.32 10.55 12.12

2021 27.41 32.11 0.00 0.00 22.58 26.46 10.55 12.36

2022 27.41 32.75 0.00 0.00 24.87 29.72 10.55 12.61

2023 27.41 33.41 0.00 0.00 27.15 33.10 10.55 12.86

2024 27.41 34.07 0.00 0.00 29.44 36.60 10.55 13.12

2025 27.41 34.76 0.00 0.00 31.72 40.23 10.55 13.38

2026 27.41 35.45 0.00 0.00 34.00 43.99 10.55 13.65

2027 27.41 36.16 0.00 0.00 36.29 47.88 10.55 13.92

2028 27.41 36.88 0.00 0.00 38.57 51.91 10.55 14.20

2029 27.41 37.62 0.00 0.00 40.85 56.08 10.55 14.48

2030 27.41 38.37 0.00 0.00 43.14 60.40 10.55 14.77

NOx & SO2 from Ventyx assumptions. CO2 (RGGI) from Auction 19 and RGGI Updated Model Rule 

Modeling, CO2 (Synapse) starting in 2020 from Synapse report of October 2012.

NOx SO2 CO2 (Synapse) CO2 (RGGI)
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐5  

The 15‐year levelized value of the embedded avoided cost of carbon compliance for AESC 2013 is 21.6

percent higher than AESC 2011 (2013$) ($19.72/ton versus $16.21/ton), primarily due to upward

pressure from RGGI Model Rule allowance prices, which was moderated somewhat by a delay in federal

GHG regulation.

The following sections summarize the existing and expected environmental regulations that are

incorporated into AESC 2013, and which are reflected in Exhibit 4‐1, above.

4.2.2 Existing Regulations

Regional Greenhouse Gas Initiative

The Regional Greenhouse Gas Initiative (RGGI) is a cap and trade greenhouse gas program for power

plants in the northeastern United States. Current participant states include Connecticut, Delaware,

Maine, Maryland, Massachusetts, New Hampshire, New York, Rhode Island, and Vermont. Pennsylvania,

Québec, New Brunswick, and Ontario are official “observers” in the RGGI process. To date, 19 rounds of

RGGI auctions have occurred.

RGGI is designed to:

 Limit CO2 emissions from power plants to 2009 levels for the period 2009 – 2013,

followed by a 42 percent reduction below those levels by 2020.

 Allocate a minimum of 25 percent of allowances for consumer benefit and strategic

energy purposes. Allowances allocated for consumer benefit will be auctioned and the

proceeds of the auction used for consumer benefit and strategic energy purposes.

 Include certain offset provisions that increase flexibility to include opportunities

outside the capped electricity generation sector.55

AESC 2013 assumes RGGI allowance prices as reported in Exhibit 4‐1 based upon the proposed RGGI

Model Rule update expected to be in place starting in 2014.  

4.2.3 Expected Regulations

In AESC 2013, our assumptions incorporate the impact of future environmental regulations on the

existing electric generation fleet. When considered individually, these rules will require generator

retrofits and associated outages, and may result in retirements and/or the repowering of existing

electric generating units across the United States. Taken together, these rules will have a significant

effect on the generating fleet and influence our assumptions regarding unit retirements. The following is

a short description of the rules anticipated to have the most economically consequential impacts on the

power sector.  

                                                            

55 Regional Greenhouse Gas Initiative website, http://www.rggi.org/design/program_review
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐6  

Expected EPA Regulations

National Ambient Air Quality Standards

National Ambient Air Quality Standards (NAAQS) set maximum air quality limitations that must be met

at all locations across the nation. Compliance with the NAAQS can be determined through air quality

monitoring stations, which are stationed in various cities throughout the United States, or through air

quality dispersion modeling. States with areas found to be in “nonattainment” of a particular NAAQS are

required to set enforceable requirements to reduce emissions from sources contributing to

nonattainment such that the NAAQS are achieved and maintained. The U.S. Environmental Protection

Agency (EPA) has established NAAQS for six pollutants: sulfur dioxide (SO2), nitrogen dioxides (NO2),

carbon monoxide (CO), ozone, particulate matter—measured as particulate matter less than or equal to

10 micrometers in diameter (PM10) and particulate matter less than or equal to 2.5 micrometers in

diameter (PM2.5)—and lead.  

In nonattainment areas, pollutant sources must comply with emission reduction requirements known as

“Reasonably Available Control Technology” (RACT) to bring the areas into attainment of the NAAQS.

New major sources, including major modifications at existing sources, must comply with very strict

emissions reductions consistent with “lowest achievable emissions reductions” (LAER) and obtain

emission offsets.

EPA is currently in the process of drafting new, more stringent NAAQS for SO2, PM2.5, and ozone.

 On June 22, 2010, EPA revised56 the standard for SO2 by establishing a new 1‐hour

standard at a level of 75 parts per billion (ppb) in place of the existing annual and 24‐

hour standards for SO2. EPA plans to make area designations for the new SO2

standard by June 3, 2013, and compliance would be required in 2017.  

 On December 14, 2012, EPA strengthened the annual PM2.5 standard from 15

μg/m3 to 12 μg/m3, and retained the current 24‐hour standard at 35 μg/m3. EPA will

make final area designations for the new standard by December 2014. Once

designations are made, states with non‐attainment areas will have to develop a State

Implementation Plan within three years outlining how they will reduce pollution to

meet the standard by 2020.

 In March 2008, EPA strengthened the 8‐hour ozone standard from 84 ppb to 75 ppb.

On September 16, 2009, EPA announced that because the 2008 standard was not as

protective as recommended by EPA’s panel of science advisors, it would reconsider

the 75 ppb standard. In 2010, EPA proposed lowering the 8‐hour ozone standard

from 75 ppb to between 60 and 70 ppb, and on September 2, 2011, the

Administration announced that EPA would not finalize its proposed reconsideration

                                                            

56 75 Fed. Reg. 35520 (June 22, 2010)
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐7  

of the 75 ppb standard ahead of the regular 5‐year NAAQS review cycle. The next 5‐

year review for 8‐hour ozone is expected in 2013. Compliance with the upcoming

standard would likely be required in the 2019‐2020 timeframe.

Cross State Air Pollution Rule

The Cross State Air Pollution Rule (CSAPR) was finalized in 2011, establishing the obligations of each

affected state to reduce emissions of NOx and SO2 that significantly contribute to another state’s PM2.5

and ozone non‐attainment problems. The rule targets electric generating units, and uses a cap and‐trade

approach to limit each state to emissions below a level that significantly contributes to non‐attainment

in downwind states.  

On August 21, 2012, CSAPR was vacated by the U.S. Court of Appeals for the District of Columbia. EPA

has filed a petition for en banc rehearing of that decision; however, even if EPA fails to salvage CSAPR

through the courts, the Agency must still promulgate a replacement rule to implement Clean Air Act

requirements to address the transport of air pollution across state boundaries. In the meantime, the

court left the requirements of the 2005 Clean Air Interstate Rule in place.

Regional Haze Rules

One of the national goals set out in the Clean Air Act is reducing existing visibility impairment from

human‐made air pollution in all “Class I” areas (e.g., most national parks and wilderness areas).57 EPA’s

Regional Haze Rule—issued in 1999, and revised in 2005—requires states to create plans to significantly

improve visibility conditions in Class I areas with the goal of achieving natural background visibility

conditions by 2064. These requirements are implemented through state plans with enforceable

reductions in haze‐causing pollution from individual sources and with other measures to meet

“reasonable further progress” milestones.58 The first progress milestone is 2018.  

A key component of this program is the imposition of air pollution controls on existing facilities that

impact visibility in Class I areas. Specifically, the rules require installation of “best available retrofit

technology” (BART) that is developed for such facilities on a case‐by‐case basis. In addition, EPA’s BART

determinations specify particular emission limits for each BART‐eligible facility. EPA evaluates BART for

the air pollutants that impact visibility in our national parks and wilderness areas—namely SO2, PM, and

NOx. Under the Clean Air Act, states develop Regional Haze requirements, but EPA approves state plans

for compliance. If EPA finds the plans are not consistent with the Clean Air Act, it adopts a federal plan

with BART and reasonable progress requirements. Affected facilities must comply with the BART

                                                            

57 42 U.S.C. § 7491(a)(1)

58 40 C.F.R. §51.308‐309
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐8  

determinations as expeditiously as practicable but no later than five years from the date EPA approves

the state plan or adopts a federal plan.59  

Mercury and Air Toxics Standards (MATS)

In 2000, EPA determined it was appropriate and necessary to regulate toxic air emissions (or hazardous

air pollutants) from steam electric generating units. As a result, EPA adopted strict emission limitations

for hazardous air pollutants that are based on the emissions of the cleanest existing sources.60 These

emission limitations are known as Maximum Achievable Control Technology (MACT). The final MATS

rule, approved in December 2011, sets strict stack emissions limits for mercury, other metal toxins,

other organic and inorganic hazardous air pollutants, as well as acid gasses. Compliance with MATS is

required by 2015, with a potential extension to 2016.

Coal Combustion Residuals Disposal Rule

Coal‐fired power plants generate a tremendous amount of ash and other residual wastes, which are

commonly placed in dry landfills or slurry impoundments. The risk associated with wet storage of coal

combustion residuals (CCR) was dramatically revealed in the catastrophic failure of the ash slurry

containment at the Kingston coal plant in Roane County, Tennessee in December 2008, releasing over a

billion gallons of slurry and sending toxic sludge into tributaries of the Tennessee River.  

On June 21, 2010, EPA proposed to regulate CCR for the first time either as a Subtitle C hazardous waste

or Subtitle D solid waste under the Resource Conservation and Recovery Act. The current rulemaking is

30 years overdue. If the EPA classifies CCR as hazardous waste, a cradle‐to‐grave regulatory system

would apply to CCR, requiring regulation of the entities that create, transport, and dispose of the waste.

Under a Subtitle C designation, the EPA would regulate siting, liners, run‐on and run‐off controls,

groundwater monitoring, fugitive dust controls, and any corrective actions required; in addition, the EPA

would implement minimum requirements for dam safety at impoundments. Under a solid waste Subtitle

D designation, the EPA would require minimum siting and construction standards for new coal ash

ponds, compel existing unlined impoundments to install liners, and require standards for long‐term

stability and closure care.

                                                            

59 EPA’s regulations allow certain states in the “Grand Canyon Visibility Transport Region” to participate in an SO2 trading

program in lieu of adopting source‐specific SO2 BART requirements, if the trading program will result in greater reasonable

progress toward attaining the national visibility goal than source‐specific BART. Although nine states were originally eligible

to participate, today only three states are opting to participate in this program – New Mexico, Utah, and Wyoming. These

states agreed to a gradually declining cap on SO2 emissions from all emission sources. If the declining caps are exceeded in

any year, then even greater SO2 emission reductions have to be achieved—although the reductions can be met through

emissions trading, rather than imposition of specific emission limitations on any one facility. This program is called the

Backstop Trading Program. As of the date of this testimony, EPA has not yet approved the Backstop Trading Program to meet

Regional Haze requirements in any of the three states’ Regional Haze plans, so the trading program is not yet federally

enforceable.

60 Clean Air Act §112(d)
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐9  

The EPA is currently evaluating which regulatory pathway will be most effective in protecting human

health and the environment. In 1999, EPA released a series of technical papers to Congress documenting

cases in which damages are known to have occurred from leakages and spills from coal ash

impoundments.61 In the current proposed rule, the EPA recognizes a substantial increase in the types

and quantities of potentially toxic CCR caused by air pollution control equipment.

Use of more advanced air pollution control technology reduces air emissions of metals and other

pollutants in the flue gas of a coal‐fired power plant by capturing and transferring the pollutants to the

fly ash and other air pollution control residues. The impact of changes in air pollution control on the

characteristics of CCRs and the leaching potential of metals is the focus of ongoing research by EPA’s

Office of Research and Development.62

Steam Electric Effluent Limitation Guidelines

Following a multi‐year study of steam‐generating units across the country, EPA found that coal‐fired

power plants are currently discharging a higher‐than‐expected level of toxic‐weighted pollutants into

waterways. Current effluent regulations were last updated in 1982 and do not reflect the changes that

have occurred in the electric power industry over the last thirty years, and do not adequately manage

the pollutants being discharged from coal‐fired generating units. Coal ash ponds and flue gas

desulfurization systems used by such power plants are the source of a large portion of these pollutants,

and are likely to result in an increase in toxic effluents in the future as environmental regulations are

promulgated and pollution controls are installed. No new rule has yet been proposed, but EPA is under a

court order to issue the proposed regulation by April 19, 2013 and a final rule in May 22, 2014.63 New

requirements will be implemented in 2014 to 2019 through the five‐year National Pollutant Discharge

Elimination System permit cycle.64

Clean Water Act Cooling Water Intake Structure Rule

On March 28, 2011, the EPA proposed a long‐expected rule implementing the requirements of Section

316(b) of the Clean Water Act at existing power plants.65 Section 316(b) requires “that the location,

design, construction, and capacity of cooling water intake structures reflect the best technology

available for minimizing adverse environmental impact.” Under this new rule, EPA set new standards

                                                            

61

EPA. March 15, 1999. Technical Background Document for the Report to Congress on Remaining Wastes from Fossil Fuel

Combustion: Potential Damage Cases. http://www.epa.gov/osw/nonhaz/industrial/special/fossil/ffc2_397.pdf

62 75 Fed. Reg. 35139 (June 21, 2010).

63 See U.S. Environmental Protection Agency website. Accessed February 21, 2013. Available at:

http://water.epa.gov/scitech/wastetech/guide/steam‐electric/amendment.cfm

64 See U.S. Environmental Protection Agency. Steam Electric ELG Rulemaking. UMRA and Federalism Implications: Consultation

Meeting. October 11, 2011. http://water.epa.gov/scitech/wastetech/guide/upload/Steam‐Electric‐ELG‐Rulemaking‐UMRA‐

and‐Federalism‐Implications‐Consultation‐Meeting‐Presentation.pdf

65 33 U.S.C. § 1326.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐10  

reducing the impingement and entrainment of aquatic organisms from cooling water intake structures

at new and existing electric generating facilities.

The rule provides that:

 Existing facilities that withdraw more than two million gallons per day are subject to

an upper limit on fish mortality from impingement, and must implement technology

to either reduce impingement or slow water intake velocities.

 Existing facilities that withdraw at least 125 million gallons per day are required to

conduct an entrainment characterization study to establish a “best technology

available” for the specific site.

Greenhouse Gas Tailoring Rule  

Under EPA’s Greenhouse Gas Tailoring Rule, large sources of greenhouse gas emissions are subject to

permitting requirements. For purposes of determining whether New Source Review applies, a “large

source” is a new facility with emissions of at least 100,000 tons per year of carbon dioxide equivalent

(CO2e) or an existing facility that emits at least 100,000 tons per year CO2e and is making modifications

that would increase greenhouse gas emissions by at least 75,000 tons per year CO2e. These sources are

required to obtain permits under the New Source Review Prevention of Significant Deterioration

program and therefore must install Best Available Control Technology (BACT) for greenhouse gases. In

the case of a modification, to a facility that does not emit at least 100,000 tons per year CO2e but will

increase greenhouse gas emissions by 75,000 tons per year CO2e, the BACT requirement only applies for

GHG if the project triggers new source review for another criteria pollutant. Any new or existing source

with emissions of 100,000 tons per year CO2e or more must obtain a Title V operating permit.

New Source Performance Standards

Under Section 111 of the Clean Air Act, EPA sets technology‐based standards for new sources on a

category‐by‐category basis. These standards are set based on the best demonstrated available

technology (BDAT) and apply to all new sources built or modified following promulgation of the

standard.  

On March 27, 2012, EPA proposed66 New Source Performance Standards for greenhouse gas emissions

from new electric generating units. The standard was set at 1,000 lbs CO2e/MWh, which is equivalent to

the emission rate that a combined‐cycle natural gas unit can achieve. The rule also allows a unit’s

emissions to be averaged over 30 years to achieve an annual average emission rate of 1,000 lbs

CO2e/MWh. This option allows the phase‐in of CCS within the first 10 years of operation.  

While New Source Performance Standards apply only to new facilities, Section 111(d) of the Clean Air

Act requires states to develop plans for existing sources of any non‐criteria pollutants (i.e., a pollutant

                                                            

66 77 Fed. Reg. 22392 (April 13, 2012)
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐11  

for which there is no NAAQS) and non‐hazardous air pollutant whenever EPA promulgates a standard for

a new source. These plans are subject to EPA review and approval, similar to state implementation plans

under the NAAQS program.  

Federal Carbon Regulation

The AESC 2013 Base Case assumes federal cap‐and‐trade carbon regulation will take effect in 2020,

consistent with the 2012 Carbon Dioxide Price Forecast developed by Synapse in October 2012, as

described in section 4.2.1.

As previously noted, embedded CO2 costs for the AESC 2013 Base Case are based on RGGI allowances

prices (as reported in Exhibit 4‐1) in the near term, and prices estimated in Synapse’s 2012 Carbon

Dioxide Price Forecast for years 2020 and beyond.  

4.2.4 Impact of Energy Efficiency Programs on CO2 Emissions under a Cap and Trade

Regulatory Framework

With CO2 emissions regulated under a cap and trade system, as assumed in this market price analysis, it

is conceivable that a load reduction from an energy efficiency program will not lead to a reduction in the

amount of total system CO2 emissions. The annual total system emissions for the affected facilities in

the relevant region are, after all, capped. In the analysis documented in this report, the relevant cap and

trade regulation is the RGGI for the period 2013 to 2019, and an assumed national cap and trade system

thereafter. There are, however, a number of reasons why an energy efficiency program could

nonetheless result in CO2 emission reductions. Specifically:

 A reduction in load that reduces the cost (marginal or total cost) of achieving an

emissions cap can result in a decision to tighten the cap. This is a complex interaction

between the energy system and political and economic systems, and is difficult or

impossible to model, but the dynamic reasonably may be assumed to exist.

 Specific provisions in RGGI provide for a tightening or loosening of the cap (via

adjustments to the offset provisions that are triggered at different price levels). It is

unknown at this point whether and to what extent such automatic adjustments might

be built into the U.S. carbon regulatory system.

 It is also possible that energy efficiency efforts will be accompanied by specific

retirements or allocations of allowances that would cause them to have an impact on

the overall system level of emissions (effectively tightening the cap).

 To the extent that the cap and trade system “leaks” outside of its geographic

boundaries, one would expect the benefits of a carbon emissions reduction resulting

from an energy efficiency program to similarly “leak.” That is, a load reduction in New

York could cause reductions in generation (and emissions) at power plants in New York,

Pennsylvania, and elsewhere. Because New York is in the RGGI cap and trade system,

the emissions reductions realized at New York generating units may accrue as a result

of increased sales of allowances from New York to other RGGI states. Since

Pennsylvania is not in the RGGI system, however, the emissions reductions at
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐12  

Pennsylvania generating units would be true reductions attributable to the energy

efficiency program.

The first three of these points, above, would also apply to a national CO2 cap and trade program. The

fourth point, regarding leakage and boundaries, would apply as well, but to a lesser extent.

4.3 Non‐Embedded Environmental Costs

Non‐embedded costs are impacts from the production of a good or service that are not reflected in

price of that good or service, and are not considered in the decision to provide that good or service.67

Air pollution generated in the production of electricity is a classic example of a non‐embedded cost:

pollutants released from a power plant impose health impacts on a population, cause damage to the

environment, or both. In this example, health impacts and ecosystem damages not reflected in the price

of electricity and not considered in the power plant owner’s decision of how much electricity to provide

are “non‐embedded,” whereas adverse impacts that are reflected in the market price of electricity (e.g.,

through regulation) and are considered in decisions regarding production are “embedded.”

For AESC 2013, we anticipate that the “non‐embedded carbon costs” will continue to be the dominant

non‐embedded environmental cost associated with marginal electricity generation in New England. This

is the case for two main reasons. First, regulations to address the greenhouse gas emissions responsible

for global climate change have yet to be implemented with sufficient stringency to reduce carbon

emissions, particularly in the United States.68 The damages from the EPA’s criteria air pollutants are

relatively bounded, and to a great extent “embedded,” as a result of existing regulations. In contrast,

global climate change is a problem on an unprecedented scale with far‐reaching and potentially

catastrophic implications.  

Second, New England avoided electric energy costs over the study period are likely to be dominated by

natural gas‐fired generation, which has minimal SO2, mercury, and particulate emissions, as well as

relatively low NOx emissions.  

4.3.1 History of Non‐Embedded Environmental Cost Policies in New England

In the 1980s and 1990s, several New England states had proceedings dealing with non‐embedded costs

that influence current utility planning and decision‐making.69 In Massachusetts, dockets DPU 89‐239 and

                                                            

67In economics, a non‐embedded impact can be positive (a non‐embedded benefit) or negative (a non‐embedded cost); in this

discussion we are focusing on negative impacts (non‐embedded costs).

68 On April 17, 2009; EPA issued a proposed finding that concluded that greenhouse gases posed an endangerment to public

health and welfare under the Clean Air Act (“Proposed Endangerment and Cause or Contribute Findings for Greenhouse

Gases Under Section 202(a) of the Clean Air Act” 74 Fed. Register 78: 18886–18910). This proposed finding initiates the

process of potentially regulating greenhouse gases as an air pollutant. http://epa.gov/climatechange/endangerment.html

69 A more detailed description of the history of electricity generation environmental externalities and policies in New England

may be found in AESC 2007 (p. 7‐6–7‐8).
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐13  

91‐131 served as models for other states. Docket DPU 89‐239 was opened to develop “Rules to

Implement Integrated Resource Planning” and included the determination and application of non‐

embedded environmental cost values. This docket adopted a set of dollar values for air emissions,

including a CO2 value of $37 per ton of CO2 (in 2013 dollars) (Exhibit DOER‐3, Exhibit. BB‐2, p. 26).70

Docket DPU 91‐131 examined environmental costs to develop recommendations of various approaches

for quantifying the non‐embedded CO2 value. The Department’s Order in Docket DPU 91‐131 was

noteworthy for its foresight regarding climate change, albeit optimistic about the timing of the adoption

of climate change regulations in the U.S.71 Based on information in the record, the Department

reaffirmed the CO2 value it had adopted in the previous case, $37 per ton (in 2013 dollars).  

4.3.2 Estimating Non‐Embedded CO2 Costs

Setting a Threshold for Global CO2 Emissions

The level of global CO2 emissions thought to be consistent with avoiding the most serious forms of

climate damage is essentially unchanged since AESC 2011.72 Sustainability targets for CO2 equivalent

concentrations in the atmosphere are roughly 350 to 450 ppm

73, consistent with an approximately 50

percent chance of limiting the change in the average global temperature to 2°C above pre‐industrial

levels.74 The Copenhagen Agreement, drafted at the 15th session of the Conference of the Parties to the

United Nations Framework Convention on Climate Change in 2009, recognizes the scientific view that in

order to prevent the more drastic effects of climate change, the increase in global temperature should

be limited to no more than 2°C.75

The Intergovernmental Panel on Climate Change (IPCC 2007, Table SPM5) indicates that reaching

concentrations of 450 to 490 ppm CO2 equivalent will require a reduction in 2050 global CO2 emissions

of 50 to 85 percent below 2000 emissions levels. Stern (2007, xi) states that global emissions would have

to be 70 percent below current levels by 2050 for stabilization at 450 ppm CO2 equivalent. To

accomplish such stabilization, the U.S. and other industrialized countries would have to reduce

greenhouse gas emissions on the order of 80 to 90 percent below 1990 levels, and developing countries

would have to achieve reductions from the baseline increase in emissions caused by improvements in

the standard of living as soon as possible (den Elzen and Meinshausen, 2006).  

                                                            

70 $22 in 1989 dollars.

71 AESC 2009 provides more detail about the Massachusetts DPU Order in Docket DPU 91‐131.

72 AESC 2011 Section 6.6.4.1 page 6‐97.

73 We are unaware of specific abatement cost estimate studies for the 350 ppm target; thus the information and analysis

presented here focuses on the 450 ppm target.

74 Ackerman and Stanton (2013) Climate Economics: The State of the Art. Routledge: NY.

75 IPCC, 2007: Summary for Policymakers. In: Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth

Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A.

Meyer (eds)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐14  

In the U.S., several states have adopted state greenhouse gas abatement targets of 50 percent or more

reduction from a baseline of 1990 levels or then‐current levels by 2050 (Arizona, California, Connecticut,

Florida, Illinois, Maine, Massachusetts, Minnesota, New Hampshire, New Jersey, New Mexico, Oregon,

Vermont, and Washington).76 In Massachusetts, the Global Warming Solutions Act (GWSA), signed into

law by Governor Deval Patrick in August 2008, calls for initial reductions in greenhouse gas emissions of

between 10 percent and 25 percent below 1990 levels by 2020.77 The Massachusetts Clean Energy and

Climate Plan for 2020, released on December 29, 2010 by the Massachusetts Executive Office of Energy

and Environmental Affairs, sets out policies, with associated emissions reductions, necessary to meet

the 2020 target of 25 percent below 1990 levels.78  

Methods to Monetize Non‐Embedded CO2

Several different methods are available to monetize environmental costs. These include “damage cost”

approaches that seek to assign a value to damages associated with a particular pollutant, and “control

cost” approaches that seek to quantify the marginal cost of controlling a particular pollutant. For the

reasons outlined below, AESC 2013 recommends using the control cost approach to estimate non‐

embedded CO2 costs for the study period.  

Damage Cost Approach: The Social Cost of Carbon

Damage cost methods generally rely on travel costs, hedonic pricing, or contingent valuation to assign

values in the absence—by definition—of market prices for non‐embedded impacts. These are forms of

“implied valuation,” asking complex and hypothetical survey questions, or extrapolating from observed

behavior, to impute a price to something that is never bought or sold in a market. For example, data on

how much people will spend on travel, subsistence, and equipment on fishing can be used to measure

the value of those fish, and the value of not killing fish with waterborne pollution. Even human lives

sometimes have been valued based on wage differentials for jobs that expose workers to different risks

of mortality. Comparing the difference in wages between two jobs—one with higher hourly pay rate and

higher risk than the other—can serve as a measure of the compensation that someone is “willing to

accept” in order to be exposed to a life‐threatening risk and, by analogy, as a controversial estimate of

the value of life itself.

Valuation of the societal damages caused by the emission of an additional ton of CO2—a measure often

called the “social cost of carbon”—typically combines cost estimates, using a variety of implied valuation

techniques, for numerous damages from climate change that are expected around the world. In 2010,

the U.S. government began to include a social cost of carbon in the valuation of federal rulemakings

with the goal of accounting for the damages resulting from climate change, defined as “an estimate of

                                                            

76 Center for Climate and Energy Solutions, “A Look at Emissions Targets,” http://www.c2es.org/what_s_being_done/targets

77 Massachusetts G.L. c. 21N

78 http://www.mass.gov/eea/docs/eea/energy/2020‐clean‐energy‐plan.pdf
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐15  

the monetized damages associated with an incremental increase in carbon emissions in a given year.”79

A range of four social cost of carbon values was initially calculated by the Interagency Working Group on

the Social Cost of Carbon (the “Working Group”), a group composed of members of the Department of

Agriculture, Department of Commerce, Department of Energy, Environmental Protection Agency, and

Department of Transportation, among others.  

The Working Group’s estimates, presented in Exhibit 4‐2, seek to represent the range of social cost of

carbon values for three discount rates as well as the high‐cost tail‐end of the uncertain distribution of

impacts in 2013 dollars per short ton (t) CO2.

80 It is important to note that social cost of carbon values

represent the damages associated with an incremental increase in CO2 emissions in a given year; for this

reason, they are time‐dependent and are expected to increase in future years as atmospheric

concentrations of CO2 increase. As of May 2012, these estimates had been used in more than 20 federal

government rulemakings, for policies including fuel economy standards, industrial equipment efficiency,

lighting standards, and air quality rules.81 In May 2013, the Working Group released a technical update

that revised its estimate of the Social Cost of Carbon.82

                                                            

79 Interagency Working Group on the Social Cost of Carbon, U. S. G. (2010). Appendix 15a. Social cost of carbon for regulatory

impact analysis under Executive Order 12866. In Final Rule Technical Support Document (TSD): Energy Efficiency Program for

Commercial and Industrial Equipment: Small Electric Motors. U.S. Department of Energy. URL http://go.usa.gov/3fH.

80 The Working Group’s 2010 social cost of carbon values are commonly reported in 2007 dollars of $5, $21, $35, and $65 per

metric tonne CO2. In Exhibit 4‐2, these values are converted to 2013 dollars and short tons.

81 Robert E. Kopp and Bryan K. Mignone (2012). The U.S. Government’s Social Cost of Carbon Estimates after Their First Two

Years: Pathways for Improvement. Economics: The Open‐Access, Open‐Assessment E‐Journal, Vol. 6, 2012‐15.

http://dx.doi.org/10.5018/economics‐ejournal.ja.2012‐15

82 Interagency Working Group on the Social Cost of Carbon, U. S. G. (2013). Technical Support Document:‐ Technical Update of

the Social Cost of Carbon for Regulatory Impact Analysis‐ Under Executive Order 12866. URL

http://www.whitehouse.gov/sites/default/files/omb/inforeg/social_cost_of_carbon_for_ria_2013_update.pdf
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐16  

Exhibit 4‐2. U.S. Interagency Working Group Social Cost of Carbon (2013 dollars per short ton CO2)

Source: U.S. EPA (2012) “The Social Cost of Carbon” http://www.epa.gov/climatechange/EPAactivities/economics/scc.html

These social cost of carbon values are the result of the Working Group’s reanalysis using the DICE, PAGE,

and FUND integrated assessment models, which simplify the relationships among complex climate and

economic systems with the goal of providing information useful in making climate policy decisions.83

The social cost of carbon values are calculated as the net present value of the discounted path of

hundreds of years of future damages computed by each of the three models resulting from the addition

of a ton of CO2 emissions in a given year.  

The Working Group based its common sets of assumptions regarding emissions, population, and gross

domestic product (GDP), used for all three models, on four business‐as‐usual scenarios from an Energy

Modeling Forum (EMF) model comparison exercise and an average of 550 ppm CO2e scenarios from the

same four EMF models.84 The process‐based integrated assessment models used in the EMF survey

contain substantially more detailed representations of the climate and energy systems than the DICE,

PAGE, and FUND models, but only provide results out to 2100. The Working Group analysis extrapolates

these trends out to 2300 based upon assumptions regarding changes in fertility rates, GDP per capita,

and carbon intensities.

DICE, PAGE, and FUND all employ simplified climate modules to convert emissions into atmospheric

concentrations, and then use a climate sensitivity parameter to convert concentrations into

temperature increases. To address the substantial uncertainty in this climate sensitivity parameter, the

                                                            

83 The DICE model was further simplified by the Working Group for use in its analysis, see Interagency Working Group 2010.

84 Clarke, L. (2009). Overview of EMF 22 international scenarios

http://emf.stanford.edu/events/emf_briefing_on_climate_policy_scenarios_us_domestic_and_international_policy_archite

ctures/.

Climate Sensitivity Median Median Median 95th Percentile

Discount Rate 5% 3% 2.5% 3%

2010 $5 $21 $35 $65

2015 $6 $24 $38 $73

2020 $7 $26 $42 $81

2025 $8 $30 $46 $90

2030 $10 $33 $50 $100

2035 $11 $36 $54 $110

2040 $13 $39 $58 $119

2045 $14 $42 $62 $128

2050 $16 $45 $65 $136
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐17  

Working Group conducted a “Monte Carlo” analysis that averages results from a distribution of likely

sensitivities. Three of the four social cost of carbon values are based on the median of this distribution,

with the fourth based on the high‐cost tail‐end 95th percentile.

The DICE, PAGE, and FUND integrated assessment models rely on implied valuations of future climate

damages to calibrate their “damage functions,” which translate temperature changes into changes in

GDP. Climate damage valuation is hampered by significant uncertainty in the climate system itself, long

time intervals separating cause and effect, and practical difficulties in assigning monetary values to

projected damages that fall outside of the range of past experience. A common practice used in these

and other climate‐economics models is to set a point estimate for the expected cost of near‐term, low‐

level climate damages and then to extrapolate the costs as rising with the square of temperature

change.85 The climate damage values used in the Working Group analysis represent the most likely level

of damage given these estimation techniques, ignoring any uncertainty in the range of damages

expected to occur from a given rise in temperature.

In a 2012 paper, Ackerman and Stanton critiqued and modified several of the assumptions in the

Working Group’s DICE model analysis—climate sensitivity, the expected level of damages at low and

high greenhouse gas concentrations, and the discount rate—and found 2010 social cost of carbon values

ranging from the Working Group’s level up to more than an order of magnitude greater, up to $892 per

short ton (in 2013 dollars) compared to the median $28/t value from DICE (see Exhibit 4‐3).86 Johnson

and Hope also explored changes in the discount rate, equity weighting to differentiate damages based

on regional income levels, and the full range of climate sensitivity uncertainty in the Working Group’s

social cost of carbon. This analysis resulted in 2010 social cost of carbon values ranging up to $1,774 per

short ton (in 2013 dollars).87

                                                            

85 Stanton, Ackerman and Kartha (2009) “Inside the Integrated Assessment Models: Four Issues in Climate Economics.” Climate

and Development 1:2(166‐184). DOI 10.3763/cdev.2009.0015

86 Ackerman and Stanton (2012) “Climate Risks and Carbon Prices: Revising the Social Cost of Carbon.” Economics: The Open‐

Access, Open‐Assessment E‐Journal, Vol. 6, 2012‐10. http://dx.doi.org/10.5018/economics‐ejournal.ja.2012‐10

87 Johnson, L. and Hope, C. (2012). “The social cost of carbon in US regulatory impact analysis: an introduction and critique.” J

Environ Stud Sci. DOI 10.1007/s13412‐012‐0087‐7
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐18  

Exhibit 4‐3. Alternate Estimates of the Working Group's Social Cost of Carbon (2013 dollars per short ton CO2)

Source: See text.

Using an updated version of PAGE, the model’s developer (Hope) also conducted an independent

analysis of the social cost of carbon based on new socioeconomic scenarios, new discount rate ranges,

updated climate sensitivity, and reduced climate‐damage adaptation from the earlier version of the

model. The mean estimate of $106/t (in 2013 dollars) for the average social cost of carbon was

substantially larger than $30/t for the median PAGE model run at the 3 percent discount rate.88

Only the Working Group’s median, 3‐percent‐discount‐rate social cost of carbon—for 2010, $21 per

short ton in 2013 dollars—is used in federal impact analysis. While the Working Group’s central case is

now employed in environmental impact analysis of many federal regulations, we find that both the

overall social cost of carbon methodology and the Working Group’s application of it include serious

flaws that should not be overlooked. The social cost of carbon methodology relies on the estimation of

damage costs for far future impacts that are expected to be outside of the range of past experience. This

type of valuation is, at best, highly speculative. The Working Group’s social cost of carbon calculations

ignore critical uncertainties in damage estimation. To the extent that the Working Group examines a

limited set of discount rates and a broader set of climate sensitivities, the distribution of results is

essentially thrown out.  

As noted previously, in May 2013, the Working Group released a technical update to its Social Cost of

Carbon that used the same methodology as 2010, but used updated versions of the DICE, FUND, and

PAGE models. The revised modeling exercise resulted in change in the working Group’s median, 3‐

percent‐discount‐rate social cost of carbon—for 2010, $21 to $33 per short ton in 2013 dollars.  

For the purposes of AESC 2013, the Working Group’s revised $33/t may be viewed as an extreme lower

bound to possible non‐embedded CO2 values in 2010.89

                                                            

88 Hope, C. (2013). “Critical issues for the calculation of the social cost of CO2: why the estimates from PAGE09 are higher than

those from PAGE2002”. Climatic Change. Vol 117. 2013‐04. DOI: 10.1007/s10584‐012‐0633‐z

89 We note that in May 2013, the DOE’s microwave appliance standard used a SCC value of $33/ton as noted previously.

$0 $200 $400 $600 $800 $1,000 $1,200 $1,400 $1,600 $1,800

Hope 2013

Johnson and Hope 2012

Ackerman and Stanton 2012

Working Group 2010
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐19  

Control Cost Approach

The Marginal Cost of Stabilizing CO2 Emissions

Control cost methods generally look at the marginal cost of abating CO2 emissions—that is, the last (or

most expensive) unit of emissions reduction required to comply with regulations. The cost of control

approach is often based on regulators’ revealed preferences. For example, if air quality regulators

require a particular technology that costs $X for each ton of emissions that it achieves, then this can be

taken as an indication that regulators value emission reductions at or above $X/t. For CO2 emissions,

however, regulators’ preferences are not as yet fully revealed.  

A marginal cost of abatement can also be based on a sustainability target of staying at or below the

highest level of damage or risk that is considered to be acceptable. In this case, the marginal cost of

abatement is the cost per ton of the most expensive technology needed to achieve the sustainability

target. A sustainability target for CO2 emissions relies on the assumption—well established in

documents related to international climate policy negotiations—that there is a threshold beyond which

the nations of the world deem climatic changes and their associated damages to be unacceptable.  

A wealth of well documented, compelling research exists both on setting an acceptable threshold for

CO2 emissions and on current and projected costs of CO2 emissions abatement technologies. Here, we

review several recent analyses of strategies and technologies that would contribute to emission

reductions consistent with an increase in average temperature of no more than 2°C above preindustrial

levels or atmospheric concentrations no greater than 450 ppm CO2 equivalent. The information and

analysis presented here focuses on the 450 ppm target, entirely because the available studies used the

450 ppm level in their analyses. The 350 ppm target has been identified and is viewed as a more current

target to maintain the global temperature increase above pre‐industrial levels at no more than 2°C.

While there is a lack of abatement cost estimates associated with a 350 ppm target, given the factors

described in the following text it is reasonable to conclude that such an abatement cost would be equal

or more than the abatement cost associated with a 450 ppm target, and could potentially be

considerably higher.

McKinsey & Company examined these technologies in a 2010 report entitled Impact of the Financial

Crisis on Carbon Economics: Version 2.1 of the Global Greenhouse Gas Abatement Cost Curve. The CO2

mitigation options identified by McKinsey and the costs of those options are reproduced in Exhibit 4‐4.

The figure represents a marginal abatement cost curve, where the per ton cost of abatement is shown

on the vertical axis and cumulative metric tons of CO2 equivalent reductions are shown on the horizontal

axis. Global CO2 mitigation technologies are ordered from least to most expensive with the width of

each bar representing each technology’s expected total emission reduction. If technologies are assumed

to be implemented in order of their costs, beginning with the cheapest abatement options, the marginal

cost of maintaining the sustainability threshold is the cost per ton of the most expensive technology

needed to provide the appropriate reduction (here, 38 metric gigatonne CO2 equivalent in 2030).  
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐20  

Exhibit 4‐4. Marginal Abatement Technologies and Associated Costs for the Year 2030

Source: McKinsey & Company. Impact of the Financial Crisis on Carbon Economics: Version 2.1 of the Global Greenhouse Gas

Abatement Cost Curve. 2010. Page 8.

As shown in Exhibit 4‐4, the marginal technology is a gas plant carbon capture and storage (CCS) retrofit

costing $106 per short ton in 2013 dollars.90 This figure also shows a variety of technologies for carbon

mitigation that are available to the electric sector, including those related to energy efficiency, nuclear

power, renewable energy, and CCS for fossil‐fired generating resources.  

In World Energy Outlook 2012, the IEA has modeled the implications of a 450 Scenario, which stabilizes

CO2 levels at 450 ppm with a goal of limiting temperature increase to 2°C.91 IEA projects regionally

specific 2035 marginal cost of abatements for OECD+ countries ($115 per short ton in 2013 dollars) and

other major economies ($91/t) for its 450 Scenario.92

In Energy Technology Perspectives 2012, the IEA examines another 450 ppm CO2 scenario, referred to as

the “2DS” with broad deployment of CCS for coal, gas, and biomass, utility‐scale PV, offshore wind, and

                                                            

90 2005 Euro to Dollar conversion factor, 1.25, http://www.oanda.com/convert/fxhistory accessed 4/28/09

91 IEA (2012). World Energy Outlook 2012. Available at: http://www.worldenergyoutlook.org/publications/weo‐2012/

92 OECD+ countries include all OECD countries, as well as non‐OECD countries in the European Union. Other Major Economies

includes Brazil, China, the Middle East, Russia, and South Africa.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐21  

geothermal. The marginal cost of abatement for this scenario reaches $141 per short ton in 2013 dollars

in 2050. This represents a decrease in abatement costs on the order of $20/t from the Energy

Technology Perspectives 2010, primarily as a result of higher projected prices for fossil fuels and more

optimistic forecasts for low‐carbon technologies.

The results of these studies are summarized below in Exhibit 4‐5. The dotted line is drawn at the value of

atmospheric stabilization of 450 ppm CO2 equivalent, which corresponds to a good chance of limiting

global temperature increase to 2°C above pre‐industrial levels. The average of the 450 ppm marginal

cost of abatement estimates shown here is $113 per short ton of CO2 equivalent in 2013 dollars. Based

on this analysis—as well as the CCS costs presented in the section below, and our own judgment and

experience—we recommended an AESC 2013 abatement cost of $100 per short ton (in 2013 dollars).  

Exhibit 4‐5. Summary Chart of Marginal Abatement Cost Studies

Source: See text.

CCS Technology Costs

CCS for electricity generation is often at or near the margin for targets of limiting temperature rise to

2°C above pre‐industrial levels. For this reason, we expect that CCS costs may be viewed as providing an

alternate, first‐order approximation of the marginal cost of abating CO2 emissions. Projected technology

costs vary widely, with gas CCS typically more expensive than coal on a per ton of avoided emissions

basis. For mature (nth‐of‐a‐kind) CCS deployment, estimates are commonly in the range of $60 to $100

per short ton of CO2 avoided in 2013 dollars (see Exhibit 4‐6). The exceptionally high values from Al

$0

$20

$40

$60

$80

$100

$120

$140

$160

0 100 200 300 400 500 600 700 800 900 1000

2013$ per short ton CO2 equivalent

Level of Atmospheric Stabilization (ppm CO2 equivalent)

Energy Technology Perspective 2012 World Energy Outlook 2012 - Current Policies

World Energy Outlook 2012 - OECD+ McKinsey 2010

World Energy Outlook 2012 - Other Major Economics McKinsey 2010 with behavioral changes

World Energy Outlook 2012 - New Policies Average of 450
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐22  

Juaied et al. reflect first‐of‐a‐kind technologies, as if they were built today, with commodity costs from

2008.

Exhibit 4‐6. Projected Costs of CCS (in 2013 dollars per short ton of CO2 avoided)

Source: Al‐Juaied, Mohammed and Adam Whitmore. Realistic Costs of Carbon Capture. Belfer Center Discussion Paper 2009‐08.

Harvard Kennedy School. July 2009; Abellera, Chester and Christopher Short. The costs of CCS and Other Low‐Carbon

Technologies. Global CCS Institute. Issues Brief 2011, No. 2; Worley, Parsons and Schlumberger. Economic Assessment of Carbon

Capture and Storage Technologies. 2011 Update. Supported by the Global CCS Institute. 2011; Caniere, H. et al. Marginal

abatement cost curves for coal‐fired power plants in Europe: CO2 reduction potential for 2020. Climate Change and

Environmental Services ‐ Energy Efficiency Centre. 2012. Antwerp, Belgium; and Rubin, Edward and Haibo Zhai. The Cost of CCS

for Natural Gas‐Fired Power Plants. Presentation to the 10th Annual Conference on Carbon Capture and Storage. Pittsburgh, PA.

May 3, 2011.

Existing deployment of CCS in electricity generation is limited to a few small‐scale demonstration

projects. The AEP Mountaineer plant in West Virginia operated CCS facilities for 2 percent of its 1,300

MW plant for nearly two years, but canceled planned scale‐up activities due to the uncertainty of

climate policy.93 Many similar examples exist across the United States. The Texas Clean Energy project,

an integrated gasification combined‐cycle (IGCC) coal plant with CCS, and perhaps one of the most

advanced U.S. commercial projects, is not scheduled for completion until at least 2015. At $2.8 billion,

                                                            

93 MIT (2013). “AEP Mountaineer Fact Sheet” Available at:

http://sequestration.mit.edu/tools/projects/aep_alstom_mountaineer.html  

$0 $20 $40 $60 $80 $100 $120 $140 $160 $180

Gas: WorleyParsons/Schlumberger 2011 (nth-of-a-kind)

Gas: WorleyParsons/Schlumberger 2011 (reference)

Gas: Rubin & Haibo 2011

Gas: Abellera & Short 2011

Coal: Caniere et al. 2012

Coal: WorleyParsons/Schlumberger 2011 (nth-of-a-kind)

Coal: WorleyParsons/Schlumberger 2011 (reference)

Coal: Abellera & Short 2011

Coal: Al Juaied et al. 2009 (nth-of-a-kind)

Coal: Al Juaied et al. 2009 (reference)

2013 dollars per short ton CO2
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐23  

the 400 MW plant is expected to cost $7,000/kW in comparison to the cost of a new natural gas

combined‐cycle plant ($1,000/kW) or a new IGCC without CCS ($3,200/kW).94

Substantial uncertainty still exists in the long‐term costs of CCS deployment. CCS costs can provide an

important cross‐check of long‐term forecasts of mitigation costs, but should be coupled with other

metrics such as complete marginal cost of abatement curves constructed from energy system modeling

results.

CO2 Abatement Cost in AESC 2013

Based on our review of the most current research on marginal abatement and CCS costs, and our

experience and judgment on the topic, we believe that it is reasonable to use a CO2 marginal abatement

cost of $100 per short ton in 2013 dollars. We contend that $100/short ton is also a practical and

reasonable measure of the total societal cost of carbon dioxide emissions. This CO2 marginal abatement

cost can be applied to the emissions reductions that result from lower electricity generation as a result

of energy efficiency, in order to quantify these reductions’ full value to society. A portion of this CO2

marginal abatement cost will be reflected in the allowance price for emissions, and thus embedded in

the avoided costs; the balance may be referred to as a non‐embedded cost.  

States that have established targets for climate mitigation comparable to the targets discussed in

section 4.3.1, or that are contemplating such action, could view the $100/t CO2 marginal abatement cost

as a reasonable estimate of the societal cost of carbon emissions, and hence as the long‐term value of

the cost of reductions in carbon emissions required to achieve those targets.

Like any long‐run projections, this estimate of the marginal abatement cost includes important

uncertainties in underlying assumptions regarding the extent of technological innovation, the selected

emission reduction targets, the technical potential of key technologies, and the evolution of

international and national policy initiatives, along with a variety of other influencing factors. It will be

necessary to review available information and reassess what value is reasonable given the best state of

knowledge at the time of future reviews.  

Estimating Non‐Embedded CO2 Costs for New England

The non‐embedded value for New England’s CO2 emissions in each year was calculated as the estimated

marginal abatement cost of $100 per short ton in 2013 dollars less the annual allowance values

embedded in the projected electric energy market prices. These values are summarized in Exhibit 4‐7.

                                                            

94 Platts (2012). “Summit signs key contracts for planned coal‐gasification plant in Texas”. Feb 2012. Available at:

http://www.platts.com/RSSFeedDetailedNews/RSSFeed/Coal/6958831. For comparison costs see: EIA. 2013. Assumptions to

the Annual Energy Outlook 2012. Table 8.2. http://www.eia.gov/forecasts/aeo/assumptions/pdf/electricity.pdf
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐24  

Exhibit 4‐7. AESC 2013 Non‐Embedded CO2 Costs (2013 dollars per short ton CO2)

The annual allowance values embedded in the projected electric energy market prices are shown in

column b of Exhibit 4‐7. These carbon prices were included in the generators’ bids in the dispatch model

runs and therefore are embedded in the AESC 2013 avoided electricity costs. The non‐embedded value

in each year is the difference between the marginal abatement cost ($100/t) and the value of the

embedded carbon trading price shown in column c of Exhibit 4‐7. Exhibit 4‐8 illustrates how the non‐

embedded CO2 cost was calculated.  

Comparison to AESC 2011

The AESC 2013 value for the CO2 marginal abatement cost of $100/ton is 20 percent higher than the

AESC 2011 value of $83/ton (2013 dollars). The change in the assessment results from three major

factors:

1) AESC 2013 incorporates new studies with different estimates than two years ago.

2) AESC 2011 used values from multiple vintages of the same studies, whereas AESC 2013

only uses values from the most up‐to‐date version of studies.

3) AESC 2013 incorporates an analysis of CCS technologies that are expected to be the

marginal technology.  

Marginal 

abatement cost

Reference 

Allowance Price 

Reference 

Externality

RGGI-Only 

Scenario 

Allowance Price 

RGGI-Only 

Scenario 

Externality

a b c = a - b d e = a - d

2013 $100 $2.80 $97.20 $2.80 $97.20

2014 $100 $4.22 $95.78 $4.22 $95.78

2015 $100 $5.28 $94.72 $5.28 $94.72

2016 $100 $6.33 $93.67 $6.33 $93.67

2017 $100 $7.39 $92.61 $7.39 $92.61

2018 $100 $8.44 $91.56 $8.44 $91.56

2019 $100 $9.50 $90.50 $9.50 $90.50

2020 $100 $20.30 $79.70 $10.55 $89.45

2021 $100 $22.58 $77.42 $10.55 $89.45

2022 $100 $24.87 $75.13 $10.55 $89.45

2023 $100 $27.15 $72.85 $10.55 $89.45

2024 $100 $29.44 $70.57 $10.55 $89.45

2025 $100 $31.72 $68.28 $10.55 $89.45

2026 $100 $34.00 $66.00 $10.55 $89.45

2027 $100 $36.29 $63.71 $10.55 $89.45

2028 $100 $38.57 $61.43 $10.55 $89.45
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐25  

Exhibit 4‐8. Determination of the Additional Cost of CO2 Emissions(2013$/short ton of CO2 equivalent)

Applying Non‐Embedded CO2 Costs in Evaluating Energy Efficiency Programs

The non‐embedded values from Exhibit 4‐7 are incorporated as a separate value in the avoided

electricity cost workbooks and expressed as dollars per kWh based upon our analysis of the CO2

emissions of the marginal generating units summarized below. We recommend that program

administrators include these values in their analyses of energy efficiency programs unless specifically

prohibited from doing so by state or local regulations. At a minimum, program administrators should

calculate the costs and benefits of energy efficiency programs with and without these values in order to

assess their incremental impact on the cost‐effectiveness of programs.  

4.4 Value of Mitigating Significant Pollutants

4.4.1 Electricity Generation

Pollutants and Their Significance

Impacts associated with electricity production and uses include a wide variety of air pollutants, water

pollutants, and land use impacts. These include the following:

 Air emissions (including SO2, NOx and ozone, particulates, mercury, lead, other toxins,

and greenhouse gases) and the associated health and ecological damages;

 Fuel cycle impacts associated with “front end” activities such as mining and

transportation, and waste disposal;

 Water use and pollution;

 Land use;

 Aesthetic impacts of power plants and related facilities;
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐26  

 Radiological exposures related to nuclear power plant fuel supply and operation

(routine and accident scenarios); and

 Other non‐embedded impacts, such as economic impacts (generally focused on

employment), energy security, and others.

Over time, regulations limiting emission levels have forced suppliers and buyers to consider at least a

portion of these costs in their production and use decisions, thereby “embedding” a portion of these

costs. As noted in section 4.3 (below), we anticipate that the “non‐embedded carbon cost” will continue

to be the dominant non‐embedded environmental cost associated with marginal electricity generation

in New England.

For AESC 2013, our approach to quantifying the reduction in physical emissions due to energy efficiency

is as follows:

 Identify the marginal unit in each hour in each transmission area from our energy

model;

 Draw the heat rates, fuel sources, and emission rates for NOx and CO2, of those

marginal units from the database of input assumptions used in our Market Analytics

simulation; and

 Calculate the physical environmental benefits from energy efficiency and demand

reductions by calculating the emissions of each of those marginal units in terms of

lbs/MWh. We do this by multiplying the quantity of fuel burned by each marginal unit

by the corresponding emission rate for each pollutant for that type of unit and fuel.

The calculations for each pollutant in each hour are as follows:

Marginal Emissions = [Fuel BurnedMU (MMBtu) x Emission RateMU (lbs/MMBtu) x 1 ton/2000

lbs]/GenerationMU (MWh)

Where:

Fuel BurnedMU = the fuel burned by the marginal unit in the hour in which that unit

is on the margin,

Emission RateMU = the emission rate for the marginal unit, and

GenerationMU = generation by the marginal unit in the hour in which that unit is on

the margin.

Value of Mitigating Significant Pollutants

The scope of work for AESC 2013 asks for the heat rates, fuel sources, and emissions of NOx, and CO2 of

the marginal units during each of the energy and capacity costing periods in the 2013 base year. It also

asks for the quantity of environmental benefits that would correspond to energy efficiency and demand

reductions, in pounds per MWh, respectively, during each costing period.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐27  

Exhibit 4‐9 summarizes the marginal heat rate and marginal fuel characteristics from the model results.

The results are based on the marginal unit in each hour in each transmission area, as reported by the

model. Once the marginal units are identified, we extracted the heat rates, fuel sources, and emission

rates for the key pollutants from the database of input assumptions used in our Market Analytics

simulation of the New England wholesale electricity market.  

Exhibit 4‐9. 2013 New England Marginal Heat Rate by Pricing Period (Btu per kWh)

Exhibit 4‐10. 2013 New England Marginal Fuel by Percentage

The avoided emissions values shown in the exhibits below represent the averages for each pollutant

over each costing period for all of New England in pounds per MWh. The emission rates are presented

by modeling zone; however, differences between zones tend to be relatively minor.

Off Peak On Peak Off Peak On Peak

Average Heat Rate (BTU/kWh) 8,085            9,061      7,918      8,348                  8,254

Season and Period

Summer Winter Grand Total

Fuel Type Off On Off On

Natural gas 91% 96% 89% 95% 92%

Oil 0% 0% 0% 0% 0%

Coal 8% 4% 8% 3% 6%

Nuclear 0% 0% 0% 0% 0%

Biomass 1% 0% 1% 1% 1%

Other 0% 1% 2% 1% 1%

Renewable 0% 0% 0% 0% 0%

Grand Total 100% 100% 100% 100% 100%

Grand Total

Season and Period

Summer Winter
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐28  

Exhibit 4‐11. 2013 New England Avoided CO2 Emissions by Modeling Zone and Pricing Period (lbs/MWh)

Exhibit 4‐12. 2013 New England Avoided NOx Emissions by Modeling Zone and Pricing Period (lbs/MWh)

Our recommended dollar values to use for relevant “embedded” avoided pollutant emissions are

summarized in Exhibit 4‐1. Our recommended dollar value to use for non‐embedded carbon costs is

provided in Exhibit 4‐7.

On Peak Off Peak On Peak Off Peak

NE ‐ BHE 969                1,033      1,103      1,043                  1,026

NE ‐ Boston 960                1,023      1,060      1,023                  1,009

NE ‐ CT NE Central 973                1,009      1,077      1,025                  1,011

NE ‐ CT Norwalk 974                1,009      1,078      1,030                  1,013

NE ‐ ME 968                1,033      1,103      1,043                  1,026

NE ‐ NEMA 963                1,019      1,061      1,020                  1,009

NE ‐ New Hampshire 968                1,028      1,099      1,046                  1,024

NE ‐ Rhode Island 960                1,019      1,058      1,026                  1,008

NE ‐ SEMA 958                1,018      1,057      1,017                  1,006

NE ‐ SME 968                1,031      1,103      1,047                  1,026

NE ‐ SWCT 974                1,009      1,078      1,030                  1,013

NE ‐ Vermont 967                1,010      1,081      1,032                  1,012

NE ‐ WCMA 965                1,011      1,073      1,028                  1,009

Average 967                1,019      1,079      1,032                  1,015

Winter Summer

Season and Period

Grand Total

Grand Total

On Peak Off Peak On Peak Off Peak

NE ‐ BHE 0.390 0.550 0.510 0.395 0.467

NE ‐ Boston 0.385 0.507 0.467 0.327 0.431

NE ‐ CT NE Central 0.383 0.475 0.481 0.331 0.422

NE ‐ CT Norwalk 0.383 0.475 0.484 0.334 0.423

NE ‐ ME 0.389 0.550 0.510 0.395 0.466

NE ‐ NEMA 0.391 0.508 0.471 0.338 0.436

NE ‐ New Hampshire 0.388 0.527 0.495 0.372 0.452

NE ‐ Rhode Island 0.370 0.501 0.458 0.343 0.426

NE ‐ SEMA 0.366 0.497 0.451 0.338 0.421

NE ‐ SME 0.389 0.539 0.509 0.395 0.462

NE ‐ SWCT 0.383 0.475 0.484 0.336 0.423

NE ‐ Vermont 0.398 0.478 0.479 0.337 0.428

NE ‐ WCMA 0.396 0.478 0.473 0.330 0.425

Average 0.385 0.505 0.482 0.352 0.437

Winter Summer

Season and Period
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐29  

4.4.2 End‐Use Natural Gas

We estimate the environmental benefit from reduced combustion of end‐use natural gas due to energy

efficiency programs with the following analyses:

 Identifying the various pollutants created by the combustion, and assessing which of them are

significant and how, if at all, the impact of those pollutants is currently embedded in the cost of

natural gas.

 Finding the value associated with mitigation of each significant pollutant and the portion that

should be treated as a non‐embedded cost.

Natural gas consists of methane (generally above 85 percent) and varying amounts of ethane, propane,

butane, and inert gases (typically nitrogen, carbon dioxide, and helium) (EPA 1999).  

In general, the combustion in boilers and furnaces generate the following pollutants (EPA 1999, 1.4‐2–

5):

 oxides of nitrogen (NOx),

 sulfur oxides (SOx) (trace levels)95,

 CO2 and other greenhouse gases,

 particulates (trace levels),

 volatile organic compounds, and

 carbon monoxide.

Pollutants and their Significance

To estimate the absolute quantities of each pollutant from the combustion of natural gas relative to the

absolute quantity of each from all sources, we began by estimating the quantity of each that is emitted

per MMBtu of fuel consumed. Exhibit 4‐13 provides emissions factors for NOx and CO2 for three

generalized boiler type categories.  

                                                            

95Sulfur is generally added as an odorant to natural gas, which generates trace quantities of sulfur oxides when combusted.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐30  

Exhibit 4‐13. Emission Rates of Significant Pollutants

Boiler Type NOx

(lbs/MMBtu)

CO2

(lbs/MMBtu)

Residential boiler 0.092 118

Commercial boiler 0.098 118

Industrial boilers 0.137 118

Notes

NOx emissions from industrial boilers without low NOx burners would

be 0.274 lb/MMBtu. We assumed these boilers were controlled in

order to be conservative.

NOx and CO2 emissions factors for all boilers utilized conversion rate of

1,020 btu/scf.

Sources

Environmental Protection Agency, AP‐42, Volume I, Fifth Edition,

January 1995, Chapter 1, External

Combustion Sources. http://www.epa.gov/ttnchie1/ap42/

We apply these pollutant emission rates to the quantity of natural gas consumed, by sector, in New

England in 2013. The estimated annual quantity of each of the two pollutants from natural gas

combustion, and from other sources, is presented in Exhibit 4‐14.

Exhibit 4‐14. Pollutant Emissions in New England from Natural Gas

Exhibit 4‐14 illustrates that combustion of natural gas is a source of both NOx and CO2 emissions.

Moreover, these emissions are not currently subject to regulation, as explained below.

 CO2. RGGI applies to electric generating units larger than 25 MW. New England CO2 emissions

for 2011 were 27.4 million tons. The total CO2 emissions from the end‐use sectors above would

represent about 50 percent of the total CO2 emissions, if such emissions were included.

 NOx. The Clean Air Interstate Rule applies only to Massachusetts and Connecticut during the

ozone season. New England NOx emissions for 2011 were approximately 3,900 tons for just the

Sector NOx (tons) CO2 (tons)

Residential 9,325 11,904,588

Commercial 7,584 9,100,294

Industrial 7,829 6,710,412

R, C & I Total 24,738 27,715,294

Electric Gen 3,904 27,380,362

Notes

Source

Environmental Protection Agency AMPD Database 

http://ampd.epa.gov/ampd/?bookmark=5342

All figures are from 2011.

Energy Information Administration 

http://tonto.eia.doe.gov/dnav/ng/ng_cons_sum_a_EPG0_vrs_mmcf_a.htm
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐31  

electric generating sector96. The total NOx emissions from the end use sectors above would

represent about 86 percent of the total NOx budget if such emissions were included.

Value of Mitigating Significant Pollutants

We estimate the value associated with mitigation of NOx and CO2 based on the 2013 emissions

allowance prices per short ton presented in Exhibit 4‐1.97 As noted previously, natural‐gas combustion is

not a significant source of SO2 emissions. Consequently, we have not included an emission value for SO2.  

In addition, for states with aggressive carbon mitigation targets, we provide a value of reducing CO2

based upon the $100/ton long‐term marginal abatement cost of carbon dioxide reduction.  

The annual pollutant‐emission values by end‐use sector are summarized below in Exhibit 4‐15. They

equal the pollutant allowance prices multiplied by the pollutant emission rates.  

                                                            

96 A few large sources in the industrial sector are included in CAIR. These include municipal waste combustors,

steel and cement plants, and large industrial boilers (such as those located at Pfizer in New London, CT and

General Electric in Lynn, MA). However, the number of NOx allowances used, sold, and traded for the industrial

sector is very small. A few allowances in each state are allocated to non‐electric generating units compared to

thousands of allowances used, sold and traded for electric generating units.

97 The full non‐embedded value associated with NOx emissions is probably not captured in the allowance price from electricity

generation; however, determining that non‐embedded value is beyond the scope of this project.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐32  

Exhibit 4‐15. Annual Pollutant Emission Values (2013$/MMBtu)

Pollutant Emission Values by Sector and by Year in 2013$/MMBtu 

 Residential Commercial Industrial 

 NOx CO2 

CO2 at 

$100/ton NOx CO2

CO2 at 

$100/ton NOx CO2

CO2 at 

$100/ton

2013 $0.001 $0.16 $5.88 $0.001 $0.16 $5.88 $0.002 $0.16 $5.88 

2014 $0.001 $0.25 $5.88 $0.001 $0.25 $5.88 $0.002 $0.25 $5.88 

2015 $0.001 $0.32 $5.88 $0.001 $0.32 $5.88 $0.002 $0.32 $5.88 

2016 $0.001 $0.40 $5.88 $0.001 $0.40 $5.88 $0.002 $0.40 $5.88 

2017 $0.001 $0.47 $5.88 $0.001 $0.47 $5.88 $0.002 $0.47 $5.88 

2018 $0.001 $0.55 $5.88 $0.001 $0.55 $5.88 $0.002 $0.55 $5.88 

2019 $0.001 $0.63 $5.88 $0.002 $0.63 $5.88 $0.002 $0.63 $5.88 

2020 $0.001 $1.37 $5.88 $0.002 $1.37 $5.88 $0.002 $1.37 $5.88 

2021 $0.001 $1.56 $5.88 $0.002 $1.56 $5.88 $0.002 $1.56 $5.88 

2022 $0.002 $1.75 $5.88 $0.002 $1.75 $5.88 $0.002 $1.75 $5.88 

2023 $0.002 $1.95 $5.88 $0.002 $1.95 $5.88 $0.002 $1.95 $5.88 

2024 $0.002 $2.15 $5.88 $0.002 $2.15 $5.88 $0.002 $2.15 $5.88 

2025 $0.002 $2.37 $5.88 $0.002 $2.37 $5.88 $0.002 $2.37 $5.88 

2026 $0.002 $2.59 $5.88 $0.002 $2.59 $5.88 $0.002 $2.59 $5.88 

2027 $0.002 $2.82 $5.88 $0.002 $2.82 $5.88 $0.002 $2.82 $5.88 

2028 $0.002 $3.05 $5.88 $0.002 $3.05 $5.88 $0.003 $3.05 $5.88 

Levelized 

(2013$/MMBtu) 

5 year (2014-18) $0.001 $0.40 $5.88 $0.001 $0.40 $5.88 $0.002 $0.40 $5.88 

10 year (2014-23) $0.001 $0.90 $5.88 $0.001 $0.90 $5.88 $0.002 $0.90 $5.88 

15 year (2014-28) $0.001 $1.43 $5.88 $0.002 $1.43 $5.88 $0.002 $1.43 $5.88 

Notes 

Based on Emission Rates of Significant Pollutants for Natural Gas in Exhibit 4-13. 

Pollutant values based on emission allowance prices detailed in Exhibit 4-1 and $100/short ton long-term marginal 

abatement cost for CO2. 

  

The entire amount of each value is a non‐embedded cost. With the exception of those industrial sources

subject to the EPA NOx budget program, which represent a small fraction of the total emissions, none of

these emissions are currently subject to environmental requirements. Therefore, none of these values

are embedded in their market prices.

4.4.3 End‐Use Fuel Oil and Other Fuels

We estimate the environmental benefit from reduced combustion of fuel oil and other fuels due to

energy efficiency programs with the following analyses:

 Identifying the various pollutants created by the combustion, and assessing which of them are

significant and how, if at all, the impact of those pollutants is currently embedded in the cost of

the studied fuels.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐33  

 Finding the value associated with mitigation of each significant pollutant and the portion that

should be treated as a non‐embedded cost.

The pollutant emissions associated with the combustion of fuel oil are dependent on the fuel grade and

composition, boiler characteristics and size, combustion process and sequence, and equipment

maintenance (EPA 1999 1.3‐2). 98  

In general, the combustion in boilers and furnaces generate the following pollutants (EPA 1999, 1.4‐2–

5):

 Oxides of nitrogen (NOx)

 Sulfur oxides (SOx)  

 CO2 and other greenhouse gases

 Particulates

 Volatile organic compounds

 Carbon monoxide

 Trace elements  

 Organic compounds

Pollutants and Their Significance

Like the combustion of natural gas, NOx, SOx, and CO2 are potentially the most significant pollutants.99

NOx is a precursor to the unhealthy concentrations of ozone that areas in New England continue to

experience. The region is also required to reduce NOx and SOx emissions by EPA programs, implement

state low sulfur fuel requirements, and participate in the RGGI program to reduce CO2 from the power

sector.100

For the electric generation sector, the forecast of emissions allowance prices value of mitigating

emissions of from the combustion of NOx, SOx, and CO2 is shown in Exhibit 4‐1.  

                                                            

98 EPA, 1999. “Stationary Point and Area Sources” v. 1 of Compilation of Air Pollutant Emission Factors 5th Ed. AP‐42. Triangle

Partk, N.C.: U.S. Environmental Protection Agency. (Section 1.3‐2)

99Wood combustion may contribute to an accumulation of unhealthy concentrations of fine particulate matter (PM2.5). This is

especially true in many valleys, where pollutants accumulate during stagnant meteorological conditions. The regulation of

PM2.5 from wood combustion is a state by state process. No comparable regionally consistent or market‐based program of

allowances have been established for PM2.5, like those described above for SOx, NOx, and CO2.

100 SO2 and NOX emissions are regulated by the EPA under the acid rain program and the regional NOX budget trading program,

as well as the new Clean Air Interstate Rule. CO2 emissions from electrical generation sources are regulated under the

Regional Greenhouse Gas Initiative (RGGI).  
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐34  

In order to estimate the absolute quantities of each pollutant from the combustion of fuels by sector,

we began by estimating the quantity of each pollutant that is emitted per MMBtu of fuel consumed.101

The pollutant emissions associated with the combustion of wood are dependent on the species of wood,

moisture content, appliance used for its combustion, combustion process, and sequence and equipment

maintenance. The pollutant emissions associated with the combustion of kerosene are similar to those

associated with the combustion of distillate oil, and depend upon boiler characteristics and size,

combustion process and sequence, and equipment maintenance (EPA 1999, 1.3‐2).

Exhibit 4‐16 provides emissions factors for each fuel based on three generalized boiler‐type categories.  

Exhibit 4‐16. Emission Rates of Significant Pollutants from Fuel Oil

Boiler type, and fuel combusted SOx

(lbs/MMBtu)

NOx

(lbs/MMBtu)

CO2

(lbs/MMBtu)

#2 Fuel Oil     

Residential boiler, combusting #2 oil 0.002 0.129 173

Commercial boiler, combusting #2 oil 0.002 0.171 164

Industrial boilers, combusting #2 oil 0.002 0.171 161

Kerosene—Residential heating 0.152 0.129 173

Wood—Residential heating   0.468 2.59 N/A

Notes:

For fuel oil, assumed sulfur content of 15ppm

Kerosene same as AESC 2011

Sources:

1) Energy Information Administration, Electric Power Annual with data for 2011. Table A3

http://www.eia.gov/electricity/annual/html/epa_a_03.html (for CO2 for industrial boilers)

2) Environmental Protection Agency, AP‐42, Volume I, Fifth Edition, January 1995, Chapter 1, External

Combustion Sources. http://www.epa.gov/ttnchie1/ap42/ (for SOx and NOx emissions factors for all boilers)

3) Environmental Benefits of DSM in New York: Long Island Case Study; Bruce Biewald and Stephen Bernow,

Tellus Institute. Proceedings from Demand‐Side Management and the Global Environment, Arlington,

Virginia, April 22‐23, 1991. (for CO2 emissions factors for residential and commercial boilers)

4) James Houck and Brian Eagle, OMNI Environmental Services, Inc., Control Analysis and Document for

Residential Wood Combustion in the MANU‐VU Region, December 19, 2006. (for wood)

                                                            

101Number‐6 fuel oil has about the same rate of SO2 emissions as distillate, about twice the rate of NOx emissions and about

seven percent higher rate of CO2 emissions.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐35  

AESC 2013 reduces emissions values for fuel oil based on standards mandating the use of low sulfur

heating oil.102 Values for kerosene were based on AESC 2011 values and updated with EIA data. The

values for emissions from wood remain unchanged from the AESC 2011 values.  

Next, we applied those pollutant emission rates to the quantity of each fuel consumed by sector in New

England in 2011.103

Exhibit 4‐17. Distillate Consumption, 2011 (Trillion BTU)

Residential Commercial Industrial

217 60 24

Notes: Data from EIA 2011

Combustion of No. 2 fuel oil is a major source of each of these pollutants, but kerosene and wood are

not, as shown in Exhibit 4‐18 below.

                                                            

102 EIA 2013 “Heating oil futures contract now uses ultra‐low sulfur diesel fuel.” May 10th, 2013. Available at:

http://www.eia.gov/todayinenergy/detail.cfm?id=11211

103 Distillate fuel oil consumption figures for 2011 come from the Energy Information Administration

(http://www.eia.gov/state/seds/data.cfm?incfile=/state/seds/sep_fuel/html/fuel_use_df.html&sid=US). Our

research did not find updated kerosene or wood consumption data , therefore we continue to use the same values as in the

AESC 2011 report.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐36  

Exhibit 4‐18. Pollutant Emissions in New England by Major Source

Sector SO2 (tons) NOx (tons) CO2 (tons)

Emissions from Electric Generation

A 63,560 40,311 41,467,433

Combustion of #2 Fuel Oil in R, C & I

I Residential 16,477 13,924 18,735,900

Ii Commercial 4,595 5,177 4,952,800

Iii Industrial 3,606 2,031 1,907,850

B = i + ii +iii R, C & I Total 24,678 21,133 25,596,550

C Combustion of

kerosene in

Residential heating

302 255 343,146

D Combustion of

wood in

Residential heating

556 3,081 N/A

E = A + B + C + D 89,096 64,779 67,407,138

Non‐electric as percent of total

(B+C+D)/E 29% 38% 38%

Notes

All figures are for 2011

Residential, commercial, and industrial SO2 emissions for 2011 are based on higher levels of

fuel SO2 content than shown in Exhibit 4‐15 of 0.152 lbs/MMBtu compared to 0.002

lbs/MMBTU for low sulfur heating oil

Value of Mitigating Significant Pollutants

Emissions of NOx, SOx, and CO2 from the combustion of these fuels are not currently subject to

regulation, as explained below.

All of these values are non‐embedded values.

 SO2 & CO2: The acid rain program and RGGI apply to electric generating units larger

than 25 MW. New England SOx emissions from electric generating units for 2011 were

approximately 63,560 tons. The total SOx emissions from the end‐use sectors above
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐37  

would represent approximately 29 percent of the total SOx emissions, if such emissions

were included.104 New England electric generation CO2 emissions for 2011 were

approximately 41.5 million tons. The calculated CO2 emissions from the end‐use

sectors above would represent approximately 38 percent of the total electric

generation CO2 emissions, if such emissions were included.

 NOx: The Ozone Transport Commission–EPA NOx budget program applies to electric

generating units larger than 15 MW and to industrial boilers with a heat input larger

than 100 MMBtu per hour. New England NOx emissions for 2011 were approximately

40,000 tons for just the electric generating sector105. The total NOx emissions from the

end use sectors above would represent approximately 38 percent of the total NOx

budget, if such emissions were included.

We base the value associated with mitigation of NOx, SOx, and CO2 on the 2013 emissions allowance

prices per short ton in Exhibit 4‐1 and the non‐embedded value of CO2 shown in Exhibit 4‐7. Using the

allowance prices associated with electricity generation for NOx, SOx, and CO2 represents applying what

AESC 2013 has internalized in its forecast consistently across fuels as noted in this chapter. For CO2, we

have also provided the value of pollutant emissions associated with the long term marginal abatement

cost of $100/short ton.

The pollutant‐emission values for 2013 based upon these allowance prices and the pollutant emission

rates, as presented in Exhibit 4‐1, are presented below.  

Exhibit 4‐19. Value of Pollutant Emissions from Fuel Oil in 2013

Generalized Boiler Type

by Sector SO2 ($/MMBtu) NOx($/MMBtu)

CO2

($/MMBtu)

Residential boiler 0 0.0018 0.2422

Commercial boiler 0 0.0023 0.2296

Industrial boiler 0 0.0023 0.2254

The emission values in Exhibit 4‐19 are non‐embedded.106 With the exception of those industrial sources

subject to the EPA NOx budget program, which represent a small fraction of the total emissions, none of

                                                            

104 The use of ultra‐low sulfur fuel (15 ppm) will be required in four of the five New England states by mid‐2018.

105 A few large sources in the industrial sector are included in the NOx budget program. These include municipal waste

combustors, steel and cement plants and large industrial boilers (such as those located at Pfizer in New London,

Connecticut, and General Electric in Lynn, Massachusetts). However, the number of NOx allowances used, sold and traded

for the industrial sector is very small. A few allowances in each state are allocated to non‐electric generating units

compared to thousands of allowances used, sold, and traded for electric generating units.

106 The full externality value associated with SOx and NOx emissions is probably not captured in the allowance price from

electricity generation associated with these two pollutants; however, determining that externality value is beyond the

scope of this project.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐38  

the emissions shown in Exhibit 4‐18 are currently subject to environmental requirements.107 None of

these values, therefore, are internalized in the relevant fuel’s market prices.

The values by year for fuel oil over the study period are presented in Appendix E.

4.5 Discussion of Non‐Embedded NOx Costs

This section addresses the request in the AESC 2013 scope of work to provide a discussion of non‐

embedded NOx costs. Please note that we are not recommending an additional non‐embedded NOx

value beyond the embedded allowance prices based on the analysis discussed in this section. Instead,

we recommend a methodology consistent with AESC 2011, and detailed below.

4.5.1 Health Impacts and Damages

NOx emitted from the combustion of coal and natural gas reacts with compounds in the air

(“precursors”) to produce ozone, particulate matter (“PM2.5”), and acid rain. Both PM2.5 and ozone are

EPA criteria pollutants that have been shown to have harmful effects on human health, and are

regulated under the Clean Air Act. Quantifying the value associated with damages from NOx emissions is

a particularly complicated process. Most studies look at incidence rates of premature death and chronic

bronchitis in order to evaluate health impacts. The reaction of NOx with precursors to form PM2.5 and

ozone is highly dependent on atmospheric conditions and local emissions of other precursors. Fowlie

and Muller use a stochastic model to estimate damages and quantify health impacts, and found impacts

on human health to be valued at $1,518/ton NOx with a standard deviation of $1,823/ton resulting from

air quality modeling uncertainty.108 Mauzerall et al. found a similar level of uncertainty in an earlier

study, citing one location where the health impact of emissions nearly doubled within a short span of

time as the temperature changed. 109 EPA has used the BenMAP tool to calculate benefits of NOx

reduction based on reduced mortality from particulate matter, and calculates national benefits of

$19,286/ton for electricity generation and $12,479/ton for non‐electricity sources, respectively.110

The analyses above do not include valuation of the impacts of environmental effects resulting from

nitrogen deposition, or visibility impairment from increased haze.

                                                            

107 EPA. Factsheet: EPA’s Final Air Toxics Standard Major and Area Source Boilers and Certain Incinerators Overview of Rules

and Impacts. Available at http://www.epa.gov/airquality/combustion/docs/overviewfsfinal.pdf. Accessed

June 20, 2011.

108 Fowlie, M. N. Muller (2013) “Market‐Based Emissions Regulation When Damages Vary Across Sources: What Are the Gains

from Differentiation?” National Bureau of Economic Research. NBER Working Paper No. 18801.

109 Mauzerall, D.L., B. Sultan, N. Kim, and D.F. Bradford. 2005. “NOx emissions from large point sources: Variability in ozone

production, resulting health damages and economic costs.” Atmos. Environ. 39(16):2851‐2866  

110 EPA (2012). “RSM‐based Benefit Per Ton Estimates.” Dec 2012. Available at:

http://www.epa.gov/oaqps001/benmap/bpt.html  
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐39  

4.5.2 Abatement Costs

Market prices for NOx emissions fall far below the estimated costs of health impacts. Values for costs of

NOx mitigation have fallen substantially from those cited in AESC 2011.111 An analysis by the EIA

published in early 2012 showed a drop in allowance prices from $870/ton in 2008 to $17/ton in 2011.112

Another report from September 2012, after the Cross‐State Air Pollution Rule (CASPR) was vacated,

estimated a price of $36/ton (the CSAPR vacature in August 2012 caused a brief price spike to

$46/ton).113 Given uncertainty in how EPA NOx policies will ultimately play out, it is difficult to predict

how these prices will change in the future.

In New England, significant progress on NOx abatement has already been made, marked by rapid

reductions over the past decade (see Exhibit 4‐20). While Connecticut and Massachusetts are subject to

Clean Air Interstate Rule (CAIR) requirements, their prior reductions already surpass CSAPR standards.

As a result, in the recent Connecticut 2012 Integrated Resource Plan, annual allowance prices were

assumed to be zero.

114  

                                                            

111 NOx allowances prices began at $238/ton and fell to $136/ton (in 2013 dollars)

112 EIA (2012). “Emissions allowance prices for SO2 and NOx remained low in 2011.” Feb. 2, 2012. Available at:

http://www.eia.gov/todayinenergy/detail.cfm?id=4830

113 Argus Air Daily (2012). US Emissions Market Prices. Sept 2012. Vol. 19, Issue 173. Available at:

http://www.argusmedia.com/Coal/~/~/media/Files/PDFs/Samples/Argus‐Air‐Daily.ashx  

114 CT DEEP (2012). “2012 Integrated Resource Plan”. Connecticut Department of Energy and Environmental Protection.

Available at: http://www.ct.gov/deep/cwp/view.asp?a=4405&q=486946&deepNav_GID=2121%20
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐40  

Exhibit 4‐20. Annual NOx Emissions Rate in New England (lb/MWh)

Source: 2011 ISO New England Electric Generator Air Emissions Report. February 2013. http://www.iso‐

ne.com/genrtion_resrcs/reports/emission/2011_emissions_report.pdf

Given the uncertainty described above, a methodology consistent with AESC 2011 is recommended.

Assumptions for NOx allowance prices will be based on revised Market Analytics default data. The

embedded value of annual NOx prices in the model are $38/ton, although most New England generators

only see seasonal prices of $27/ton.

4.6 Emissions from Hydraulic Fracking

The AESC 2013 scope of work requested a discussion of greenhouse gas emissions from hydraulic

fracturing. These are air emissions that occur when extracting natural gas from the ground, and may be

avoided as a result of energy efficiency programs reducing demand for natural gas. Although calculating

upstream avoided externalities associated with fracking is outside the scope of work for AESC (our

calculations associated with natural gas include only those externalized emission costs avoided due to

reduced combustion of gas at electricity‐generating facilities, or at end‐user boilers and furnaces),

discussion of “front end” emissions for gas fracking is important and is included here because of the

large amount of greenhouse gas emissions associated with this fuel extraction process.

Electricity generation from natural gas is widely recognized as the second largest source (after coal

generation) of CO2 emissions in the U.S. electric sector. Conventional production, processing, and

distribution of natural gas have also long been sources of greenhouse gas emissions into the

atmosphere. These emissions are largely made up of methane (CH4) but can also include CO2. Natural

gas wells must be drilled in order to access underground formations of raw gas, and a portion of that gas

is often emitted into the atmosphere through energy‐consumption‐related combustion (flaring),

equipment leaks, and venting during the drilling process and completion of the wells. Emissions occur

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1999 2001 2003 2005 2007 2009 2011

Annual NOx Emissions Rate (lb/MWh)
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐41  

through these same mechanisms (flaring, equipment links, venting, etc.) as raw natural gas is processed

into “pipeline quality” gas. Finally, natural gas is also released through pipeline and service line leakage

as it is transported and distributed to end users.

Recent technological innovations have expanded the use of unconventional natural gas drilling

techniques and enabled access to large volumes of new natural gas resources contained in shale, tight

sands, or coal bed methane formations. These drilling techniques include the combined use of

horizontal drilling and hydraulic fracturing (“fracking”).115 Approximately 90 percent of oil and gas wells

in the United States now require the use of hydraulic fracturing, and the use of fracking techniques in

shale gas deposits has changed the trajectory of natural gas supply across the country from one of

declining gas production to one of rising production.116

Fracking involves the drilling of conventional wells below the surface, and then using horizontal drilling

techniques to add lateral sections that run parallel to the rock layer containing the natural gas that will

be extracted. These lateral sections can extend several thousand feet. A mixture of water, sand, and

chemical additives (“frack fluid”) is then injected at high pressure into the rock formation, creating and

reopening fractures and releasing trapped gas. The frack fluid is then drawn back out, during a period

known as “flowback,” in order to prepare the well for production.  

There are many potential environmental issues associated with fracked natural gas wells. The most

prominent is that of groundwater contamination, with the concern being that fracking chemicals will

accidentally be injected near or into aquifers, or that chemicals will remain underground and leach into

aquifers over time. Spills of chemicals and wastewater can also occur above ground, resulting in surface

contamination. Water use is also of concern, as millions of gallons may be used in the drilling and

fracking process. Much of the water may remain underground after wells have been fracked, rather than

being returned to its source, and drillers have been criticized for depleting smaller water sources.

117 The

equipment and chemicals used to frack natural gas wells must be trucked in from off‐site, and any

resulting waste or wastewater must be trucked away for disposal. This truck transit can take a heavy toll

on local roads, creating traffic congestion near drilling sites and leading to increased vehicular air

emissions. Finally, a significant amount of methane is brought up during the flowback period leading to

additional gas venting or flaring, and thus greater methane or CO2 emissions than at conventional

wells.118 As a result, lifecycle estimates of greenhouse gases emitted from fracked natural gas, which

                                                            

115 A Litovitz, et. al. Estimation of regional air‐quality damages from Marcellus Shale natural gas extraction in Pennsylvania.

RAND Corporation. Environmental Research Letters: 8(2013). January 31, 2013. Available at:

http://iopscience.iop.org/1748‐9326/8/1/014017/pdf/1748‐9326_8_1_014017.pdf

116 A Davis Vaughan and David Pursell. Frac Attack: Risks, Hype, and Financial Reality of Hydraulic Fracturing in the Shale Plays.

Reservoir Research Partners and Tudor Pickering Holt & Co. July 8, 2010.

117 A Davis Vaughan and David Pursell (2010).

118 U.S. EPA. Greenhouse Gas Emissions Reporting from the Petroleum and Natural Gas Industry: Background Technic al

Support Document. 2010. Available at: http://www.epa.gov/ghgreporting/documents/pdf/2010/Subpart‐W_TSD.pdf
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐42  

include significant flowback emissions, are thought to be larger than estimates of emissions from

conventional natural gas.

119 The focus of this section is on the emissions leakage associated with the

flowback period.

The average emissions rate of natural gas‐fired electricity generation in the United States is 1,135

pounds per MWh of CO2, compared to an average emissions rate for coal‐fired generation of 2,249

pounds per MWh of CO2. The average emissions rate for oil‐fired generation falls in between the two at

1,672 pounds per MWh of CO2.

120 In a 2011 study by Hultman et al., the authors conclude that when

production is taken into account, electricity generated from natural gas from fracked wells produces 11

percent more greenhouse gas emissions (in CO2 equivalents) than electricity from conventional gas.

Using this estimate, the emissions from electricity generation powered by natural gas from fracked wells

are still less in CO2 equivalent terms than the emissions from coal‐fired electricity generation.121 While

natural gas, which is mostly methane, releases fewer CO2 emissions than other fossil fuels, when

uncombusted methane leaks into the atmosphere it acts as a powerful, short‐term greenhouse gas.

Over a 20‐year period, each pound of methane is 72 times more powerful at increasing the retention of

heat in the atmosphere than a pound of CO2.

122

The EPA estimates that 11,400 new natural gas wells are fractured each year, and that another 1,400

existing wells are re‐fractured in order to increase production or to extract natural gas from a different

production zone.

123 Given this large number of wells, and increasing volume of natural gas production,

the rate of methane leakage associated with fracking becomes of particular importance. Research to

date has been inconclusive, and rates of methane leakage will often vary by drilling site or field,

equipment used, and level of care taken by the natural gas producers. Exhibit 4‐21, below, shows nine

recent estimates of methane leakage as a percentage of lifetime production of natural gas. The Hultman

et al. estimate, cited above, falls in the middle of this range of estimates.

                                                            

119 A Litovitz, et. al. Estimation of regional air‐quality damages from Marcellus Shale natural gas extraction in Pennsylvania.

RAND Corporation. Environmental Research Letters: 8(2013). January 31, 2013. Available at:

http://iopscience.iop.org/1748‐9326/8/1/014017/pdf/1748‐9326_8_1_014017.pdf

120 U.S. Environmental Protection Agency, “Air Emissions,” accessed on March 19, 2013, available at:

http://www.epa.gov/cleanenergy/energy‐and‐you/affect/air‐emissions.html.  

121 N Hultman et al. (2011). The greenhouse impact of unconventional gas for electricity generation. Environ. Res. Lett.

doi:10.1088/1748‐9326/6/4/044008

122 Steven Hamburg, Environmental Defense Fund, “Measuring Fugitive Methane Emissions from Fracking,” Eco Watch,

January 4, 2013.

123 U.S. EPA. Overview of Final Amendments to Air Regulations for the Oil and Natural Gas Industry: Fact Sheet. April 17, 2012.

Available at: http://www.epa.gov/airquality/oilandgas/pdfs/20120417fs.pdf
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐43  

Exhibit 4‐21. Estimates of Methane Losses, Shown as a Percent of Lifetime Production.

  

Source:

A. T Stephenson et al. (2011). Modeling the Relative GHG Emissions of Conventional and Shale Gas Production. Environ. Sci. Tech.

45: 10757–10764.

B. LM Cathles et al. (2012). A commentary on “The greenhouse‐gas footprint of natural gas in shale formations” by R.W. Howarth,

R. Santoro, and Anthony Ingraffea. Climatic Change, doi: 10.1007/s10584‐011‐0333‐0.

C. A Burnham et al. (2011). Life‐cycle greenhouse gas emissions of shale gas, natural gas, coal, and petroleum. Environ. Sci.

Technol., doi:10.1021/es201942m

D. M Jiang et al. (2011). Life cycle greenhouse gas emissions of Marcellus shale gas. Environ. Res. Lett., doi:10.1088/1748‐

9326/6/3/034014

E. N Hultman et al. (2011). The greenhouse impact of unconventional gas for electricity generation. Environ. Res. Lett.

doi:10.1088/1748‐9326/6/4/044008

F. US EPA (2011). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990‐2009. April 15, 2011. Available at:

http://epa.gov/climatechange/emissions/usinventoryreport.html

G. Howarth et al. (2011). Methane and the greenhouse gas footprint of natural gas from shale formations. Climatic Change

Letters, doi: 10.1007/s10584‐011‐0061‐5

H. G Petron et al. (2012). Hydrocarbon Emissions Characterization in the Colorado Front Range – A Pilot Study. Journal of

Geophysical Research. doi:10.1029/2011JD016360.

I. J Tollefson. (2013). Methane leaks erode green credentials of natural gas. Nature 493:12. January 2, 2013. Available at:

http://www.nature.com/news/methane‐leaks‐erode‐green‐credentials‐of‐natural‐gas‐1.12123

Only two of the nine studies listed above—both led by National Oceanic and Atmospheric

Administration (NOAA) scientist Gabrielle Petron—attempt to measure actual leakage rates in basins

where methane is being extracted. The first NOAA study examined leakage rates in the Denver‐

Julesburg Basin in Colorado, and relied on measurements taken in 2008 on the ground and from a

nearby tower. The team calculated leakage rates based on chemical analysis of the pollutants it

measured—a methodology that remains in dispute—and resulting figures were twice as high as official

figures. Michael Levi, an energy analyst at the Council on Foreign Relations in New York, has questioned

the findings and presented an alternative interpretation of the data in a peer‐reviewed comment that

brings the study’s leakage rates in line with official estimates.124

The second study led by Petron examined methane leakage in the Uinta Basin in Utah and suggested

leakage rates of nine percent of total production. The team used ground‐based equipment and an

aircraft to take measurements of various pollutants, and used atmospheric modeling to calculate the

                                                            

124 J Tollefson. Methane leaks erode green credentials of natural gas. Nature 493:12. January 2, 2013. Available at:

http://www.nature.com/news/methane‐leaks‐erode‐green‐credentials‐of‐natural‐gas‐1.12123

Author Affiliation Study Date Estimate of Losses (Does not include T&D)

Stephenson et al.A Shell Oil December 2011 0.6%

Cathles et al.B Cornell University October 2011 0.9%

Burnham et al.C Argonne National Laboratory December 2011 1.3%

Jiang et al.D Carnegie Mellon University August 2011 2.0%

Hultman et al.E University of Maryland October 2011 2.8%

EPAF April 2011 3.0%

Howarth et al.G Cornell University April 2011 3.3% (mean; range = 2.2% to 4.3%)

Petron et al.H NOAA, University of Colorado at Boulder February 2012 4.0% ("best estimate;" range = 2.3% to 7.7%

Petron et al.I NOAA, University of Colorado at Boulder January 2013 9.0% (preliminary results)
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐44  

level of methane emissions required to reach those concentrations. The resulting leakage rates were

compared with industry data on gas production to obtain the percentage of methane that was escaping

into the atmosphere through venting and leakage.

In order to give these numbers additional context, a study published by scientists at Princeton University

and the Environmental Defense Fund suggests that a shift from coal‐fired power plants to those fueled

by natural gas has a climate benefit as long as the methane leakage rate—distinct from the total CO2

equivalent emissions rate cited from Hultman et al. above—from natural gas production is less than 3.2

percent.125 These calculations assume an average leaking rate for the entire U.S. natural gas supply, and

the authors emphasize that much work needs to be done to determine actual emissions on a site‐

specific basis with any degree of accuracy.

Efforts to measure methane leakage rates in the natural gas industry are ongoing. NOAA scientists are

participating in a series of studies with the University of Texas at Austin, the Environmental Defense

Fund, and various industry partners to analyze emissions from all aspects of the natural gas industry:

production, gathering, processing, long‐distance transmission, and local distribution of gas. Researchers

are reviewing industry data and collecting field measurements at various sites across the United States.

The results of these studies are expected to be submitted for publication in 2013.126

4.6.1 Abatement Options for Emissions from Flowback

Emissions resulting from the flowback that occurs during natural gas production can be reduced through

a process called “reduced‐emissions completion,” or “green completion.” During green completion,

special equipment separates gas and liquid hydrocarbons from the flowback. The gas and hydrocarbons

can then be treated and used, or can be sold, thereby avoiding venting or flaring into the atmosphere.127

The states of Wyoming and Colorado and the cities of Fort Worth and Southlake (both in Texas) already

require green completion. In April 2012, the EPA adopted new rules requiring that operators of natural

gas wells use green completion to capture fugitive emissions and make them available for use or sale.

These requirements will take effect in 2015. In its cost‐benefit analysis of the requirements, the EPA

estimates that green completion will reduce methane emission leakage during the well completion

process by 95 percent, and that revenues from the sales of captured gas will result in a cost savings of

$11 to $19 million on an industry‐wide basis.128 This analysis assumes a natural gas price in 2015 of

$4.22 per million cubic feet, as forecast by the EIA. A break‐even analysis conducted by the EPA suggests

                                                            

125 R Alvarez, et al. Greater focus needed on methane leakage from natural gas infrastructure. Proceedings of the National

Academy of Sciences of the United States of America: 109(17). February 13, 2012.  

126 J Tollefson. Methane leaks erode green credentials of natural gas. Nature 493:12. January 2, 2013. Available at:

http://www.nature.com/news/methane‐leaks‐erode‐green‐credentials‐of‐natural‐gas‐1.12123

127 U.S. EPA. Overview of Final Amendments to Air Regulations for the Oil and Natural Gas Industry: Fact Sheet. April 17, 2012.

Available at: http://www.epa.gov/airquality/oilandgas/pdfs/20120417fs.pdf

128 Id.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐45  

that natural gas prices of $3.66 per million cubic feet or greater in 2015 result in revenue from the

recovery of natural gas that had previously been vented or flared that exceeds the annualized costs of

the required green completion.129

On a per well basis, a report commissioned by the Natural Resource Defense Council estimates that the

investment cost for green completions ranges from $8,700 to $33,000. Methane capture per well is

estimated to be 7,000 to 23,000 million cubic feet, resulting in a profit of $28,000 to $90,000, exceeding

expected costs. The payback period for investing in green completion equipment is projected to be less

than one year.

130

Some natural gas developers, including Southwestern Energy Co. and Devon Energy Corp., already use

green completion systems to capture fugitive methane. Mark Boling, president of the V+ Development

Division at Southwestern Energy Co., said in an interview that “what we do today with reduced

emissions completions in our wells doesn’t cost us any more than just venting the gas into the

atmosphere,” and noted that Southwestern has cut the cost of emissions capture from $20,000 per well

to $0. The Company estimates that it captures an average of 16 million cubic feet of natural gas that

would have been vented or flared. 131

At the other end of the range, the American Petroleum Institute (API) estimates that the cost of leasing

the equipment needed for a green completion is approximately $180,000 per well, plus $30,000 to

transport the equipment from one well to the next. As a result, industry expenses would increase by

$783 million over four years.

132 According to the API, these costs, combined with an insufficient supply

of equipment needed to comply with the regulations, would lead to a 52 percent reduction in drilling,

causing gas output to fall by nine to eleven percent.

133

It should be noted that the number of permit applications for drilling in Colorado did not decline after

green completion standards were implemented, but instead more than doubled. Permit applications

                                                            

129 U.S. EPA. Regulatory Impact Analysis: Final New Source Performance Standards and Amendments to the National Emissions

Standards for Hazardous Air Pollutants for the Oil and Natural Gas Industry. April 2012.

130 S Harvey, et. al. Leaking Profits: The US Oil and Gas Industry Can Reduce Pollution, Conserve Resources, and Make Money

by Preventing Methane Waste. Natural Resources Defense Council. March 2012. Available at:

http://www.nrdc.org/energy/files/Leaking‐Profits‐Report.pdf

131 Efstathiou, Jim. Drillers Say Costs Manageable from Pending Gas Emissions Rule. Bloomberg. April 17, 2012. Available at:

http://www.bloomberg.com/news/2012‐04‐17/drillers‐say‐costs‐manageable‐from‐pending‐gas‐emissions‐rule.html

132 Comment submitted by Howard J. Feldman, Director, Regulatory and Scientific Affairs, American Petroleum Institute, on

Proposed Rule: Oil and Natural Gas Sector, New Source Performance Standards and National Emission Standards for

Hazardous Air Pollutants Reviews. Docket ID: EPA‐HQ‐OAR‐2010‐0505.

133 Efstathiou, Jim. Drillers Say Costs Manageable from Pending Gas Emissions Rule. Bloomberg. April 17, 2012. Available at:

http://www.bloomberg.com/news/2012‐04‐17/drillers‐say‐costs‐manageable‐from‐pending‐gas‐emissions‐rule.html
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐46  

have also not fallen in Wyoming. Rather, in both states the supply of green completion equipment and

installation has expanded to meet demand.134

4.7 Compliance with State‐Specific Climate Plans

The AESC 2013 scope of work required the Synapse project team to determine if there was some

component of compliance with state‐specific regulations or climate plans that would directly impact

generators and that the project team could quantify and credibly support. The scope of work further

required the project team, if it made such a determination, to include their estimate of that compliance

cost in one of the three categories of costs related to emissions control reflected in the AESC 2013

avoided energy cost forecast. (Those three categories of emissions control costs are “currently

enforced,” “enacted, but not yet in effect,” and “reasonably expected to be enacted.”) This is because,

due to the nature of the regional market, the costs of complying with one state’s law may also affect

avoided costs in other states in the New England market. The scope notes that AESC 2013 was not to

determine the value of full compliance with these plans, laws, or regulations or the impact of energy

efficiency on other sectors that may also be covered by them, such as transportation or industry, in

achieving the overall objectives of the plan, law or regulation.

The AESC 2013 scope of work identified the GWSA of 2008 and Connecticut’s generator tax as examples

of state‐specific regulations or climate plans. The Synapse project team ultimately limited its analysis to

the GWSA.135 136 As required under the GWSA, the Secretary of Energy and Environmental Affairs

established a statewide limit for Massachusetts greenhouse gas (GHG) emissions for 2020, and a plan to

achieve the 2020 target, in the Massachusetts Clean Energy Climate Plan (CECP). 137 Our analysis of the

components required to comply with the GWSA relative to the AESC 2013 Base Case finds the following:

1. The current Massachusetts Greenhouse Gas Emissions Inventory method does not

provide an accurate accounting of electricity sector emission reductions for GWSA

compliance. Synapse presents an example alternate inventory method that would

provide an accurate accounting.  

2. The Massachusetts Clean Energy Climate Plan (CECP) assumes the electricity sector will

achieve significant reductions in emissions by 2020 under its Business as Usual Forecast.

The CECP then identifies six policy measures the electricity sector could use to comply

with GWSA targets in 2020 and beyond, as well the quantity of reductions and cost per

                                                            

134 Id.

135 The Connecticut Generator Tax is scheduled to sunset on June 30, 2013. However, a final decision on the fate of the tax has

not been made. For AESC 2013, we have assumed that the generator tax sunsets.  

136 On April 12, 2013, Synapse polled the Study Group to determine the likelihood of similar regulations in the other New

England states.  

137 Massachusetts Executive Office of Energy and Environmental Affairs, December 29, 2010,

http://www.mass.gov/eea/docs/eea/energy/2020‐clean‐energy‐plan.pdf
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐47  

ton of reduction from each. The AESC 2013 Base Case reflects the GWSA compliance

components that are currently enforced for the Massachusetts electricity, (i.e., RPS,

RGGI, and EPA Power Plant Rules). The remaining compliance measures are all cost‐

effective energy efficiency, the Clean Energy Import Strategy (CEI) and a Clean Energy

Performance Standard (CEPS).

3. The Massachusetts electricity sector will require reductions from a CEPS or other

additional component in order to comply with the GWSA at some point from 2020

onward. However, there are unresolved policy questions regarding the CECP targets for

the electricity sector beyond 2020 and the inventory method for accounting for

reductions in that sector. As a result, the project team could not determine the size of

reductions that would be required in the electricity sector each year and therefore could

not quantify and credibly support an estimate of the cost of the marginal resource

required to achieve those reductions.

4. In the absence of detailed modeling, the project team identified additional renewable

generation, incremental to RPS quantities, as the marginal resource for electric‐sector

compliance with the GWSA. If the quantity of additional renewable generation required

for GWSA compliance in a given year is comparable to the AESC 2013 projected quantity

of renewable generation added to meet RPS requirements in that year, it is reasonable

to expect the cost of that additional renewable generation in that year to be

comparable to the REC prices estimated for Massachusetts for that year (e.g.

$18.40/MWh in 2020 per Exhibit 6‐30) plus the AESC 2013 estimate of electric energy

costs for Massachusetts in that year. If the quantity of additional renewables required

for GWSA compliance is significantly larger than those added to meet RPS requirements,

the cost of the marginal resource required to achieve those larger reductions would

have to be determined through new modeling.  

4.7.1 Electricity Sector Compliance with GWSA: Current Regulatory Background

In accordance with the requirement of the 2008 Green Communities Act138 to implement all cost‐

effective energy efficiency resources, Massachusetts Department of Public Utilities (MassDPU) clarified

its policies with regard to the avoided costs of energy efficiency programs, including policies regulating

the types of costs and benefits that can be included in cost‐effectiveness screening in Massachusetts. In

2009, MassDPU affirmed the use of the Total Resource Cost test, and detailed how environmental

benefits could be used in evaluating cost‐effectiveness. MassDPU cited a Supreme Judicial Court (SJC)

case that addressed the circumstances under which the Department may require Program

Administrators (PAs) to account for environmental impacts in evaluating energy resources. The SJC

found that MassDPU could not require PAs to consider environmental externalities in evaluating energy

resources, as it did not have the statutory authority to do so.

139  

                                                            

138 An Act Relative to Green Communities, Acts of 2008, Chapter 169, July 2, 2008.

139 Investigation by the Department of Public Utilities on its Own Motion into Updating its Energy Efficiency Guidelines

Consistent with an Act Relative to Green Communities, Order, DPU 08‐50‐A, March 16, 2009, pages 14 and 15.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐48  

The SJC, however, has asserted that MassDPU does have the authority to require PAs to include the

costs of compliance with current and reasonably foreseeable future environmental regulations, as these

compliance costs would be incorporated in electricity prices over which the Commonwealth has clear

jurisdiction. MassDPU identified the GWSA and federal measures to control greenhouse gas emissions as

examples of existing and reasonably anticipated future environmental regulations, stating that “the

Department expects Program Administrators to include estimates of such compliance costs in the

calculation of future avoided energy costs.”140

In the case of GWSA, these compliance costs may be developed in a broader stakeholder process per

MassDPU Docket 11‐120, which states that:

In light of the required GHG emissions reductions set forth in the GWSA and the [Clean

Energy and Climate Plan (CECP)] 2020 Plan, the Department will investigate the extent

to which the current approach of calculating the benefits associated with reduced CO2

emissions (i.e., the internalized cost approach included in the 2011 AESC Study) may

undervalue the actual benefits. If the Department concludes that the current method

understates actual benefits, we seek to identify whether and, if so, how the reasonably

anticipated costs of complying with the GWSA and the 2020 Climate Plan can be

incorporated into the cost‐effectiveness analyses for both electric and gas energy

efficiency programs.

141

After receiving extensive public comments—many of which urged MassDPU to adopt a proxy value

based on Massachusetts Renewable Energy Portfolio REC market prices or alternative compliance

mechanism prices until a complete analysis of supply curves for the GWSA can be completed142—

MassDPU issued the following finding:

At this point in our investigation, we have not developed record evidence as is required

by G.L. 30A, § 14(7) (e), and the case law discussed above, that would support the use of

an interim proxy value for CO2 emissions. Accordingly, we decline to adopt an interim

proxy value for CO2 to be used in the cost‐effectiveness determination of energy

efficiency programs. Our investigation into environmental compliance costs is ongoing

and will not conclude until after our review of the 2013‐2015 three‐year energy

efficiency plans is complete. To the extent that the Department determines that it is

appropriate to update environmental compliance cost values, the Department will

                                                            

140 Ibid, page 17.

141 MassDPU 11‐120. November 29, 2011. “Investigation by the Department of Public Utilities on its own Motion into Updating

its Energy Efficiency Guidelines.”

142 See for example, MassDPU 11‐120. January 11, 2010. Massachusetts Department of Energy Resources Initial Comments.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐49  

consider how best to incorporate any updated values into the three‐year energy

efficiency plans mid‐term.143

4.7.2 Electricity Sector Compliance with GWSA: Emission Reduction  

The GWSA calls for a 25 percent reduction from Massachusetts’ 1990 greenhouse gas emissions by

2020, and an 80 percent reduction from 1990 by 2050. The GWSA does not establish specific targets for

each sector of the Massachusetts economy.

The CECP establishes target reductions for various sectors of the Massachusetts economy including the

electricity sector. The CECP begins with a Business‐as‐Usual (BAU) forecast that assumes the electricity

sector will emit 4.4 million short tons less CO2 in 2020 than in 1990. The CECP then identifies a further

total emission reduction target for 2020 of 13.3 million short tons based on the following measures:

144

 All Cost‐Effective Energy Efficiency: 5.2 million short tons CO2 (CECP, p.18, and ERG, April 2010,

“Initial Estimates of Emission Reductions from Existing Policies Related to Reducing Greenhouse

Gas Emissions,” p.4, http://www.mass.gov/dep/air/climate/gwsa_docs.htm#erg)

 Renewable Portfolio Standard: 1.2 million short tons CO2 (CECP, p.40)

 Regional Greenhouse Gas Initiative: 0 short tons CO2 (CECP, p.42)

 More Stringent EPA Power Plant Rules: 1.3 million short tons CO2 (CECP, p.44)

 Clean Energy Imports: imports of low‐carbon energy from Canada via a new transmission line,

5.6 million short tons CO2 (CECP, p.45)145

 Clean Energy Performance Standard: 0 short tons CO2 (CECP, p.47)

For years after 2020, the CECP describes two possible “Scenarios for a Clean Energy Future”: the

Electrification Scenario, in which there is a significant conversion to electric vehicles and 100 percent of

electricity consumed in Massachusetts comes from “near zero carbon” sources, and the Efficiency

Scenario, in which there are far stronger energy efficiency standards and only 80 percent of electricity

comes from near zero carbon sources.

146 CECP Figure 13 reports 2050 electricity sector emissions for

both scenarios: 0.6 million short tons CO2 for the Electrification Scenario and 5.5 million short tons for

Efficiency Scenario. Expected 2050 emission reductions from 1990 electricity sector emissions are 98

and 82 percent, respectively. Required emission reductions for 2030 and 2040 are linearly interpolated

using expected CECP 2020 and 2050 reductions.

                                                            

143 MassDPU 11‐120‐A. August 10, 2012. “Investigation by the Department of Public Utilities on its own Motion into Updating

its Energy Efficiency Guidelines.”

144 The CECEP reports its targets in metric tons; we have prepared our analysis in short tons.

145 The CECP assumptions regarding the quantity of GHG reductions from the CEI and its average cost per ton of reduction are

not uniformly accepted by all parties. In addition the GWSA method for estimating GHG emissions does account for life‐

cycle emissions of GHG associated with the development of a resource to provide generation.

146 CECP, p.95‐102.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐50  

Exhibit 4‐22 reports the GWSA’s required electricity‐sector emission reductions for the modeled years.

The 44‐percent emission reduction target for 2020 is the sum of electricity‐sector emission reductions

designated in the CECP (13.3 million short tons divided by Massachusetts’ 1990 electricity‐sector

emissions (30.6 million short tons CO2).147

Exhibit 4‐22. GWSA Required Electricity‐Sector Emission Reductions

   2020 2030 2040 2050

Emission reduction from 1990 (million short tons CO2)

CECP Electrification Scenario: Electricity‐Sector Target 13.3 18.9 24.5 30.1

CECP Efficiency Scenario: Electricity‐Sector Target 13.3 17.3 21.2 25.1

Emission reductions as share of 1990 electricity sector emissions

CECP Electrification Scenario: Electricity‐Sector Target 44% 62% 80% 98%

CECP Efficiency Scenario: Electricity‐Sector Target 44% 56% 69% 82%

4.7.3 Electricity Sector Compliance with GWSA: Synapse Methodology

The project team estimated the projected costs and CO2 emissions savings of the specific GWSA electric‐

sector policies set out in the CECP for 2013, 2020, 2030, 2040, and 2050. The project team prepared this

analysis using a “supply curve” methodology.148 The supply curve for a given year compares estimated

costs per short ton and CO2 emissions reductions in short tons of the specific electric‐sector policies set

out in the CECP. Elements of each supply curve depict GWSA policies as blocks or columns with a width

representing policy‐specific emission reductions and a height representing the average cost of that

policy’s implementation per ton of emission reductions (see Exhibit 4‐23).

                                                            

147 Statewide Greenhouse Gas Emissions Level: 1990 Baseline & 2020 Business As Usual Projection, and Appendix 1: Statewide

Greenhouse Gas Emissions Level: Final 1990 Baseline & 2020 Business As Usual Projection. Available from the MassDEP.

148 For illustrative purposes, we estimate cost and emissions savings for 2040 and 2050 to represent the full extent of GWSA

compliance through its 2050 target and to inform future AESC calculations.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐51  

Exhibit 4‐23. Illustrative supply curve of CECP policies

Note: EE is energy efficiency; CEI is the Clean Energy Import Strategy; EPA is more stringent EPA regulation of power plants;

RGGI is the Regional Greenhouse Gas Initiative; and RPS is Massachusetts’ Renewable Portfolio Standard.

The supply curve for a given year presents the compliance policies in order of their cost per ton of

emission reductions, from least to most expensive. The avoided cost of GWSA compliance is the cost per

ton of the last policy needed to achieve GWSA required electricity‐sector emission reductions, where

policies are assumed to be adopted in order of their costs, starting with the least expensive measures.

Because the AESC 2013 Base Case includes three of the CECP policies (i.e., Massachusetts’ Renewable

Portfolio Standard, the RGGI, and more stringent EPA rules for power plants), the effective supply curve

of policy measures for the purpose of modeling GWSA compliance is limited to (1) all cost effective

energy efficiency, (2) the CEI and (3) a CEPS which might include a portfolio of compliance measures. Of

these three, the CECP provides estimates of the size of reductions, and cost per ton of reductions, for

the first two of those policy measures.  

Before the project team could estimate the incremental cost of electricity sector compliance with the

GWSA, it had to first identify the marginal compliance policy in each year, and the reductions that would

be required from that marginal compliance policy. The project team included the CECP policy measure

of reductions from energy efficiency in order to determine whether the electricity sector would require

marginal compliance policy measures over and above energy efficiency and CEI, and if so, what quantity

of additional reductions would be required in each year. That approach is different from the approach

used to estimate avoided costs in the AESC 2013 Base Case, which is to estimate the costs of meeting

future sales in each year while complying with the quantitative policy and regulatory goals, such as

specific RPS percentages, applicable in each year assuming no reductions from new energy efficiency.

The difference in approach arose because the project team was attempting to determine the specific

goal and quantity of reductions the electricity sector would require in order to comply with the GWSA

EE

CEI

EPA

RGGI

RPS

-$250

-$200

-$150

-$100

-$50

$0

$50

$100

$150

$200

$250

0 5 10 15 20 25 30 35

Net Cost of Policy ($/ton)

Avoided CO2-equivalent (million tons)
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐52  

each year. The results and implications of this approach for the calculation of the marginal cost of

compliance are presented in section 4.7.7.

The project team determined whether the Massachusetts electricity sector would require a CEPS or

some other additional component in order to comply with the GWSA and, if so, the size of reductions

that component would have to achieve using an “inventory” model, i.e., a spreadsheet which accounts

for the reductions in GHG emissions from each compliance policy measure. The team used this model to

estimate CO2 emissions associated with Massachusetts electricity consumption under several scenarios,

and comparing those emissions to Massachusetts 1990 electricity‐sector emissions.  

The major components of the project team analysis are:

 Two sets of CECP emission reduction targets and futures:  

o The CECP electricity sector targets for the Efficiency Scenario are 44 percent less than

1990 emissions by 2020 and 82 percent less than 1990 emissions by 2050. In this

scenario, there are stronger energy efficiency requirements, less conversion to electric

vehicles, and slower adoption of zero‐carbon generation resources.

o The CECP electricity sector targets for the Electrification Scenario are 44 percent less

than 1990 emissions by 2020 and 98 percent less than 1990 emissions by 2050. In this

scenario, reductions from energy efficiency measures are offset by significant

conversion to electric vehicles; and

 Three emission reduction policy cases:  

 AESC 2013 Base Case (which includes several CECP policies: Massachusetts’ Renewable

Portfolio Standard, the RGGI, and more stringent EPA rules for power plants);  

 The AESC 2013 Base Case with the all‐cost effective energy efficiency measures

described in the CECP; and  

 The AESC 2013 Base Case with additional energy efficiency measures and Clean Energy

Import Strategy from the CECP.

 Two inventory methods:  

 The current Massachusetts Greenhouse Gas Emission Inventory method; and

 An example of an alternate inventory method (described below).

4.7.4 Electricity Sector Compliance with GWSA: Carbon Emission Inventory Methods  

Current Massachusetts Method

The current methodology used for the Massachusetts Greenhouse Gas Emission Inventory follows a

hierarchy illustrated in Exhibit 4‐24.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐53  

Exhibit 4‐24. Illustration of Current Massachusetts Inventory Method

First, the method assumes that Massachusetts retail electricity sales are satisfied by Massachusetts

generation to the greatest extent possible.

Next, residual sales (Massachusetts total retail sales less Massachusetts generation) are then satisfied to

the greatest extent possible by electricity exports from other New England states. Massachusetts is

awarded a share of each state’s exports equal to the Massachusetts share of all New England electricity

imports (from other New England states and from outside of New England combined).  

Residual sales (Massachusetts total retail sales less Massachusetts generation and the Massachusetts

share of exports from other New England states) are then satisfied in their entirety by electricity

imported from outside of New England. Massachusetts is awarded a share of each region’s exports into

New England equal to the Massachusetts share of all New England electricity imports (from other New

England states and from outside of New England combined).149

We note that this current method: (1) does not account for REC certificates for zero‐carbon resources

purchased by Massachusetts LSEs in compliance with RPS and APS regulations; (2) would only award

Massachusetts a share of the emission reductions expected from additional transmission of Canadian

relatively low‐carbon energy in the Clean Energy Import Strategy; and, (3) depending on the design of

the standard, might not account for emission reductions from a Clean Energy Performance Standard.

We found that it is not possible to determine the marginal policy for GWSA compliance using this

inventory method.

                                                            

149 Note that in our GWSA compliance calculations we abstract from the official inventory’s state‐specific allocations of imports

and exports, and instead model the imports and exports of an “other New England” (excluding Massachusetts) five‐state

group.

Current MA Inventory Method (numbers are illustrative)

GWh

10^6 short

tons CO2‐e

MA Retail Electricity Sales 60,000

less MA Generation (C) 35,000 13.7

Residual: MA Sales less MA Generation 25,000

less MA share of Intra‐NE Imports (D) 14,000 5.2

Residual: MA Sales less NE Generation 11,000

less MA share of Extra‐NE Imports (E) 11,000 0.6

MA Emissions from Electricity Consumption (C+D+E) 19.4
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐54  

Synapse alternative example method

The spreadsheet model used to estimate the emissions associated with GWSA compliance includes an

example alternative inventory method labeled as the “Synapse alternative example” method for clarity

and illustrated in Exhibit 4‐25.  

Exhibit 4‐25. Illustration of “Synapse alternative example” Inventory Method

In this method, Massachusetts retail electricity sales are first satisfied to the greatest extent possible by

RPS and APS REC purchases.

Next, residual sales (Massachusetts total retail sales less Massachusetts LSEs’ RPS and APS REC

purchases) are next satisfied to the maximum extent possible by imports from the Clean Energy Import

Strategy.

Residual sales (Massachusetts total retail sales less REC purchases and Clean Energy Import Strategy

imports) are then satisfied by Massachusetts generation to the greatest extent possible.

Residual sales (Massachusetts total retail sales less REC purchases, Clean Energy Import Strategy

imports, and Massachusetts generation) are then satisfied to the greatest extent possible by electricity

exports from other New England states, after accounting for these states’ own REC requirements.

Massachusetts is awarded a share of each state’s exports equal to the Massachusetts share of all New

New Synapse Inventory Method (numbers are illustrative)

GWh

10^6 short

tons CO2‐e

MA Retail Electricity Sales 60,000

less MA RPS/APS REC purchases(A) 6,000 0.0

Residual: MA Sales less MA RPS 54,000

less MA Clean Energy Import Strategy (B) 4,000 0.0

Residual: MA Sales less MA RPS and CEIP 50,000

less MA Generation (C) 35,000 13.7

Residual: MA Sales less MA Generation 15,000

less MA share of Intra‐NE Imports (D) 14,000 5.2

Residual: MA Sales less NE Generation 1,000

less MA share of Extra‐NE Imports (E) 1,000 0.1

MA Emissions from Electricity Consumption (A+B+C+D+E) 18.9
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐55  

England electricity imports (from other New England states and from outside of New England

combined).150

Residual sales (Massachusetts total retail sales less REC purchases, Clean Energy Import Strategy

imports, Massachusetts generation, and the Massachusetts share of exports from other New England

states) are then satisfied in their entirety by electricity imported from outside of New England.

Massachusetts is awarded a share of each region’s exports into New England equal to the

Massachusetts share of all New England electricity imports (from other New England states and from

outside of New England combined).

In this alternative method, the emission reductions of all CECP policies are accounted for in the

inventory, making it possible to determine the marginal policy for GWSA compliance. The spreadsheet

model designed for estimating the emission reductions associated with GWSA compliance replicates

both inventory methodologies; the model allows the user to choose to display results either from the

current Massachusetts inventory method or the alternative inventory method.

4.7.5 Electricity Sector Compliance with GWSA: Policy Measure Assumptions

Our analysis begins by determining if the Massachusetts electricity sector will require reductions from a

CEPS or other additional component in order to comply with the GWSA at some point from 2020

onward. We do this by modeling the annual CO2 emissions of the Massachusetts electricity sector for

three policy cases –the AESC 2013 Base Case, the AESC 2013 Base Case plus the CECP estimate of all‐

cost‐effective energy and the AESC 2013 Base Case plus the CECP estimate of all‐cost‐effective energy

and the CEI.

GHG Emission Rates

Our estimates of CO2 emissions per MWh of generation from each resource are drawn from the AESC

2013 Base Case, with three exceptions where the project team uses CECP emission rate assumptions.

These exceptions are:

 All RECs purchased by Massachusetts load serving entities (LSEs) are zero carbon, except for

RECs for waste‐to‐energy and combined heat and power.

 Waste‐to‐energy generation has one‐half the emission rate implied by AESC 2013 Base Case

emissions to generation ratio in order to account only for non‐biogenic emissions.151

 Electricity imports from outside of New England have the 2006 electricity generation

greenhouse gas intensity for Quebec.152

                                                            

150 Note that in our GWSA compliance calculations we abstract from the official inventory’s state‐specific allocations of imports

and exports, and instead model the imports and exports of an “other New England” (excluding Massachusetts) five‐state

group.

151 See Massachusetts Department of Environmental Protection, http://www.mass.gov/eea/docs/dep/air/climate/rse11cal.xls
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐56  

Policy Case Measures  

Our modeling assumptions for the policy case measures are as follows:  

All Cost‐Effective Energy Efficiency: Our analysis uses the CECP assumption that the average cost per ton

of energy efficiency measures in the electricity sector is negative.153 Our analysis models the quantity of

reductions from this measure as the difference between the AESC 2013 Base Case forecast of

Massachusetts retail sales and the CECP forecast of retail sales under each of the CECP scenarios (see

Exhibit 4‐26).154

Exhibit 4‐26. Projected Massachusetts Retail Sales (GWh)

2013 2020 2030 2040 2050

AESC 2013 Base Case 61,368 67,189 76,011 85,968 97,230

CECP Electrification Scenario 61,368 62,026 62,684 63,342 64,000

CECP Efficiency Scenario   61,368 55,226 49,084 42,942 36,800

Clean Energy Import Strategy: Our analysis uses the CECP assumption that the average cost per ton of

clean energy imports is zero or negative. 155 Our analysis models the CEI Strategy using the CECP

assumption of 1,200 MW of new clean energy import capacity by 2020.156 However, our analysis

assumes the CEI will provide 4,730 GWh based upon an assumed 45 capacity factor, which is consistent

with the AESC 2013 Base Case assumptions but lower than the capacity factor assumed in the CECP

projections.157  

4.7.6 Electricity Sector Compliance with GWSA: Marginal Compliance Measure Reductions  

The results of our estimates of Massachusetts electricity sector emissions under the CECP Efficiency

Scenario and Electrification scenarios are summarized in Exhibit 4‐27. These results indicate that, at

some point in time, the Massachusetts electricity sector will require some level of reductions from a

CEPS or other additional component in order to comply with the GWSA.  

                                                            

152 See Environment Canada, http://www.ec.gc.ca/pdb/ghg/inventory_report/2006_report/a9_eng.cfm#ta9_6

153 CECP, p.18 fn.25.

154 CECP, Figure 12.

155 CECP, p.45‐46.

156 CECP, p.45‐46.

157 Hydro‐Quebec Annual Report 2012, http://www.hydroquebec.com/publications/en/annual_report/pdf/annual‐report‐

2012.pdf
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐57  

Exhibit 4‐27. GWSA Compliance Emissions Results

The project team prepared these estimates using the current Massachusetts Greenhouse Gas Emissions

Inventory method as well as an example alternate inventory method because, as noted earlier, the

current method does not provide an accurate accounting of emission reductions for GWSA compliance.

The top half of Exhibit 4‐27 provides results for the Efficiency Scenario. Under that scenario, the

Massachusetts electricity sector would not require reductions from a CEPS or other new compliance

measure until after 2030, if all of the underlying modeling assumptions prove correct. However, the

Massachusetts electricity sector could require reductions from CEPS as early as 2020 if the state does

not achieve a 4.4 million short ton reduction by 2020 through BAU improvements and/or if actual

reductions from energy efficiency and the CEI prove to be less than assumed.  

The bottom half of Exhibit 4‐27 provides results for the Electrification Scenario. Under that scenario, the

Massachusetts electricity sector would require some level of reduction as early as 2020.  

Our estimates of the reductions in GHG emissions in 2020 from all cost‐effective energy efficiency and

the CEI are lower than the CECP target reductions for those two compliance measures. In particular, our

2013 2020 2030 2040 2050 2013 2020 2030 2040 2050

Reduction from 1990 Electricity‐Section Emissions

Target: CECP Efficiency Electricity‐Sector 44% 56% 69% 82% 44% 56% 69% 82%

AESC 2013

Base Case Emissions (10^6 Short Tons CO2‐e ) 19.2 23.3 24.9 26.1 30.0 19.3 23.0 23.3 23.8 23.6

Emission Reduction (10^6 Short Tons CO2‐e ) 11.4 7.3 5.7 4.5 0.6 11.3 7.6 7.3 6.8 7.0

Percent Reduction from 1990 Emissions 37% 24% 19% 15% 2% 37% 25% 24% 22% 23%

AESC 2013 Efficiency Scenario

EE Emissions (10^6 Short Tons CO2 ‐e) 19.2 19.3 17.3 15.3 13.3 19.3 17.2 13.5 10.1 7.1

Emission Reduction (10^6 Short Tons CO2‐e ) 11.4 11.3 13.3 15.3 17.3 11.3 13.4 17.1 20.5 23.5

Percent Reduction from 1990 Emissions 37% 37% 43% 50% 57% 37% 44% 56% 67% 77%

EE + CEI Emissions (10^6 Short Tons CO2 ‐e) 19.2 19.3 17.3 15.3 13.3 19.3 17.2 13.5 10.1 7.1

Emission Reduction (10^6 Short Tons CO2‐e ) 11.4 11.3 13.3 15.3 17.3 11.3 13.4 17.1 20.5 23.5

Percent Reduction from 1990 Emissions 37% 37% 43% 50% 57% 37% 44% 56% 67% 77%

Efficiency Scenario

Inventory Method:

Current MA

Inventory Method:

Synapse Example Alternative

2013 2020 2030 2040 2050 2013 2020 2030 2040 2050

Reduction from 1990 Electricity‐Section Emissions

Target: CECP Electrification Electricity‐Sector 44% 62% 80% 98% 44% 62% 80% 98%

AESC 2013

Base Case Emissions (10^6 Short Tons CO2‐e ) 19.2 23.3 24.9 26.1 30.0 19.3 23.0 23.3 23.8 23.6

Emission Reducti on (10^6 Short Tons CO2‐e ) 11.4 7.3 5.7 4.5 0.6 11.3 7.6 7.3 6.8 7.0

Percent Reduction from 1990 Emissions 37% 24% 19% 15% 2% 37% 25% 24% 22% 23%

AESC 2013 Electrification Scenario

EE Emissions (10^6 Short Tons CO2 ‐e) 19.2 21.6 21.0 22.5 23.0 19.3 21.2 19.5 17.5 15.5

Emission Reducti on (10^6 Short Tons CO2‐e ) 11.4 9.0 9.6 8.1 7.6 11.3 9.4 11.1 13.1 15.1

Percent Reduction from 1990 Emissions 37% 30% 31% 26% 25% 37% 31% 36% 43% 49%

EE + CEI Emissions (10^6 Short Tons CO2 ‐e) 19.2 21.6 21.0 22.5 23.0 19.3 19.4 17.6 15.6 13.6

Emission Reducti on (10^6 Short Tons CO2‐e ) 11.4 9.0 9.6 8.1 7.6 11.3 11.2 13.0 15.0 17.0

Percent Reduction from 1990 Emissions 37% 30% 31% 26% 25% 37% 37% 42% 49% 55%

Inventory Method:

Current MA

Inventory Method:

Electrification Scenario Synapse Example Alternative
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐58  

analysis estimates that the CEI would reduce emissions by 1.8 million short tons in 2020 as compared to

the CECP target of 5.6 million short tons. The difference between these two estimates is due to

differences in two key assumptions. First, our analysis assumes approximately 50 percent less

generation from the CEI, based upon it operating at a 45 capacity factor rather than a 90 percent

capacity factor. Second, our analysis assumes generation from the CEI will displace conventional fossil

generation with an emissions rate of 0.38 million short tons CO2 per MWh, based on average emissions

of generation located outside of Massachusetts under the AESC 2013 Base case. The rate of displaced

emissions is approximately 30 percent less than the 0.54 short tons CO2 per MWh underlying the CECP

estimates of the reductions from the CEI. (This difference in assumptions regarding emissions per MWh

of displaced generation is the primary reason our estimate of reductions in emissions from energy

efficiency in 2020 is lower than the CECP target.)

4.7.7 Electricity Sector Compliance with GWSA: Cost of Marginal Compliance Measure

Our results indicate that, at some point in time, the Massachusetts electricity sector will require some

level of reductions from a CEPS or other additional component in order to comply with the GWSA.

However, our results also indicate that there are unresolved policy questions regarding the CECP targets

beyond 2020 and the inventory method for accounting for reductions in that sector. As a result, the

project team could not determine the size of reductions that would be required in the electricity sector

each year and therefore could not estimate the cost of the marginal resource required to achieve those

reductions.  

The marginal cost of GWSA compliance is determined by several modeling assumptions for which there

are no obvious or well‐supported choices at present:

1) What greenhouse gas inventory method for the electricity sector should be used to compare

GWSA emission savings to GWSA target emission reductions? The current method used for the

Massachusetts Greenhouse Gas Emission Inventory158 does not account for emission reductions

from some of the policies described in the CECP. The project team developed an example

alternative inventory method that accounts for these policies’ effect on emissions.

2) To what emission reduction targets for the electricity sector should emissions savings from

GWSA policies after 2020 be compared? The CECP electricity‐sector target is 44 percent less

than 1990 emissions by 2020. After 2020, the CECP targets are higher in the Electrification

Scenario than in the Efficiency Scenario. By 2050, the target reductions are either 98 or 82

percent less than 1990 emissions—depending on the scenario.

3) If emission reduction targets for the electricity sector are not met by policies with specific

costs and emissions savings described in the CECP, what quantity of additional reductions are

required for compliance and what marginal cost can be assigned to GWSA compliance? The

                                                            

158 Massachusetts Department of Environmental Protection, July 2012, Final 2006‐2008 Massachusetts Greenhouse Gas

Emissions Inventory, http://www.mass.gov/dep/air/climate/ghg08inf.pdf
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐59  

CECP provides for a “Clean Energy Performance Standard” that could provide additional

emissions savings, but provides no details on exactly how it would be designed and

implemented, the quantity of reductions it might provide, or its costs. The GWSA 2012 Annual

Report notes that “an analysis of the risks and opportunities of a Clean Energy Performance

Standard will be performed by summer 2013.”159 For AESC 2013, treat the Clean Energy

Performance Standard’s costs and emission reductions as unknown.

As a result, the project team could not determine the size of reductions that would be required in the

electricity sector each year and therefore could not quantify and credibly support an estimate of the

cost of reductions from a CEPS or other additional component the Massachusetts electricity sector

would require in order to comply with the GWSA over the planning horizon. In the absence of detailed

modeling the project team identified additional renewable generation, incremental to RPS quantities, as

the marginal resource for electric‐sector compliance with the GWSA. It is reasonable to expect the cost

of that marginal compliance resource, and its impact on wholesale electric energy prices, will vary

according to its size and timing.  

If the quantity of additional renewable generation the Massachusetts electricity sector requires for

GWSA compliance in a given year is comparable to the AESC 2013 projected quantity of renewable

generation added to meet RPS requirements in that year, the avoided cost of that additional renewable

generation in that year would be comparable to the REC prices estimated for Massachusetts in that year

and would not have a material impact on the marginal cost of energy in the New England market. For

example, the cost of adding a limited quantity of renewable resources in 2020 would be comparable to

the incremental REC premium for Massachusetts in 2020 (e.g. $18.40/MWh per Exhibit 6‐30) plus the

AESC 2013 estimate of wholesale electric energy and capacity costs for Massachusetts in that year.  

In contrast, it is possible that the quantity of additional renewables the Massachusetts electricity sector

will require for GWSA compliance in 2020 or beyond will be significantly larger than the quantity added

to meet RPS requirements in the AESC 2013 Base Case. For example, an updated BAU forecast may

indicate that the Massachusetts electricity sector is not on track to emit 4.4 million short tons less CO2 in

2020 than in 1990. Similarly, updated estimates may project a smaller emission reduction from current

energy efficiency programs than the CECP had anticipated. Such analyses may indicate that the

Massachusetts electricity sector will require reductions much greater than the renewable generation

additions for RPS compliance modeled in AESC 2013. If so, the cost of the marginal resource required to

achieve those larger reductions would have to be determined through new modeling. The addition of

such a significant quantity of renewable generation could have a material impact on the marginal cost of

energy in the New England market, and hence could affect the AESC 2013 estimates of avoided

wholesale energy costs for each state. For this reason, the impact of a significant additional quantity of

                                                            

159 Massachusetts Executive Office of Energy and Environmental Affairs, April 2013,

http://www.mass.gov/eea/docs/eea/gwsa/2012‐annual‐report.pdf, p.19.
Synapse Energy Economics, Inc. – AESC 2013 Page 4‐60  

renewable energy should be estimated through additional modeling that is not within the scope of the

current analysis.  

Value of energy efficiency as a GWSA compliance measure

The value of reductions from energy efficiency in Massachusetts that would enable the electricity sector

to avoid the cost of adding renewable generation to comply with the GWSA will vary according to the

size and cost of the marginal compliance resource.  

The incremental value of energy efficiency in Massachusetts that would avoid a limited addition of

renewable generation can be estimated using the Massachusetts avoided Class I REC price shown in

Exhibit F‐1 (in Appendix F) adjusted for ISO‐NE line losses per Exhibit F‐1 and for the retail risk premium

per the Massachusetts Statewide “inputs to avoided costs” table on page B‐14 of Appendix B. Program

administrators in Massachusetts could add this incremental value to the avoided costs applicable to

their efficiency programs.

Following is an example calculation of the incremental value of energy efficiency based on avoiding the

cost of a limited addition of renewable generation in 2020 to comply with the GWSA. Multiply the 2020

Class I REC price for Massachusetts of $18.40/MWh from Exhibit F‐1 by 1.08 to reflect the ISO‐NE line

losses of 8 percent in Exhibit F‐1. This results in an avoided cost of $19.47/MWh at the customer meter.

Multiply that $19.47 by 1.09 to reflect the wholesale risk premium of 9 percent from the Massachusetts

Statewide “inputs to avoided costs” table on page B‐14 of Appendix B. This results in an avoided electric

energy value of $21.66/MWh or $0.022/kWh for electric sector GWSA compliance in 2020.  

The value of energy efficiency in Massachusetts that would avoid the need for additional renewable

generation significantly larger than the RPS additions modeled in the AESC 2013 Base Case would need

to be calculated through new modeling.

If another New England state had to achieve reductions in emissions from its electricity sector to meet

its state climate goal, it could follow the approach described in this section to estimate the value of

energy efficiency as a compliance measure. To use this approach the state would first have to determine

if incremental renewables were the marginal compliance resource and, if so, whether the quantities

were comparable to the quantities added for RPS compliance in the AESC Base Case. If so, the state

would also have to use the AESC 2013 REC value for its state.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐1  

Chapter 5: Avoided Electric Capacity Costs

5.1 Introduction  

Avoided electric capacity costs are an estimate of the value of a load reduction by retail customers

during hours of system peak demand.160 The major input to this calculation is the wholesale forward

capacity price to load (in dollars per kilowatt‐month), which is set for a capacity year (June–May) roughly

three years before the start of the capacity year. To develop an avoided cost at the meter, the wholesale

electric capacity price is first increased by the reserve margin requirements forecasted for the year, then

increased by eight percent to reflect ISO‐NE’s estimate of distribution losses.

The major drivers of the avoided wholesale capacity price are: 1) system peak demand, 2) capacity

resources, and 3) the detailed ISO‐NE rules governing the auction. ISO‐NE rules indicate which resources

are allowed to bid in the auction, how the resources’ capacity values are computed, and what range of

prices each resource category is allowed to bid. The load‐resource balance is determined by load

growth, retirements of existing capacity, addition of new capacity from resources to comply with RPS

requirements, imports, exports, and new non‐RPS capacity additions. AESC 2013, which is based on the

counter factual assumption of no new energy efficiency, projects that new capacity, other than RPS‐

related renewable resources, will have to be added starting in 2020.  

The 15‐year levelized projection of capacity prices from AESC 2011 was $49.69/kW‐year in each pricing

zone (in 2013 dollars), while the corresponding levelized value from AESC 2013 is $79.88/kW‐year. The

AESC 2013 estimate of levelized capacity prices is approximately 61 percent higher than the estimate

from AESC 2011. The higher values are primarily due to the AESC 2013 forecast of expected retirement

of larger quantities of capacity due to environmental requirements and changes in the Forward Capacity

Market (FCM).  

The actual amount of wholesale electric capacity costs avoided by kW reductions from energy

efficiency measures will vary according to the approach that the PA responsible for those

measures takes towards the FCM. PAs achieve the maximum avoided cost by bidding the entire

anticipated kW reduction from measures in a given year into the Forward Capacity Auction (FCA)

for that power year. However, PAs have to submit those bids when the FCA is held, which is

approximately three years in advance of the applicable power year. Some expected load

reductions may not be bid into the first FCA for which the reduction would be effective, due to

                                                            

160 The benefit arises from two sources: the reduction of load at the system annual peak hour and the capacity credit

attributed to energy‐efficiency programs (called “passive demand response” in the ISO‐NE forward capacity mechanism),

measured as the average load reduction of the on‐peak hours in high‐load months or the hours with loads over 95 percent

of forecast peak.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐2  

uncertainty about future program funding and savings.161 Information provided by various PAs

indicates that the majority of expected savings will be bid into the first applicable auction (75

percent to 100 percent, depending on PA), with the remainder bid in over the next two years.  

This chapter is organized as follows:

 Section 5.2 describes the basic assumptions and methodologies underlying our

projections of avoided electric capacity costs and avoided electric energy costs. This

includes components such as the load forecast, transmission assumptions, generating

unit retirements, and resource additions.

 Section 5.3 describes additional assumptions that are specific to our projection of

elements influencing avoided electric capacity costs, such as results of FCAs, reserve

margin requirements, and reliability contracts.  

 Section 5.4 describes the wholesale capacity market in New England and expected

changes to that market during the study period.

 Section 5.5 describes the spreadsheet model used in AESC 2013 to estimate electric

capacity market prices by simulating future FCAs in the FCM.  

 Section 5.6 provides our projections of avoided capacity costs for each year of the study

period.

5.2 Basic Assumptions and Methodologies Shared with Electric Energy Forecast

5.2.1 Load Forecast

In order to forecast electric energy and capacity prices that would occur in the absence of new energy

efficiency (EE) programs, the project team developed a forecast of peak demand and energy

requirements in the absence of such new EE programs.

162

The forecasts of annual energy and peak load used to calculate avoided costs in AESC 2013 are derived

from ISO‐NE’s 2012–2021 Forecast Report of Capacity, Energy, Loads and Transmission (CELT 2012), as

                                                            

161 PAs also avoid capacity costs from kW reductions that are not bid into FCAs, since those kW reductions lower actual

demand, and ISO‐NE eventually reflects those lower demands when setting the maximum demand to be met in future FCAs

and the allocation of capacity requirements to load. However, the total amount of avoided capacity costs is lower because

of the time lag, up to four years, between the year in which the kW reduction first causes a lower actual peak demand and

the year in which ISO‐NE translates that kW reduction into a reduction in the total demand for which capacity has to be

acquired in a FCA. Since the load reduction in one year will affect the allocation of capacity responsibility in the next year,

the PA’s customers experience a one‐year delay in realized savings that are not bid into the auctions at all.

162 The purpose of the overall study is to develop avoided costs for program administrators to use in their economic

evaluations of measures for inclusion in EE program budgets for calendar years 2014 and beyond. The program

administrators will submit those proposed budgets in regulatory filings from mid‐2013 onward. If the program budgets are

approved, the measures would be installed after January 1, 2014, causing savings from that point onward.  
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐3  

discussed below.163 Beyond 2021, we extrapolate using the last five years’ (2017–2021) Compound

Annual Growth Rate (CAGR) reflected in that report.  

Load Forecast for 2013 through 2021 (CELT 2012)

ISO‐NE developed the CELT 2012 forecast of peak demand and energy requirements through 2021

based upon econometric models.164

ISO‐NE forecasts of annual energy for New England as a whole and for each individual state and load

zone is based on previous usage along with real electricity price, real personal income, gross state

product, and heating and cooling degree days.165 ISO‐NE developed the model and its coefficients by

analyzing the historical relationships between energy requirements and those independent variables

since 1984. Therefore, the forecast implicitly contains some level of reductions from efficiency programs

because the programs in effect during the historical period would have influenced the actual levels of

energy used, and would be reflected in the derived model coefficients, most likely for the personal

income and electricity price variables. Since 2008, the econometric models have sought to compensate

for those effects by explicitly accounting for energy efficiency in the load by representing them as

passive demand resources (PDR). Thus to a large degree the effects of energy efficiency programs are

excluded from the econometric forecasts. However, the econometric forecast results are adjusted for

the expected effects of federal energy efficiency standards.

ISO‐NE then produces an adjusted forecast representing actual system loads based on its expectations

of EE program effects. These EE effects are represented by ISO‐NE as PDR, and their levels are based on

what has cleared in the future capacity markets and future expectations. While this may not capture all

EE effects, it is a fairly complete and well‐documented value that we will use to adjust the load forecast

in the absence of new programs.  

For its forecast of peak load, ISO‐NE develops peak‐load models for each calendar month, for New

England as a whole, and for each state using daily historical data. The models are based on the annual

energy load, a temperature humidity index, and several dummy variables for weekends and holidays.

The historical and forecast loads are then explicitly modified by PDRs based on EE programs that

qualified in the capacity market. These resources are called passive because they cannot be dispatched,

but do have identified effects on loads and qualify as capacity resources.  

                                                            

163 We are using CELT 2012 for this study, as that report has been completed, and a full set of supporting documentation is

currently available. CELT 2013 was released on May 1, 2013, too late to be incorporated in a timely manner for this study.  

164 Further information about the CELT forecasting process can be found at ISO‐NE’s web page, http://www.iso‐

ne.com/trans/celt/fsct_detail/2012/index.html.

165 The CELT 2012 econometric models vary by state, as shown in the “prelim_ne_ene_model.xls” document on the above

website.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐4  

CELT 2012 includes explicit calculations of PDR effects to develop its estimates of system net peak and

energy loads. CELT 2012 estimates that PDRs would lower the New England summer peak (relative to

the econometric forecast) by 978 MW in 2012, 1,136 MW in 2013, and 1,398 MW in 2014. To represent

the 2013 year‐end value for existing EE programs, we use the average of the 2013 and 2014 values,

which is 1,267 MW. Then, going forward we project that those savings from the existing programs

would decline over a 20‐year period.

The forecast of annual energy load for AESC 2013 applies the same methodology as for the AESC peak

load forecast.  

The capacity requirements forecast excludes the PDR reductions, since those resources can now

participate in the capacity market.

Load Forecast Post 2021

Beyond 2021, we extrapolate using the CAGR from the last five years reflected in the CELT 2012

forecast. This represents a period of sustained economic growth in the drivers used to develop the

econometric forecast. For context, the summer peak growth (2012 – 2021) without the PDR effects is

1.46 percent, but when PDRs are included that rate comes down to 0.79 percent.  

The following two exhibits show historical and projected summer peak loads as well as the adjusted

peak load used for the AESC project excluding the effects of future EE programs. Note that the historical

values prior to about 2008 are actuals and represent the embedded effects of EE.

Exhibit 5‐1. ISO‐NE Summer Peak Load

  

20,000

22,000

24,000

26,000

28,000

30,000

32,000

2000200120022003200420052006200720082009201020112012201320142015201620172018201920202021

Historic Forecast

Summer Peak (MW)

Summer Peak ‐ Historic and CELT 2012

Econometric

With PDR
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐5  

Exhibit 5‐2. AESC 2013 New England Summer Peak Forecast

  

Energy‐ Efficiency Forecast Working Group

In 2012, ISO‐NE formed an Energy‐Efficiency Forecast Working Group (EEFWG) that produced an EE

forecast for 2015‐2021.166 The EEFWG identified EE savings beyond that incorporated in the CELT

report. To illustrate, the additional summer peak load reduction in 2021 was projected to be 1,444 MW

above the 2,841 MW PDR value used in CELT 2012.167 An updated version of that report released in

February 2013 for 2016‐2022 forecasts an additional summer peak load reduction of 1,353 MW by 2022.  

The EEFWG methodology is based on taking expected future expenditures in EE programs and then

projecting the energy savings based on actual previous and anticipated future program performance.

While these projections are useful for the purpose of forecasting future EE savings, it is not relevant for

the AESC 2013 forecast, which is based on loads without future EE program savings.

5.2.2 Transmission Assumptions

The interface limits used in the simulations reflect the existing system, ongoing transmission upgrades

including those discussed in the ISO‐NE 2012 Regional System Plan,168 related transmission planning

documents, and the reference Market Analytics database.  

Transmission‐path assumptions are based on those developed by Market Analytics for the Base Case

version 9.5. We have modified those assumptions based on ISO data and proposed projects to represent

                                                            

166 EEFWG documents are available at: http://www.isone.com/committees/comm_wkgrps/othr/enrgy_effncy_frcst/index.html  

167 Slide 32, “Final Energy‐Efficiency Forecast 2015‐2021,” April 12, 2012.

168 ISO‐New England. November 2, 2012. 2012 Regional System Plan. Available at: http://www.iso‐ne.com/trans/rsp/

20,000

22,000

24,000

26,000

28,000

30,000

32,000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Summer Peak (MW)

AESC 2013 New England Summer Peak Forecast

CELT Econometric

CELT with PDR

AESC
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐6  

future additions. These transmission assumptions, like our other resource assumptions, are not

intended to represent specific forecasts or projections, but a reasonable allowance for likely additions.  

The transmission system within Market Analytics is represented by links between transmission areas.

These links represent aggregated actual physical transmission paths between locations. Each link is

specified by the following variables:  

 “From” location,

 “To” location,

 Transmission capability in each direction,

 Line losses in each direction, and

 Wheeling charges.

Exhibit 5‐3 shows the AESC 2013 assumptions for transmission capabilities of each path between New

England zones and between New England and external areas as indicated in the Market Analytics

database, reconciled to the interface limits reported in recent ISO reports and transmission planning

documents.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐7  

Exhibit 5‐3. Existing Transmission Paths and Future Upgrades

Path

Type Name

"From"

Zone "To" Zone

Capacity

"From‐To"

(MW) Notes

Capacity

Back

(MW) Notes

Transmission Paths within New England

BHE‐ME BHE ME 1200 1050    

CMA‐BOSTON CMA‐NEMA BOST 3200 3000    

CMA‐NH CMA‐NEMA NH 912 925    

CMA‐WMA CMA‐NEMA WEMA 1360 2000    

CT‐RI

  

CT‐CNE

  

RI

  

720 720 (a) Part of CT import

1170 As of 1/1/2016 1170 (b) As of 1/1/2016

CTSW‐CT CT‐SW CT‐CNE 2000 3550

CTSW‐NOR CT‐SW CT‐NOR 1650 1650

MPS‐BHE MPS BHE 10 10    

NH‐BOSTON NH BOST 900 912    

NH‐SME NH SME

1400 1525 As of 1/1/2013

1400 As of 1/1/2015 1500 As of 1/1/2015

2400 As of 1/1/2016 2500 (c) As of 1/1/2016

NH‐VERMONT NH VT 720 715    

RI‐BOSTON RI BOST 400 400    

RI‐CMA RI CMA‐NEMA 1480 600    

RI‐SEMA

  

RI

  

SEMA

   1000

3000    

3300 (d) As of 1/1/2017

SEMA‐BOSTON SEMA BOST 400 400    

SME‐ME SME ME 1250 1250    

VERMONT‐WMA VT WEMA 875 875    

WEMA‐CT WEMA CT‐CNE 2880 As of 1/1/2013 710 (a) Part of CT import

Transmission Paths between

New England and External

Control Areas

BHE‐NBPC BHE NBPC 728 1000    

CMA‐HYQB (Phase II) CMA‐NEMA HYQB 1467 1600    

EMEC‐NBPC EMEC NBPC 20 20    

HYQB‐VT (Highgate) HYQB VT 214 170    

MPS‐NBPC MPS NBPC 100 90    

NOR‐NYZK CT‐NOR NYZK 300 300    

NYZD‐VERMONT NYZD VT 150 150 (e) part of NY‐NENG

NYZF‐WEMA NYZF WEMA 575 650 (e) part of NY‐NENG

NYZG‐CT NYZG CT‐CNE 700 300 (e) part of NY‐NENG

NYZK‐CT (CSC) NYZK CT‐CNE 346 330    

Notes   

(a) Connecticut import total of 2,500 MW distributed among several paths.   

(b) Interstate Reliability Project (IRP) or equivalent increase CT‐RI ties by 450 MW by 2016.    

(c)

Increased Maine interconnection associated with the Maine Power Reliability Project (MPRP) of 1000 MW in

2016.

(d) Per ISO‐NE projections on SEMA exports, based on NEEWS in‐service dates.    

(e) Based on NY ‐ New England import limit.      

A discussion of our major transmission assumptions is provided below.

The New England East‐West Solutions (NEEWS) transmission program consists of four major

components:  
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐8  

1) The Rhode Island Reliability Project (RIRP),  

2) The Greater Springfield Reliability Project (GSRP),  

3) The Interstate Reliability Project (IRP), and

4) The Central Connecticut Project (CCP).  

These components, when in service, will enhance several transmission interface limits, but most

importantly, the Connecticut import capability.  

 In the AESC 2013 Base Case, the interface limit between Western Massachusetts and

Central Connecticut is increased to 2,880 MW, reflecting the effect of the GSRP.  

 In 2016, we add an additional 450 MW between Connecticut and Rhode Island for

the IRP project. The GSRP required approval in two states; the IRP will apparently

require siting review in three states (Massachusetts, Rhode Island, and Connecticut).

Hence, 2016 appears to be a realistic in‐service date for the next phase of NEEWS for

our modeling purposes. The IRP project was recently approved by regulators in

Connecticut; studies are underway to evaluate the CCP project.

Most of the additional transfer capability into Connecticut (and on the East‐West and SE Massachusetts‐

Rhode Island export interfaces, as well) results from the IRP and CCP. These two projects were justified

primarily by the objective of meeting Connecticut’s load with combined generation and transmission

outages at times of extraordinary (once in ten years) high‐load conditions, even if more than 1,200 MW

of Connecticut generation is retired. Since the original analyses, Connecticut has contracted for over

1,500 MW of additional capacity, load forecasts have fallen, and the GSRP is expected to increase import

capacity, greatly reducing the prospect of shortfalls in the Connecticut transmission‐security analysis.  

AESC 2013 leaves the interface limits between Central Massachusetts and Hydro Quebec intact and does

not include the effects of the proposed Northern Pass project at this time. The Northern Pass project is

still in the preliminary phases of development, and there are uncertainties regarding the project

schedule and in‐service dates.  

AESC 2013 assumes a 1,000 MW increase in the transmission capacity between Maine and the rest of

New England, effective 2016. This assumption is based in part on estimates of the transfer effects in the

Maine Power Reliability Plan (MPRP). Additional transmission is also necessary to allow new renewable

resources access to load. Our preliminary modeling results indicate that if new capacity is not added,

then energy prices in Maine fall substantially below the rest of New England, which provides a strong

economic argument for increased interties.

5.2.3 Generating Unit Retirements

In general, AESC 2013 assumes that plants that have been operating since the implementation of

restructured markets will continue to operate in the absence of any major changes in market and
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐9  

regulatory conditions. AESC 2013 assumes that retirements of existing plants will be driven by the

following factors:

 Requirements for environmental retrofits due to regulatory changes. A discussion of changing

environmental and economic conditions that will drive retirements is presented in Chapter 4,

Embedded and Non‐Embedded Environmental Costs.  

 Failure of major components in old and marginally cost‐effective units. In these situations,

restoring the plant to service may not be cost‐effective. Component failure is inherently

unpredictable. Our assumptions about the retirement of older capacity reflect anticipated

effects of equipment failure.

 The expiration of nuclear, hydro, or other licenses for plants that cannot economically meet

requirements for license extension. We describe the relicensing of New England nuclear units in

Chapter 6, Avoided Electric Energy Costs. Relicensing of hydroelectric plants has resulted in

reduced capacity or retirement of a few small units; we do not anticipate any significant effects

on hydro capacity in the future.

 Construction of new capacity at the site of existing capacity, requiring retirement due to lack of

space, transmission capacity, or emission compliance. No pending capacity additions are

expected to drive retirements of existing units. When new generic units are added, some

existing units on those sites may retire. We assume that such additions will occur primarily at

sites with units that would be close to retirement for other reasons, so this factor will have little

or no effect on market prices.

The specific units that we assume will be retired are presented in Exhibit 5‐4, representing 7,400 MW of

fossil generation capacity. The basis for these assumptions is presented in the sections below. AESC

2013 treats retirements as occurring on January 1 of the relevant year. AESC 2013 retires about 10 MW

of old gas turbines annually.  

While most of the remaining steam‐electric units in New England have cleared in the FCAs through FCA

7, this does not imply that they are committed to remaining in service until May 2017.  
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐10  

Exhibit 5‐4. Fossil Unit Retirements for Energy Modeling

MW In‐service

date Fuel Retirement Age atretirement

Salem Harbor 3 150 Aug‐58 Coal 2014 56

Salem Harbor 4 437 Aug‐72 Oil 2014 42

Norwalk Harbor 1 162 Jan‐60 Oil 2016 56

Norwalk Harbor 2 168 Jan‐63 Oil 2016 53

Mt. Tom 143 Jun‐60 Coal 2016 56

Middletown 4 400 Jun‐73 Oil 2017 44

Montville 6 407 Jul‐71 Oil 2017 46

Wyman 1 52 Jan‐57 Oil 2017 60

Wyman 2 51 Jan‐58 Oil 2017 59

Bridgeport Harbor 3 383 Aug‐68 Coal 2017 49

Montville 5 81 Jan‐54 O/G 2020 66

Brayton 4 435 Dec‐74 O/G 2020 46

Canal 1 573 Jul‐68 Oil 2020 52

Canal 2 545 Feb‐76 Oil 2020 44

Wyman 3 116 Jul‐65 Oil 2020 55

Brayton 1 243 Aug‐63 Coal 2020 57

Brayton 2 244 Jul‐64 Coal 2020 56

Schiller 4 48 Apr‐52 Coal 2020 68

Schiller 6 48 Jul‐57 Coal 2020 63

New Haven Harbor 448 Aug‐75 O/G 2021 46

Mystic 7 578 Jun‐75 O/G 2021 46

Middletown 2 117 Jan‐58 O/G 2022 64

Middletown 3 236 Jan‐64 O/G 2022 58

Cleary 8 26 Jan‐66 Oil 2022 56

West Springfield 3 94 Jan‐57 O/G 2022 65

Wyman 4 603 Dec‐78 Oil 2022 44

Brayton 3 612 Jul‐69 Coal 2024 55

Merrimack 1 113 Dec‐60 Coal Not Retired

Merrimack 2 320 Apr‐68 Coal Not Retired

Newington 400 Jun‐74 O/G Not Retired

Vermont Yankee Retirement

The AESC 2013 Base Case assumes Vermont Yankee retires in 2015.

The Nuclear Regulatory Commission has granted Vermont Yankee a 20‐year license extension, but the

plant also requires state certification to operate beyond March 2012. According to Vermont law (30

V.S.A. § 248), the Vermont general assembly must vote affirmatively to allow the Public Service Board to

issue such certification; however, in February 2010 the state senate voted 26–4 against such issuance.

The plant owner, Entergy Nuclear Vermont Yankee, has sued to force the state to allow it to continue

operating the plant on the grounds that the state’s actions are preempted by federal authority over

nuclear safety issues. The plant currently operates under federal court order pending resolution of this

suit and, if Entergy is successful, certification by the Public Service Board. While we cannot predict with
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐11  

certainty the outcome of these proceedings, we assume a 2015 retirement as a reasonable proxy for

when the owner would have to come into compliance with Vermont law.

Retirements of Coal Plants

Six coal plants (consisting of ten units) are operating in New England. Four additional units have been

retired in recent years:

 Somerset 6 (100 MW, Massachusetts) did not clear in any of the FCAs and was not even

qualified in FCA 7.

 AES Thames (182 MW, Connecticut) shut down in 2011 in a contract dispute with its

steam host, entered bankruptcy, and was sold for dismantlement.

 Salem Harbor 1 and 2 (158 MW, Massachusetts) delisted in the third and fourth FCA

and retired permanently in June 2012.

Exhibit 5‐5 summarizes recent operating characteristics of most of the coal fired units in New

England.169  

Exhibit 5‐5. Recent Performance by New England Coal‐Fired Plants

MW In

Service

Controls Capacity Factor 2012

Heat

Rate

Typical

Energy Bid

$/MWh

FGD NOx PM Cooling

Tower

2012 2011 2010

Brayton 1 243 8/63 Y SCR FF Y 29% 41% 78% 10.5 $35

Brayton 2 244 7/64 Y SCR FF Y 18% 31% 79% 10.5 $35

Brayton 3 612 7/69 Y SCR FF Y 18% 38% 61% 9.8 $45

Bridgeport

Harbor 3

383 8/68 ESP 3% 14% 37% 11.4 $52

Mt. Tom 143 6/60 ESP 10% 9% 42% 12.1 $100

Merrimack 1 113 12/60 Y SCR ESP P 35% 59% 68% 11.5 $46

Merrimack 2 320 4/68 Y SCR ESP P 30% 50% 71% 10.7 $50

Schiller 4 48 4/52 SNCR ESP 11% 29% 54% 14.0 $50

Schiller 6 48 7/57 SNCR ESP 11% 26% 52% 13.9 $50

Notes:

From EIA Forms 860 & 923, EPA Air Markets Program database

FGD = Flue‐Gas Desulfurization (scrubber)

SCR = Selective Catalytic Reduction

SNCR = Selective Non‐Catalytic Reduction

FF = Fabric Filter

ESP = Electrostatic Precipitator

Y = Yes

P = Planned

Bridgeport 2011 and 2012 and Mt. Tom 2012 energy are gross output, overstating capacity factor and understating heat rate

                                                            

169 Information was not available for Salam Harbor 3.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐12  

The AESC 2013 Base Case assumes eight of the region’s remaining coal units will retire over the study

period.

 Salem Harbor 3 (150 MW, Massachusetts) submitted high bids for the third and fourth

FCAs but has been required to stay online for reliability until June 2014, at which point

it will retire.  

 Mt. Tom (143 MW, Massachusetts) has installed a scrubber, SCR, and a baghouse, but

is rather small and probably faces 316(b) issues with its withdrawal of cooling water

from the Connecticut River. The unit did not clear in FCA7, has been bidding into the

energy market at over $100/MWh, and has been operating at capacity factors at or

below 10 percent. We assume this unit retires in 2016.

 Schiller 4 and 6 (48 and 48 MW, New Hampshire) are small, old (1952 and 1957 in‐

service dates) units, but they are adjacent to the wood‐fired Schiller 5 and the large

gas‐ and oil‐fired steam unit Newington, which may reduce these units’ operating

costs. They have SNCR to reduce NOx emissions and a precipitator for particulates, but

no sulfur controls. In addition, the units are owned by Public Service of New Hampshire

and are rate‐regulated, so they are not under the same market pressures as other

plants. We assume the 2020 retirement data projected by PSNH (NHPUC Docket No.

11‐215, Record Request 1, 12/19/2011).  

 Bridgeport Harbor 3 (383 MW, Connectictut) has relatively low NOx emission rates

(about 0.14 lb/MMBtu in 2010–2012) for a coal plant, and a baghouse to control

particulate and mercury emissions, but does not have a scrubber or post‐combustion

NOx controls. The plant burns very‐low‐sulfur (0.8 percent S, 9200 Btu/lb), sub‐

bituminous coal from Indonesia. Bridgeport 3 operated at capacity factors up to the 80

percent range a few years ago, but in only the 30 to 40 percent range in 2009 and

2010, about 15 percent in 2011, and single digits in 2012, presumably due to lower gas

prices (and hence lower electric energy prices) and higher coal prices. 170 The high fuel

prices have also resulted in Bridgeport 3 bidding into the ISO energy markets at about

$50/MWh. Even at the high gas prices in December 2012, this unit ran at only about 9

percent capacity factor. The expected changes in the capacity markets would likely

render Bridgeport 3 substantially uneconomic, leading to its retirement. We assume

that retirement would occur in 2017.

 Brayton Point 1–3 (243, 244, and 612 MW, Massachusetts) has installed SCR,

scrubbers, baghouses, and cooling towers. The owner, Dominion, invested over a

billion dollars in the last few years to meet emission and water‐use requirements and

keep the plant in operation. However, a recent analysis suggests that Brayton’s cash

flow is very limited (Schlissel and Sanzillo).171 Dominion recently sold a package of

                                                            

170 In the first quarter of 2009, PSEG renegotiated its coal contract for Bridgeport Harbor with its Indonesian supplier, Adaro,

resulting in a 75 percent price increase. (PSEG Outlook, May 2009, pp. 1, 6)

171 Dominion recently sold Brayton Point (about 1,100 MW of coal, 435 MW of oil/gas capacity), the 1,158 MW Kincaid coal

plant in Illinois, and 712 MW of modern combustion turbine capacity at the Elwood plant in Illinois, for a total of $472
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐13  

assets including Brayton Point at a price consistent with attributing essentially zero

value to Brayton; the new owner, Energy Capital Partners, is not bothering to offer

Brayton Point as security for the loans supporting the purchase, again suggesting that

the plant has little value. The future of this plant remains uncertain; it may retire as

soon as 2017 or operate throughout the modeling period. As an intermediate case, we

include retirement of Brayton 1 and 2 in 2020 and Brayton 3 in 2024.  

 Merrimack 1 and 2 (113 and 338 MW, New Hampshire) share a scrubber and SCR, and

the owner, PSNH, has committed to adding a cooling tower. PSNH appears to be

committed to keeping the plant online and, given rate‐of‐return regulation, may well

succeed. We treat Merrimack as continuing to operate.

Retirements of Oil‐ and Oil‐and‐Gas‐Fired Steam Plants

We have less complete information on the older steam plants fired by oil and/or gas. None of these

plants are likely to be able to support the cost of major emissions controls.  

The AESC 2013 Base Case assumes the following steam units, which burn only residual oil, will retire

over the study period:  

 Bridgeport Harbor 2 (130 MW, Connecticut) has delisted for FCA 4 through FCA 6, and

did not qualify for FCA 7. We assume it is retired in June 2013.

 Cabot 6 and 8 (19 MW, Massachusetts), owned by the Holyoke Municipal Light Plant,

delisted in FCA 4 and beyond, and appear to be out of service.

 Salem Harbor 4 (437 MW, Massachusetts) burns only oil, and it is committed to

retirement in June 2014, along with Unit 3.

 Norwalk Harbor 1 and 2 (162 and 168 MW, Connecticut) burns only oil and has

reported very high O&M costs (both under regulation and in its RMR cost claim). The

plant has SNCR installed, and hence relatively low emissions but higher variable

operating costs than other oil units, and operated at less than 1 percent capacity factor

in 2012. These units cleared through FCA 6 (except for a play for higher RMR payments

in FCA 1), but delisted for FCA 7. Considering the changes in future capacity auctions,

we assumed that both units will retire in 2016.172  

                                                            

million (Dominion 10‐Q, 1Q2013, p. 17), a price consistent with attributing essentially zero value to Brayton. The new

owner, Energy Capital Partners, is not bothering to offer Brayton Point as security for the loans supporting the purchase,

again suggesting that the plant has little value. As a result of the bids for Brayton Point, Dominion wrote down its

investment in Brayton by $1.22 billion in December 2013 and Brayton and Kincaid by another $450 million in February

2013, bringing the book value of the “long‐lived assets” at these plants down to $216 M (Dominion Resources 2012 10‐K

Report, pp. 78, 127).

172 After we estimated the retirement schedule, the owner of Norwalk Harbor announced that the plant would be shut down in

June 2013. It has now been retired.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐14  

 Middletown 4 and Montville 6 (~400 MW each, Connecticut) are relatively large and

modern (early 1970s), and have moderate NOx emission rates, but burn only oil,

operate at low capacity factors (0.5 percent in 2012), and have particularly high heat

rates. They have cleared through FCA 7. We assume that they will be retired in 2017.

 Cleary 8 (26 MW, Massachusetts) is very small and has the highest NOx emission rates

in New England. It has cleared through FCA 7, and is owned by a municipal utility. We

assume that this unit will retire in 2022.

 Wyman 1–4 (50, 51, 115, and 603 MW, Maine) runs on higher‐sulfur, and hence less

expensive, fuel than other oil plants in New England (which generally burn 0.5 percent

sulfur oil in most Massachusetts plants, and 0.3 percent in Connecticut and at the Canal

units). As a result, they operate more often, even though they are in Maine, the zone

with the lowest market energy and capacity prices.173 Other than a requirement to

switch to 0.5 percent sulfur oil in 2018, Wyman does not appear to face any

environmental challenges. Maine, like New Hampshire, has not been subject to as

stringent NOx control regulations as southern New England. The Wyman units are

subject to 136(b). ISO‐NE determined in May 2009 that both Units 1 and 2 are needed

for reliability until completion of transmission upgrades in southern Maine. These units

have not filed above‐market delist bids, suggesting that their forward‐going costs are

less than the FCM prices through FCA 4, when the price paid to generation in Maine fell

to $2.336/kW‐month, or $28/kW‐year.

174 The four units all operated at about one

percent capacity factors in 2012. The operating costs reported by the regulated co‐

owners of unit 4 (PSNH and GMP) indicate that at least that unit’s non‐fuel O&M has

been about $13/kW in 2011 and $5/kW in 2012. The completion of the Maine Power

Reliability Project will apparently eliminate the reliability need for Wyman 1 & 2, and

NextEra (owner of Wyman 1–3 and 84 percent of Wyman 4) is considering sale of the

plant.175 We assume the retirement of Units 1 and 2 in June 2017. We assume that the

larger units will hold on somewhat longer, with Unit 3 retiring in 2020 and Unit 4 in

2022.

 Canal 1 (547 MW, Massachusetts) has installed selective catalytic reduction (SCR) and

operates with very low NOx emissions. Canal 1 has been the most efficient of the New

England oil plants, but was designed for baseload operation and does not follow load

well. When oil prices were more competitive with gas prices, this unit had relatively

high capacity factors, but in 2012, with low gas prices, its capacity factor was about 0.4

                                                            

173 This plant is also sometimes referred to as Yarmouth 1–4.

174 The Wyman owner has asserted that “Units No. 1 and 2 are not expected to realize any energy revenues in the foreseeable

future. Additionally, a bleak capacity revenue outlook makes it unlikely that the subject units will recover their full

operations and maintenance costs, and capital expenditures. Since it is not economically feasible to maintain the units, FPL

Energy is seriously contemplating retiring Units No. 1 and 2 in the near future.” (Request for Determination of Need for

System Reliability and Consideration of RMR Cost‐of‐Service Agreement for Wyman Units No. 1 and 2; December 11, 2008).

Despite these warnings, Wyman 1 & 2 have continued clearing with only market capacity prices.  

175 “NextEra Weighs Maine Peaker Sale,” Power Intelligence, March 25, 2013.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐15  

percent. We assume that the unit is retired in 2020, due to more stringent

performance rules for capacity and/or stricter limits on use of cooling water.  

The information we have regarding the remaining major dual‐fueled steam units is summarized below:

 New Haven Harbor (448 MW, Connecticut) is not as flexible as some other dual‐fuel

units, but it has moderate NOx emissions and capacity factors.  

 Middletown 2 and 3 (117 and 236 MW, Connecticut) have relatively low NOx

emissions, dual‐fuel capability, and high capacity factors for oil/gas units (although

those are still in single digits).

 Montville 5 (81 MW, Connecticut) has very low NOx emissions, dual‐fuel capability, and

relatively high capacity factors. The owner (NRG) has proposed converting the unit to

fire primarily biomass (at up to 40 MW) while retaining the ability to operate at full

capacity on natural gas or (if necessary) distillate oil.  

 Canal 2 (545 MW, Massachusetts) has installed selective non‐catalytic reduction (SNCR)

and has moderate NOx emissions. While it can burn gas, its capacity is reduced

substantially in gas‐only operation. Even with low gas prices, Canal 2 has tended to run

less than Canal 1. Like Canal 1, Unit 2 is subject to continuing proceedings with EPA

regarding compliance with 316(b) requirements.  

 West Springfield 3 (94 MW, Massachusetts) has moderately low NOx emissions and

relatively high capacity factors and does not appear to face any specific environmental

challenges. West Springfield did not clear in FCA 5 or 6, but did clear in FCA 7.

 Brayton Point 4 (435 MW, Massachusetts) has low NOx emissions, and shares the

cooling towers with the coal plants, but has operated at low capacity factors. It was

recently sold with the Brayton coal plants.

 Newington (400 MW, New Hampshire) has relatively high capacity factors, has been

allowed to burn higher‐sulfur oil than most New England plants, and does not appear

to face any special environmental challenges. It is owned by PSNH, which assumes the

unit will continue operating until 2039.

 Mystic 7 (406 MW, Massachusetts) has very low NOx emissions and moderate capacity

factors, and does not appear to face any environmental challenges.

Exhibit 5‐6 summarizes the capacity factors of the region’s oil‐ and gas‐fired steam plants over the last

few years.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐16  

Exhibit 5‐6. Recent Performance by Gas‐ and Oil‐Fired Steam Plants

Capacity Factor

Dual‐fuel?

MW In‐service

date 2012 2011 2010

Middletown 2 117 Jan‐58 6.9% 3.7% 12.7% Y

Middletown 3 236 Jan‐64 4.5% 2.1% 7.3% Y

Middletown 4 400 Jun‐73 0.5% 0.6% 1.8%

Montville 5 81 Jan‐54 1.5% 1.8% 7.4% Y

Montville 6 407 Jul‐71 0.5% 0.4% 1.4%

New Haven Harbor 448 Aug‐75 3.7% 2.8% 3.5% Y

Norwalk Harbor 1 162 Jan‐60 0.7% 1.0% 2.7%

Norwalk Harbor 2 168 Jan‐63 0.7% 1.0% 3.1%

Brayton Point 4 435 Dec‐74 2.2% 0.8% 1.1% Y

Canal 1 573 Jul‐68 0.4% 0.7% 2.2%

Canal 2 545 Feb‐76 0.1% 0.0% 0.4% Partial

Cleary 8 26 Jan‐66 0.5% 1.4% 1.6%

Mystic 7 578 Jun‐75 3.3% 2.0% 4.4% Y

Salem Harbor 4 437 Aug‐72 0.1% ‐0.3% 0.4%

West Springfield 3 94 Jan‐57 6.4% 4.0% 7.7% Y

Wyman 1 52 Jan‐57 1.1% 2.0% 2.2%

Wyman 2 51 Jan‐58 1.1% 1.3% 2.6%

Wyman 3 116 Jul‐65 1.0% 1.6% 2.4%

Wyman 4 603 Dec‐78 0.7% 1.1% 2.5%

Newington 1 400 Jun‐74 2.1% 3.6% 6.9% Y

Notes: From EIA Forms 860 & 923

West Springfield capacity factors are based on gross output

We assume for the purposes of this analysis that all the oil‐fired steam generation and a large portion of

the dual‐fueled steam generation will retire in the period 2017 through 2022, as changes in the capacity

markets make them less economic to operate. The specific units we model as retiring are listed in

Exhibit 5‐6 above.

5.2.4 Resource Additions

Over the course of the AESC 2013 study period, new generation resources will be needed in addition to

the existing mix of generating capacity in order to satisfy renewable portfolio standards, meet future

load growth, and respond to retirements. Since Market Analytics is not a capacity expansion model,

these additions are inputs to the model. Our assumptions regarding new capacity additions are

presented in the following sections.

Resource Additions to Meet Renewable Portfolio Standards

Specific renewable energy resource additions (including fuel cells in Connecticut) were based on

generation in the interconnection queues and other sources in the near‐term, and based on a supply

curve analysis in the longer term.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐17  

The Synapse project team found the operating characteristics of renewable generation units to be

reasonably consistent between the Market Analytics modeling inputs and the SEA analysis.  

Renewable Portfolio Standards in New England

Each New England state has adopted some form of Renewable Portfolio Standard (RPS) or renewable

energy goal. Connecticut, Maine, Massachusetts, New Hampshire, and Rhode Island each have a

mandatory RPS which requires either Renewable Energy Certificate (REC) purchases or alternative

compliance payments (ACPs) to demonstrate compliance. Vermont currently has a voluntary renewable

energy goal, with a legislatively driven option to convert to a binding RPS requirement if the voluntary

target is not met.176 There are no alternative compliance payments if these goals are not achieved.

While AESC 2011 assumed that Vermont would adopt a mandatory RPS target of 5 percent by 2017,

after consulting with the Study Group, the AESC 2013 analysis assumes that Vermont does not establish

a binding RPS during the study period. The region’s RPS requirements and eligibility criteria are

summarized, by state and by RPS sub‐category, in Appendix F.

The quantity of new or incremental renewables that will be added each year during the study period is

driven by the state RPS requirements. In particular, new renewable additions are driven by demand

from the “premium RPS tiers,” which consist of:

 The “Class I” tiers in Connecticut, Maine, Massachusetts, and New Hampshire;

 The “New” tier in Rhode Island;

 The “Class II” (solar) tier in New Hampshire;  

 The MA Class I Solar Carve‐Out;177 and  

 The NH Class I Useful Thermal Energy Carve‐Out.178  

Some states have also implemented additional requirements that specific percentages of energy be

provided by unconventional non‐renewable or efficiency resources. Two examples of such alternative

requirements are the Massachusetts Alternative Portfolio Standard (which includes combined heat and

power, flywheel storage, coal gasification, and efficient steam technologies) and the Connecticut Class III

RPS requirement (which includes CHP, conservation and load management, and waste heat or pressure

recovery).  

                                                            

176 The Vermont Public Service Board issued its first report on the "Progress Towards SPEED Goals" in January 2012 and

concluded that the state is making adequate progress toward meeting the goal of 20 percent by 2017. As such, no binding

RPS is yet required.  

177 The Massachusetts Solar Carve‐Out is a sub‐component of the MA Class 1 RPS target.

178 The Useful Thermal Energy Carve‐Out is a sub‐component of NH Class 1 and is defined as renewable energy delivered from

Class I sources that can be metered and for which fuel or electricity would otherwise be consumed.  
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐18  

It is important to note that while past experience has favored the creation of new or accelerated RPS

requirements, the delay or reduction of future RPS targets is also proposed and discussed from time to

time. For example, the New Hampshire legislature made a downward adjustment to the state’s RPS

targets during the 2012 session, and Connecticut policymakers are currently discussing the merits of

adjusting their RPS targets (and associated eligibility) between now and 2020.

Demonstrating Compliance with RPS Policies

With the exception of Vermont, all states require the use and retirement of NEPOOL Generation

Information System (GIS) certificates in order to demonstrate RPS compliance.179 In the marketplace

where this commodity is traded, NEPOOL GIS Certificates are often referred to as Renewable Energy

Certificates (RECs). While the definition of a GIS Certificate is narrower than that of a REC, the two terms

are used interchangeably and their reciprocal meaning is commonly understood.  

For AESC 2013, we assumed full compliance with established RPS requirements via one of two possible

mechanisms. First, entities subject to RPS requirements are expected to comply primarily through the

acquisition and retirement of GIS Certificates/RECs. In the alternative, an obligated entity can comply

with RPS requirements by making an Alternative Compliance Payment (ACP).180 ACP levels have been

set at prices above the minimum REC price level expected to be necessary to allow plants to be financed

and built. Because the ACP exists as a valid form of compliance, actual non‐compliance with RPS

requirements will be extremely rare. In other words, if the market is short on supply, there is a valid

alternative route to comply. Given these options, we expect load‐serving entities to comply each year,

particularly since regulators have the authority to impose penalties or ultimately withdraw the load‐

serving entity’s and/or generator’s right to participate in the RPS market. The rate at which the ACP is

set—which is not uniform across the New England states—will, however, influence the manner in which

compliance is achieved. All else equal (e.g., in the absence of bilateral contracts or asset ownership

which would dictate otherwise), states with lower ACPs (CT and NH) will tend to receive more

Alternative Compliance Payments than REC compliance during periods of shortage, while RECs flow to

markets where the ACP and REC prices are higher.

                                                            

179 Currently, Vermont’s voluntary goal allows the contracting utilities to resell the RECs associated with SPEED‐qualifying

facilities to other load‐serving entities in satisfaction of RPS obligations in other New England states. Therefore, Vermont’s

policy does not lead to incremental renewable energy additions beyond what would be predicted in the presence of other

states’ requirements. It has been argued, however, that Vermont’s policy supports financing and therefore leads to

incremental renewable capacity and therefore less regional reliance on Alternative Compliance Payments.  

180 The Class 1/New Renewable ACPs in Massachusetts, Rhode Island, and Maine are harmonized. For these states, the 2013

ACP is $65.27/MWh, and escalates with the Consumer Price Index (CPI) thereafter. New Hampshire recently parted

company from this group and now has an ACP of $55/MWh in 2013 with annual escalation at 1⁄2 of CPI. In Connecticut, the

penalty for non‐compliance is set at $55/MWh, with no annual escalation. While it is called a penalty payment rather than

ACP in Connecticut, its effect is the same and it is often referred to as an ACP, which has become the generic term in the

industry.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐19  

Gross and Net Demand for Renewable Resources

The gross demand for new renewable generation resources is derived by multiplying the load of

obligated entities (those retail load‐serving entities subject to RPS requirements, often excluding public

power) by the applicable annual RPS percentage target for the RPS tier.

The net demand for incremental renewable generation within New England is derived by subtracting

from the gross demand: (a) existing eligible generation already operating (including biomass co‐fired at

existing fossil‐fueled facilities); and (b) the current level of RPS certified imports.

Over time, the net demand to be met by resources within ISO‐NE will be further reduced by an estimate

of additional RPS‐eligible imports over existing tie lines, phased in towards a maximum level of usage

(consistent with competing uses of the lines and appropriate capacity factors of imported resources) at

a rate consistent with the recent historical rate of increase in RPS‐eligible imports over a ten‐year

period.

Renewable resources eligible to satisfy state RPS requirements have considerable overlap, but vary by

state. With the exception of Maine, AESC 2013 assumes that renewable resources eligible in one or only

a few states are insufficient to completely fulfill the demand of the limited states in which they are

eligible. This means that beginning in the first year of the study period (2013), we assume that all states

other than Maine are competing at the margin to satisfy their requirements for new renewables, other

than the solar tiers, from the same group of eligible supply resources. Maine’s inclusion of refurbished

biomass facilities in its Class I RPS creates the potential that the state will remain in RPS surplus until at

least 2022—after taking into account the limited ability to bank state RPS compliance. In this scenario,

Maine Class I REC prices would be expected to remain suppressed throughout this period and separated

from REC prices in the rest of the region. As such, the Maine RPS may encourage a modest amount of

retooling at existing biomass facilities, but is unlikely to spur the development of incremental renewable

energy supply. Connecticut’s current eligibility definitions also allow for certain biomass supply to be

uniquely eligible in Connecticut, but its RPS targets have increased at a pace such that this supply is now

sub‐marginal. In the near term (from 2013 to 2017), we assume that the aggregate net demand for new

RPS supply will be met by a mix of renewable resources consistent with: (1) RPS‐eligible resources in the

New England administered systems and Maine Public Service interconnection queues, plus (2) other

expected RPS‐eligible generation in the development pipeline, which has not entered the queue. This

includes both large projects that have not yet filed for interconnection studies, and distributed wind,

solar, and fuel cell projects, which—due to their size—are not required to go through the large

generator interconnection process. Due to the increasing expense of entering and maintaining a position

in the interconnection queue, some proposed projects must delay this stage of the process until early

site evaluation and permitting progress has been sufficient to attract substantial development capital.  

The Impact of Policy Uncertainty on RPS Supply

In some cases, the development and interconnection processes are also delayed by regulatory

uncertainty. Examples of such uncertainty are available in each state in today’s market—making the

regional RPS marketplace increasingly complex and challenging for developers and investors. The critical
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐20  

example in today’s market is Connecticut’s seemingly perennial legislative discussion regarding whether

or not to adjust RPS eligibility criteria, RPS demand targets, or both. Connecticut legislators and

regulators both are considering these issues as this study is being drafted. In Vermont, after receiving a

detailed proposal from the Public Service Board for an RPS substantiated by REC retirement, the

legislature firmly defeated the RPS bill and continued to support the resale of RECs associated with

SPEED program resources into other New England RPS markets. During the 2012 legislative session, New

Hampshire reduced its Class I RPS targets and lowered the ACP to $55/MWh for 2013. In the 2013

session, the NH legislature has proposed to reduce the Class III RPS target and to return the ACP to its

previous level (which would restore consistency with MA and RI). The Class III bill had passed the House

but had not cleared the Senate when this report was drafted. As previously described, Maine has

introduced the potential for a flood of RECs eligible only in Maine through its Class I refurbishment

provisions. Because these RECs would come from existing facilities, Maine’s role in encouraging new

renewable energy supply would be greatly reduced. Finally, the MA DOER recently revised the RPS‐

eligibility of biomass generators and feedstock. After a lengthy stakeholder and rule promulgation

process (which delayed the development of nearly all of the region’s proposed biomass projects), DOER

and the legislature approved new regulations which effectively foreclosed the MA RPS market to new

biomass through efficiency requirements that are expected to be unattainable by all but small combined

heat and power facilities.  

Additional Assumptions for Renewable Energy Supply

All proposed generators for which information has entered the public domain are included in this

analysis. This generation is derated to reflect the likelihood that not all proposed projects will ultimately

be built, and may not be built on the timetable reflected in the queue. This information is grouped by

load area as an input to the Market Analysis model.

For the longer term (generally after 2017), we estimate the quantity and types of renewables that will

be developed using a supply‐curve approach based on resource potential studies. In this approach,

potentially available resources are sorted from least to greatest REC premium required to attract

financing. This approach identifies the incremental resources required to meet net incremental demand

in each year through 2028.

The one exception to this methodology is solar PV. We assume that resource is developed in proportion

to various state policies intended to promote solar, including solar RPS tiers and other factors. In AESC

2013, we assume that the MA Solar Carve‐Out (a sub‐set of MA Class I) reaches its 400 MW target in

2014 and that the policy is expanded—either through a supplementary or parallel program—to require

an additional 600 MW (for 1 GW total), which we assume is installed by 2019. Opt‐in terms for the initial

400 MW are expected to expire in approximately 2022, and assuming that the same market design

principles are applied to the next 600 MW, the opt‐in terms for this supply are expected to expire in

approximately 2024. Beginning in 2023, we assume that the Solar Carve‐Out begins to sunset into MA

Class I at the same rate as it ramped up, reaching zero carve‐out shortly after the study period ends.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐21  

Planned Additions and Uprates

The non‐renewable generation resources used as inputs to our simulations are drawn from the

capacities in CELT 2012. So far as we have determined, the only planned non‐renewable generation

addition with a specific construction schedule and a high probability of completion is the Footprint

Power project, a 670 MW combined‐cycle facility on the site of the retiring Salem Harbor power plant.

This project cleared in FCA 7 and locked in a capacity rate of $15/kW‐month for the first five years, so it

has high incentives to reach commercial operation by June 2016. 181

Demand‐Response Resources

Demand Response (DR) resources participate in the FCA. For simulation purposes we start with the

quantities of DR that cleared in FCA 7 and project a modest decline in DR capacity in response to the

changes in market structure in future FCAs. DR resources, when dispatched, affect energy prices

primarily in peak hours.

Generic Non‐Renewable Additions

In order to reliably serve the forecasted load in the mid‐ to long‐term portion of the forecast period, the

model adds new generic additions as needed. New generic non‐renewable resources were added to

meet any residual installed capacity requirements after adding planned and RPS additions. We

developed our assumptions regarding the quantity, type, and timing of these generic additions in

coordination with our simulation of the FCM because revenues from FCA prices help support those

investments.

Based on the mix of resources in the interconnection queue, and the constraints on construction of new

coal or nuclear units in New England in the foreseeable future, we assumed generic additions consisted

of a 50/50 mix of capacity from gas/oil‐fired 375‐MW combined‐cycle (CC) units and 180‐MW

combustion turbines (CT). No coal or nuclear units were added.  

New resources were dispersed geographically based on a combination of zonal need and historical zonal

capacity surplus/deficit patterns. Maine’s surplus of capacity, low energy prices, and export constraints

tended to suppress development of new generic capacity in that zone. The locational markets for energy

and forward reserves tended to provide incentives to build new generation in import‐constrained zones,

principally Connecticut.

5.2.5 Environmental Regulations

Assumptions regarding environmental regulations are discussed in Chapter 4, Embedded and Non‐

Embedded Environmental Costs.  

                                                            

181 The financing of the Footprint is not in place, and it is possible that the plant will not be built without a purchased‐power

agreement. The DPU rejected a PPA with the Massachusetts utilities in DPU Docket No. 12‐77.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐22  

5.2.6 Wholesale Risk Premium

The retail price of electricity supply from a full‐requirements fixed‐price contract over a given period of

time is generally greater than the sum of the wholesale market prices for energy, capacity, and ancillary‐

service in effect during that supply period.

This premium over wholesale prices, or wholesale risk premium, is attributable to various costs that

retail electricity suppliers incur in addition to the cost of acquiring wholesale energy, capacity, and

ancillary‐service at wholesale market prices. These additional costs include costs incurred to mitigate

cost risks associated with uncertainty in charges that will be borne by the supplier but whose unit prices

cannot be definitely determined or hedged in advance. These cost risks include costs of hourly energy

balancing, transitional capacity, ancillary services, and uplift.  

The larger component of the risk is the difference between projected and actual energy requirements

under the contract, driven by unpredictable variations in weather, economic activity, and/or customer

migration. For example, during hot summers and cold winters, load‐serving entities (LSEs) may need to

procure additional energy at shortage prices, while in mild weather they may have excess supply under

contract that they need to “dump” into the wholesale market at a loss. The same pattern holds in

economic boom and bust cycles. In addition, the suppliers of power for utility standard‐service offers

run risks related to migration of customer load from utility service to competitive supply (presumably at

times of low market prices, leaving the supplier to sell surplus into a weak market at a loss) and from

competitive supply to the utility service (at times of high market prices, forcing the supplier to purchase

additional power in a high‐cost market).

AESC 2013 applies the same wholesale risk premium to avoided wholesale energy prices and to avoided

wholesale capacity prices.182 Estimates of the appropriate premium range from less than 8 percent to

around 10 percent, based on analyses of confidential supplier bids, primarily in Massachusetts,

Connecticut, and Maryland, to which the project team or sponsors have been privy. Short‐term

procurements (for six months or a year into the future) may have smaller risk adders than longer‐term

procurements (upwards to about three years, which appears to be the limit of suppliers’ willingness to

offer fixed prices). Utilities that require suppliers to maintain higher credit levels will tend to see the

resulting costs incorporated into the adders in supplier bids.  

                                                            

182 Capacity costs present a different risk profile than energy costs. With the advent of the Forward Capacity Market, suppliers

have a good estimate of the capacity price three years in advance and of the capacity requirement for any given set of

customers about one year in advance. (Reconfiguration auctions may affect the capacity charges, but the change in average

costs is likely to be small.) On the other hand, since suppliers generally charge a dollars‐per‐MWh rate, and energy sales are

subject to variation, the supplier retains some risk of under‐recovery of capacity costs. There is no way to determine the

extent to which an observed risk premium in bundled prices reflects adders on energy, capacity, ancillary services, RPSs,

and other factors. Given the uncertainty and variability in the overall risk premium, we do not believe that differentiating

between energy and capacity premiums is warranted under this scope of work. We thus apply the retail premium uniformly

to both energy and capacity values.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐23  

In the absence of robust information on the retail premium implicit in the prices being bid for retail

supply in New England, we assumed a 9 percent premium as a default risk premium. The risk premium is

a separate input to the avoided‐cost spreadsheet. Therefore, program administrators will be able to

input whatever level of risk premium they feel best reflects their specific experience, circumstances,

economic and financial conditions, or regulatory direction.

The details of the risks and costs of serving load are somewhat different for Vermont, Public Service of

New Hampshire (PSNH), and various municipal utilities, where vertically integrated utilities procure

power from owned resources and a variety of long‐ and short‐term contracts. For Vermont, we have

included the 11.1 percent risk premium mandated by the Vermont Public Service Board. For PSNH and

the municipal utilities, program administrators should use a risk premium less than the 9 percent

premium default.

5.3 Additional Assumptions Specific to Electric Capacity

5.3.1 Results of Forward Capacity Auctions  

As noted above, revenues from FCAs will influence decisions regarding continued operation of existing

generating units and investments in new generating units.

5.3.2 Reserve Margin Requirements

The New England ISO acquires sufficient capacity to ensure reliability in each power year. In the FCM,

the absolute cost of that capacity equals the required capacity, i.e., the installed capacity requirements

(ICR) times the FCA auction price. The percentage by which the ICR exceeds the projected system peak is

the reserve margin. Based on the average requirement for the auctions in 2011 and 2012, we have

projected future reserve requirements of 17.2 percent from 2017 onward, as described in section 5.6.

5.3.3 Reliability Contracts

In the past, ISO‐NE granted special reliability‐must‐run (RMR) contracts to a set of power plants. The ISO

determined that these plants needed to continue to operate in order to ensure reliability—typically

because of their unique location—but that they would not be economically viable based solely upon the

revenues from then‐current market prices. The prices in the RMR contracts covered the plants’ variable

production costs (e.g., operations and maintenance) as well as their fixed costs (mostly capital).

All of the RMR contracts have expired—the last of them on June 1, 2010. A few units have received

special reliability contracts in connection with transmission constraints in the FCAs, the last of which

appear to be Salem 3 and 4, which appear to be eligible to receive $5.005/kW‐month in 2013/14. Since

future FCAs will allow for different prices in each of the eight pricing zones (western, northeastern, and

southeastern Massachusetts, and the five other states), future reliability contracts are unlikely, although

they may be required for very local supply conditions.  
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐24  

Lower loads and other system changes allowed the ISO to cancel above‐market payments for Norwalk

Harbor 2 in FCA 1 and Vermont Yankee in FCA 4. It thus appears that some of the costs of reliability

contracts have been avoidable. Additional reliability contracts may have been avoided by load

reductions that have already occurred, or are reflected in the demand resources bid into the FCAs.

Continuing reductions may avoid reliability contracts for other generators that may seek to delist in

future years.  

5.3.4 Other Wholesale‐Load‐Cost‐Components

In addition to the locational marginal energy prices and capacity prices, the ISO‐NE monthly “Wholesale

Load Cost Report” includes the following cost components:

 First‐Contingency Net Commitment Period Compensation (NCPC),

 Second‐Contingency NCPC,

 Regulation (automatic generator control),

 Forward Reserves,

 Real‐Time Reserves,

 Inadvertent Energy,

 Marginal Loss Revenue Fund,

 Auction Revenue Rights revenues,

 ISO Tariff Schedule 2 Expenses,

 ISO Tariff Schedule 3 Expenses, and

 NEPOOL Expenses.

These cost components are described in more detail in the Wholesale Load Cost Reports, available from

the ISO’s website, www.isone.com.

None of these components vary clearly enough with the level of load to warrant inclusion in the

avoided‐cost computation. More specifically:

 The NCPC costs are compensation to generators that comply with ISO instructions to

warm up their boilers, ramp up to operating levels, remain available for dispatch,

possibly generate some energy, and then shut down without earning enough energy‐ 

or reserve‐market revenue to cover their bid costs. Older boiler plants may take many

hours to reach full load and have minimum run‐times and shut‐down periods, requiring

plants to continue running at minimum levels overnight. Smaller loads would tend to

reduce the need for bringing these plants into warm reserve, thus reducing NCPC costs.

On the other hand, lower energy prices would tend to increase the net compensation

due to these units when they were required, since they would earn less when they
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐25  

actually operated. Hence, while energy efficiency may affect NCPC costs, the direction

and magnitude of the effects are not clear.

 Regulation costs are associated with units that follow variations in load and supply in

the range of seconds to a few minutes. Reduced load due to efficiency is likely to result

in reduced variation in load (in megawatts per minute), reducing regulation costs. On

the other hand, some controls may increase regulation costs, if end‐use equipment

responds more quickly to changing ambient conditions. Overall, energy efficiency

programs will probably reduce regulation costs, but we cannot estimate the magnitude

of the effect.

 Forward and real‐time reserve requirements should decrease slightly with energy

efficiency, for two reasons. First, lower load will tend to leave more available capacity

on transmission lines, which will tend to reduce the need for local reserves. Second, a

portion of real‐time reserves are priced to recover forgone energy for units that remain

in reserve; lower energy prices will tend to depress reserve prices. We expect that

these effects would be small and difficult to measure.

 Inadvertent energy exchanges with other system operators (NY ISO, Hydro Quebec,

and New Brunswick) are small and probably not affected by energy efficiency.

 The Marginal Loss Revenue Fund returns to load the difference between marginal

losses included in locational energy prices and the average losses actually experienced

over the pool transmission facilities. That fund is—by definition—generated by infra‐

marginal usage, and will not be affected by reduction of loads at the margin.

 Auction Revenue Right revenues are generated by the sale of Financial Transmission

Rights (FTR), to return to load the value of transfers on the ISO transmission facilities.

To the extent that efficiency programs reduce energy congestion, the value of these

rights will tend to decrease.

 Expenses (ISO Tariff Schedules 2 and 3 and NEPOOL) are largely fixed for the pool as a

whole, although a portion of the ISO tariffs are recovered on a per‐MWh basis. Some of

the ISO costs may decrease slightly as energy loads decline, if that leads to a reduction

in the number of energy transactions, dispatch decisions, and other ISO actions

required. Any such effect is likely to be small and slow to occur, and energy‐efficiency

programs add their own costs in load forecasting, resource‐adequacy planning, and

operation of the FCM.

5.4 Wholesale Market Prices for Electric Capacity

The capacity markets previously operated by ISO‐NE were superseded in June 2010 by the FCM. The

power year for the FCM, also referred to as an FCM year, is from June through May. Thus, the calendar

year unit cost of capacity (expressed as dollars per kW‐year) is the average of January through May from

one power year, and June through December of the following power year.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐26  

Under the FCM, ISO‐NE acquires sufficient capacity to satisfy the installed capacity requirement (ICR) it

has set for a given power year through a Forward Capacity Auction for that power year.

183 The price for

capacity in that power year is based upon the results of the FCA for that year. The FCA for each power

year is conducted roughly three years in advance of the start of that year. ISO‐NE has held seven FCAs to

date, plus annual reconfiguration auctions (ARAs) every year or so between the FCA and the start of the

power year. Exhibit 5‐7 summarizes the auction megawatt requirements and supply to date. Exhibit 5‐7

also provides the local supply requirement (LSR) for the import‐constrained sub‐regions (Connecticut

and northeastern Massachusetts) that the ISO modeled and the maximum capacity limit (MCL) for

export‐constrained Maine.  

Exhibit 5‐7. Summary of ISO‐NE Capacity Requirement and Supply (MW)

Local Supply

Requirement  

Maximum

Capacity

Limit Load Forecast

Capability Year ICR

Net

ICR CT

NEMA/

Boston ME

Summer Peak

Forecast

Year of Load

Forecast   Filing Date

2016 FCA 7 34,023 32,968 7,603 3,209 3,709 29,400 2012 6‐Nov‐2012

2015 FCA 6 34,498 33,456 7,542 3,289 3,888 29,380 2011 3‐Jan‐2012

2015 1st ARA 33,813 32,771 7,402 3,036 3,818 28,840 2012 30‐Nov‐2012

2014 FCA 5 34,154 33,200 7,478 3,046 3,702 29,025 2010 8‐Mar‐2011

2014 2nd ARA 33,163 32,209 7,262 2,917 3,682 28,275 2012 30‐Nov‐2012

2013 FCA 4 33,043 32,127 7,419 2,957 3,187 28,570 2010 4‐May‐2010

2013 2nd ARA 33,463 32,547 7,489 3,118 3,584 28,525 2011 30‐Nov‐2011

2013 3rd ARA 32,550 31,552 7,310 2,799 3,632 27,765 2012 30‐Nov‐2012

2012 FCA 3 32,879 31,965 6,640 2,019 3,257 29,020 2009 7‐Jul‐2009

2012 2nd ARA 32,841 31,927 7,284 2,718 3,517 28,165 2010 1‐Dec‐2010

2012 3rd ARA 32,987 32,010 7,312 3,013 3,707 28,095 2011 30‐Nov‐2011

2011 FCA 2 33,439 32,528 6,817 2,016 3,395 29,405 2008 9‐Sep‐2008

2011 2nd ARA 32,652 31,741 5,666 1,956 3,140 28,575 2009 2‐Feb‐2010

2011 3rd ARA 32,463 31,552 7,244 2,668 3,406 27,660 2010 1‐Dec‐2010

2010 FCA 1 33,705 32,305 7,017 2,246 3,855 29,035 2007 11‐Oct‐2007

2010 2nd ARA   33,537 32,137 6,737 1,990 3,725 28,955 2008 30‐Jan‐2009

2010 3rd ARA 32,510 31,110 6,496 1,838 3,697 28,160 2009 15‐Dec‐2009

                                                            

183 Some of the ICR (varying from 911 MW in the fourth FCA to 1,055 MW in the seventh FCA) was met by installed capacity

credits from the Phase I/II interconnection, which are allocated to the transmission owners with entitlements in the line.

The Hydro Quebec Interconnect Certificate rights are valued at the market‐clearing price, and the actual auction acquires

the remaining ICR, called the net ICR or NICR.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐27  

Through FCA 7, each FCA had a ceiling price and a floor price. Each of these first seven auctions

concluded when the price in the principal pricing zone (Rest of Pool, or ROP) reached the floor price,

although the amount of capacity offered at that price still exceeded the requirement. Existing resources

have been allowed to withdraw capacity from the auction in four ways:  

 In a dynamic bid during the auction, once the price falls below a preset level (typically

$4 to $5/kW‐month);

 In a static bid offered prior to the auction, at a price justified by the costs of continuing

to operate the resource;

 In a permanent delist bid, which is similar to a static bid but, if the bid is accepted, the

resource is permanently delisted; or

 In a non‐price bid, which requests permission from ISO‐NE to terminate the resource

regardless of price.

The floor price for FCA 7 was $3.15/kW‐month. Since more capacity cleared at the floor price than was

required to satisfy the ICR, each cleared resource was required to choose between downward proration

of the quantity of capacity that it bid or downward proration of the final auction price. For example,

when the capacity clearing was roughly 6 percent above the net ICR in FCA 1, each resource chose

between being paid 94 percent of the floor price (about $4.23 in FCA 1) for all its bid capacity, or the

floor price for 94 percent of its bid capacity.184 Exhibit 5‐8 summarizes the market‐clearing prices and

the payment rates for auctions to date. The payment rate for each auction is listed for the rest of pool

(ROP), the zones for which no constraints were binding. Where the payment rate differed among zones,

Exhibit 5‐8 includes the prices for the zones that varied from the ROP price.

Exhibit 5‐8. FCA Price Results ($/kW‐month)

FCA Year Clearing

Price

Payment Rate

ROP ME CT NEMA

4 2013 – 2014 $2.951 $2.516 $2.336

5 2014 – 2015 $3.209 $2.855

6 2015 – 2016 $3.434 $3.129

7 2016 – 2017 $3.150 $2.744 $2.744 $2.883 $6.665

Suppliers of capacity whose bids are accepted in the FCA are paid an amount equal to the quantity of

capacity they bid multiplied by the final prorated auction price. In each month of the capacity year, this

amount is reduced by peak energy rents (PER), an estimate by ISO‐NE of the annual wholesale energy

                                                            

184 Emergency generation and resources in Maine have been subject to additional constraints and proration in some years, and

the Northeastern Massachusetts (NEMA) zone cleared with a price higher than the ROP price in FCA 7.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐28  

market revenues that a hypothetical generator with a heat rate of 22,000 Btu/kWh would earn.

185

Suppliers are also subject to penalties for any failure to meet the ISO’s tests for capacity in the power

year.

The buyers of capacity, i.e., load‐serving entities, pay an amount approximately equal to the quantity of

capacity ISO‐NE requires them to support in the power year times the auction‐clearing price for that

power year.

186 The quantity of capacity that a particular load is required to hold in the power year is set

by ISO‐NE and is called the Capacity Load Obligation (ISO‐NE Market Rule 1 §III.13.7.3). This obligation is

based on the estimated contribution of that load to the ISO annual peak in the preceding power year.

Thus, the total cost of capacity to a load‐serving entity for a given power year, i.e., required kW of

capacity multiplied by FCA price in dollars per kW, is mostly set in advance of that power year. The price

is mostly determined over three years in advance of the power year, and each load’s individual share of

the cost is set to the summer preceding the power year.

5.4.1 Proposed Changes to the Forward Capacity Market  

A number of important changes are pending in the Forward Capacity Market that will affect the price of

capacity to consumers and the payments for capacity for generators and other resources. In the eighth

FCA, the floor will no longer exist, and dynamic delist bids will only be allowed when the price falls under

$1/kW‐month. Any existing resource that wished to delist at a higher price would have to submit a static

delist bid (or permanent delist bid, if it wished to retire) and justify the economics of the delisting prior

to the FCA. Depending on the stringency of the ISO’s review of the resource’s economics, delisting may

be difficult. Under those circumstances, if a capacity surplus persists, the capacity price could fall

dramatically in FCA 8.

In addition, the ISO has proposed that, starting in June 2018 (FCA 9), resources should be penalized for

not being online when the system experiences a shortfall in operating reserves, an event that might

happen 25 or 30 times annually. Some of those events would be on high‐load days, when all generation

is operating or ready to operate (other than generation on forced outage). Any power plant (baseload,

peaker, cycling steam) faces similar risks of penalties on those days, which can be mitigated by bidding

in less than the full capacity of the resource, so that it earns enough rewards on days it is available to

compensate for its outages.187 But some events could occur on low‐load days, and even in off‐peak

hours, depending on outages and other factors, which would present very serious problems for plants

that are not online at those times and are not able to start quickly. The ISO has proposed (at least in its

                                                            

185 Our analyses do not adjust for PER as it appears to be minimal. In 2012, PER was above zero for only one hour, 1/28/2013

HE18, for an adjustment of no more than 16¢/kW.  

186 These costs are reduced by the PER and credits for supplier performance penalties, as well as by adjustments due to

reconfiguration auctions (in which the ISO can buy back unnecessary capacity obligations, or purchase additional

obligations). Load‐serving entities can also self‐supply a portion of their capacity requirements.

187 It is not clear how much flexibility the eventual rules will give the resources, but it seems likely that they would be able to

derate enough to reflect their forced‐outage rates.  
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐29  

examples) very high penalty rates for non‐ or under‐performance (and corresponding reward for

resources that operate above their capacity obligations), which would have a number of effects on the

behavior of existing resources, including these three:

 Resources that are not online in most hours and cannot come online on short notice

will not be able to earn any capacity revenues. This is not a problem for baseload plants

(e.g., nuclear, biomass, waste‐to‐energy, industrial cogenerators), which are usually

online; peakers (which can ramp up rapidly); hydro (which ramps quickly); or combined‐

cycle plants (which are generally online in most hours, and generally ramp quickly).

However, it would be a serious problem for the steam‐electric plants that do not

operate baseload, including those fueled with residual oil, natural gas, and in recent

years, coal. The loss of the capacity revenues may lead to the retirement of many of

these plants.

 Even resources that could operate profitably in the performance‐based system would

have an incentive to understate their capacity to minimize penalties and maximize

rewards. While the ISO would likely attempt to block these strategic deratings, some

erosion of cleared capacity is likely.188

 Due to the surplus of capacity that has cleared at the floor price, and the fact that much

of that capacity is then prorated, there is a substantial amount of uncommitted capacity

that can participate in the reconfiguration auctions. The prices in the annual

reconfiguration auctions have been far below the FCA prices, on the order of $0.50–

$1/kW‐month. Under these circumstances, there is little to be lost and much to gain by

clearing a resource’s capacity in the FCA, purchasing replacement capacity, and retiring

the resource.

189 Starting in FCA 8, this strategy will no longer be effective, potentially

triggering decisions to delist and retire resources.

The ISO’s proposals are still largely conceptual. A large amount of negotiation, drafting and vetting of

rules, and litigation would need to occur before the performance‐based system comes into effect. We

doubt that the proposal will be implemented in time for FCA 9 in early 2015. For the purposes of this

analysis, we have assumed that some variant on the ISO proposal is phased in between 2020 and 2022,

roughly FCAs 11 through 13. This reflects other delays in the evolution of the FCM, such as the extension

of the price floor (originally established for only the first three FCAs) to seven FCAs. While we do not

know the form of the eventual changes, we assume that the revised FCA will result in delisting of

significant amounts of supply resources and demand response.

                                                            

188 It is not clear how energy‐efficiency resources, which are not metered on an hourly basis, would be affected by this scheme.

189 For example, NRG’s Norwalk Harbor 1 and 2 cleared in FCA 4, 5, and 6, but was retired June 1, 2013. It is likely that NRG will

be able to find replacement capacity from its other prorated units and from other suppliers for much less than the prices it

will be paid for clearing in the FCAs.  
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐30  

The ISO’s External Market Monitor has also suggested that it may be appropriate for the ISO to

implement an administrative “demand curve,” similar to that used to artificially maintain capacity prices

in PJM and the NYISO.190 An administrative demand curve increases the amount of capacity purchased

as price falls, in a manner similar to (but more moderate than) the floor price and proration used in the

first seven ISO‐NE FCAs. If the ISO imposes such a demand curve, the price of capacity will be increased

in periods of capacity surplus. At this time, we cannot determine the probability, timing, and nature of

this potential change in the FCM, but experience suggests that such a major change is unlikely to be

implemented for several years.  

5.4.2 Energy Efficiency Programs and the Capacity Market  

An energy efficiency program that produces a reduction in peak demand has the ability to avoid the

wholesale capacity costs associated with that reduction. The capacity‐cost amount that a particular

reduction in peak demand will avoid in a given year will depend upon the approach that the program

administrator responsible for that energy efficiency program takes towards bidding all, or some, of that

reduction into the applicable FCAs.

A program administrator (PA) can choose an approach that ranges between bidding 100 percent of the

anticipated demand reduction from the program into the relevant FCAs, to bidding zero percent of the

anticipated reduction into any FCA.

 A PA that wishes to bid 100 percent of the anticipated demand reduction from the

program into the relevant FCA has to do so when that FCA is conducted, which can be

up to three years in advance of the program implementation year. For example, a PA

responsible for an efficiency program that will be implemented starting January 2015

would had to have bid 100 percent of the forecast demand reduction for June 2015

onwards from that program into FCA 6, which was held in January 2012. Since a bid is a

firm financial commitment, there is an associated financial risk if the PA is unable to

actually deliver the full demand reduction for whatever reason. The value of this

approach is the compensation paid by ISO‐NE, i.e., the quantity of peak reduction each

year times the FCA price for the corresponding year.

 If a PA does not bid any of the anticipated demand reduction into any FCA, the

program can still avoid some capacity costs if it has a measure life longer than three

years.

191 Under this approach, a PA responsible for an efficiency program starting

January 2015 simply implements that program. The customers’ contribution to the ISO

peak load, whenever that occurs in the summer of 2015, would be lower due to the

program. This PA’s customers would see some benefit from a lower capacity share

                                                            

190 2011 Assessment of the ISO New England Electricity Markets, Patton, et al., Potomac Economics, June 2012, pp. xxi, xxiv,

106, 117–121

191 In many cases, the PA is a utility; in other cases it is a state agency or other entity. In any case, the reduction in load benefits

the customers served by the PA, whether they pay for generation supply through a utility standard‐offer supply, an

aggregator, or a competitive supplier.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐31  

starting in June 2016 (the following year). The reduced capacity requirement will

reduce the capacity acquired in future FCAs, so the entire region will benefit from the

reduction of capacity purchases.

5.5 Wholesale Electric Capacity Market Simulation Model and Inputs

5.5.1 Description of Forward Capacity Market Simulation Model

AESC 2013 uses a spreadsheet model to develop FCM auction prices for power years from June 2014

onward. The major input assumptions regarding the forecasts of peak load and available capacity in

each power year are coordinated with, and consistent with, the input assumptions used in the Market

Analytics energy market simulation model.

The major assumptions used to simulate the future operation of the FCM are listed below:

 The FCM changes described in Section 5.4.1 above, with the elimination of the floor in FCA 8 and

the imposition of performance standards in FCA 11–13.

 Installed capacity requirements (including the Hydro Quebec capacity credits), estimated from

the peak loads in the 2012 CELT and the required reserve margins (ICR ÷ peak load–1) in FCA 7.

Both are extrapolated through the analysis period.  

 Resources generally continue to bid FCM capacity in a manner similar to their bidding in FCA 7.

Most existing resources (renewables, nuclear, hydro, combined‐cycle, and modern combustion

turbines) continue to bid in as price‐takers, at or below the $1/kW‐month FCM price at which

dynamic delist bids are allowed.  

 Generators will submit bids high enough to cover their costs for maintenance, equipment

replacement, and environmental compliance, net of their energy margins. If the FCM price falls

below that level, the generators will not clear in the FCA and will be free to shut down.

 In the event of a major drop in the New England capacity price, a large amount of capacity now

imported to New England from Quebec and New York (including imports from Quebec through

New York and New Brunswick) could withdraw from the New England market, and instead sell

capacity into the markets in New York or PJM. Some domestic New England capacity could

probably also delist to sell capacity out of the region, while continuing to be available to serve

energy loads in New England. The same is true in reverse; if New England prices rise, New York

and Quebec resources are more likely to bid into New England. It is not clear how appealing

other capacity markets will be. Capacity prices in upstate New York have been even lower than

in New England, under $0.50/kW‐month in 2009–2011, rising to about $2/kW‐month in 2012.

Prices on Long Island are somewhat higher. The clearing price for capacity imports to PJM has
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐32  

been volatile, ranging from about $0.50/kW‐month for 2012/13 to about $4/kW‐month for

2015/16, and falling below $2/kW‐month in the May 2013 auction for 2016/17.192 Both New

York and PJM are likely to experience large amounts of generation retirement (mostly of coal

plants) in the next several years.

193 While some of those retirements are reflected in PJM’s

latest forward auction, New York’s prices do not reflect future retirements at all.

 FCA 7 cleared at the floor price with over 3,000 MW of excess capacity. This surplus would cause

the capacity price in FCA 8 to fall substantially with the removal of the price floor, unless the

factors listed above result in delisting and retirement of a large amount of capacity.

 Once the existing surplus no longer exists, due to retirements and load growth, FCM prices

would be determined by the price of new peaking units under long‐term contracts, net of

energy profits and operating‐reserve revenues. Following ISO‐NE’s estimate, we set this price at

$10/kW‐month in 2017 dollars, or $9.28/kW‐month in 2013 dollars.194 Capacity will be added

preferentially in the areas with the lowest reserves and the highest market prices, gradually

equalizing reserves across the region. Connecticut is most likely to have energy and possibly

FCM prices higher than average, and Maine is the zone most likely to have energy and possibly

effective FCM prices below average.

 Assumptions regarding FCM prices will be based upon the slope of the supply curve. We assume

that the average slope of future supply curves will equal the slope from auction round 4 (80

percent of CONE, the highest price at which the ISO allows dynamic delisting of existing

resources) to the bottom of FCA 7 supply curve, or about $0.0014/kW‐month/MW.

 While the ISO will run the auctions for all eight pricing zones (three zones for Massachusetts,

plus the five other states), three factors will greatly reduce the probability of the pricing

separating among zones:  

1. Existing and planned transmission additions, including the remaining components of the

NEEWS project (increasing capacity into Connecticut by 1,000 MW in 2016 and 2017 and

bringing the 745 MW Lake Road plant into the Connecticut capacity zone),195 and the

                                                            

192 Differences in the capacity markets in the three regions (New England, New York, and PJM) make precise comparisons

difficult.

193 The drop in prices in the latest PJM auction is partly the result of imports from MISO and other areas that will also

experience significant retirements over the next few years.

194 Forward Capacity Market Redesign Compliance Filing and Request for Waiver of Compliance Obligation, ISO‐NE, December

3, 2012, Docket Nos. ER10‐787, at 10.

195 “the New England East‐West Solution (NEEWS) transmission project, scheduled for completion in 2016, ...would allow

Connecticut to meet its Transmission Security Analysis requirement even if all fossil steam units in Connecticut retired”

(2012 Integrated Resource Plan for Connecticut, Connecticut Department of Energy and Environmental Protection, June 14,

2012, p. 11).
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐33  

Greater Boston transmission upgrades, increasing capacity into NEMA by 800–1,000

MW by 2018;196

2. Retirement of some of the surplus capacity in Maine; and  

3. The construction of the Footprint plant, eliminating the shortfall in NEMA capacity.197

AESC 2013 uses these assumptions to estimate FCM prices for power years from June 2017 onward. We

start with the capacity that cleared in FCA 7, adding the capacity and subtracting the retirements

described in section 5.2.3, Generating Unit Retirements. We compare the resulting capacity available to

bid in each power year to the future ICR.  

5.5.2 Values for Input Assumptions to FCM Model  

The underlying driver to the Forward Capacity Auctions is the ICR. The ICR is calculated by applying a

percentage reserve requirement to the CELT peak load forecast. The owners of capacity entitlements on

the Hydro Quebec Phase I/II interconnection (the New England utilities that pay for the HVDC

transmission link) are price‐takers, and the auction is actually for the remaining capacity need, the Net

Installed Capacity Requirement (NICR). Holders of Hydro Quebec Interconnect Certificates (HQICC)

receive the resulting auction price, although they do not participate in the auction itself.

Our analysis is based on the ICR values established by the ISO for the FCAs and the follow‐up Annual

Reconfiguration Auctions (ARAs). Exhibit 5‐9 shows the reserve margin (ICR ÷ peak load) for each FCA

and ARA to date, with a line connecting the values for each capacity year for which an ARA has

occurred.198 There appears to be an upward trend of about 0.8 percent per year in the estimates since

2011/12, but recent estimates have flattened out.  

                                                            

196 Exhibit DPU‐G‐1, D.P.U. 12‐77, November 26, 2012.  

197 Zonal capacity balances and the effect on capacity pricing are subject to even greater uncertainty than regional capacity

prices, for several reasons. First, the ISO is considering the revision of its procedures for defining capacity zones, so prices

may be set for zones that do not currently exist (ISO New England’s Strategic Transmission Analysis, New England Electricity

Restructuring Roundtable: Generation Retirement Study & 2020 Resource Options, Stephen Rourke, June 14, 2013).

Second, Footprint may not be built, which could result in higher FCM prices in NEMA for some additional years. Third, while

the failure of Footprint could result in high prices for NEMA in some future FCAs, planned transmission upgrades would add

some 800–1,000 MW of NEMA import capacity. Fourth, some strategic bidding may be possible in some situations. For

example, if regional prices were to fall very low (and neither Footprint nor transmission are built), the owner of Mystic

might be able to maintain slightly higher prices in NEMA(and hence for Mystic 8 and 9) by finding an excuse to post a static

delist bid for Mystic 7, depending on the ISO’s mitigation decisions.

198 “Summary of ICR, LSR & MCL for FCM and the Transition Period,” ISO‐NE, April 15, 2013.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐34  

Exhibit 5‐9. Required Reserve Margins by Capacity Year and Filing Date

For AESC 2013, we assume that future required reserve margins will be 17.2 percent, the average of the

eight ICRs set between 2011 and 2012. The value of the HQICCs has also varied from 911 MW to 1,400

MW, with an average value in 2011 through 2012 analyses of 992 MW.

The net ICR that must be acquired in each FCA is the forecast peak load plus the peak load multiplied by

the reserve margin, and minus the HQICCs. Exhibit 5‐10 summarizes the peak load, ICR, and Net ICR we

assume for our Base Case for each power year.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐35  

Exhibit 5‐10. Derivation of Net Installed Capacity Requirement (MW)

Year Starting Peak

Load

ICR NICR

a b=a*1.172 c=b‐992

2016 29,400 34,457 33,465

2017 29,895 35,037 34,045

2018 30,275 35,482 34,490

2019 30,605 35,869 34,877

2020 30,930 36,250 35,258

2021 31,255 36,631 35,639

2022 31,605 37,041 36,049

2023 31,958 37,455 36,463

2024 32,315 37,874 36,882

2025 32,677 38,297 37,305

2026 33,042 38,726 37,734

2027 33,412 39,159 38,167

2028 33,786 39,597 38,605

2029 34,163 40,040 39,048

2030 34,546 40,487 39,495

Notes:  

Load from Exhibit 5‐2.

Required Reserve: 17.20%

   HQ ICCs = 992 MW

We assume that the capacity price for each future power year will be determined by the difference

between the Net ICR and the amount of capacity available, based on the historical relationship between

the price in each round of FCA 7 and the amount of capacity offered at that price. For prices lower than

those observed in FCA 7, we extrapolated the slope of the FCA 7 supply curve.

Exhibit 5‐11 shows the lower portions of the supply curves for FCAs 2 through 7. We do not include the

supply curve for FCA 1 because it was far to the left of the other supply curves (prices were higher than

for the later FCAs, at much lower supply levels), and was truncated by a $4.50/kW‐year floor price. The

flat section between the third and fourth points from the left of each supply curve (the relatively flat

portion of the curve) represents the point at which existing resources were allowed to dynamically

delist, without prior ISO‐NE approval.  
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐36  

Exhibit 5‐11. Supply Curves, FCA #2 to #7

Exhibit 5‐12 extrapolates the price trend of FCA 7 to zero price.

Exhibit 5‐12. FCA 7 Supply Curve, Extrapolated to Zero Price

Exhibit 5‐12 illustrates how extrapolating the price trend of the last four rounds of FCA 7 indicates that

the market price would have to fall by about $0.144/kW‐month to reduce supply by about 100 MW,

below the 36,016 MW that cleared at FCA 7 floor price.

For AESC 2013, we note several factors that are different between FCA 7 and future FCAs for the

purpose of our analysis:

$2.90

$3.40

$3.90

$4.40

$4.90

$5.40

$5.90

$6.40

$6.90

36,000 36,500 37,000 37,500

$/kW-month

MW Bid

FCA #2

FCA #3

FCA #4

FCA #5

FCA #6

FCA #7

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

34,000 34,500 35,000 35,500 36,000 36,500 37,000 37,500

$/kW-month

MW Bid

FCA #7

Extrapolated
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐37  

 FCA 7 included energy‐efficiency demand resources that we are eliminating from our

analysis. Between FCA 3 and FCA 7, the energy‐efficiency programs that cleared as on‐

peak and seasonal‐peak resources increased from about 1,000 MW to about 1,550

MW, or a total of 550 MW.199 A small amount of the growth in cleared energy‐

efficiency resources is from pre‐2014 projects that were not bid into FCA 3.200 On the

other hand, some energy‐efficiency resources bid into FCA 3 will have retired by FCA 7.

We assume that the 210 MW of new energy‐efficiency bid into FCA 3 will be completed

by 2013 and another 80 MW of energy‐efficiency that will be completed by 2013 will

be bid into auctions from FCA 4 onward. Thus we reduce the 550 MW by 80 MW for a

total of 470 MW.  

 Peak load grows over time in the AESC 2013 Base Case of no new energy efficiency.

 We expect considerable retirements of generation in 2017 and beyond as detailed in

section 5.2.3.

 A declining capacity price in ISO‐NE would tend to result in some capacity resources

that were sold into New England in FCA 7 being diverted to New York or PJM, while an

increasing price would cause additional resources to bid into the New England market.  

 Renewable resources above those in FCA 7 will be added to meeting RPS requirements,

as discussed in section 5.2.4. Without new energy‐efficiency programs, the amount of

renewables would be higher than is likely in the real world.

5.6 Computation of Avoided Capacity Price

Exhibit 5‐13 summarizes these adjustments to the demand‐supply balances and the extrapolated FCM

price. As retirements tighten the capacity market, the price of capacity would rise, attracting imports,

additional demand response, and low‐cost expansion of existing resources, until the capacity price

reaches the cost of new peaking capacity.  

It is important to note that the capacity prices in Exhibit 5‐13 apply for the AESC 2013 no‐new‐efficiency

Base Case. With planned energy‐efficiency peak reductions, the FCA prices are likely to be on the order

of $1/kW‐month for FCA 8 through FCA 10, rising only as retirements increase around 2020 (or perhaps

earlier, as lower capacity and energy prices accelerate retirements).

                                                            

199 These numbers are approximate, since some of the resources cannot be clearly differentiated between energy‐efficiency

and other resources, for example load shifting and distributed generation.

200 United Illuminating and Efficiency Vermont report bidding 100 percent of expected load reductions into the first capacity

auction for which they are eligible, while National Grid and Northeast Utilities report that they bid somewhat less than 75

percent to 80 percent of their expected load reductions for 2012 into FCA3.  
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐38  

Exhibit 5‐13. FCA Price Extrapolation

FCA Year

Starting NICR

Increase

from FCA

7

Cumulative

Retirements

Renewable

Additions

Net

Surplus

at $3.15

FCM Prices

$/kW‐month

Actual

Nominal$

Extrapolated

2016$ 2013$

    a b c d e f g h

4 2013           $2.95    $2.95

5 2014           $3.21    $3.15

6 2015           $3.43    $3.30

7 2016 32,968         $3.15    $2.97

8 2017 34,045 1,077 1,897 101  ‐295    $3.57 $3.30

9 2018 34,490 1,522 1,897 235  ‐607    $4.02 $3.72

10 2019 34,877 1,909 1,897 325  ‐903    $4.45 $4.11

11 2020 35,258 2,290 4,230 422  ‐3,520    $8.22 $7.59

12 2021 35,639 2,671 5,256 501  ‐4,848    $10.13 $9.24

13 2022 36,049 3,081 6,332 536  ‐6,299    $12.22 $9.24

14 2023 36,463 3,495 6,332 653  ‐6,596    $12.65 $9.24

15 2024+               $9.24

Notes:

a From Exhibit 5‐10

b [1] ‐ 32,968 MW for FCA 7

c From Exhibit 5‐4

d Energy values from Exhibit 6‐28 converted to capacity values

e 3,048 MW of FCA7 surplus – [2] –[3] + [4] – 470 MW of post‐2014 efficiency bid into FCA 7

f From Exhibit 5‐8

g $3.15 ‐ [5] × 0.00144

h deflated at 2%

Starting in 2021, AESC 2013 assumes that FCM prices would be determined by the price of new peaking

units under long‐term contracts, net of energy profits, and operating‐reserve revenues. Following ISO‐

NE’s estimate, we set this price at $9.24/kW‐month in 2013 dollars.201  

The avoided cost at the meter is equal to the avoided wholesale electric capacity price, increased by:

 The reserve margin requirement (17.2%)

 Distribution losses (which ISO‐NE estimates at 8%)202

                                                            

201 Forward Capacity Market Redesign Compliance Filing and Request for Waiver of Compliance Obligation, ISO‐NE, December

3, 2012, Docket Nos. ER10‐787, at 10.

202 Various PAs may have different estimates of marginal distribution losses at peak, which would determine the avoided costs

to the PAs customers.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐39  

 Losses on the transmission system (1.5%)

 The Wholesale Risk Premium (for which we use a default value of 9%; see section 5.2.6)

As noted earlier, the actual amount of wholesale electric capacity costs avoided by kW reductions from

energy efficiency measures will vary according to the approach that the PA responsible for those measures

takes towards the FCM. PAs achieve the maximum avoided cost by bidding the entire anticipated kW

reduction from measures in a given year into the FCA for that power year. However, PAs have to submit

those bids when the FCA is held, which is approximately three years in advance of the applicable power

year. Some expected load reductions may not be bid into the first FCA for which the reduction would be

effective, due to uncertainty about future program funding and savings.203 Information provided by various

PAs indicates that the majority of expected savings will be bid into the first applicable auction (75 percent

to 100 percent, depending on PA), with the remainder bid in over the next two years. Exhibit 5‐14

summarizes the FCA price to load at wholesale, as well as the avoided capacity cost at the meter.

   

                                                            

203 PAs also avoid capacity costs from kW reductions that are not bid into FCAs, since those kW reductions lower actual

demand, and ISO‐NE eventually reflects those lower demands when setting the maximum demand to be met in future FCAs

and the allocation of capacity requirements to load. However, the total amount of avoided capacity costs is lower because

of the time lag, up to four years, between the year in which the kW reduction first causes a lower actual peak demand and

the year in which ISO‐NE translates that kW reduction into a reduction in the total demand for which capacity has to be

acquired in a FCA. Since the load reduction in one year will affect the allocation of capacity responsibility in the next year,

the PA’s customers experience a one‐year delay in realized savings that are not bid into the auctions at all.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐40  

Exhibit 5‐14. FCA Price and Avoided Capacity Cost (2013 dollars)

Year

Starting

FCM

Price

Avoided Capacity Cost at

Meter

  

$/kW‐

month $/kW‐month

$/kW‐

year

   a

b=a*(1+reserve)*

(1+ISO loss)*

(1+PTF)*

(1+WRP) c=b*12

2013 $2.95 $4.13 $49.59

2014 $3.15 $4.41 $52.87

2015 $3.30 $4.62 $55.47

2016 $2.97 $4.16 $49.88

2017 $3.30 $4.62 $55.50

2018 $3.72 $5.21 $62.48

2019 $4.11 $5.76 $69.10

2020 $7.59 $10.63 $127.59

2021 $9.24 $12.94 $155.25

2022 $9.24 $12.94 $155.25

2023 $9.24 $12.94 $155.25

2024 $9.24 $12.94 $155.25

2025 $9.24 $12.94 $155.25

2026 $9.24 $12.94 $155.25

2027 $9.24 $12.94 $155.25

2028 $9.24 $12.94 $155.25

2029 $9.24 $12.94 $155.25

2030 $9.24 $12.94 $155.25

17.2% reserve margin requirement

8% ISO default distribution losses  

1.5% PTF losses    

9% default wholesale risk premium

The cost of capacity in each month is determined by load in two ways. The total cost of capacity

purchased is determined by the projected NICR for the current capacity year (which is largely driven by

the peak loads in the summer at the beginning of the capacity year); the capacity‐market revenues for

PA efficiency savings (which benefit consumers) is determined by capacity bid into the current summer;

and the allocation of those costs among power consumers is determined by their contributions to the

system peak in the previous year. Hence, the total capacity charges for June 2017 through May 2018 will

be determined by the loads expected for the summer of 2017, and offset by the energy‐efficiency

savings bid into FCA 8 for 2017/18 (which is constrained by the 2017 summer load reduction). The

allocation of the capacity costs to each PA’s customers will be determined by their peak loads in the

summer of 2016.  

While the costs paid in calendar 2017 will include both the capacity rates set in FCA 7 (for January to

May 2017) and in FCA 8 (for June to December), the payments in the first five months will simply be
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐41  

delayed payments for the peak loads in summer 2016, and the peak loads in summer 2017 will

determine total charges and energy‐efficiency revenues for the twelve months from June 2017 through

May 2018. We therefore treat the avoidable FCA8 capacity charges as being avoidable by summer 2017

load, rather than treating 2017 peak reductions as saving some mix of FCA 7 and FCA 8 capacity

prices.204 Thus, we do not make any adjustments between power year to calendar year.

5.7 Adjustment of Capacity Costs for Losses on ISO‐Administered Pooled

Transmission Facilities

There is a loss of electricity between the generating unit and ISO‐NE’s delivery points, where power is

delivered from the ISO‐NE administered pooled transmission facilities (PTC) to the distribution utility

local transmission and distribution systems. Therefore, a kilowatt load reduction at the ISO‐NE’s delivery

points, as a result of DSM on a given distribution network, reduces the quantity of electricity that a

generator has to produce by one kilowatt plus the additional quantity that would have been required to

compensate for losses.205 The energy prices forecast by the Market Analytics model reflect these losses.

However, the forecast of capacity costs from the FCM do not. Therefore, the forecast capacity costs

should be adjusted for these losses.  

ISO‐NE does not appear to publish estimates of the losses on the ISO‐administered transmission system

at system peak. ISO‐NE does release hourly values for System Load, which it defines as the sum of

generation and net interchange, minus pumping load, and Non‐PTF Demand, the term that the ISO uses

for the load delivered into the networks of distribution utilities. Losses on the PTF system are thus the

difference between the System Load and Non‐PTF Demand. While PTF losses probably vary among

zones, marginal losses by zone could not be identified using the available data.206

AESC 2009 estimated the marginal peak losses on the PFT system for each summer 2006 to 2008 by

regressing the system losses against real‐time demand for the top 100 summer hours in each year. The

marginal loss ratio was 3.4 percent in 2006, 2.0 percent in 2007 and 1.8 percent in 2008. AESC 2009

used the average of the latter two values, or 1.9 percent.

For AESC 2013, we analyzed the system losses against the six days of each year with the most hours of

the top 100 hours of load for 2010, 2011, and 2012. The methodology and accompanying exhibits are

detailed in Appendix H.  

                                                            

204 Summer 2017 peak reductions will also affect the allocation of FCA 9 capacity charges in 2018/19; we have not attempted

to reflect this complication.

205 Computations of avoided costs sometimes assume that only average, and not marginal, losses are relevant at the peak

hour. The reasoning for that approach is that changes in peak load will lead to changes in transmission and distribution

investment, keeping average percentage losses approximately equal. The AESC 2013 avoided costs do not include any

avoided PTF investments, so marginal losses are relevant in this situation.  

206 Since losses in any zone depend both on loads in that zone and flows into and out of that zone to the rest of the region,

marginal losses as a function of load in each zone would be difficult to estimate from historical data.
Synapse Energy Economics, Inc. – AESC 2013 Page 5‐42  

The results of the regression equations (with all variables in MW) were:  

 2010: PTF Losses = 1.71%  

 2011: PTF Losses = 1.60%  

 2012: PTF Losses = 1.12%

It is not clear whether there is actually a downward trend in the slopes over the years, or whether the

differences are random variations in daily conditions, perhaps influenced by the unusual fuel‐price

relationships in 2012.  

Taking into account the daily regression results and the 2010 100‐hour regression, AESC 2013 uses a

marginal PTF demand loss factor for capacity costs of 1.5 percent.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐1  

Chapter 6: Avoided Electric Energy Costs

6.1 Introduction

This chapter addresses avoided electric energy market prices, as well as Renewable Portfolio Standard

(RPS) compliance costs that are not embedded in those energy market prices. It describes the

methodology and assumptions used to develop projections of avoided energy costs and non‐embedded

RPS costs, and presents the results of our analyses. This chapter is organized as follows:

 Section 6.2 provides an overview of wholesale energy markets in New England.

 Section 6.3 describes the simulation model used to project avoided electric energy

market prices for AESC 2013 (Market Analytics) and the assumptions used by that

model.

 Section 6.4 provides an overview of our forecast of avoided electric energy market

prices for AESC 2013, and compares those results to AESC 2011. Detailed results for

each year of the study period, by zone, are provided in Appendix B.  

 Section 6.5 describes our forecast of RPS compliance costs that are not embedded in the

wholesale market prices for energy. Detailed renewable energy certificate (REC) price

forecasts and avoided RPS costs by state for each year of the study period are provided

in Appendix F.  

6.2 Wholesale Energy Markets

The wholesale energy markets are managed by ISO New England (ISO‐NE). There are two primary

markets: (1) the Day‐Ahead Market, where the majority of the transactions occur, and (2) the Real‐Time

Market, where the remaining energy supplies and demands are balanced. These two markets represent

the bulk of the electricity transactions, and their prices on average are very close to each other,

although there is greater volatility in the real‐time market.

According to the ISO‐New England 2010 Annual Market Report (2011, 29–30)207

:

The primary objective of the electricity markets operated by ISO New England is to ensure a reliable and

economic supply of electricity to the high‐voltage power grid. The markets include a Day‐Ahead Energy

Market and a Real‐Time Energy Market. In what is termed a multi‐settlement system, each of these

markets produces a separate but related financial settlement.  

                                                            

207 We cite from the 2010 report because it has the most concise explanation of the NE markets.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐2  

The Day‐Ahead Energy Market produces financially binding schedules for the sale and purchase of

electricity one day before the operating day. However, supply or demand for the operating day can

change for a variety of reasons, including generator reoffers of their supply into the market, real‐time

hourly self‐schedules (i.e., generators choosing to be on line and operating at a fixed level of output

regardless of the price of electric energy), self‐curtailments, transmission or generation outages, and

unexpected real‐time system conditions. Physically, real‐time operations balance instantaneous changes

in supply and demand and ensure that adequate reserves are available to operate the transmission

system within its limits. Financially, the Real‐Time Energy Market settles the differences between the day‐

ahead scheduled amounts of load and generation and the actual real‐time load and generation.  

Participants either pay or are paid the real‐time locational marginal price (LMP) (see below) for the

amount of load or generation in megawatt‐hours (MWh) that deviates from their day‐ahead schedule.  

This section summarizes the key features of the ISO’s Day‐Ahead and Real Time Energy Markets, including

locational marginal pricing; the factors influencing electric energy supply offers, demand bids, and LMPs;

and virtual and real‐time trading.

2.1.1 Locational Marginal Prices and Pricing Locations

Locational marginal pricing is a way for wholesale electric energy prices to efficiently reflect the value of

electric energy at different locations, accounting for the patterns of load, generation, and the physical

limits of the transmission system. In New England, wholesale electricity prices are identified at 900 pricing

points (i.e., pnodes) on the bulk power grid. If the system were entirely unconstrained and had no losses,

all LMPs would be the same, reflecting only the cost of serving the next increment of load. This

incremental megawatt of load would be served by the generator with the lowest‐cost energy offer

available to serve that load, and electric energy from that generator would be able to flow to any node on

the transmission system. LMPs differ generally among locations because transmission and reserve

constraints prevent the next‐cheapest megawatt (MW) of electric energy from reaching all locations of

the grid. Even during periods when the cheapest megawatt can reach all locations, the marginal cost of

physical losses will result in different LMPs across the system.  

New England has five types of pnodes: one type is an external proxy node interface with neighboring

balancing authority areas, and four types are internal to the New England system. The internal pnodes

include individual generator‐unit nodes, load nodes, load zones (i.e., aggregations of load pnodes within a

specific area), and the Hub. The Hub is a collection of locations with a load‐weighted price intended to

represent an uncongested price for electric energy; facilitate trading; and enhance transparency and

liquidity in the marketplace. New England is divided into the following eight load zones: Maine (ME), New

Hampshire (NH), Vermont (VT), Rhode Island (RI), Connecticut (CT), Western/Central Massachusetts

(WCMA), Northeast Massachusetts and Boston (NEMA), and Southeast Massachusetts (SEMA).

Generators are paid the real‐time LMP for electric energy at their respective nodes, and participants

serving demand pay the price at their respective load zones. The load‐zone price is a load‐weighted

average price of the load‐node prices in that zone.

Import‐constrained load zones are areas within New England that must use more expensive generators

than the rest of the system because local, inexpensive generation or transmission‐import capability is

insufficient to meet both local demand and reserve requirements. Export‐constrained load zones are

areas within New England where the available resources, after serving local load, exceed the areas’

transmission capability to export excess electric energy.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐3  

6.3 Wholesale Electric Energy Market Simulation Model and Inputs

6.3.1 The Energy‐Market‐Simulation Model

Market Analytics is a zonal locational marginal‐price‐forecasting model that simulates the operation of

the energy and operating reserves markets. The simulation engine used is PROSYM. The modeling

system and the default data is provided by the model vendor, Ventyx.  

The model does not simulate the forward capacity market and, therefore, does not require assumptions

regarding the capital costs of new generation capacity and the interconnection costs associated with

such capacity. However, the model does require assumptions about the quantity and type of existing

and new capacity over the study horizon, fuel prices, and other factors. Section 6.3.2 catalogues the

input assumptions to the model.  

Zonal Locational Marginal Price‐Forecasting Model

This section provides a high‐level overview of the Market Analytics data‐management and production‐

simulation‐model functionality. Market Analytics uses the PROSYM simulation engine to produce

optimized unit commitment and dispatch options. The model is a security‐constrained chronological

dispatch model that produces detailed and accurate results for hourly electricity prices and market

operations.

The smallest location in Market Analytics is a Location (typically representing a utility service territory)

which for modeling purposes is mapped into a Transmission Area (TA). A TA may represent one or more

Locations. TAs represent sub regions of Control Areas such as ISO New England. TAs are defined in

practice by actual transmission constraints within a control area. That is, power flows from one area to

another in a control area are governed by the operational characteristics of the actual transmission lines

involved. PROSYM can also simulate operation in any number of control areas. Groups of contiguous

control areas were modeled in order to capture regional impacts of the dynamics under scrutiny.

PROSYM uses highly detailed information on generating units. Data on specific units in the Market

Analytics database are based on data drawn from various sources including the U.S. Energy Information

Administration, U.S. Environmental Protection Agency, North American Electric Reliability Corporation,

Federal Energy Regulatory Commission (FERC), and ISO‐New England databases, as well as various trade

press announcements and Ventyx’s own professional assessment. Total existing capacity in the Market

Analytics database was compared with that of ISO‐NE CELT 2012 and found to be reasonably consistent,

although we made a few adjustments to reflect retirements as detailed below.

For larger units, emission rates and operating characteristics are based on unit‐specific data reported to

EPA and EIA rather than on data based on unit type. Operating costs for each unit are based on plant‐

level operating costs reported to FERC and assessment of unit type and age. For smaller units (e.g.,

combustion turbines), most input data are based on unit type. All generating units in PROSYM operate

at different heat rates (efficiencies) at different loading levels. This distinction is especially important in

the case of combined‐cycle units, which often operate in a simple‐cycle mode at low loadings. PROSYM
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐4  

determines the fuel a unit burns by placing each generating unit into a “fuel group.” PROSYM does not

limit the number of fuel groups used, and creating new fuel groups to simulate a few unusual units is a

simple matter. In New England, for example, it is especially important to model the operation of dual‐

fueled units as accurately as possible.

Based upon hourly loads, PROSYM determines generating unit commitment and operation by

transmission zone based upon economic bid‐based dispatch, subject to system operating procedures

and constraints. PROSYM operates using hourly load data and simulates unit dispatch in chronological

order. In other words, 8,760 distinct hourly load levels are used for each TA for each study year. The

model begins on January 1st and dispatches generating units to meet hourly loads. Using this

chronological approach, PROSYM takes into account time‐sensitive dynamics such as transmission

constraints and operating characteristics of specific generating units. For example, one power plant

might not be available at a given time due to its minimum down time (i.e., the period it must remain off

line once it is taken off). Another unit might not be available to a given TA because of transmission

constraints created by current operating conditions. These are dynamics that system operators wrestle

with daily, and they often cause generating units to be dispatched out of merit order. Few other electric

system models simulate dispatch in this kind of detail.

The model’s fundamental assumption of behavior in competitive energy markets is that generators will

bid their marginal cost of producing electric energy into the energy market. The model calculates this

marginal cost from the unit’s opportunity cost of fuel or the spot price of gas at the location closest to

the plant, variable operating and maintenance costs, and opportunity cost of tradable permits for air

emissions.

PROSYM does not make capacity‐expansion decisions internally. Instead, the user specifies capacity

additions, a practice that increases transparency and allows the system‐expansion plans to be specified

to reflect non‐market considerations. As discussed in more detail below, PROSYM also models randomly

occurring forced outages of generating units probabilistically rather than as deterministic capacity de‐

rating, thereby producing more accurate estimates of avoided costs, particularly for peak‐load periods.

PROSYM models generating units with a much higher level of detail, including inputs for unit specific

ramp rates, minimum up/down times, and multiple capacity blocks, all of which are critical for

accurately modeling hourly prices. This modeling capability enabled production of locational prices by

costing period in a consistent manner at the desired level of detail.

PROSYM simulates the effects of forced (i.e., random) outages probabilistically, using one of several

Monte Carlo simulation modes. These simulation modes initiate forced outage events (full or partial)

based on unit‐specific outage probabilities and a Monte Carlo‐type random number draw. Many other

models simulate the effect of forced outages by “de‐rating” the capacity of all generators within the

system. That is, the capacities of all units are reduced at all times to simulate the outage of several units

at any given time. While such de‐rating usually results in a reasonable estimate of the amount of annual

generation from baseload plants, the result for intermediate and peaking units can be inaccurate,

especially over short periods.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐5  

PROSYM calculates emissions of NOx, SO2, CO2, and mercury based on unit‐specific emission rates.

Emissions of other pollutants (e.g., particulates and air toxics) are calculated from emissions factors

applied to fuel groups.

6.3.2 Input Assumptions to Electric‐Energy‐Price Model

Our projection of avoided electric energy market prices incorporates several assumptions that are

shared with other analyses in AESC 2013. Exhibit 6‐1 shows the input assumptions to the Market

Analytics locational‐price‐forecasting model that are detailed in other chapters.

Exhibit 6‐1. Assumptions shared with other AESC 2013 analyses

Assumption Chapter/Section

Forecasted annual peak demand and total energy Chapter 5, Electric Capacity

Fuel prices Chapter 2, Natural Gas; and Chapter 3, Fuel Oil and

Other Fuels

Transmission resources Chapter 5, Electric Capacity

Generating unit retirements Chapter 5, Electric Capacity

Resource additions Chapter 5, Electric Capacity

Emission allowance costs Chapter 4, Embedded and Non‐Embedded

Environmental Costs

Wholesale risk premium Chapter 5, Electric Capacity

Input assumptions to the Market Analytics model that are detailed in this chapter include: market rules

and topology; hourly load profiles; generating unit characteristics for thermal, nuclear, and conventional

hydro and pumped storage resources; and demand‐response resources.  

Market Rules and Topology

The major assumptions are described below as inputs to the model.

Marginal‐Cost Bidding

In deregulated markets, generation units are assumed to bid marginal cost (opportunity cost of fuel plus

variable operating and maintenance costs [VOM] plus opportunity cost of tradable permits). It is

reasonable to assume that the real markets are not perfectly competitive and thus the model prices

based on marginal costs tend to underestimate the prices in the real markets. To represent that effect,

the default data includes bid strategies with adders to represent more realistic market behavior. The

resulting energy‐price outputs are benchmarked against historical and futures prices.

Installed Capacity

Installed‐capacity requirements for the resource‐addition model include reserve requirements

established by ISO‐NE on an annual basis. Current estimates of the reserve‐margin and installed‐capacity

requirement (with and without the Hydro Quebec (HQ) installed capacity credits) are described in

Chapter 5, Avoided Electric Capacity Costs. Installed capacity for the energy model in each model year is
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐6  

consistent with the values assumed in the FCA analysis, although the values are not the same, due to

imports and exports.

Ancillary Services

Market Analytics allows users to define generating units based on their ability to participate in various

ancillary services markets including Regulation, Spinning Reserves, and Non‐Spinning Reserves. The

database includes specifications for these abilities based on unit type. Market Analytics generates prices

for these markets in conjunction with the energy market. The spinning reserves market affects energy

prices since units that spin cannot produce electricity under normal conditions. The energy prices are

higher when reserves markets are modeled. Reserves requirements for New England are applied to the

model.

Electric Model Topology

Market Analytics represents load and generation areas at various levels of aggregation. Assets within the

model, including physical or contractual resources such as generators, transmission links, loads, and

transactions, are mapped to physical locations which are then mapped to TAs. Multiple TAs are linked by

transmission paths to create the control area. The load and generation areas modeled for AESC 2013 are

presented in Exhibit 6‐2.

CELT 2012 forecasts load for 13 subareas which correspond to the locations used in the Market Analytics

data. Our modeling maps those 13 load subareas into 10 TAs, which is the level of detail required to

report results for the 14 reporting zones specified for AESC 2013.208 Neighboring regions that are

modeled in this study are New York, Quebec, Ontario, and the Maritime Provinces.209  

Areas outside of New England are represented with a high level of zonal aggregation to minimize model

run time.

                                                            

208 We produce results for four of the AESC zones by aggregating the results for certain of the areas we model. For example,

the results for Massachusetts are the aggregate results for SEMA, WCMA, and NEMA. The results for the aggregate zones

are based on the weighted averages of their constituent subzones.

209The Maritimes zone includes Maine Public Service (MPS) and Eastern Maine Electric Cooperative (EMEC) which are not part

of ISO‐New England and, therefore, are not included in any of the New England pricing zones used in this study. MPS and

EMEC are not modeled as part of the Maine pricing zone and were modeled as part of the New Brunswick Transmission

Area.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐7  

Exhibit 6‐2. Load Areas Used to Model New England

AESC Reporting Zones 210

Load Area

CELT SubArea  

Market Analytics

Modeling Areas  

AESC

Zone Mapping

1 Maine ME + BHE + SME BHE + ME Central +

ME Southwest Direct

2 Vermont VT Vermont Direct

3 New Hampshire NH New Hampshire Direct

4 Connecticut (Statewide) CT +SWCT + NOR = Aggregated

5 Massachusetts (Statewide) BOST + CMA/NEMA

+ SEMA + WMA = Aggregated

6 Rhode Island RI Rhode Island Direct

7 SEMA (Southeast Massachusetts) SEMA MA Southeast Direct

8 WCMA (West‐Central Massachusetts) WMA MA Western Direct

9 NEMA (Northeast Massachusetts) CMA/NEMA MA Central &

Northeast Direct

10 Rest of Massachusetts (Massachusetts

excluding NEMA)

BOST + SEMA +

WMA = Aggregated

11 Norwalk/Stamford NOR CT Norwalk Direct

12 Southwest Connecticut, including

Norwalk/Stamford SWCT + NOR = Aggregated

13 Southwest Connecticut, excluding

Norwalk/Stamford SWCT   CT Southwest Direct

14 Rest of Connecticut (Connecticut

excluding all of Southwest Connecticut) CT   CT Central‐Northeast Direct

This study explicitly models neighboring control areas that have direct connections to the New England

grid, including New York ISO, the Maritimes region (New Brunswick, Nova Scotia, and Prince Edward

Island), and Quebec. These external markets are modeled in the same manner and simultaneously with

New England. The Market Analytics database is used as the primary data source for external regions.

New capacity is added to meet RPS requirements, and generic gas capacity is added based on the same

methodology that is used in New England. Consistent assumptions are also used for fuel prices, with the

primary one being for natural gas.

The forecasts of electricity prices for each load area from the model are mapped and load‐weighted into

the AESC zones.

Load Forecast and Hourly Load Profiles

Forecasts of peak demand and energy by month for each of the areas modeled in Market Analytics were

derived from CELT 2012, as described in Chapter 5, Avoided Electric Capacity Costs. Historical profiles for

                                                            

210 The AESC reporting zones are the six New England states and eight subareas representing various parts of Connecticut and

Massachusetts.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐8  

each utility were developed by Ventyx for Market Analytics based on a set of annual historical load

shapes. Hourly load profiles based on historical profiles were calculated for each load serving entity.

Loads were then mapped to TAs based on location ratios. Hourly load data for future years were scaled

based on forecasted peak demand and energy.  

The area ISO‐NE load forecasts (shown in Exhibit 6‐3 and Exhibit 6‐4) are used to produce the area loads

required for the Market Analytics modeling.211  

Exhibit 6‐3. Summer Peak Forecast by Model Load Area

Load Area 2013 (MW) 2020 (MW)

2017‐2021

CAGR 2028 (MW)

BHE 302 336 0.95% 370

ME 906 1,022 1.17% 1,148

SME 697 758 0.64% 816

NH 2,086 2,404 1.32% 2,700

VT 1,182 1,318 1.01% 1,470

BOSTON 5,482 6,174 1.13% 6,867

CMA/NEMA 1,653 1,869 1.20% 2,086

WMA 2,079 2,344 1.16% 2,613

SEMA 2,850 3,256 1.36% 3,676

RI 2,530 2,921 1.61% 3,367

CT 3,351 3,734 0.83% 4,065

SWCT 2,270 2,532 0.84% 2,758

NOR 1,240 1,376 0.77% 1,489

ISO‐NE 26,629 30,043 1.12% 33,405

2028 values were developed by escalating 2021 values by the 2017‐2021

Compound Annual Growth Rate.

Loads include the effects of 2013 Passive Demand Resources.

                                                            

211 i.e., BHE, ME and SME are combined into Maine. Connecticut is represented by three load areas CT, SWCT and NOR that are

aggregated in different ways
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐9  

Exhibit 6‐4. Energy Forecast by Model Load Area

Load Area 2013 (GWh) 2020 (GWh)

2017‐2021

CAGR 2028 (GWh)

BHE 1,788 1,939 0.74% 2,106

ME 5,560 6,116 0.93% 6,737

SME 3,982 4,232 0.44% 4,491

NH 10,536 11,556 0.94% 12,639

VT 6,815 7,350 0.75% 8,058

BOSTON 27,045 29,377 0.90% 32,189

CMA/NEMA 8,096 8,791 0.89% 9,608

WMA 10,557 11,419 0.83% 12,434

SEMA 13,945 15,424 1.15% 17,176

RI 11,652 12,961 1.18% 14,492

CT 15,718 16,981 0.68% 18,374

SWCT 10,714 11,592 0.70% 12,566

NOR 5,827 6,265 0.61% 6,745

ISO‐NE 132,236 144,003 0.87% 157,595

2028 values were developed by escalating 2021 values by the 2017‐2021

Compound Annual Growth Rate.

Loads include the effects of 2013 Passive Demand Resources.

Generic Generating Unit Operating Characteristics

Thermal Units

Market Analytics represents generation units in detail, in order to accurately simulate their operational

characteristics and therefore project realistic hourly dispatch and prices. These characteristics include:

 Unit type (steam‐cycle, combined‐cycle, simple‐cycle, cogeneration, etc.)

 Heat rate values and curve

 Seasonal capacity ratings (maximum and minimum)

 Variable operation and maintenance costs

 Forced and planned outage rates

 Minimum up and down times

 Quick start and spinning reserves capabilities

 Startup costs

 Ramp rates

 Emission rates (SO2, NOx, CO2, and mercury)
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐10  

The Market Analytics data are based on a variety of reliable public sources such as EIA reports and FERC

filings, although some sources are proprietary.212  

Exhibit 6‐5. Characteristics of Market Analytics Generic Unit Additions

Characteristics NG CC   NG CT

Typical Size 375 180

Heat Rate (Btu/kWh) 6,625 10,500

Variable O&M costs (2013

dollars per MWh) $2.18 $3.56

Availability 85.2% 92.3%

NOX (lb/MMBtu) 0.01 0.03

SO2 (lb/MMBtu) 0 0

CO2 (lb/MMBtu) 120 120

Notes:      

NG CC Natural Gas Combined Cycle

NG CT Natural Gas Combustion Turbine

Nuclear Units

There are four nuclear plants and five nuclear units in New England (Millstone 2 and 3, Pilgrim,

Seabrook, and Vermont Yankee) with a combined summer capacity of 4,541 MW, representing

approximately 15 percent of the total New England capacity.

Exhibit 6‐6. New England Nuclear Unit Capacity and License Expirations

Nuclear Unit Load Area

Capacity

(MW)a

License‐Expiration

Yearb

Millstone 2 CT 875 2035

Millstone 3 CT 1,225 2045

Pilgrim SEMA 677 2032

Seabrook NH 1,246 2030

Vermont Yankee VT 604 2032 a

CELT 2012 Summer capability; bU.S. Nuclear Regulatory Commission

Of the five operating nuclear units in New England, the Nuclear Regulatory Commission (NRC) has

relicensed the Pilgrim unit and Millstone 2 and 3, along with 60 other reactors outside New England,

without denying a single extension. Based on this track record and the lack of evidence suggesting that

the NRC would deny the license renewals for any of these plants, we assume that all of the nuclear

                                                            

212 Specific details about the Market Analytics Model inputs can be requested and provided under appropriate confidentiality

agreements.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐11  

plants in New England will receive NRC licenses to operate for another 20 years, through the entire

modeling period.  

As discussed in Chapter 5, the NRC recently granted Vermont Yankee a 20‐year license extension, but

the plant also requires state permission to operate past March 2012. The future is somewhat uncertain

for this plant, but our expectation is that it will cease operation in 2015.  

Pilgrim’s operating license was renewed by the NRC in March of 2012 for 20 more years. Its design and

vintage is very similar to that of Vermont Yankee and Fukushima Daiichi, and it is also located on the

coast. Serious earthquakes along the Massachusetts coast are very rare, but not unknown. Pilgrim is

thus among the U.S. nuclear units most likely to be affected by increased safety requirements following

the Fukushima disaster, either as part of an extended relicensing review or subsequently. Many such

measures (hardening of spent‐fuel pools and back‐up power supply, transferring spent fuel to dry casks,

building higher seawalls) would have little effect on Pilgrim’s power output. Nor are those measures

likely to result in economic retirement of the plant. On the other hand, if the NRC were to require

fundamental design changes in the Mark I reactors, Pilgrim would likely retire. The NRC has rarely

required such major modifications to licensed reactors. We thus assume that Pilgrim will continue

operating.  

Conventional Hydro and Pumped Storage Unit Characteristics

The Market Analytics database was used as the primary source for all hydro unit information.

Conventional reservoir and run‐of‐river hydro resources are considered a “fixed energy” station or

contract in the model. Like thermal stations, these stations have a maximum and minimum generating

capacity, but they also have a fixed amount of energy available within a specified time (i.e., a week or a

month). Hydro stations operate generally on peak in a manner that levels the load shape served by

other stations. Hydro stations are scheduled over the horizon of a week, subject to hourly constraints

for minimum and maximum generation, and weekly constraints for ramp rates and total energy.

Although the load shape they intend to level is the overall system load, a hydro station can be scheduled

against the load of a specified Transmission Area or control area.

Pumped‐storage type resources (with exchange contracts) have slightly different modeling

requirements, typically involving a series of reservoirs used to release water for energy generation

during peak load periods and pump water back uphill during off‐peak times when energy demand and

price is lower. The water (fuel) of pumped hydro generation is valued at the cost of pumping, allowing

for net plant efficiency. Hourly reservoir levels are computed and a look‐ahead is employed to prevent

drawing the reservoir below the level where pumping space allows refilling to the desired level before

the beginning of the next peak period.

Demand Resources

Demand resources were included in the model consistent with the ISO‐NE 2012 Regional System Plan

(RSP) and the Forward Capacity Auction results (through FCA 7). These resources were modeled as

generating units that act as load reduction resources that are committed only if all other available
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐12  

generating resources are operating at full capacity and load is about to be lost. These resources do not

set the marginal clearing price.

6.3.3 Model Calibration

Since a key objective of this study is the calculation of avoided electric energy costs, we took steps to

ensure that the model is forecasting energy market prices accurately. The calibration approach we use is

to compare the prices forecast by the model to electric energy historical and futures prices at the ISO‐NE

hub. The ability to make this comparison is complicated by the requirement for the model to forecast

prices assuming no continuation of energy‐efficiency activities, i.e., no “new” reductions. The

complication is that the electric‐energy future prices will reflect the expectations of buyers and sellers in

the actual market, who are likely assuming continuation if not escalation of existing efficiency programs.

Consequently, we model the current market situation using the loads that reflect the energy efficiency

resources in place through 2013. We then make appropriate model adjustments (e.g., bidding

strategies, etc.) to reasonably match the electric‐energy historical and futures prices at the ISO‐NE hub

over 2012 and 2013. Detailed results of our calibration analyses are shown in sections 6.4.1 and 6.4.2.

6.4 Avoided Electric Energy Costs

6.4.1 Forecast of Energy Prices Assuming No New DSM

The projected wholesale energy prices for the AESC 2013 Base Case presented below are outputs from

the Market Analytics simulation model for a hypothetical future in which no new energy efficiency

resources are implemented after 2013. As such, they represent the wholesale price of avoided energy in

a future with no new efficiency. These prices are NOT meant to be used as energy price forecasts in the

most likely future, i.e., one in which there will be some level of new energy efficiency measures installed

each year over the planning horizon.

The projected level and mix of capacity in the Base Case is presented in Exhibit 6‐7. New capacity

additions include renewable resources to comply with RPS requirements, as well as new natural gas

generators added to meet energy and reserve margin requirements. Substantial portions of the existing

oil (Pet Prod) and coal capacity are forecasted to retire before 2025. Because of the relatively high price

of oil compared to other fuels, these generating plants are now rarely used.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐13  

Exhibit 6‐7. Base Case Capacity by Source (MW)

The projected level and mix of generation in the Base Case is presented in Exhibit 6‐8. Generation from

nuclear declines slightly with the closure of Vermont Yankee in 2016, and coal generation declines

substantially as most units are retired. Generation from natural gas is the dominant resource, and

renewable generation increases substantially in compliance with RPS requirements. The generation

from oil (Pet Prod) plants is negligible on this graph.

 ‐

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

MW

New Renewables

Existing

Renewables

DR/Pumped

Storage

PetProd

New Generic

Natural Gas

Existing NG

Coal

Nuclear

Hydro
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐14  

Exhibit 6‐8. Base Case Generation by Source (GWh)

Forecast of Wholesale Electric Energy Prices

For AESC 2013, we present streams of energy values for all of New England in the form of the hub price.

This is separately presented for four periods—summer on‐peak, summer off‐peak, winter on‐peak,

winter off‐peak.213

The hub price representing the ISO‐NE Control Area is located in central Massachusetts, and the Central

Massachusetts zone in the Market Analytics model is used as the proxy for that location. Exhibit 6‐9

presents summer and winter, on‐peak and off‐peak energy prices as produced by the model through

2028 for Central Massachusetts.214 The higher winter on‐peak price in the initial years represents the

current high winter natural gas basis prices, which moderate as more pipeline capacity is added.

                                                            

213 Summer is defined as the four months June through September, with winter the other eight months. By combining the true

winter season within Spring and Fall, the effects of high prices during the coldest months are moderated. For AESC 2013,

“on‐peak” hours are defined as 7 am – 11 pm, consistent with ISO‐New England’s definition. (See: http://www.iso‐

ne.com/support/training/glossary/index‐p5.html.) This is a small deviation from the scope of work requested by the AESC

2013 Study Group, which defined on‐peak hours as 6 am to 10 pm.

214 Beyond 2028, AESC 2013 escalates wholesale energy prices based on the compound annual growth rate of the last five

years of values from the energy model. Escalation rates are specific for each of the four costing periods.  

 ‐

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

GWh

New Renewables

Existing

Renewables

DR/Pumped Stor.

PetProd

New Generic

Natural Gas

Existing NG

Coal

Nuclear

Hydro
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐15  

Exhibit 6‐9. Wholesale Energy Price Forecast for Central Massachusetts

Exhibit 6‐10 provides these prices in tabular form.  

Exhibit 6‐10. Wholesale Energy Price Forecast for Central Massachusetts (2013$/MWh)

0

10

20

30

40

50

60

70

80

90

2013 2018 2023 2028

2013$/MWh

Winter On‐Peak

Summer On‐Peak

Winter Off‐Peak

Summer Off‐Peak

Summer Winter

Year Off-Peak On-Peak All-Hours Off-Peak On-Peak All-Hours

2013 39.3 45.1 42.2 47.3 55.7 51.5

2014 38.1 43.9 41.0 47.7 54.6 51.2

2015 38.3 45.3 41.8 47.7 53.9 50.8

2016 37.8 45.8 41.8 46.2 51.9 49.0

2017 40.4 51.4 45.9 43.8 49.9 46.8

2018 43.1 55.6 49.3 46.4 52.8 49.6

2019 45.7 60.0 52.8 49.5 57.0 53.3

2020 52.5 64.2 58.4 56.7 64.8 60.7

2021 53.5 66.9 60.2 57.8 64.6 61.2

2022 55.8 66.5 61.2 60.4 67.8 64.1

2023 58.1 71.2 64.7 62.3 70.2 66.3

2024 59.7 73.4 66.6 64.3 72.1 68.2

2025 61.9 77.2 69.5 66.4 75.2 70.8

2026 63.8 79.8 71.8 68.4 77.0 72.7

2027 65.7 83.0 74.3 70.3 79.9 75.1

2028 67.4 84.5 75.9 71.9 83.0 77.5

All prices expressed in 2013$ per MWh.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐16  

6.4.2 Comparison with Other Forecasts

The AESC 2013 scope of work requested the following analyses of the AESC 2013 wholesale electric

energy price forecast:

 Comparisons with other trends and forecasts, including comparisons to a trend of actual

monthly prices (real time) from ISO‐NE, a forecast as represented by the NYMEX futures

market and the most recent EIA forecast;

 A high‐level discussion of reasons for differences identified in the comparisons; and

 Explanation of any apparent price spikes and key variables that affect the outcome, as

well as identification of potential scenarios worthy of investigation.

Exhibit 6‐11 provides a comparison of 1) historical prices, 2) futures prices, 3) AESC 2011, and 4) AESC

2013 forecasts of the annual wholesale energy prices in the Central Massachusetts zone.  

Exhibit 6‐11 indicates that the AESC 2013 forecast is below AESC 2011. The lower AESC 2013 forecast

reflects significant reductions in the cost of natural gas, which is generally the marginal generation fuel.

It also reflects somewhat lower annual loads as well as delayed CO2 prices.  

The AESC 2013 Base Case forecast of Henry Hub natural gas prices starts in 2013 at $3.84/MMBtu, which

is about $0.60/MMBtu below AESC 2011. Over time that gap narrows, but still remains. The

irregularities in the annual electricity price curve primarily represent the natural gas price changes as

exemplified by winter basis differences that decline through 2017, when our assumptions of additional

New England pipelines remove some constraints. The 2020 increase is associated with the start of the

AESC 2013 assumed federal CO2 emission pricing.

Exhibit 6‐11. Historical and Forecast Annual Wholesale Price Comparisons (2013$/MWh)

The following sections detail our comparisons of the AESC 2013 forecast with other forecasts.

0

10

20

30

40

50

60

70

80

90

100

2003 2008 2013 2018 2023 2028

2013$/MWh

Historic

Futures

AESC 2011

AESC 2013
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐17  

Comparison with AEO 2013 Forecast

The Annual Energy Outlook is released annually by the EIA, and forecasts energy usage and price for the

U.S. as a whole and for its constituent regions. Table 55.5 of the report presents generation, capacity,

and prices for New England. 215 Although the AEO does not produce a wholesale market price per se, the

generation service category price comes fairly close. It should also be noted that the AEO price

represents a New England Average, while the AESC 2013 forecast is for the New England hub. Exhibit

6‐12 compares the AEO generation price with the AESC 2013 forecast, and shows that it is moderately

above the AESC price and the current futures.  

Exhibit 6‐12. Forecast Comparison with AEO 2013 (2013$/MWh)

Comparison with Trends in ISO NE Prices

We examined variations in historical monthly prices in ISO‐NE in 2010, 2011, and 2012 by investigating

changes in monthly electricity loads and natural gas prices. Exhibit 6‐13 shows the electricity monthly

prices in each of the last three calendar years. The general pattern is that high loads in the summer

increase prices above the spring and autumn periods because the summer marginal generation is less

efficient and more expensive. And moderately higher winter loads combined with sometimes much

higher spot natural gas prices can result in even higher winter electricity prices for these three years. In

2010 and 2012, years with generally lower loads, the winter prices were higher than the summer prices.

In 2011, electricity prices were higher in the summer since the winter was abnormally mild. As discussed

elsewhere, the primary driver of electricity prices in New England are the spot natural gas prices, which

                                                            

215 Table 55.5 “Electric Power Projections for EMM Region – Northeast Power Coordination Council / Northeast”

0

10

20

30

40

50

60

70

80

90

100

2013 2018 2023 2028

2013$/MWh

Futures

AEO2013

AESC 2013
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐18  

tend to be low in the summer but can spike considerably during cold winter periods. The AESC 2013

forecast of monthly prices is consistent with this historical trend.

Exhibit 6‐13. ISO‐NE Control Area Monthly Real‐Time Prices ($/MWh)  

The Chicago Mercantile Exchange (CME) maintains the NYMEX futures market for electricity prices at

the New England hub. There is a moderate amount of trading out about a year or two, but further out

the market is quite thin. Nevertheless, these futures prices provide one source of comparison with the

AESC 2013 forecast. We use futures as of March 15, 2013.

Exhibit 6‐14 shows the comparisons on a monthly basis. The prices through March 2013 are historical

values; those thereafter are from the NYMEX futures. Considering the volatility of the futures markets,

the correspondence is close.  

The price spikes in January and February 2013 represent a spike in the spot prices of natural gas as a

result of pipeline constraints. The purpose of the simulation model is to generate long‐term avoided

costs; thus, we do not see the model as predictive of short‐term price spikes.

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12

$/MWh

Month

ISO-NE Control Area Real-Time Monthly LMP

2010

2011

2012
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐19  

Exhibit 6‐14. AESC vs. NYMEX New England Futures

The AESC 2013 Base Case prices are within 5 percent of the historical prices and the NYMEX futures (as

of March 15, 2013) for ISO‐NE for 2011 through 2016.  

The next exhibit compares the futures and the AESC forecast energy prices on an annual average basis.

The correspondence is extremely close and represents both the assumptions about natural gas prices

and the AESC 2013 calibration process. Note too that the electric energy future prices may embed

different assumptions about future fuel prices, and definitely represent lower load growth assumptions.

0

20

40

60

80

100

120

140

123456789

101112

123456789

101112

2012 2013

Nominal $/MWh

AESC 2013 Model Calibration Results

Actuals & Futures

Model
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐20  

Exhibit 6‐15. Comparison of Futures and Base Case Annual Prices

Comparison to AESC 2011 Forecast

The following section summarizes differences between the AESC 2013 and AESC 2011 forecasts. Exhibit

6‐16 compares the two AESC forecasts on a levelized basis. Differences between the two forecasts occur

in all years and periods on the order of 3.6 to 21.0 percent. The lower summer prices reflect overall

lower natural gas prices. That is less apparent in the winter because of the high winter basis prices in the

initial years.

Exhibit 6‐16. 15‐Year Levelized Cost Comparison for Central Massachusetts (2013$/MWh)

On a levelized annual basis, the AESC 2013 Base Case wholesale energy prices for WCMA are

approximately 7.4 percent below those of AESC 2011. The reductions are generally more for summer

periods and less for winter, as shown in Exhibit 6‐9.216

                                                            

216 Levelized values have been calculated for AESC 2013 using a discount rate of 1.36 percent, and for AESC 2011 using a

discount rate of 2.46 percent.

0

10

20

30

40

50

60

2013 2014 2015 2016 2017

2013$/MWh

Futures

AESC 2013

Winter 

Peak 

Energy

Winter 

Off-Peak 

Energy

Summer 

Peak 

Energy

Summer 

Off-Peak 

Energy

Annual 

All-Hours 

Energy

AESC 2013 $64.39 $56.79 $63.80 $51.53 $59.86

AESC 2011 $67.90 $58.93 $80.74 $57.80 $64.68

% Difference -5.2% -3.6% -21.0% -10.8% -7.4%

Notes:

All prices expressed in 2013$ per MWh.

Levelization periods: 2014-2028 for AESC 2013, 2012-2026 or AESC 2011

Discount Rate 1.36% for AESC 2013, 2.46% for AESC 2011
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐21  

There are two primary factors causing the current forecast to differ from that of AESC 2011:

 Natural gas price – Natural gas prices are the primary determinant of electricity prices in

the New England wholesale market. The current natural gas price forecast is 13.4

percent lower on a 15‐year levelized basis than the previous one.  

 CO2 price – The current forecast for a national price for CO2 starts two years later (in

2020), but on a levelized basis (2012‐2026 for AESC 2011 and 2014‐2028 for AESC 2013)

the AESC 2013 CO2 price forecast is 17 percent higher than AESC 2011, due in large part

to higher forecast prices through 2019 under the RGGI Model Rule Update.217  

The impact of each of these factors is discussed in more detail below.

New England Natural Gas Price Forecast

Prices in the New England electric energy market have been historically very volatile. This volatility is

very strongly linked to the price that electric generators pay for natural gas. The graph below shows

these prices on a monthly average basis for the previous five years. One thing to note is that although

electricity prices closely follow natural gas prices, they tend to be proportionally higher in the summer

when loads are greater.218  

                                                            

217 On levelized basis for the same period (2014‐2028), AESC 2013 is 10 percent lower than AESC 2011, due the higher national

CO2 price forecast under AESC 2011.

218 As described above, there have been some recent exceptions to this general trend.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐22  

Exhibit 6‐17. Historic Monthly New England Electricity and Natural Gas Prices

Exhibit 6‐18 compares the current natural gas forecast for electric generation in New England—which

reflects historical margins in the spot market—with the corresponding forecast for AESC 2011. The AESC

2013 forecast has much lower prices in all years with the exception of 2013, which reflects current

constraints in New England pipeline capacity. The decline through 2017 of electric generation natural

gas prices reflects the return in winter basis prices to historical patterns as the pipeline capacity is added

in future years. On a levelized basis (2012‐2026 for AESC 2011 and 2014‐2028 for AESC 2013) the

current natural gas price forecast is $0.93/MMBtu or 13.4 percent below AESC 2011.219  

                                                            

219 For the same levelization period (2012‐2026), the AESC 2013 New England natural gas forecast is $1.18/MMBtu or 16.8

percent lower than AESC 2011.

0

4

8

12

16

20

24

28

0

20

40

60

80

100

120

140

160

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Electricity ($/MWh)

Nat Gas ($/MMBtu)

NE Monthly Electricity and Natural Gas Prices

OnPkDALMP OffPkDALMP NE NG Price
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐23  

Exhibit 6‐18. AESC 2013 vs. AESC 2011 Annual Gas Price Forecast Comparison

In terms of the seasonal differences, the winter (eight month) natural gas prices average 6 percent

above the annual average and the summer (four month) prices average 12 percent below. This differs

slightly from AESC 2011, where those seasonal differences were 3.5 percent higher for winter and 6.9

percent lower for summer.

CO2 Price Forecast

The CO2 price forecast used for AESC 2013 is different than that used in AESC 2011, reflecting

expectations of higher RGGI prices, but delayed national regulation (until 2020) as shown in Exhibit 6‐19.

The levelized (2014‐2028 for AESC 2013 and 2012‐2026 for AESC 2011) cost for AESC 2013 is $19.72/ton,

compared to $16.21/ton for AESC 2011, a $3.51 or 21.6 percent increase reflecting primarily an increase

in RGGI prices between 2013 and 2017.

3.00

4.00

5.00

6.00

7.00

8.00

9.00

2012 2017 2022 2027

2013$/mmBtu

New England Generation Natural Gas Prices

AESC 2011

AESC 2013
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐24  

Exhibit 6‐19. AESC 2013 & AESC 2011 CO2 Price Forecasts

Load Forecast

As discussed in section 5.2.1, the CELT 2012 loads used for AESC 2013 are below those used in AESC

2011. The summer peak loads are about 1 to 2 percent less, but the annual energy differences are

somewhat smaller, at 0 to 1 percent less. This overall reduction in load and energy means, for a similar

amount of available capacity, marginal heat rates and energy prices tend to be lower.  

Analysis of Forecast Differences

There are many factors that go into the wholesale electricity price, including fuel, environmental costs,

and system operation. The following exhibit focuses on a comparative analysis of the summer peak

prices for AESC 2011 and AESC 2013 on a levelized basis. As noted earlier, the AESC 2013 summer peak

price on a levelized basis is 21 percent below the corresponding AESC 2011 price. Exhibit 6‐20 presents

an illustrative calculation of those two summer prices and the resulting differences, keeping in mind that

there are numerous year‐by‐year variations.  

The table starts by showing the levelized wholesale prices over a comparable period. That is followed by

values for two of the key inputs—natural gas and CO2 prices. The system parameters represent overall

system behavior and are consistent with the behavior we see and expect from the dispatch modeling. In

addition to lower gas prices, AESC 2013 includes even more retirements of old coal units than AESC

2011. Those retirements shift the generation supply curve to the left, which causes more efficient units

to set the market price in summer peak periods, when loads are highest, as compared to AESC 2011. The

result is a decrease in summer peak period prices in AESC 2013 relative to AESC 2011 due to lower

natural gas and offset by higher CO2 prices.

$0

$5

$10

$15

$20

$25

$30

$35

$40

$45

$50

2013 2018 2023 2028

2013$/short ton CO2

AESC 2011

AESC 2013
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐25  

Exhibit 6‐20. AESC 2013 vs. AESC 2011 Summer On‐Peak Levelized Cost Comparisons

WCMA Summer On-Peak Period Price Comparison (2013$ per MWh) 

 

AESC 

2011 

AESC 

2013 

% 

Difference

Wholesale Price from Simulation Model $80.74 $63.80 -21.0% 

 

Analysis 

 

Input Values 

Summer NG Price ($/MMBtu) $6.70 $5.38 -19.7% 

CO2 Price ($/ton) $16.21 $19.72 21.6% 

NG CO2 (lbs/MMBtu) 118 118 0.0% 

Marginal Heat Rate (Btu/kWh) 10,150 9,061 -10.7% 

Marginal CO2 Rate (tons/MWh) 0.60 0.53 -10.7% 

 

Price and Heat Rate Effects 

Fuel Cost ($/MWh) $68.05 $48.79 -28% 

CO2 Cost ($/MWh) $9.71 $10.54 9% 

Other variable & bid costs ($/MWh) $3.00 $3.00 0% 

Wholesale Price Estimated from Price 

and Heat Rate effects + other variable 

costs $80.76 $62.33 -23% 

 

Notes 

Values may not sum due to rounding 

AESC 2011 levelized (2012-2026) 

AESC 2013 levelized (2014-2028) 

CO2 costs are embedded portion only 

Marginal heat rates are for the single years of 2011 and 2013 

As indicated previously, the AESC 2013 annual wholesale energy price forecast on a levelized basis

(2014‐2028) is 7.4 percent below that of AESC 2011, while the summer on‐peak difference is much

higher. On a levelized basis, the AESC 2013 summer natural gas price for New England electric

generators is 19.7 percent lower than in AESC 2011, and the CO2 price forecast is 21.6 percent higher.

Exhibit 6‐21 provides the summer on‐peak prices by year for AESC 2011 and AESC 2013.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐26  

Exhibit 6‐21. AESC 2013 vs. AESC 2011 Summer On‐Peak Prices for WCMA  

6.4.3 Forecast of Electric Energy Prices by State

The forecast of energy values by zone by year for each period—summer on peak, summer off‐peak,

winter on‐peak, and winter off‐peak—are presented in Appendix B.  

Exhibit 6‐22 illustrates the zonal annual peak period prices in descending order by price. Note how some

zones have nearly identical prices. The highest price zone is southwestern Connecticut and the lowest

price zone is Maine. The price decline up to 2017 is associated with the decline in spot natural gas

prices, as a result of increased pipeline capacity. The large increase in 2020 is associated with the

implementation of a federal carbon price for electric generation. Prices in Maine are lower because of

continued excess generation and transmission constraints to the rest of New England.

Year AESC 2013 AESC 2011

2011 57.83

2012 63.55

2013 45.06 65.28

2014 43.93 67.74

2015 45.27 72.67

2016 45.84 78.72

2017 51.38 78.29

2018 55.60 88.19

2019 59.95 86.75

2020 64.18 82.89

2021 66.87 84.52

2022 66.53 86.13

2023 71.21 90.21

2024 73.42 91.61

2025 77.16 92.84

2026 79.82 96.91

2027 82.96

2028 84.46

Annual Summer On-Peak Price 

(2013$/MWh)
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐27  

Exhibit 6‐22. New England Annual Peak Locational Price Forecast

6.4.4 Transmission Energy Losses

Our forecast for marginal energy clearing prices includes inter‐area losses for energy coming into load

area across transmission links between modeling zones. These losses are not reported by the model by

time of day; therefore, we have presented the loss factors for summer and winter periods only. The

losses are presented in Exhibit 6‐23 as a percentage of imports into each zone or state. These are losses

only for transmission between zones and do not include losses within individual zones. Exhibit 6‐24

presents the generation within each zone as a percentage of zonal load. Some zones produce energy

equivalent to several times their own load, while others have nearly zero local generation.

30

40

50

60

70

80

90

2013 2018 2023 2028

2013$/MWh

New England Annual Locational Wholesale Energy 

Prices

NE - SWCT

NE - Boston

NE - CT NE

Central

NE - CT Norwalk

NE - NEMA

NE - Rhode

Island

NE - New

Hampshire

NE - SEMA

NE - Vermont

NE - WCMA

NE - BHE

NE - SME

NE - ME
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐28  

Exhibit 6‐23. AESC 2013 Modeling Zone and State Transmission Losses

Modeling Zone Summer Winter

NE ‐ BHE 6.0% 6.0%

NE ‐ ME 0.0% 0.0%

NE ‐ NEMA 3.0% 4.1%

NE ‐ New Hampshire 8.8% 8.6%

NE ‐ Rhode Island 0.5% 1.1%

NE ‐ SEMA 0.5% 0.5%

NE ‐ SME 0.0% 0.0%

NE ‐ SWCT 2.0% 2.0%

NE ‐ Vermont 3.6% 4.6%

NE ‐ WCMA 1.6% 1.4%

NE ‐ Boston 1.0% 1.1%

NE ‐ CT NE Central 0.6% 1.2%

NE ‐ CT Norwalk 0.5% 0.9%

New England Average 2.1% 2.4%

State Summer Winter

CT 1.2% 1.4%

MA 1.9% 2.3%

ME 0.2% 0.7%

NH 8.8% 8.6%

RI 0.5% 1.1%

VT 3.6% 4.6%

New England Average 2.1% 2.4%

Modeling Zone Losses

State Losses
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐29  

Exhibit 6‐24. AESC 2013 Generation within Each Zone as a Percentage of Zonal Load

Zone Percentage of

Zonal Load

BHE 274%

NH 191%

RI 154%

CT 119%

SEMA 109%

VT 92%

WMA 91%

SWCT 83%

ME 81%

SME 80%

BOSTON 38%

CMA/NEMA 8%

NOR 0%

ISO‐NE 91%

6.5 Avoided Cost of Compliance with RPS

6.5.1 Introduction

Our estimate of avoided costs includes the expected impact of the region’s five existing Renewable

Portfolio Standards. The annual quantity of renewable energy that LSEs need to acquire in order to

comply with RPS requirements is directly proportional to the annual load that the LSEs supply.  

To the extent that the price of renewable energy exceeds the market price of electric energy, LSEs incur

a cost to meet the RPS percentage target. That incremental unit cost is the price of a Renewable Energy

Certificate (REC). This annual compliance cost ($) equals the quantity of renewable energy purchased

(MWh) multiplied by the REC price ($/MWh).

Energy‐efficiency programs reduce the cost of compliance with RPS requirements by reducing the total

load, or MWh, that must be supplied. Reduction in load due to DSM will reduce the RPS requirements of

LSEs and therefore reduce the costs they seek to recover associated with complying with these

requirements. The RPS compliance costs that retail customers avoid through reductions in their energy

usage is equal to the price of renewable energy in excess of market prices, multiplied by the percentage

of retail load that a supplier must meet from renewable energy under the RPS regulations. RPS targets

for Connecticut, Maine, Massachusetts, New Hampshire, and Rhode Island are a percentage of retail

load as defined by state‐specific legislation and regulation in effect as of April 2013. While AESC 2011

assumed that Vermont would adopt a mandatory RPS target of 5 percent by 2017, after consulting with

the Study Group, the AESC 2013 analysis assumes that Vermont does not establish a binding RPS during

the study period.  
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐30  

This section forecasts those avoided RPS costs where the key input to the calculations is a forecast of the

price of renewable energy in excess of market prices each year, i.e., the forecast price of RECs. This

section presents a forecast of the expected future cost of renewable energy certificates and RPS

compliance. We deduct the market price of energy from the forecast cost of renewable energy in order

to calculate the forecast price of RECs for each RPS subcategory, by state and by year. For all Class 1

requirements, the forecasted price of RECs for the remainder of 2013 and all of 2014 is based on

historical average broker quotations regarding short‐term forward transactions consummated between

January and April 2013. Beginning in 2018, Class 1 REC prices reflect the forecasted cost of new entry.

Class 1 prices are interpolated for 2015 through 2017 by scrutinizing the expected balance between RPS‐

eligible supply and RPS demand and by including the expected impact of banked compliance.220 For

Class 2 requirements, the 2013 REC prices are based on a 12‐month (May 2012 to April 2013) historical

average of broker quotes and/or bid‐ask spreads. These REC prices are summarized in Appendix F.  

The following exhibit summarizes the change in Avoided RPS costs between AESC 2011 and AESC 2013.

Exhibit 6‐25. Comparison of Avoided RPS Costs

Comparison of Avoided RPS Costs 2013$/MWh of Load Levelized Price Impact 2014 ‐ 2028

(2013 $, unless noted)

   CT ME MA NH RI VT

AESC 2011

(2011$) $2.17 $0.92 $4.98 $2.30 $1.43 $0.50

AESC 2011

(2013$) $2.24   $0.95 $5.14 $2.38 $1.48   $0.52

AESC 2013 $4.62   $1.82 $6.25 $5.05 $3.45   $0.00

Percent

Difference 106% 91% 22% 112% 134%  ‐100%

Notes

Conversion from 2011$ to 2013$: 1.033

AESC 2011 levelization period (2012‐2026) using a 2.46 percent discount rate.

The avoided RPS costs are generally higher for AESC 2013 than AESC 2011 due to an increase in

shortage‐induced near‐term REC prices and delays in the renewable energy development pipeline due

to a dearth of creditworthy long‐term contracts in the market. The percent difference in the

Massachusetts Avoided RPS costs are lower than the other states primarily due to the change in

methodology to exclude the Massachusetts Solar Carve‐Out that had been included in prior AESC

studies.

                                                            

220 In the event that an LSE purchases RECs in excess of its current year RPS obligation, each state allows LSEs to save and count

that quantity of compliance against either of the following two compliance years. This compliance flexibility mechanism is

referred to as banking. LSEs may only bank compliance within a single state, and may not transfer banked compliance credit

to other entities.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐31  

6.5.2 Methodology

As described in more detail in section 5.2.4, AESC 2013 assumes LSEs will comply fully with established

RPS requirements each year—either by securing RECs or by making Alternative Compliance Payments.

For ease of presentation, this AESC 2013 discussion generally refers to all of these requirements as RPS

requirements, which must be met with RECs, even though some of the resources are not renewable.

Our estimate of avoided costs includes a forecast of the REC costs that reduction in load will enable an

LSE to avoid. Reduction in load due to DSM will reduce the RPS requirement of the LSE and therefore

reduce the cost they incur to comply with that requirement.  

Estimating the Cost of Entry for New or Incremental Renewable Energy

As with AESC 2011, the AESC 2013 analysis assumes that in the long run, the price of renewable energy

certificates (and therefore the unit cost of RPS compliance) will be determined by the cost of new entry

of the marginal renewable energy unit. To estimate the new or incremental REC cost of entry, we

constructed a supply curve for incremental New England renewable energy potential based on various

resource potential studies that sorts the supply resources from the lowest cost of entry to the highest

cost of entry.221 The resources in the supply curve model are represented by 307 blocks of supply

potential from resource studies, each with total MW capacity, capacity factor, and cost of installation

and operation applicable to projects installed in each year.

The supply curve consists of land‐based wind, biomass, hydro, landfill gas, offshore wind, and tidal

resources. While offshore wind is the largest potential resource by MW, land‐based wind is the largest

source by number of blocks (modeled as 86 separate categories), varying by state, number and size of

turbines in each project, wind speed, and distance from transmission.

Resources from the supply curve are modeled to meet net demand (as described earlier), which consists

of the gross demand for new or incremental renewables, less:

a) Existing eligible generation already operating (including biomass co‐firing in

existing facilities);

b) The current level of RPS imports; and

c) Additional imports over existing ties to neighboring control areas.

                                                            

221 These assumptions are based on technology assumptions compiled by Sustainable Energy Advantage, LLC from a range of

studies and interviews with market participants. Some characteristics are adapted from those used in a New England

renewable energy supply curve analysis prepared by Sustainable Energy Advantage for the New England States Committee

on Electricity. Other characteristics are adapted from those used in a New England renewable energy supply curve analysis

prepared by Sustainable Energy Advantage, LaCapra Associates and AWS Truewind for the Maine Governors Wind Task

Force Study on behalf of the Natural Resources Council of Maine. Typical generator sizes, heat rates, availability, and

emission rates are consistent with technology assumptions used by ISO‐New England in its scenario planning process. The

resulting supply curve is proprietary to Sustainable Energy Advantage, LLC.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐32  

For solar and fuel‐cell resources, which tend not to be resource‐constrained, we separately estimated

the amounts that would be driven by various policy initiatives; these amounts were also netted from

gross demand.

The estimated levelized cost of marginal resources is based on several key assumptions, including

projections of capital costs, capital structure, debt terms, required minimum equity returns, and

depreciation, which are combined and represented through a carrying charge. The estimated levelized

cost of marginal resources also includes fixed and variable operations and maintenance costs,

transmission and interconnection costs (as a function of voltage and distance from transmission), and

wind integration222 costs. The Federal Production Tax Credit is assumed to be phased out over a five‐

year period ending in 2018. Capital and operating costs were escalated over time using inflation. The

levelized commodity revenue over the life of each resource was determined based on the sum of energy

and capacity prices, both utilizing preliminary AESC 2013 Base Case estimates of the FCM price and all‐

hour zonal LMP.

Revenues for wind resources were adjusted in three ways:

 The value of wind energy was adjusted to reflect wind’s variability, production

profile, and historical discount of the real‐time market (in which wind plants will

likely sell a significant portion of their output) versus the day‐ahead market.

 Energy prices were further discounted to reflect the lower prices typical in long‐term

contracts, especially for wind plants, with their fluctuating energy output.223

 Wind generators were assumed to receive FCM revenues corresponding to only 15

percent of nameplate capacity, reflecting the poor performance of most on‐shore

wind plants on summer afternoons. This assumption may be conservative for

commercial wind farms, reflecting developer, investor, and lender risk‐aversion

regarding future capacity valuation.

The price for each block of the supply curve is estimated for each year. For each generator, we

determined the levelized REC premium, or additional revenue the project would require in order to

attract financing, for market entry by subtracting the nominal levelized value of production consistent

with the AESC 2013 projection of wholesale electric energy prices from the nominal levelized cost of

marginal resources:

224

                                                            

222We assume that reinforcement of major transmission facilities (e.g., improved connections between Maine and the rest of

New England) will be socialized.

223Our forecast of REC prices assumes that most renewables will be financed with long‐term contracts for most of their

capacity and/or RECs.

224 SEA calculated these levelized analyses using discount rates representative of the cost of capital to a developer of

renewable resource projects.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐33  

 The nominal levelized cost of marginal resources is the amount the project needs in

revenue on a levelized $/MWh basis;

 The nominal levelized value of production is the amount the project would receive

from selling its commodities (energy, capacity, ancillary services) into the various

wholesale markets; and

 The difference between the levelized cost and the levelized value represents the REC

premium.

Unless the revenue from REC prices can make up the REC premium, a project is unlikely to be

developed. Resource blocks are sorted from low to high REC price, and the intersection between

incremental supply and incremental demand determines the market‐clearing REC price for market entry.

Our projections assume that REC prices for new renewables will not fall below $2/MWh, which is the

estimated transaction cost associated with selling renewable resources into the wholesale energy

market. This estimate is consistent with market floor prices observed in various markets for renewable

resources.

Calculating Avoided RPS Compliance per MWh Reduction

The RPS compliance costs that retail customers avoid through reductions in their energy usage is equal

to the price of renewable energy in excess of market prices (e.g., the REC price) multiplied by the

portion of retail load that a supplier must meet from renewable energy under the RPS. In other words,

Avoided RPS cost = REC price × RPS percentage

For example, in a year in which REC prices are $30/MWh and the RPS percentage is 10%, the avoided

RPS cost to a retail customer would be $30 × 10% = $3/MWh.  

We calculated the RPS compliance costs that retail customers in each state avoid through reductions in

their energy usage in each year for each major applicable RPS tier as follows:

(REC Pricen × RPS %n)/(1‐L)

Where:

n = the RPS tier

L = the load‐weighted average loss rate from ISO wholesale load accounts to retail

meters

We forecasted annual REC prices for three major RPS tiers. These are new renewables (primarily Class I),

all New Hampshire Class II solar, and all other renewables. For 2013 and 2014 we relied upon recent

broker quotes to estimate the market prices at which RECs are transacted. REC markets in New England

continue to suffer from a lack of depth, liquidity, and price visibility. Broker quotes for RECs represent

the best visibility into the market’s view of current spot prices. However, since RPS compliance must be

substantiated annually, and actual REC transactions occur sporadically throughout the year, the actual
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐34  

weighted average annual price at which RECs are transacted will not necessarily correspond to the

straight average of broker quotes over time. Broker quotes for RECs may span several months with few

changes and no actual transactions (being represented by offers to buy or sell), and at other times may

represent a significant volume of actual transactions. As a result, care should be taken to filter such data

for reasonableness. Exhibit 6‐26 provides the type of REC prices we used to characterize the near‐term

REC market prices.225  

Exhibit 6‐26. REC and APS Prices: 2012 Annual Average and 2013 Year‐to‐Date (2013$/MWh)

Annual Avg. REC Prices, 2012

(2013$/MWh)

Avg. REC Prices, Jan‐Mar 2013

(2013$/MWh)

CT Class I $48.00 $54.00

Class II $0.40 $0.55

Class III $10.75 $10.25

MA Class I $58.00 $63.00

Class II – renewable $24.50 $26.00

Class II – WTE $5.75 $7.75

APS $19.50 $19.75

RI New $57.50 $62.75

Existing $0.65 $0.60

ME Class I $32.00 $22.75

Class II $0.40 $0.10

NH Class I $56.00 $54.50

Class II – Solar $64.00 $51.00

Class III $28.25 $28.75

Class IV $28.75 $23.50

Data from confidential REC brokers’ quotations compiled by Sustainable Energy Advantage, LLC

The AESC 2013 estimates of Class I REC prices in the longer term (after 2014) are based on analysis of

the near‐term supply and demand balance, banking limits and observed practices, and the cost of entry

of new renewable energy resources in each applicable year. That analysis relies on SEA’s renewable

energy supply curve model to determine the marginal (or market‐clearing) resource in each year

through 2028—after which REC prices are assumed to remain stable. REC prices were estimated based

on the difference between the levelized cost for the marginal renewable resource and the resource’s

commodity market value based on our Base Case forecast of wholesale electric‐energy‐market prices.  

We forecasted REC prices for the remaining two tiers as follows:

                                                            

225 This table was developed from a representative sampling of REC brokers quotes, which is comprised of both consummated

transactions and bid‐ask spreads in periods where transactions were not reported.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐35  

 For New Hampshire Class II (solar) REC prices were estimated at the lesser of (1) the

alternative compliance payment rate and (2) the difference between a levelized cost

of energy estimate for solar and our production‐weighted Base Case forecast of

wholesale electric‐energy‐market prices.

 For all other RPS tiers we escalated recent broker‐derived prices at inflation. The

exception to this methodology was for RPS classes focused on existing supply but for

which such existing supply has not been certified by the applicable RPS authority in a

quantity sufficient to meet demand. Near‐term REC prices for such classes were

estimated based on current broker quotes and the applicable ACP. REC prices were

assumed to trend toward values which reflect a market in equilibrium or modest

surplus over time, as existing generators become certified and participate in the

program.

6.5.3 New or Incremental Additions to RPS Supply

New or incremental renewable resources are those that qualify as “Class I” in CT, MA, NH, ME, and as

“New” in RI. We refer to those categories in those states collectively as Class I. REC prices will be driven

both by the costs of renewable resources eligible in each state and by the quantity of state‐specific

supply compared to state‐specific demand. Because RPS eligibility criteria differ by state, REC prices are

differentiated by state and reflect state‐specific expectations with respect to generator certification and

LSE banked compliance. A more detailed description may be found in section 5.2.4.

In AESC 2011, we assumed that the MA Solar Carve‐Out (a sub‐set of MA Class I) reaches its 400 MW

target in 2018 and that the target remains at this level through 2022. In AESC 2013, we assumed that

Governor Patrick’s April 2013 announcement targeting 1,600 MW of solar installed by 2020 increases

the MA Solar Carve Out by an incremental 800 MW, for a total Solar Carve‐Out obligation of 1,200 MW

by 2020. Massachusetts is unique in its treatment of the solar carve‐out portion of its Class 1 obligation.

While the carve‐out itself is not unique, Massachusetts establishes an annual MWh obligation, which is

then allocated among the obligated LSEs. In aggregate, this solar target is converted into a percentage of

state load and is removed from the Class 1 percentage target for that year—thereby reducing the Class 1

RPS compliance obligation, which is then avoidable through energy efficiency activities. In AESC 2011,

we assumed that the MA Solar Carve Out obligation was similarly avoidable. Upon further review and

discussion with LSEs, however, AESC 2013 concludes that the solar carve‐out represents an LSE

obligation to include a fixed quantity (MWh) of Solar RECs (SRECs) each year and is therefore not

avoidable through energy efficiency measures that reduce all other RPS obligations.  

While Class I RPS requirements generally spur the development of new renewable resources, Class II, III,

and IV requirements are generally designed as “maintenance tiers.” These programs are intended to

provide just enough financial incentive to keep the existing fleet of renewable resources in reliable

operation. Due to their maintenance orientation, Class II, III and IV percentage targets are generally held

constant, with annual obligations varying only based on changes in the demand forecast. CT Class II, MA

Class II‐WTE, ME Class II, and RI "Existing" REC markets are in surplus. Therefore, REC prices in these
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐36  

markets are expected to remain relatively constant at levels just above the transaction cost. The MA

Class II market has overlapping eligibility with CT Class I. In addition, while there is theoretically ample

supply to meet MA Class II, fewer generators than expected have undertaken the steps necessary to

comply with the eligibility criteria and become certified. Therefore, the MA Class II market is currently in

shortage. As a result, in 2012 the Massachusetts legislature suggested that the Department of Energy

Resources (DOER) consider adjustments to the Class II (non‐Waste‐to‐Energy) program in order to bring

the market into a balance more consistent with a policy targeting existing resources. While DOER is

expected to act in a manner consistent with the legislature’s suggestions, such action will likely include a

lengthy stakeholder process which will extend the schedule of any proposed adjustments. As a result,

long‐run MA Class II REC prices are assumed to be the lesser of CT Class I REC prices and 50 percent of

the MA Class II Alternative Compliance Payment (ACP) rate. REC prices for MA APS are forecasted at 90

percent of the ACP rate. The CT Class III market has an administratively set REC price floor of $10. Based

on the performance of this market to date, CT Class III compliance prices are expected to remain at $10

per MWh throughout the study period. Existing solar facilities across New England are eligible for NH

Class II. As such, this market is expected to remain in balance, trend toward the MA Class I REC price by

2014, and settle marginally above the MA Class I REC price for the remainder of the study period. The

NH Class III and NH Class IV markets226 have overlapping eligibility with CT Class I. In the long‐run,

therefore, NH‐III and NH‐IV REC prices are assumed to be the lesser of CT Class I and 90 percent of their

respective ACP rates.

Class I requirements will outpace the other classes on a GWh basis over time. This phenomenon is

shown in Exhibit 6‐27, which summarizes New England’s total renewable energy requirements by year,

based on the RPS percentage targets by state and ISO‐NE’s 2012 CELT forecast, as discussed in Chapter

5. Exhibit 6‐27 distinguishes between the quantity of Class I renewables that are required and the

aggregate quantity of all other classes of renewables combined.  

   

                                                            

226 Several Class III biomass and Class IV hydroelectric facilities have been certified in both NH III or IV, respectively, and CT

Class I.
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐37  

Exhibit 6‐27. Summary of New England RPS Demand

New England Annual RPS Demand

Year Class 1 (GWh) Other Classes (GWh) Total (GWh)

2013 9,279 10,625 19,904

2014 10,530 10,772 21,302

2015 12,083 11,082 23,165

2016 13,657 11,246 24,903

2017 15,253 11,392 26,644

2018 16,752 11,529 28,281

2019 18,276 11,662 29,938

2020 19,691 11,789 31,481

2021 20,617 11,919 32,537

2022 21,570 12,054 33,624

2023 22,542 12,189 34,731

2024 23,534 12,325 35,859

2025 24,546 12,462 37,008

2026 25,481 12,599 38,081

2027 26,434 12,738 39,172

2028 27,406 12,878 40,284

Notes: Class 1 includes voluntary demand. Based on CELT 2012 and RPS % targets as of 4/1/2013.

The requirements for each RPS class were derived by multiplying the load of obligated entities (those

retail LSEs subject to RPS requirements, often excluding public power) by the applicable annual class‐

specific RPS percentage target. The RPS percentage requirements by class and year are listed in

Appendix F. The load by state is based on CELT 2012 as detailed in section 5.2.1.

The major sources of the renewable supply forecast used to meet the RPS requirements by year are

shown in Exhibit 6‐28. These sources include wind, biomass, natural gas fuel cells, and hydro. The

“other” category is included to represent the aggregate contribution of solar, landfill gas, and tidal

resources.

   
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐38  

Exhibit 6‐28. Cumulative Incremental Supply of Class 1 Renewable Energy Resources in New England, by Fuel

Type (excludes resources already in the 2012 CELT Report)

Class 1 Renewable Energy Supply, by Fuel Type (GWh)

Year Wind Biomass NGFC Hydro Other Total

   a c d e f g = sum a to f

2013 709 1,035 110 105 319 2,278

2014 852 1,594 159 122 343 3,070

2015 1,727 2,114 241 131 380 4,592

2016 2,993 2,439 241 131 385 6,189

2017 4,122 2,575 241 131 385 7,455

2018 4,989 2,692 241 205 672 8,800

2019 5,907 2,702 241 327 690 9,866

2020 7,113 2,702 241 367 707 11,130

2021 7,705 2,702 241 575 725 11,948

2022 7,923 2,702 241 575 867 12,308

2023 8,341 2,702 241 575 1,047 12,905

2024 8,673 2,702 241 575 1,193 13,384

2025 9,439 2,702 241 575 1,328 14,284

2026 9,854 2,702 241 575 1,463 14,835

2027 10,421 2,702 241 659 1,598 15,620

2028 11,716 2,702 241 659 1,733 17,050

The expected distribution of Class 1 RPS supplies between ISO‐NE and adjacent control areas is

summarized in Exhibit 6‐29. Supply is categorized as follows:

 Existing eligible generation already operating (including biomass co‐firing in existing

facilities)

 The quantity of (energy and) RECs currently imported from RPS‐eligible facilities

located outside of ISO‐NE

 The assumed incremental level of (energy and) RECs imported from RPS‐eligible

facilities located outside of ISO‐NE

 The assumed incremental renewable resources by fuel type

   
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐39  

Exhibit 6‐29. Expected Distribution of New Renewable Energy between ISO‐NE and Adjacent Control Areas

Summary of Renewable Energy Supply & Demand for AESC 2013

Class 1 RPS Supply (GWh) Class 1

RPS

Demand

(GWh)

New Renewable

Energy

Surplus/(Shortage)

(GWh)

ISO‐NE Supply Imported Supply

Total Supply Year Operating Incremental Current Expected

a b c d e = sum a to

d f g = e‐f

2013 7,913 49 1,369 164 9,494 9,279 215

2014 7,960 794 1,431 180 10,365 10,530 (166)

2015 7,960 2,315 1,507 261 12,043 12,083 (40)

2016 7,960 3,913 1,501 536 13,909 13,657 252

2017 7,960 5,178 1,491 796 15,425 15,253 173

2018 7,960 6,523 1,482 1,089 17,053 16,752 301

2019 7,960 7,590 1,472 1,280 18,302 18,276 26

2020 7,960 8,854 1,463 1,487 19,762 19,691 71

2021 7,960 9,672 1,453 1,708 20,793 20,617 176

2022 7,960 10,032 1,444 1,947 21,382 21,570 (188)

2023 7,960 10,629 1,434 2,204 22,227 22,542 (316)

2024 7,960 11,108 1,425 2,480 22,972 23,534 (562)

2025 7,960 12,008 1,415 2,778 24,160 24,546 (386)

2026 7,960 12,558 1,406 3,258 25,182 25,481 (299)

2027 7,960 13,344 1,397 3,280 25,980 26,434 (454)

2028 7,960 14,774 1,388 3,303 27,424 27,406 18

Notes: A portion of the Operating supply (that which is not included in the CELT) was already provided

to Synapse. This supply is composed of wind, solar, biomass, biogas, hydro, and natural gas fuel cell

projects (which are eligible in CT), including behind‐the‐meter installations within ISO‐NE.

Exhibit 6‐29 also compares total Class I RPS supply to total Class 1 RPS demand. The combination of

operating supply, projects currently under development, imported supply and resource potential from

the renewable energy supply curve analysis are expected to keep supply and demand in balance through

2028.  

In addition to new or incremental renewables, several states also have minimum requirements for

existing renewable energy sources, or other eligible sources. The eligibility details and target

percentages are summarized in Appendix F.

6.5.4 Estimated Cost of Entry for New or Incremental Renewable Energy

Our general approach to estimating the cost of entry for new or incremental renewable supply is

described in section 6.5.2.

As previously stated, 2013 and 2014 REC prices were estimated using broker quotes. Due to an increase

in renewable energy imports, the price responsiveness of operating biomass supply, upcoming long‐

term contract solicitations in several states, and the ability to bank RPS compliance, the cost of new
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐40  

entry is not expected to be determined by generic supply curve supply until roughly 2018. Until then,

REC prices are estimated by scrutinizing the expected balance between RPS‐eligible supply and RPS

demand and by including the expected impact of banked compliance. Beginning in 2018, regional REC

prices are expected to converge on the cost of new entry as all states rely on new or incremental

renewable resources to meet their RPS demands—with only modest price differentials between states

based on eligibility, bank balances and utility‐specific decisions to retire the RECs from long‐term

contracts in satisfaction of RPS obligations. Our projection of the cost of new entry for each state is

summarized in Exhibit 6‐30.  

Exhibit 6‐30. REC Premium for Market Entry ($/MWh)

REC Premium for Market Entry Class 1 (2013$/MWh)

Year CT ME MA NH RI

2018 $14.33 $8.49 $15.80 $15.00 $15.80

2019 $15.46 $9.88 $17.02 $16.23 $17.02

2020 $16.74 $11.51 $18.40 $17.62 $18.40

2021 $16.65 $13.44 $18.39 $18.08 $18.38

2022 $16.18 $15.70 $18.02 $18.18 $17.99

2023 $15.31 $18.36 $17.24 $17.86 $20.84

2024 $19.05 $21.49 $21.06 $21.24 $24.61

2025 $26.19 $24.90 $28.28 $27.56 $28.28

2026 $26.19 $24.90 $28.28 $27.56 $28.28

2027 $26.19 $24.90 $28.28 $27.56 $28.28

2028 $26.19 $24.90 $28.28 $27.56 $28.28

These results are highly dependent upon the forecast of wholesale electric energy market prices,

including the underlying forecasts of natural gas and carbon allowance prices, as well as the forecast of

inflation. A lower forecast of market energy prices would yield higher REC prices than shown,

particularly in the long term. In all cases, project developers will need to be able to secure long‐term

contracts and attract financing based on the aforementioned natural gas, carbon, and resulting

electricity price forecasts in order to create this expected REC market environment. This presents an

important caveat to the projected REC prices, as such long‐term electricity price forecasts (particularly

to the extent that they are influenced by expected carbon regulation) are not easily taken to the bank.

In contrast to the long‐term REC cost of entry, spot prices in the near term will be driven by supply and

demand, but are also influenced by REC market dynamics and to a lesser extent to the expected cost of

entry (through banking), as follows:

 Market shortage: Prices approach the cap or Alternative Compliance Payment

 Substantial market surplus, or even modest market surplus without banking: Prices crash to

approximately $0.50 to $2/MWh, reflecting transaction and risk management costs
Synapse Energy Economics, Inc. – AESC 2013 Page 6‐41  

 Market surplus with banking: Prices tend towards the cost of entry, discounted by factors

including the time‐value of money, the amount of banking that has taken place, expectations of

when the market will return to equilibrium, and other risk management factors

Detailed projections of REC prices by state for Class I renewables are presented in Appendix F.

6.5.5 Avoided RPS Compliance Cost per MWh Reduction

Our approach to calculating avoided RPS compliance costs per MWh of reduction is described in section

6.5.2. The RPS compliance costs that retail customers avoid through reductions in their energy usage is

equal to the price of renewable energy in excess of market prices multiplied by the percentage of retail

load that a supplier must meet from renewable energy under the RPS regulation. In other words,

l

Rn i



 

1

n

Pn,i ,

Where:

i = year

n = RPS classes

Pn,i = projected price of RECs for RPS class n in year i,

Rn,i = RPS requirement, expressed as a percentage, for RPS class n in year i, from Exhibit 3‐9 in

Deliverable 3‐1.

l = losses from ISO wholesale load accounts to retail meters

For example, in a year in which REC prices are $30/MWh and the RPS percentage target is 10%, the

avoided RPS cost to a retail customer would be $30 × 10% = $3/MWh. Detailed results from Appendix C

are incorporated into the Appendix B Avoided Cost Worksheets by costing period. The year‐by‐year RPS

percentages for each RPS tier are shown in Appendix F. The levelized RPS price impact for the 2014 to

2028 period, in 2013$ per MWh of load, is shown below.

Exhibit 6‐31. Avoided RPS Cost by Class 2013$/MWh of Load Levelized Price Impact 2014 ‐ 2028 (2013$)

CT ME MA NH RI VT

Class 1 $4.23 $1.78 $4.43 $2.43 $3.44 $0.00

All Other Classes $0.39 $0.04 $1.83 $2.62 $0.01 $0.00

Total $4.62 $1.82 $6.25 $5.05 $3.45 $0.00
Synapse Energy Economics. Inc. — AESC 2013 Page 7‐1  

Chapter 7: Demand Reduction Induced Price Effects

7.1 Introduction  

Demand Reduction Induced Price Effect (DRIPE) refers to the reduction in prices in the wholesale

markets for capacity and energy, relative to the prices forecast in the Base Case, resulting from the

reduction in quantities of capacity and of energy required from those markets due to the impact of

efficiency and/or demand response programs. Thus, DRIPE is a measure of the value of efficiency in

terms of the reductions in wholesale prices seen by all retail customers in a given period.

Price suppression, the general term including DRIPE, is not a new concept. For a review of the literature

on the effect of load reductions (or alternatively, of load increases or the addition of other resources) on

market prices in competitive electricity markets, see AESC 2011 pages 6‐61 to 6‐69.

This chapter describes our methodology and assumptions for DRIPE, and presents our estimates of

capacity DRIPE and energy DRIPE in the first two sections. The remaining section of this chapter details

our estimate of natural gas DRIPE, which is new to AESC 2013.

The following exhibit provides an overview of our conceptualization of electric and gas DRIPE.

Exhibit 7‐1. DRIPE Overview

Reduction in Retail Load Affected Cost Categories Affected Cost Component  

Natural Gas

Own‐price (retail gas prices) Gas Supply

Cross‐fuel (electric energy prices) Gas Supply

Basis to New England

Electricity

Own‐price (electric prices) Electric Energy  

Electric Capacity

Cross‐fuel (natural gas supply prices) Gas Supply

  

Our estimates indicate that the DRIPE effects are very small when expressed in terms of an impact on

market prices, i.e., reductions of a fraction of a percent. However, the DRIPE impacts are significant

when expressed in absolute dollar terms. Very small impacts on market prices, when applied to all

energy and capacity being purchased in the market, translate into large absolute dollar amounts.

On a 15‐year levelized basis, the AESC 2013 estimates of capacity DRIPE are approximately 45.3 percent

lower than those from AESC 2011.227 This decrease is primarily due to: 1) the extension of the floor

                                                            

227 AESC 2011 values for 2012 Installations levelized from 2012‐2026.  
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐2  

price; 2) the projection of generic new generation in 2020; and 3) a change in the assumed duration of

DRIPE. The AESC 2013 projections assume the phase‐out, or dissipation, of capacity DRIPE will last up to

eight years, versus 11 years assumed in AESC 2011. The projected dissipation of capacity DRIPE is based

upon an analysis of the various factors that tend to offset the reduction in capacity prices. Those factors

include timing of new capacity additions, timing of retirements of existing capacity, elasticity of

customer demand, and the portion of capacity that LSEs acquire from the FCM.

The AESC 2013 estimates of intrastate energy DRIPE are approximately 18 percent lower than those

from AESC 2011 on a levelized annual load weighted basis. These lower estimates are primarily due to

lower wholesale energy prices and shorter DRIPE dissipation.  

Although uncertainty remains regarding the projections of energy DRIPE and capacity DRIPE, the

consensus among the Study Group members and the Project Team is that these projections are

comprehensive and reasonable based on the available information.

7.2 Electricity DRIPE  

AESC 2013 provides estimates of the effect of reductions in demand and energy from energy efficiency

programs on wholesale market prices for capacity and energy within the realm of the AESC 2013

construct of no new energy efficiency in New England. We estimate DRIPE in each wholesale market in

three steps:

 First, we estimate the impact a reduction in load will have on the price in that wholesale market,

assuming all else is held constant (gross DRIPE). We estimate this impact by analyzing the

relationship between the quantity of capacity or energy required in the relevant market and the

market price.

 Second, we estimate the pace at which market participants will respond to the reduction in

price with actions that offset that reduction and ultimately cause the market price to eventually

return to the level it would have been under the Base Case (net DRIPE). To estimate the pace of

this offset or dissipation, we estimate the material differences in actions that suppliers would

take each year in the DRIPE case relative to the actions they are projected to take under the

Base Case. The pace of dissipation of capacity DRIPE will likely be different from the pace of

energy DRIPE, because of the differences in the types of responses available to participants in

those markets. Estimating the dissipation of DRIPE involves the exercise of considerable

judgment, and reasonable analysts may develop different estimates.

 Third, we estimate the percentage of net DRIPE that retail customers will experience based

upon the portion of their supply that is acquired from wholesale capacity and energy markets.

7.2.1 Wholesale Electric Capacity Market Effects

AESC 2013 estimates capacity DRIPE from the slope of the capacity market‐clearing price in each FCA as

a function of the ISO’s net installed capacity requirements and available resources. (See Chapter 5,
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐3  

Avoided Electric Capacity Costs, for more information on capacity market assumptions.) From June 2017

onward, we assume that the FCM price will be set by the market, rather than ISO‐NE setting floor prices.

From that point onward, FCM prices will be determined by the prices at which generators choose to

delist. (By delisting, generators in New England are able to sell into another market such as New York, or

to shut down.) We use the supply curve from FCA 7, adjusted for changes in resources, above the FCA

floor price of $3.15/kW‐month, and extrapolate that adjusted curve below the $3.15/kW‐month level.

The value of capacity DRIPE will vary over time.

Capacity prices cannot be affected by future energy‐efficiency measures in the years for which capacity

prices have been determined by the auction floor price. Thus, we treat capacity DRIPE effects as starting

in 2018.

After estimating the magnitude of capacity DRIPE, we estimate its duration. We estimate the phase‐out

of capacity DRIPE based upon the assumption that the effect of reductions from efficiency programs on

market prices will not last indefinitely. Over time, a number of effects of lower capacity prices will tend

to affect the markets in ways that will offset the price reductions. Low market prices will result in the

reduction of investments in maintaining and expanding existing generation resources, resulting in less

capacity being available for future auctions (or increasing the cost of reclaiming that capacity). Lower

prices will also result in earlier retirement of some generation resources, which will not be available for

future auctions. We develop a phase‐out of DRIPE effects consistent with the AESC 2013 retirements

and additions described in Chapter 5.

As shown in Chapter 5 for FCA 8 and beyond, we estimate that the slope of the capacity supply curve

over a wide range in FCA 8 and beyond would average about $0.144/kW‐month in 2016 dollars, or

$0.136/kW‐month in 2013 dollars, per 100 MW of capacity.228  

If all capacity were procured in the market and load reductions did not affect future supply curves, the

capacity DRIPE effect in a state, in dollars per MW saved, would be the product of:  

 State peak load in MW,  

 One plus the percentage reserve requirement,  

 12 months per year, and  

 $0.00136 per kW‐month per MW saved.  

For Massachusetts, with a 2014 AESC forecast of 12,626 MW, in‐state capacity DRIPE would be about

$235/kW‐year of peak savings. For the rest of the pool, a kW saved in Massachusetts would reduce

capacity bills in the rest of New England by another $280/kW‐year. In FCAs 4 through 7 (which have

                                                            

228 In AESC 2011, we used different DRIPE values for various parts of the supply curve. On reflection, we recognize that the

anticipated (and still poorly defined) changes in the market and retirements will change the shape of the supply curve, and

use only the average slope from FCA7 as a guide to future price effects.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐4  

already occurred and set the FCM price through May 2017), the auctions ended at the floor price, and

would have been at the same price without any new energy‐efficiency bids. As a result, capacity DRIPE is

zero through FCA 7.

However, not all load is served by capacity purchased in the market, and we expect that the supply

curve would change as a result of load reductions (e.g., lower current and projected capacity prices

would impair the economics of capital additions to maintain or expand capacity, even at resources that

are not at the margin in the short‐run FCA). At some point, the market price will be set by new generic

resources and the supply curve will probably flatten, further reducing DRIPE.  

Exhibit 7‐2 summarizes the capacity obligation in each state that is hedged (not subject to changes in

market prices), including the following:  

 Investor‐owned utility (IOU) contracts (pre‐restructuring legacy contracts, post‐

restructuring reliability contracts in Connecticut, renewables purchases, and utility‐

owned resources in Vermont);229

 Generation resources owned by PSNH; and

 The load of the public utilities (municipals and coops), estimated from the percentage of

sales in each state that are from the public utilities, and assuming that the public utilities

are hedged to the same extent as Vermont.

The capacity hedged in each state is the sum of (1) the generation entitlements and (2) the public utility

hedged capacity.230

                                                            

229 The Vermont PSD provided a table of resources that included both owned and purchased resources for all the state’s

utilities, so these categories are combined here.  

230 For Vermont, we received combined entitlement data for the IOUs and public utilities.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐5  

Exhibit 7‐2. Hedged Capacity Requirement by State (MW)

IOU Contracts Owned Hedged Public Utilities Capacity Capacity Hedged

CT MA ME NH RI VT PSNH CT MA ME NH RI VT CT MA ME NH RI VT

Percent sales by public utilities 4.03% 9.73% 2.69% 1.27% 0.53%

2014 1,296 230 92 87 961 1,131 232 1,004 44 26 8 1,304 1,053 37 100 81 820

2015 1,296 230 89 87 822 1,131 199 864 37 22 7 1,276 933 32 95 80 701

2016 1,296 244 89 87 800 1,131 195 849 37 22 7 1,272 933 31 94 80 683

2017 1,296 263 88 87 777 1,131 190 828 36 21 7 1,268 931 30 93 80 663

2018 1,296 194 88 87 777 1,131 189 826 35 21 7 1,267 871 30 93 80 663

2019 1,296 194 79 87 790 1,131 193 846 36 22 7 1,270 887 31 86 80 674

2020 1,196 147 75 87 785 1,131 191 841 36 22 7 1,183 843 31 82 80 670

2021 1,196 147 75 87 754 1,035 183 808 34 21 6 1,176 815 29 82 80 643

2022 1,196 147 72 87 733 1,035 177 787 33 20 6 1,172 797 29 79 79 626

2023 1,196 147 68 87 733 1,035 177 788 33 20 6 1,171 798 29 75 79 626

2024 1,196 140 59 87 733 1,035 176 789 33 20 6 1,171 793 29 67 79 626

2025 1,136 140 59 87 733 1,035 176 790 33 20 6 1,119 794 28 67 79 626

Notes: 1. Connecticut IOU contracts from spreadsheet provided by Brian Rice (4/24/2013)

2.   Massachusetts IOU contracts from AESC 2011 (NGrid) and spreadsheet provided by Brian Rice (4/24/2013)

(WMECo and NStar)

3.   New Hampshire IOU (PSNH) contracts from spreadsheet provided by Brian Rice (4/24/2013)

4. Rhode Island IOU contracts from AESC 2011

5. Vermont contracts from spreadsheet provided by Sean Foley, VT PSD (4/30/2013)

6. PSNH owned capacity from CELT 2013.

7. Share of load served by public utilities (municipal and cooperative) from 2011 EIA 861 data, adjusted per SEA.

8.   Capacity hedged = sum of contracts, owned and public utilities.

Exhibit 7‐3 shows the results from subtracting the hedged load from total load by state, to derive the

load subject to the market. As noted above, DRIPE values are zero for 2014 through 2016, so the loads in

those years do not matter for this analysis.

Exhibit 7‐3. Load Subject to Market Capacity Prices by State (MW)

Capacity Hedged   Peak Load 2013 AESC Peak Load Subject to FCM Price

CT MA ME NH RI VT CT MA ME NH RI VT CT MA ME NH RI VT

1,304 1,053 37 100 81 820 7,049 12,626 1,991 2,471 1,870 1,003 5,745 11,572 1,954 2,371 1,789 183

1,276 933 32 95 80 701 7,192 12,947 2,032 2,544 1,903 1,022 5,917 12,014 2,000 2,450 1,823 321

1,272 933 31 94 80 683 7,331 13,264 2,068 2,613 1,942 1,037 6,059 12,331 2,037 2,519 1,862 355

1,268 931 30 93 80 663 7,489 13,521 2,105 2,661 1,981 1,052 6,221 12,590 2,074 2,568 1,901 389

1,267 871 30 93 80 663 7,592 13,742 2,136 2,705 2,015 1,072 6,325 12,872 2,106 2,612 1,935 409

1,270 887 31 86 80 674 7,671 13,949 2,162 2,748 2,043 1,082 6,400 13,062 2,131 2,662 1,964 408

1,183 843 31 82 80 670 7,749 14,151 2,188 2,787 2,072 1,097 6,565 13,308 2,158 2,704 1,992 427

1,176 815 29 82 80 643 7,822 14,353 2,214 2,825 2,101 1,112 6,646 13,538 2,185 2,743 2,021 468

1,172 797 29 79 79 626 7,907 14,566 2,242 2,867 2,132 1,127 6,735 13,769 2,214 2,788 2,052 501

1,171 798 29 75 79 626 7,992 14,783 2,271 2,910 2,163 1,142 6,821 13,985 2,242 2,835 2,084 516

1,171 793 29 67 79 626 8,078 15,001 2,299 2,953 2,195 1,157 6,907 14,208 2,270 2,886 2,115 532

1,119 794 28 67 79 626 8,164 15,222 2,327 2,997 2,226 1,173 7,045 14,429 2,299 2,930 2,147 547

Load subject to market price = peak – Hedged capacity ÷ 1.172
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐6  

The DRIPE effect for each state’s capacity charges, as a result of one kilowatt of load reduction in the

state, is shown in Exhibit 7‐4, as the product of:  

1) The load subject to market price in MW,  

2) The DRIPE coefficient of $0.136/kW‐month per MW,  

3) One plus the 17.2 percent reserve requirement, and

4) One minus a decay factor.

The decay factor reflects the increasing probability over time that (a) lower capacity prices will result in

retirement of existing units or the delay of power‐plant upgrades, and (b) the FCM price will be set by

the cost of new generic resources with small differences in their bid prices. This particular decay

trajectory is subjective, since we have limited or no publicly available data on the underlying behavior of

capacity suppliers.

Exhibit 7‐4. In‐State Capacity DRIPE by State (MW)

Peak Load Subject to FCM Price 2014 $/kW‐month capacity DRIPE

CT MA ME NH RI VT Decay CT MA ME NH RI VT ISO

2014 5,745 11,572 1,954 2,371 1,789 183 10% ‐ ‐ ‐ ‐  ‐    ‐   ‐

2015 5,917 12,014 2,000 2,450 1,823 321 20% ‐ ‐ ‐ ‐  ‐    ‐   ‐

2016 6,059 12,331 2,037 2,519 1,862 355 30% ‐ ‐ ‐ ‐  ‐    ‐   ‐

2017 6,221 12,590 2,074 2,568 1,901 389 40% $5.06 $10.25 $1.69 $2.09 $1.55 $0.32 $20.96

2018 6,325 12,872 2,106 2,612 1,935 409 50% $4.29 $8.73 $1.43 $1.77 $1.31 $0.28 $17.82

2019 6,400 13,062 2,131 2,662 1,964 408 60% $3.47 $7.09 $1.16 $1.44 $1.07 $0.22 $14.45

2020 6,565 13,308 2,158 2,704 1,992 427 70% $2.67 $5.42 $0.88 $1.10 $0.81 $0.17 $11.05

2021 6,646 13,538 2,185 2,743 2,021 468 80% $1.80 $3.67 $0.59 $0.74 $0.55 $0.13 $7.49

2022 6,735 13,769 2,214 2,788 2,052 501 85% $1.37 $2.80 $0.45 $0.57 $0.42 $0.10 $5.71

2023 6,821 13,985 2,242 2,835 2,084 516 90% $0.93 $1.90 $0.30 $0.38 $0.28 $0.07 $3.86

2024 6,907 14,208 2,270 2,886 2,115 532 95% $0.47 $0.96 $0.15 $0.20 $0.14 $0.04 $1.96

Exhibit 7‐4 also provides similar results for the effect of load reduction in any state on capacity bills for

consumers in the entire ISO.

On a levelized basis, the AESC 2013 regional capacity DRIPE value of $68.26/kW‐year is about 45.3

percent lower than the AESC 2011 (2013$) value of $124.74/kW‐year.  

The AESC 2011 study attempted to replicate the specific shape of the FCA 4 supply curve, and assumed

that the floor price would extend only to FCA 6. AESC 2013 uses the slope of the FCA 7 supply curve, but

does not incorporate every twist and turn in the curve, resulting in smoother DRIPE transitions. In

addition, AESC 2013 recognizes that the ISO has extended the price floor through FCA 7. Finally, since

AESC 2011, we have drawn closer to the end of a capacity surplus (with the passage of time and the

expected retirement of most fossil‐steam generators).
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐7  

7.2.2 Wholesale Electric Energy Market Effects

AESC 2013 estimates the magnitude of wholesale energy market DRIPE by year by conducting a set of

regressions of historical zonal hourly market prices against zonal and regional load, similar to the

process conducted in AESC 2007, 2009, and 2011. We have simplified the analysis by:

 Normalizing prices in each zone in each month in each period (on‐ or off‐peak) to the

average price in that zone/month/period (such that the average price is set to 1.0);  

 Conducting a single regression on the normalized prices over several years (2009‐2012),

rather than conducting hundreds of separate regressions for each month and then

normalizing and averaging the results; and

 Summing load and averaging prices across the three Massachusetts zones before

conducting the regressions.

More specifically, we estimated regression equations for two formulations:

normalized state LMP = a + b × normalized state load + c × normalized ROP load   (1)

and  

normalized state LMP = a + b × normalized ISO load   (2)

The coefficients thus represent the percent change in state LMP as a function of a percent change in

load. We use Equation 1 (state and rest‐of‐pool load) where the load coefficients are sensible (both

positive and the state coefficient, adjusted for load level, exceeds the ROP coefficients) and otherwise

use Equation 2. We use the regressions with only the ISO load variable for Vermont, Maine, and

Connecticut on‐peak energy; and the regressions with both state and ROP load for Massachusetts, New

Hampshire, Rhode Island, and Connecticut off‐peak energy.

231 The regression results are summarized in

Exhibit 7‐5. The bolded values in the exhibit are the coefficients used in subsequent calculations.

                                                            

231 The Vermont and Maine own‐state coefficients are negative, which does not make sense. The Connecticut on‐peak own‐

state coefficient is positive, but even normalized for the three‐fold ratio of ROP load to Connecticut load, the own‐state

coefficient would imply in a smaller effect on Connecticut price from a MWh of Connecticut load than a MW of ROP load.

Similar computations for New Hampshire, Rhode Island, and the Connecticut off‐peak regression result in own‐state effects

per MWh four to seven times the ROP effects.  
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐8  

Exhibit 7‐5. Energy DRIPE Coefficients by State

Off Peak (18,711 hours) On Peak (16,352 hours)

Normalized LMP vs. Normalized (State Load, Rest‐of‐Pool)

CT MA ME NH RI VT CT MA ME NH RI VT

Adjusted R2 51.1% 50.2% 47.5% 50.9% 43.5% 51.1% 44.0% 42.6% 42.4% 43.8% 43.2% 47.2%

Intercept ‐0.202 ‐0.181 ‐0.203 ‐0.099 ‐0.109 ‐0.108 ‐1.288 ‐1.253 ‐1.189 ‐1.185  ‐1.163  ‐0.913

State Load 0.788 0.809 ‐0.207 0.488 0.245 ‐0.263 0.473 1.818 ‐0.299 0.653 0.713 ‐0.594

Rest‐of‐Pool Load 0.414 0.372 1.411 0.611 0.864 1.371 1.815 0.435 2.489 1.532 1.450 2.507

t‐stat State load 23.23 18.96 ‐19.10 23.22 10.78 ‐17.82 11.81 32.19 ‐18.62 22.53 23.67  ‐32.42

t‐stat ROP load 11.3 9.7 92.1 24.8 35.4 89.8 40.2 7.9 99.7 44.1 37.4 118.0

   

Normalized LMP vs. Normalized ISO Load

CT MA ME NH RI VT CT MA ME NH RI VT

Adjusted R2 50.6% 50.1% 45.5% 50.0% 43.3% 50.0% 44.0% 42.0% 39.5% 43.0% 42.0% 42.8%

Intercept 0.234 ‐0.153 ‐0.151 ‐0.143 ‐0.110 ‐0.144 ‐1.282 ‐1.235 ‐1.195 ‐1.199  ‐1.232  ‐1.191

ISO Pool Load 1.234 1.152 1.151 1.143 1.110 1.144 2.282 2.235 2.195 2.199 2.232 2.191

t‐stat ISO load 138.6 137.2 125.0 136.9 119.6 136.7 113.4 108.7 103.4 111.0 108.9 110.7

The results are remarkably stable across states. A 1.0 percent reduction in load throughout New England

results in a 1.1 to 1.2 percent reduction in off‐peak price, and a 1.9 to 2.2 percent reduction in peak

price.  

As is true for capacity DRIPE, energy DRIPE is applicable only to energy purchased at market prices, and

the effect of DRIPE decays over time. In addition, while energy DRIPE starts immediately (there is no

floor price in the energy market), most energy purchased at market price for retail load is priced months

or a couple years in advance of delivery, through utility contracting for standard service or a third‐party

contract. Hence, the magnitude of energy DRIPE is reduced in the early years following measure

implementation.  

Exhibit 7‐6 summarizes the hedged (not subject to changes in market prices) energy obligation in each

state, including the following:232  

 IOU contracts (pre‐restructuring legacy contracts, post‐restructuring reliability contracts

in Connecticut, renewables purchases, and utility‐owned resources in Vermont);233

                                                            

232 These factors are the energy equivalents of the factors discussed in the Capacity DRIPE section.

233 Based on the energy‐modeling runs, we assume that the Connecticut peaker contracts would operate at a 6 percent

capacity factor through 2019 and at 4 percent thereafter. The Vermont resources include a large purchase from HQ that is

partially tied to market prices, with lags and fixed components. Since the pricing terms are not public, we assumed that 50

percent of the Vermont HQ contract price is tied to current market prices.  
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐9  

 Generation resources owned by PSNH, at the unit output from 2011; and

 The load of the public utilities (municipals and coops), estimated from the percentage of

sales in each state that are from the public utilities, and assuming that the public utilities

are hedged to the same extent as Vermont.

The energy hedged in each state is the sum of (1) the generation entitlements and (2) the public utility

hedged energy.

Exhibit 7‐6. Hedged Energy Requirement by State (GWh)

Contracts Owned Public Utilities Entitlements

CT MA ME NH RI VT PSNH CT MA ME NH RI VT

4.03% 9.73% 2.69% 1.27% 0.53% included

2014 266 1,311 739 760 4,135 3,001 1,244 5,624 294 145 42

2015 266 2,317 726 760 3,612 3,001 901 4,082 214 105 30

2016 266 2,798 725 760 3,524 3,001 832 3,775 198 97 28

2017 266 2,798 724 760 3,500 3,001 768 3,495 183 90 26

2018 266 2,798 723 760 3,500 3,001 767 3,500 183 90 26

2019 266 2,798 655 760 3,618 3,001 792 3,624 189 93 27

2020 177 2,750 621 760 3,600 2,772 787 3,609 187 93 27

2021 177 2,616 621 760 3,444 2,772 751 3,454 179 89 25

2022 177 2,616 603 760 3,443 2,772 750 3,458 179 89 25

2023 177 2,387 573 760 3,283 2,772 714 3,302 170 84 24

2024 177 2,387 500 760 3,283 2,772 714 3,306 170 84 24

2025 177 2,387 500 760 3,242 2,772 704 3,269 168 83 24

Exhibit 7‐7 subtracts the hedged energy from total energy by state, to derive the load subject to the

market. Based on our knowledge of the procurement policies for standard service and of the length of

third‐party contracts, and information provided by some of the participating utilities, we estimate that

80 percent of energy is pre‐contracted for the year of measure installation, 20 percent in the following

year, and 10 percent in the third year.
Synapse Energy Economics. Inc. — AESC 2013 Page 7‐10

Exhibit 7‐7. Load Subject to Market Energy Prices, by State, 2014 installations, GWh

Energy Hedged Energy 2013 AESC Short‐term

Contract

Energy 

CT MA ME NH RI VT CT MA ME NH RI VT CT MA 

2014 1,510 6,935 294 3,885 802 4,135 32,966 61,791 11,695 12,248 8,405 6,091 80% 6,291 10,971 2015 1,167 6,399 214 3,832 790 3,612 33,428 62,776 11,901 12,458 8,526 6,166 20% 25,808 45,102 2016 1,098 6,573 198 3,823 788 3,524 33,869 63,737 12,082 12,644 8,658 6,232 10% 29,494 51,447 2017 1,034 6,293 183 3,815 786 3,500 34,261 64,642 12,233 12,804 8,780 6,297 33,227 58,350 2018 1,033 6,298 183 3,813 786 3,500 34,623 65,513 12,375 12,950 8,897 6,373 33,590 59,215 2019 1,058 6,422 189 3,748 787 3,618 34,975 66,358 12,506 13,101 9,003 6,443 33,917 59,936 2020 964 6,359 187 3,485 787 3,600 35,321 67,189 12,627 13,251 9,095 6,519 34,357 60,830 2021 929 6,070 179 3,481 785 3,444 35,673 68,004 12,758 13,402 9,182 6,594 34,744 61,934 2022 928 6,074 179 3,464 785 3,443 36,030 68,862 12,891 13,554 9,284 6,669 35,102 62,788 2023 892 5,689 170 3,430 784 3,283 36,389 69,727 13,025 13,708 9,387 6,744 35,497 64,039 2024 891 5,693 170 3,356 784 3,283 36,749 70,600 13,160 13,863 9,491 6,820 35,859 64,907 2025 881 5,656 168 3,355 784 3,242 37,112 71,479 13,295 14,020 9,595 6,895 36,231 65,823 
Synapse Energy Economics. Inc. — AESC 2013 Page 7‐11  

Exhibit 7‐8 provides the same computation for 2015 installations, for which the effects of the short‐term

contracts occur one year later than for 2014 installations.

Exhibit 7‐8. Load Subject to Market Energy Prices, by State, 2015 installations

Short‐

term

Contracts

Energy Subject to LMP, 2015 EE

CT MA ME NH RI VT

2015 80% 6,452 11,275 2,337 1,725 1,547 511

2016 20% 26,217 45,731 9,507 7,057 6,296 2,166

2017 10% 29,904 52,515 10,846 8,091 7,195 2,517

2018 33,590 59,215 12,192 9,137 8,111 2,872

2019 33,917 59,936 12,317 9,352 8,217 2,825

2020 34,357 60,830 12,440 9,766 8,309 2,919

2021 34,744 61,934 12,579 9,921 8,397 3,151

2022 35,102 62,788 12,713 10,091 8,499 3,226

2023 35,497 64,039 12,855 10,279 8,603 3,461

2024 35,859 64,907 12,989 10,507 8,706 3,536

2025 36,231 65,823 13,127 10,664 8,811 3,653

We estimate the phase‐out of energy DRIPE based upon four factors:

1) Over time, customers would respond to lower energy prices by using somewhat more

energy, pushing prices back up somewhat.

2) Lower loads would reduce acquisition mandates under renewable and other alternative‐

energy standards that specify the percentage of energy that must be provided by

various categories of resources. The reduced acquisition of renewables would tend to

increase prices.

3) Owners of existing generating capacity would tend to allow their energy‐producing

assets to become less efficient and reliable as low energy prices make continued

operation of the units less attractive, leading to more outages and higher market‐

clearing prices.

4) The addition of new resources would tend to be delayed, and the mix of new resources

would tend to be shifted toward peakers and away from baseload resources that would

otherwise have reduced energy prices.

Exhibit 7‐9 summarizes our estimate of the decay of energy DRIPE for 2014 installations. The demand

elasticity is driven by rebound of sales as a result of price reduction, using the price elasticity estimated
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐12  

by ISO‐NE in the 2012 CELT forecast and assuming that LMP would be 50 percent of retail revenues.

234

The RPS offset equals the load‐weighted average of the new‐renewable (Class 1) RPS requirement for

each calendar year; each MWh of load reduction reduces the required renewable build‐out by a fraction

of a MWh, which pushes prices up.  

The adjustments for existing and new generation are more judgmental. As prices fall, the incentive for

generation owners to make investments to maintain the capacity, reliability, and heat rate for existing

units declines, leading to additional retirements, de‐rating, and lower availability. We assume that effect

rises gradually from the time the energy‐efficiency measures are implemented. We estimate in Chapter

5 that new generic capacity will be required around 2020, and recognize the uncertainty in the timing of

the new capacity, in whether load reduction will delay new capacity, and whether the avoided capacity

will be peaking combustion turbines (which provide little energy) or combined‐cycle units (which

provide large amounts of energy). We estimate total elimination of DRIPE by 2024.  

Exhibit 7‐9. Energy DRIPE Decay, 2014 installations

Year Demand

Elasticity

RPS Existing

Generation

New

Generation

Total

2014 1 4.0% 7.9% 2.0% 13%

2015 2 6.0% 8.9% 4.0% 18%

2016 3 6.9% 10.0% 6.0% 21%

2017 4 7.4% 11.0% 8.0% 5.0% 28%

2018 5 7.6% 11.9% 10.0% 10.0% 34%

2019 6 7.7% 12.8% 12.0% 25.0% 47%

2020 7 7.8% 13.7% 14.0% 40.0% 59%

2021 8 7.8% 14.2% 16.0% 55.0% 70%

2022 9 7.8% 14.6% 18.0% 70.0% 81%

2023 10 7.8% 15.1% 20.0% 85.0% 91%

2024 11 7.8% 15.6% 22.0% 100.0% 100%

Exhibit 7‐10 shows the same computation for 2015 installations, with the price‐elasticity and existing‐

generation effects starting one year later.

                                                            

234 This assumption somewhat overstates DRIPE decay, since the actual ratio of day‐ahead LMP to retail rates averaged about

30% in 2009–2012.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐13  

Exhibit 7‐10. Energy DRIPE Decay, 2015 installations

Year Demand

Elasticity

RPS Existing

Generation

New

Generation

Total

2015 1 4.0% 8.9% 2.0% 14%

2016 2 6.0% 10.0% 4.0% 19%

2017 3 6.9% 11.0% 6.0% 5.0% 26%

2018 4 7.4% 11.9% 8.0% 10.0% 32%

2019 5 7.6% 12.8% 10.0% 25.0% 46%

2020 6 7.7% 13.7% 12.0% 40.0% 58%

2021 7 7.8% 14.2% 14.0% 55.0% 69%

2022 8 7.8% 14.6% 16.0% 70.0% 80%

2023 9 7.8% 15.1% 18.0% 85.0% 90%

2024 10 7.8% 15.6% 20.0% 100.0% 100%

The energy DRIPE values for each state’s energy on‐peak and off peak periods, as a result of one MWh

of load reduction in the state, are shown in Exhibit 7‐11, Exhibit 7‐12, Exhibit 7‐13, and Exhibit 7‐14, as

the product of:

1) The load subject to market price in GWh, from Exhibit 7‐7;

2) The own‐state or ISO DRIPE coefficient, from Exhibit 7‐5;  

3) One divided by the own‐state or ISO energy load, to convert from MWh to percent of

load (the variable for which the coefficient was computed);

4) The regional market energy price; and

5) One minus the decay factor, from Exhibit 7‐9.

Comparable data for off‐peak energy DRIPE for 2014 and 2015 installations are shown for each state in

the four exhibits for both intrastate and rest‐of‐pool energy DRIPE. The rest of pool energy DRIPE effects

are generally parallel to those for own‐state DRIPE, but are determined by summing across the other

states: the energy subject to market energy prices, times the coefficient for the rest of pool (or ISO)

regression variable, times the regional market energy price and one minus the decay factor.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐14  

Exhibit 7‐11. Energy DRIPE Values for 2014 Installation On Peak Periods ($/MWh)

Winter On Peak

   Intrastate     Rest of Pool

   CT MA ME NH RI VT     CT MA ME NH RI VT

2014 5.10 15.26 1.78 4.22 6.11 0.30     7.27 16.56 9.62 10.63 10.44 10.76

2015 19.30 57.86 6.72 16.02 22.95 1.47     27.95 63.29 36.77 40.57 39.88 40.70

2016 20.06 60.00 7.00 16.77 23.87 1.59     29.17 66.03 38.32 42.25 41.55 42.33

2017 19.61 58.99 6.84 16.48 23.35 1.58     28.63 64.71 37.57 41.39 40.72 41.45

2018 18.97 57.22 6.62 16.04 22.65 1.56     27.79 62.78 36.43 40.11 39.48 40.16

2019 16.46 49.70 5.75 14.11 19.71 1.32     24.14 54.54 31.62 34.78 34.27 34.90

2020 14.50 43.81 5.05 12.81 15.44 1.18     21.38 48.25 27.97 30.65 30.29 30.82

2021 10.52 31.98 3.66 9.34 11.11 0.92     15.63 35.17 20.40 22.34 22.08 22.40

2022 7.14 21.76 2.49 6.38 7.46 0.63     10.64 23.93 13.88 15.18 15.01 15.22

2023 3.58 11.00 1.25 3.23 3.69 0.34     5.38 12.07 7.00 7.65 7.57 7.66

Summer On Peak

   Intrastate     Rest of Pool

   CT MA ME NH RI VT     CT MA ME NH RI VT

2014 4.10 12.28 1.43 3.39 4.91 0.24     5.85 13.32 7.74 8.55 8.40 8.65

2015 16.21 48.60 5.65 16.02 19.27 1.47     23.48 53.15 30.89 34.07 33.49 34.19

2016 17.72 53.00 6.18 16.77 21.08 1.59     25.76 58.32 33.85 37.32 36.70 37.39

2017 20.20 60.76 7.05 16.48 24.05 1.58     29.49 66.66 38.70 42.63 41.95 42.69

2018 19.97 60.22 6.97 16.04 23.84 1.56     29.25 66.06 38.34 42.21 41.54 42.26

2019 17.30 52.25 6.04 14.11 20.72 1.32     25.37 57.33 33.25 36.56 36.02 36.69

2020 14.36 43.38 5.00 12.81 15.03 1.18     21.17 47.78 27.70 30.35 29.99 30.52

2021 10.89 33.11 3.79 9.34 11.31 0.92     16.18 36.41 21.12 23.13 22.86 23.19

2022 7.00 21.34 2.44 6.38 7.15 0.63     10.44 23.47 13.61 14.89 14.72 14.93

2023 3.64 11.16 1.27 3.23 3.67 0.34     5.46 12.24 7.10 7.76 7.68 7.77
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐15  

Exhibit 7‐12. Energy DRIPE Values for 2014 Installation Off Peak Periods ($/MWh)

Winter Off Peak

   Intrastate     Rest of Pool

   CT MA ME NH RI VT     CT MA ME NH RI VT

2014 6.22 5.94 0.81 2.76 1.83 0.14     3.91 4.63 3.22 3.78 3.57 3.78

2015 23.87 22.79 3.12 10.60 6.97 0.68     15.17 18.00 12.52 14.65 13.84 14.50

2016 24.96 23.74 3.26 11.15 7.28 0.74     15.89 18.90 13.11 15.33 14.49 15.15

2017 24.11 23.03 3.15 10.81 7.03 0.73     15.39 18.29 12.71 14.84 14.03 14.66

2018 23.41 22.38 3.05 10.54 6.83 0.71     14.97 17.79 12.36 14.42 13.64 14.24

2019 20.10 19.21 2.62 9.16 5.87 0.60     12.84 15.27 10.61 12.36 11.71 12.24

2020 17.85 17.04 2.32 8.37 4.70 0.54     11.43 13.61 9.45 10.97 10.42 10.88

2021 13.27 12.73 1.72 6.25 3.43 0.43     8.55 10.17 7.07 8.20 7.79 8.10

2022 8.97 8.61 1.16 4.24 2.29 0.29     5.79 6.89 4.79 5.54 5.27 5.48

2023 4.50 4.35 0.58 2.14 1.13 0.16     2.92 3.47 2.42 2.80 2.66 2.75

Summer Off Peak

   Intrastate     Rest of Pool

   CT MA ME NH RI VT     CT MA ME NH RI VT

2014 4.97 4.74 0.65 2.20 1.46 0.11     3.12 3.70 2.58 3.02 2.85 3.02

2015 19.14 18.28 2.50 8.51 5.59 0.54     12.17 14.44 10.04 11.75 11.10 11.63

2016 20.47 19.47 2.68 9.14 5.97 0.61     13.03 15.49 10.75 12.57 11.88 12.42

2017 22.24 21.24 2.90 9.97 6.48 0.67     14.20 16.87 11.72 13.69 12.94 13.52

2018 21.72 20.77 2.83 9.78 6.34 0.66     13.89 16.51 11.47 13.38 12.66 13.21

2019 18.54 17.72 2.42 8.45 5.42 0.55     11.85 14.09 9.79 11.41 10.80 11.29

2020 16.54 15.80 2.15 7.76 4.31 0.50     10.60 12.62 8.77 10.17 9.66 10.09

2021 12.29 11.79 1.59 5.78 3.13 0.40     7.92 9.42 6.55 7.59 7.21 7.50

2022 8.30 7.97 1.07 3.93 2.08 0.27     5.35 6.37 4.43 5.13 4.88 5.07

2023 4.19 4.05 0.54 2.00 1.04 0.14     2.72 3.24 2.25 2.60 2.48 2.57
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐16  

Exhibit 7‐13. Energy DRIPE Values for 2015 Installations On Peak Periods ($/MWh)

Winter On Peak

   Intrastate     Rest of Pool

   CT MA ME NH RI VT     CT MA ME NH RI VT

2015 5.03 15.07 1.75 4.17 5.98 0.38     7.28 16.48 9.58 10.57 10.39 10.60

2016 18.40 55.03 6.42 15.37 21.89 1.46     26.75 60.55 35.15 38.75 38.11 38.82

2017 18.13 54.52 6.32 15.23 21.58 1.46     26.46 59.81 34.72 38.25 37.64 38.31

2018 19.45 58.64 6.79 16.44 23.21 1.60     28.48 64.33 37.33 41.11 40.46 41.16

2019 16.85 50.89 5.89 14.45 20.18 1.35     24.71 55.84 32.38 35.62 35.09 35.74

2020 14.84 44.86 5.17 13.12 15.81 1.21     21.89 49.40 28.64 31.38 31.01 31.56

2021 10.78 32.76 3.75 9.56 11.38 0.94     16.01 36.02 20.89 22.88 22.61 22.94

2022 7.32 22.29 2.55 6.54 7.64 0.65     10.90 24.52 14.22 15.55 15.38 15.60

2023 3.67 11.27 1.28 3.31 3.79 0.34     5.52 12.37 7.18 7.84 7.76 7.85

Summer On Peak

   Intrastate     Rest of Pool

   CT MA ME NH RI VT     CT MA ME NH RI VT

2015 4.22 12.66 1.47 4.17 5.02 0.38     6.12 13.84 8.04 8.88 8.72 8.90

2016 16.25 48.60 5.67 15.37 19.33 1.46     23.63 53.48 31.04 34.22 33.66 34.29

2017 18.67 56.15 6.51 15.23 22.23 1.46     27.26 61.61 35.77 39.40 38.77 39.46

2018 20.46 61.71 7.14 16.44 24.43 1.60     29.98 67.70 39.29 43.26 42.58 43.31

2019 17.72 53.50 6.19 14.45 21.22 1.35     25.98 58.71 34.04 37.44 36.89 37.57

2020 14.70 44.42 5.12 13.12 15.39 1.21     21.67 48.92 28.36 31.08 30.71 31.25

2021 11.16 33.91 3.88 9.56 11.59 0.94     16.57 37.29 21.63 23.68 23.41 23.75

2022 7.18 21.86 2.50 6.54 7.32 0.65     10.69 24.05 13.94 15.26 15.08 15.30

2023 3.73 11.44 1.30 3.31 3.76 0.34     5.60 12.55 7.28 7.96 7.87 7.96
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐17  

Exhibit 7‐14. Energy DRIPE Values for 2015 Installations Off Peak Periods ($/MWh)

Winter Off Peak

   Intrastate     Rest of Pool

   CT MA ME NH RI VT     CT MA ME NH RI VT

2015 6.22 5.94 0.81 2.76 1.81 0.18     3.95 4.69 3.26 3.82 3.60 3.78

2016 22.89 21.77 2.99 10.22 6.68 0.68     14.57 17.33 12.03 14.06 13.29 13.89

2017 22.28 21.28 2.91 9.99 6.50 0.67     14.22 16.90 11.75 13.72 12.97 13.55

2018 23.99 22.93 3.13 10.80 7.00 0.73     15.34 18.23 12.67 14.78 13.98 14.59

2019 20.58 19.67 2.68 9.38 6.01 0.61     13.15 15.64 10.87 12.66 11.99 12.53

2020 18.27 17.45 2.37 8.57 4.81 0.55     11.70 13.94 9.68 11.23 10.67 11.14

2021 13.59 13.04 1.76 6.40 3.52 0.44     8.75 10.42 7.24 8.39 7.98 8.30

2022 9.19 8.82 1.19 4.35 2.35 0.30     5.93 7.06 4.91 5.68 5.40 5.61

2023 4.61 4.46 0.60 2.20 1.16 0.16     2.99 3.56 2.48 2.87 2.73 2.82

Summer Off Peak

   Intrastate     Rest of Pool

   CT MA ME NH RI VT     CT MA ME NH RI VT

2015 4.99 4.76 0.65 2.22 1.46 0.14     3.17 3.76 2.62 3.06 2.89 3.03

2016 18.77 17.85 2.45 8.38 5.47 0.56     11.95 14.21 9.86 11.53 10.89 11.39

2017 20.56 19.63 2.68 9.22 5.99 0.62     13.12 15.59 10.84 12.65 11.96 12.50

2018 22.26 21.28 2.90 10.03 6.50 0.68     14.23 16.92 11.76 13.72 12.97 13.54

2019 18.99 18.15 2.47 8.66 5.55 0.56     12.13 14.43 10.02 11.68 11.06 11.56

2020 16.94 16.18 2.20 7.95 4.41 0.51     10.85 12.92 8.97 10.41 9.89 10.33

2021 12.59 12.08 1.63 5.92 3.20 0.41     8.11 9.65 6.71 7.77 7.39 7.68

2022 8.50 8.16 1.10 4.02 2.14 0.28     5.48 6.53 4.54 5.25 4.99 5.19

2023 4.30 4.15 0.56 2.05 1.06 0.15     2.79 3.32 2.31 2.67 2.54 2.63

7.3 Natural Gas DRIPE

Just as reducing electric load reduces electric energy prices, reducing gas usage reduces demand for gas

in producing regions and therefore reduces the market price of that gas supply. This report refers to that

gas price reduction effect as gas DRIPE.  

As discussed in Chapter 2, the wholesale cost of gas for gas consumers (the customers of the local

distribution companies, or LDCs) and the cost of gas for electric generation in New England can each be

broken into two components:  

 The supply component, determined by North American demand and supply conditions on a

largely annual basis, and

 Transportation costs or basis, determined by contract prices for LDCs and by the balance of

regional demand and supply (mostly from pipelines) on a daily and seasonal basis.

The following sections of this chapter consider, in turn, the effects of gas‐use reductions on gas supply

prices and on the market basis for delivery to New England. The final section estimates the cross‐fuel

DRIPE effects caused by the linkage of gas and electricity through the gas used in electric generation.  
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐18  

7.3.1 Wholesale gas supply market effects

Interest in the effect on natural gas prices of reduced consumption has considerable history. Exhibit 7‐15

summarizes the results of a number of analyses in the period 1998 to 2004 that estimated the effect on

North America natural gas prices of reducing gas use through gas or electric energy‐efficiency programs

and/or renewable energy.

235 Most of these studies estimated gas DRIPE based upon projections from

the EIA’s National Energy Modeling System (NEMS), which the EIA uses to develop its Annual Energy

Outlook.236

Exhibit 7‐15. Estimates of Gas Price Suppression from Reduced Usage for 2020

Author

Reduction in

U.S. Annual

Gas

Consumption

quads

Reduction in

annual average

Gas Wellhead

Price $/MMBtu

(2000$)

Reduction in

wellhead price pre

reduction in

consumption

$/MMBtu per

quad (2000$)

EIA (1998) 1.12 $0.34 $0.30

EIA (1999) 0.41 $0.19 $0.46

EIA (2001) 1.45 $0.27 $0.19

EIA (2001) 3.89 $0.56 $0.14

EIA (2002a) 0.72 $0.12 $0.17

EIA (2002a) 1.32 $0.22 $0.17

EIA (2003) 0.48 $0.00 $0.00

UCS (2001) 10.54 $1.58 $0.15

UCS (2002a) 1.28 $0.32 $0.25

UCS (2002a) 3.21 $0.55 $0.17

UCS (2002b) 0.72 $0.05 $0.07

UCS (2003) 0.10 $0.14 $1.40

UCS (2004a) 0.49 $0.12 $0.24

UCS (2004a) 1.80 $0.07 $0.04

UCS (2004b) 0.62 $0.11 $0.18

UCS (2004b) 1.45 $0.27 $0.19

Tellus (2002) 0.13 $0.00 $0.00

Tellus (2002) 0.23 $0.01 $0.04

Tellus (2002) 0.28 $0.02 $0.07

ACEEE (2003) 1.35 $0.76 $0.56

Source: Wiser (2005)

                                                            

235 The city‐gate wholesale price of gas varies by region throughout the United States. For any point on the pipeline system, the

national average wellhead gas price is always well over half the city‐gate price on an annual basis, as shown for New

England in Chapter 2.  

236 The ACEEE study used the proprietary model of Energy and Environmental Analysis, Inc.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐19  

Exhibit 7‐16 summarizes the supply elasticities for these studies.237 Most of these analyses estimated

that a 1 percent reduction in annual U.S. gas consumption would reduce annual average gas wellhead

prices by about 1 to 3 percent, with a few outliers on each side. For the roughly $4 gas supply prices

AESC 2013 projects for 2014–2016, a price reduction of 1 to 3 percent would be about $0.04 to

$0.12/MMBtu (2013$). For the $6/MMBtu supply price that AESC projects for 2021–2030, a 1 percent

load reduction would reduce prices $0.06 to $0.18/MMBtu. Load changes in New England alone would

have much smaller effects on supply prices, since annual New England gas consumption is only about

0.9 quads, or 3.5 percent of national consumption of 25 quads. Only about half of New England gas

consumption is by end users, with electric generators using the other half. With the elasticity range of 1

to 3 percent, a 1 percent reduction in New England end‐use gas would reduce supply prices by only

about $0.001 to $0.004/MMBtu in the 2020s. That range of price reductions would reduce gas bills to

New England end users by one to a few million dollars annually, enough to warrant further analysis.

Exhibit 7‐16. Gas‐Supply Elasticities

The structure of natural gas supply has changed considerably since these studies were performed, with

the growing importance of shale gas and the transition from forecasts of large LNG imports to potential

LNG exports. As a result, we have not used these older analyses to estimate gas‐supply DRIPE. Instead,

                                                            

237 The elasticities cannot be directly computed from Exhibit 7‐11, due to rounding and the fact that Exhibit 7‐11 does not list

the base consumption and price for each study.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐20  

we have used EIA’s most recent set of sensitivity analyses, from the 2012 AEO. Exhibit 7‐17 lists the

cases we identified as changing natural gas demand without affecting the gas supply curve, and

identifies the differences in consumption and Henry Hub prices between the case listed and the AEO

Reference Case in 2020.  

Exhibit 7‐17. AEO 2012 Gas‐Demand Sensitivity Cases for 2020

  

Forecast Case

Change from 2020 Reference Case

Consumption

(quads/year)

Henry Hub Price

(2010$/MMBtu)

High economic growth 0.48 0.31

Low economic growth (0.53) (0.35)

Low nuclear uprates, lives and additions 0.07 0.05

High nuclear uprates, lives and additions 0.00 0.01

Low coal cost (0.32) (0.20)

High coal cost 0.45 0.26

2011 Residential and Commercial Demand

Technology

0.37 0.17

High Residential and Commercial Demand

Technology

(0.49) (0.47)

Best Residential and Commercial Demand

Technology

(0.74) (0.83)

High coal retirement (Reference 05 case) 0.36 0.17

Low demand and supply technology 0.35 0.18

High demand and supply technology (0.55) (0.55)

Low renewable technology cost (0.08) (0.10)

Extended taxes and standards for efficiency and

renewables

(0.15) (0.08)

No sunset on tax policies for efficiency and

renewables

(0.06) (0.02)

Exhibit 7‐18 plots those changes from the Reference Case, over all the years reported in AEO 2012. The

results are remarkably linear, with the small changes in the early years clustered near the origin, and the

large changes in later years closer to the ends of the trend line.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐21  

Exhibit 7‐18. Gas Demand and Price Changes, AEO 2012, 2010$

The linear trend line in Exhibit 7‐18 implies a $0.632/MMBtu decrease in Henry Hub gas price for every

quad (quadrillion Btu or 109 MMBtu) decrease in annual gas consumption. As with the electric DRIPE

effects, the price reduction per MMBtu saved is a miniscule portion of the price per MMBtu, but each

MMBtu saved reduced prices for a very large number of MMBtus. The benefit to end‐use gas consumers

is a significant price change per MMBtu, for every billion MMBtus of reduced load. Since DSM avoided

costs are stated in dollars per MMBtu, the price slope would be $0.632 × 10‐9

/MMBtu per MMBtu saved,

which is a very small number. On the other hand, that price decrease applies to about 450 million

MMBtu of annual end‐use gas in New England, of which about 260 million MMBtu are in Massachusetts.

Hence, saving one MMBtu in Massachusetts (or anywhere else in North America) reduces bills to other

customers by:  

($0.632 × 10‐9

/MMBtu) × (0.26 × 109 MMBtu) = $0.162.

This $0.162/MMBtu would be a small, but not trivial, addition to the avoided costs of gas in

Massachusetts. The gas supply DRIPE for each New England state, and the total benefit for all New

England gas end‐use consumers, is shown in Exhibit 7‐19.  
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐22  

Exhibit 7‐19. Supply DRIPE Benefit in Annual MMBtu Load Reduction, by State

CT MA ME NH RI VT New England

Use (quads) 0.1155 0.2559 0.0347 0.0222 0.0353 0.0085 0.4722

DRIPE $0.073 $0.162 $0.022 $0.014 $0.022 $0.005 $0.298

These benefits would continue as long as the efficiency measure continues to reduce load. While the

electric energy DRIPE we observe with our regressions in intra‐month price variation represents only the

short‐term supply curve, the AEO gas prices (at least after the first few years) reflect the full long‐term

costs of gas development, not just the operation of existing wells. In addition, gas supply DRIPE is

measuring the effect of demand on the marginal cost of extraction for a finite resource.

238 If anything,

lower gas usage in 2014 will leave more low‐cost gas in the ground to meet demand in 2015, causing the

DRIPE effect to accumulate over time. A program that saves 100 MMBtu annually from 2015 onward

would have kept another 500 MMBtu in the ground by 2020, in addition to reducing 2020 demand by

100 MMBtu. The shape of the scatter plot in Exhibit 7‐18 does not suggest strong effects of either DRIPE

decay or accumulation. For each of the AEO scenarios, the later years have the largest changes from

reference, since the total difference in the underlying forecasts (e.g., economic growth, nuclear capacity,

coal use, installed efficiency measures) typically rises over time. DRIPE decay would produce an S curve,

with the price effect slowing in the out years, at the far end of the demand range. An accumulation of

price effects (due to low‐cost gas staying in the ground, shifting the supply curve to the left) would result

in rising effects in the out years more extreme than the trend line.239  

In the early years following energy‐efficiency implementation, the effect of DRIPE on gas consumers is

reduced by the fact that utilities, marketers, and self‐supplying customers typically purchase at least

some of their gas under contracts ranging from under a month to more than a year into the future.

Based on the information provided by the LDCs, and assuming that marketers and self‐supplying

customers on average hedge in a manner similar to the LDCs, we assume that gas supply is 50 percent

hedged in the year of energy‐efficiency implementation, 30 percent for the next year, and 0 percent

thereafter.

The following exhibit shows how a natural gas program administrator could apply the gas supply DRIPE

value stream to a natural gas efficiency measure.  

                                                            

238 As technology changes, the size of the resource changes, but once gas is removed from the ground, it is gone forever. Less

gas will be available from that play in the future, forcing the marginal supply to more expensive plays. In contrast, how

much energy a power plant produces this year has little effect on its ability to produce power next year. If anything,

increased energy generation at a plant in the near term may tend to justify keeping the plant online.

239 The EIA analyses of the price effects of LNG exports (Effect of Increased Natural Gas Exports on Domestic Energy Markets,

January 2012), in contrast, show large price increases in the early years, followed by smaller price increases and eventually

price decreases, compared to the no‐export case. The reason for the differences between the AEO cases and the LNG

sensitivities is not clear.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐23  

Exhibit 7‐20. State Supply DRIPE Benefit (2013$ per MMBtu for installation in year 2014)

CT MA ME NH RI VT New England

2014 $0.039 $0.085 $0.012 $0.007 $0.012 $0.003 $0.157

2015 $0.054 $0.119 $0.016 $0.010 $0.016 $0.004 $0.220

2016+ $0.077 $0.171 $0.023 $0.015 $0.024 $0.006 $0.315

     

Notes:    

Supply DRIPE benefit stream extends for gas efficiency measure life  

Based on Exhibit 7‐19

LDC gas supply hedge estimated at 50% Year 1, 30% Year 2, 0% Year 3

7.3.2 Wholesale Gas Transportation Market Effects

In addition to its effect on prices in the supply areas, reductions in annual gas use will reduce the basis,

or price differential between the wholesale market price of gas in New England and the prices in the

supply areas. The basis component of the wholesale market price of gas in New England has risen

rapidly in the last year or so, as discussed in Chapter 2. The majority of that basis is attributable to

constraints on gas delivery capacity into New England from the Mid‐Atlantic region. As a result, our

analysis focused on the basis, or price differential, between the Texas Eastern Transmission Zone M‐3 (in

Pennsylvania and New Jersey) and the Algonquin Gas Transmission citygates in Connecticut, Rhode

Island, and eastern Massachusetts.  

Price data is readily available for both of those locations because there is extensive trading in the

forward and spot markets at both of these points (AGT and TETCo M‐3). In addition, prices are very

similar for other trading points in the same region (e.g., Transco Zone 6 non‐NY prices are very similar to

those for TETCo M‐3, and Tennessee Gas Pipeline Zone 6 New England citygate prices are very similar to

those for AGT). In particular, AGT prices are a widely accepted proxy for New England prices, since AGT

prices are similar to prices on the Massachusetts, New Hampshire, and Connecticut portions of TGP,

while prices in Maine, Vermont, and parts of New Hampshire are determined in large part by the price

of gas at the TGP interconnection with Iroquois and at the TGP and AGT connections with PNGTS and

Maritimes & Northeast Pipeline at Dracut, Beverly, and Salem, Massachusetts.  

We estimated the DRIPE effect for the New England gas price basis using a method similar to the

method we used for electric energy DRIPE, with some adjustments for the differences in the markets.

First, gas markets are primarily daily, rather than hourly, so our analysis uses daily prices and loads.

Second, no data appears to be available on daily (or even weekly) consumption by state or region, so we

use as our measure of load the daily day‐ahead scheduled net deliveries in New England on the AGT and
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐24  

TGP pipelines, minus deliveries from the Maritimes & Northeast (MNE) and Iroquois pipeline, and from

the Distrigas LNG facility at Everett.240

We regressed daily load against basis for the winters (December through February) of 2011/12 and

2012/13 and the summers (June to September) of 2011 and 2012.  

For the winter of 2011‐12 (December through February), the ICE prices show that the average basis

from TETCo M‐3 to Algonquin was $1.79/MMBtu, and that the daily basis varied from about

$0.10/MMBtu to over $4.50/MMBtu. As shown in Exhibit 7‐21, the basis was under 50¢/MMBtu (typical

of summer conditions) for when total loads were less than about 2,200 MMcf/day.241 Above 2,200

MMcf/day, basis rose gradually at about $0.004/MMBtu per MMcf/day, with three trading days in

January setting prices over $3.50/MMBtu for a total of eight days. Each of these higher prices was

associated with average scheduled net load over 2,400 MMcf/day. Prices on these days may have

reflected temporary supply constraints or concern about potential restrictions or cold waves, especially

over weekends.

Exhibit 7‐21. Basis from TETCo M‐3 to Algonquin Citygates as function of net TGP and Algonquin load, Dec 2011

to Feb 2012

                                                            

240 Our computation of basis is from ICE’s day‐ahead trade data, so the timing of the two data sets is roughly equivalent. Since

ICE uses a single Friday trading price for Saturday, Sunday, and Monday, and the scheduling and pricing data may come

from slightly different times of day, the matching is not perfect.

241 A million cubic feet (MMcf) of gas is essentially the same as a billion Btu (BBtu).
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐25  

In the winter of 2012‐13, colder weather, declining deliveries from the Maritimes (due to off‐shore

production problems and depletion), and LNG (due to high gas prices in Europe and Asia) dramatically

increased New England price basis. Exhibit 7‐22 shows the relationship between scheduled deliveries

and basis for 2012/13, broken out by month. Note that the vertical scale in Exhibit 7‐22 covers a range

six times that of Exhibit 7‐21, and that the top end of the range of scheduled deliveries is about 140

MMcf/day higher than in 2011/12.  

Basis in December 2012 was already higher than the 2011/12 basis for similar loads, and the basis rose

even higher in January 2013 and again in February 2013. The increase in basis per MMcf/day was about

$0.008/MMBtu in December, $0.020/MMBtu in January, and $0.035/MMBtu in February.

Exhibit 7‐22. Basis from TETCo M‐3 to Algonquin Citygates as function of net TGP and Algonquin load, Dec 2012

to Feb 2013

Similar, but less dramatic, variation in delivery basis is apparent in the non‐winter months.

In the future, we assume that the sensitivity of winter basis to gas load will decline to match the

2011/12 relationship by 2017. We interpolate from the slope in January 2013 ($0.020/MMBtu/MMcf‐

day) down to the 2011/12 slope ($0.004/MMBtu/MMcf‐day), starting with a large drop in 2014

(reflecting the fall from the January 2013 basis to the difference in 2014 forward prices for AGT and

TETCo M‐3 basis) of about 25 percent of the difference between January 2013 and the 2011/12 average.

In each year from 2015 to 2017, we assume another 10 percent convergence towards 2011 basis levels,

reflecting the gradual decline in the basis forwards. Finally, we assume that major new transportation

comes online around 2017, dropping the marginal basis to 2011/12 conditions in 2017, with continuing

decline to 2019, at about half the supply slope of 2011/12. The resulting winter DRIPE coefficients are

shown in Exhibit 7‐23.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐26  

Review of the load and price data for other months suggests that the DRIPE per MMcf is about half as

large in the shoulder months (November, March, and April) as in the winter, and only about

$0.0004/MMBtu per MCFd reduction in the summer months (May to October) as in the winter. The

resulting shoulder and summer DRIPE coefficients are also shown in Exhibit 7‐23.

Exhibit 7‐23. Gas Basis DRIPE Coefficients by Year, $/MMBtu reduction per Mcf/day saved

Year Three‐month

Winter

Three‐month

Shoulder

Six‐month Winter Summer

2014 0.0160 0.0080 0.0120 0.0004

2015 0.0118 0.0059 0.0089 0.0004

2016 0.0106 0.0053 0.0080 0.0004

2017 0.0040 0.0020 0.0030 0.0004

2018 0.0030 0.0015 0.0023 0.0004

2019 and

after

0.0020 0.0010 0.0015 0.0004

The six‐month winter basis DRIPE would be appropriate to apply to gas space‐heating measures.

The DRIPE coefficients in Exhibit 7‐23 are stated in terms of reductions in average daily gas load in each

time period in each year. For example, a one MMBtu/day of load reduction throughout the winter is a

load reduction of 90 MMBtu. Therefore the DRIPE coefficient for one MMBtu reduction in total for a

given time period is much lower than the coefficient for a one MMBtu/day reduction during that same

time period. Exhibit 7‐24 converts the gas basis price effect per MMBtu saved per day into a gas basis

price effect per quad saved in each time period.

Exhibit 7‐24. Gas Basis DRIPE Coefficients by Time‐Period by Year, $/MMBtu reduction per quad saved

Year Three‐month

Winter

Three‐month

Shoulder

Six‐month

Winter

Summer Annual

Baseload

2014 178 88 133 2.2 33.8

2015 131 65 98 2.2 25.0

2016 118 58 88 2.2 22.5

2017 44.4 22.0 33.2 2.2 8.8

2018 33.3 16.5 24.9 2.2 6.8

2019 and

after 22.2 11.0 16.6 2.2 4.7

One MMBtu reduction in gas use would reduce prices by one billionth of the coefficient in Exhibit 7‐24

for the applicable year and period, but since this reduction would apply to all the gas purchased in New

England at citygate prices (and thus subject to market basis cost), the effect per MMBtu saved could still

be quite large. However, only a small portion of daily net deliveries to end‐use gas users in New England

is subject to market basis. The vast majority of the gas delivered to those end‐use customers is
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐27  

purchased outside the region and transported under long‐term cost‐of‐service rates, and is therefore

sheltered from daily volatility in the local market. A relatively small portion of the total quantity of end‐

use gas appears to be purchased at market prices at New England citygates. We believe it is reasonable

to ignore the DRIPE effect on that small amount of purchases.

On the other hand, we expect that all electric energy prices for gas‐fired plants (including pumped

storage shifting energy from and/or to hours in which gas would otherwise be marginal) would be driven

by the market price of gas in New England, including basis. Even a generator with firm transportation

and/or storage will price its fuel at the opportunity cost, which will generally be close to the market

price. This cross‐price DRIPE factor is included in the next section.

7.4 Cross‐Fuel Market Price Effects

As discussed above, reduced use of natural gas, including reductions resulting from reduced operation

of gas‐fired power plants, will reduce natural gas prices. Thus, the value of electric energy DRIPE should

include the benefit to New England gas customers from the resulting reduction in gas prices.

Similarly, reduced end‐use gas consumption would reduce gas prices to electric generators. Lower gas

prices would tend to reduce electricity prices by reducing the dispatch costs of electric generation.

While generators are free to set their bid prices, the optimal pricing strategy for any fossil‐fueled

generator that may be setting the market price is likely to be affected by its fuel price.  

Exhibit 7‐25 summarizes the methods used for estimating the various DRIPE effects that flow through

various aspects of gas prices. The effect of conserving gas on the supply price paid by gas consumers is

measured in the own‐DRIPE computation in Section 7.3.1. The same method is used for estimating the

gas‐supply price effects of electric conservation on gas consumers, gas conservation on electric

generation, and electric conservation on electric generation.  

Basis costs are assumed to be fully hedged for LDC customers. The effect of electric load on gas basis

and hence electric price is at least partially reflected in the hourly regressions by month of market

electricity prices against electric load (section 7.2.2). Since we cannot determine what portion of basis

DRIPE is captured in the electric energy DRIPE, we do not include any allowance for electricity usage

affecting gas basis and hence electric market prices. Finally, the effect of gas conservation on the basis

prices reflected in market electric energy prices are estimated from the analysis in section 7.3.2.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐28  

Exhibit 7‐25. Summary of Gas‐Related DRIPE Effects

Gas Price Affected

Conservation of Energy

Gas Electricity

To LDC gas

consumers

Supply Own‐DRIPE Cross‐DRIPE

Basis Hedged Hedged

To gas‐fired

electric

generation

Supply Cross‐DRIPE Cross‐DRIPE

Basis Cross‐DRIPE Own‐DRIPE

(hourly regression)

Key:

Gas supply curve slope from AEO 2012

No effect

Basis supply curve analysis

7.4.1 Effect of gas prices on market electric energy price  

Natural‐gas‐fired generators set the market energy price in about 74 percent of the hours in 2011, and

in 81 percent in 2012, and must strongly affect energy prices in the hours for which pumped storage sets

the market price (roughly 13 to 14 percent in 2011 and 2012), since gas is likely to fuel most of the

energy used for pumping and most of the energy that pumped‐storage generation displaces.242 The ISO‐

NE marginal energy price per MWh in 2012 averaged about 9.5 times the price of gas per MMBtu at the

Algonquin citygates, representing an effective marginal heat rate of 9,500 Btu/kWh. The actual heat rate

in the hours in which gas is at the margin may be slightly different from this value, but the ISO does not

provide data in sufficient detail to determine whether the average marginal gas heat rate is higher or

lower than the implied average heat rate.243  

Gas Supply DRIPE Effect on Electric Prices

Assuming that gas sets the marginal price (directly or indirectly) in 85 percent of hours, at an average

heat rate of 9,500 Btu/kWh, a $1/MMBtu change in the price of gas would change the price of electricity

by about $8/MWh (= $1/MMBtu  9.5 MMBtu/MWh  0.85 gas‐fired MWh per MWh saved). We

therefore multiply the gas supply price reduction by 8 and by the projected portion of annual electric

energy consumption in each state that is not subject to some form of price hedge. This indirect gas‐

                                                            

242 2011 Annual Markets Report, May 15, 2012, p. 14; 2012 Annual Markets Report, May 15, 2013, p. 18.

243 If the marginal energy supply when gas was not marginal were always less expensive than gas (e.g., some coal), the energy

price (and hence the implicit heat rate) when gas is running would be higher than average. Conversely, if the marginal

energy supply when gas was not marginal were always more expensive than gas (e.g., some coal and most oil), the energy

price (and hence the implicit heat rate) when gas is running would be lower than average. It is not clear how these two

factors balance out.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐29  

electric DRIPE would decay in a manner similar to the direct electric DRIPE discussed in section 7.2.2,

except that gas prices have no effect on the RPS requirements.

The DRIPE effect on annual average wholesale electric energy prices in New England due to a reduction

in annual average gas well‐head prices from a one MMBtu reduction in annual gas use would be:  

$0.632 × 10‐9/MMBtu × 8 MMBtu/MWh = $5.1 × 10‐9/MWh per MMBtu saved

The cross‐price DRIPE effect in each state would be a function of that state’s annual electric use.

244

Annual electric load in Massachusetts is about 62,000 GWh, so the gas supply DRIPE effect of that

reduction on Massachusetts electric bills would be $0.31/MMBtu saved (= $5.06  10‐9

/MWh  0.062 

109MWh), before any hedging or decay.

Gas Basis DRIPE Effect on Electric Prices

In 2014, one MMBtu of reduced gas use from space‐heating gas conservation (at the six‐month winter

basis DRIPE in Exhibit 7‐24) would reduce electric price by basis DRIPE of:

$133/MMBtu/quad saved ÷ 109 quad/MMBtu × 8 MMBtu/MWh = $1.1 × 10‐6

/MWh

In Massachusetts, unhedged electric energy in 2014 totals about 5,200 GWh in the six winter months

(Exhibit 7‐7). If all that energy were purchased in the spot market, and there were no DRIPE decay, the

basis DRIPE value per MMBtu saved would be about:  

$1.1 × 10‐6

/MWh per MMBtu saved × 5.2 × 106 MWh = $5.7/MMBtu saved

Gas‐on‐Electric Cross‐Fuel DRIPE Decay

Electric energy DRIPE from reductions in annual gas use rises in 2015 as the hedged portion of energy

declines. That effect then falls after 2015 as gas basis declines and electric DRIPE decays. Exhibit 7‐26

summarizes the decay of cross‐fuel DRIPE. It is essentially the same as Exhibit 7‐9, which shows the

decay estimated for electric energy DRIPE, except that:  

1. There is no RPS effect.

2. The existing‐generation effect is reduced by one third, reflecting the tendency for lower

gas prices to improve the economics of gas‐fired plants, even though the lower electric

energy prices would reduce the economics of all plants.

3. The new‐generation effect is increased by 50 percent, reflecting the tendency for lower

gas prices to discourage investment in combined‐cycle plants, rather than combustion

turbines, in addition to the effect of lower electric energy prices.

                                                            

244 Since generation everywhere in ISO‐NE serves load throughout New England, the cross‐price effect on electric consumers in

a state is not dependent on the amount of gas burned for electric generation in that state.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐30  

Exhibit 7‐26. Decay of Electric DRIPE Caused by Reduction in Gas Use, 2014 Installation

Year Demand

Elasticity

Existing

Generation

New

Generation

Total

2014 1 4.0% 1.3% 5%

2015 2 6.0% 2.7% 8%

2016 3 6.9% 4.0% 11%

2017 4 7.4% 5.3% 7.5% 19%

2018 5 7.6% 6.7% 15.0% 27%

2019 6 7.7% 8.0% 37.5% 47%

2020 7 7.8% 9.3% 60.0% 67%

2021 8 7.8% 10.7% 70.0% 75%

2022 9 7.8% 12.0% 80.0% 84%

2023 10 7.8% 13.3% 90.0% 92%

2024 11 7.8% 14.7% 100.0% 100%

For 2015 installations, the demand‐elasticity and existing‐generation components would be lagged one

year.

Summary of Gas‐on‐Electric Cross‐Fuel DRIPE  

Exhibit 7‐27 summarizes the gas‐on‐electric cross‐fuel basis DRIPE, stated in dollars per TWh (million

MWh) per MMBtu saved, based on the basis DRIPE coefficients in Exhibit 7‐24, the supply DRIPE

coefficient, and the decay factors from Exhibit 7‐26.

Exhibit 7‐27. Cross‐fuel DRIPE ($/TWh per MMBtu Gas Saved)

DRIPE $/TWh per MMBtu saved

Supply Basis total DRIPE

    Heating Baseload Heating Baseload

2014 0.005 1.063 0.270 1.068 0.275

2015 0.005 0.784 0.200 0.789 0.205

2016 0.005 0.704 0.180 0.709 0.185

2017 0.005 0.266 0.071 0.271 0.076

2018 0.005 0.199 0.054 0.204 0.059

2019 0.005 0.133 0.038 0.138 0.043

2020 0.005 0.133 0.038 0.138 0.043

2021 0.005 0.133 0.038 0.138 0.043

2022 0.005 0.133 0.038 0.138 0.043

Exhibit 7‐28 summarizes the own‐state and ISO‐wide cross‐fuel DRIPE values for 2014 gas efficiency

installations based upon the coefficients in Exhibit 7‐27, the unhedged energy in Exhibit 7‐7 and an

estimate that about 50 percent of electric energy usage occurs in the heating season.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐31  

Exhibit 7‐28. Gas‐to‐Electric Cross‐Fuel Heating DRIPE, $/MMBtu, 2014 gas efficiency installations

Winter or Space Heating Annual Baseload

CT MA ME NH RI VT ISO CT MA ME NH RI VT ISO

2014 $3.0 $5.3 $1.1 $1.1 $0.7 $0.2 $11.5 $1.6 $2.9 $0.6 $0.6 $0.4 $0.1 $6.2

2015 $8.9 $15.6 $3.3 $3.3 $2.1 $0.8 $34.0 $4.9 $8.5 $1.8 $1.7 $1.2 $0.4 $18.4

2016 $8.9 $15.6 $3.4 $3.3 $2.1 $0.8 $34.1 $4.9 $8.5 $1.8 $1.8 $1.2 $0.4 $18.5

2017 $3.5 $6.1 $1.3 $1.3 $0.8 $0.3 $13.4 $2.0 $3.6 $0.7 $0.7 $0.5 $0.2 $7.8

2018 $2.4 $4.2 $0.9 $0.9 $0.6 $0.2 $9.2 $1.5 $2.6 $0.5 $0.5 $0.4 $0.1 $5.6

2019 $1.2 $2.1 $0.4 $0.4 $0.3 $0.1 $4.6 $0.8 $1.4 $0.3 $0.3 $0.2 $0.1 $2.9

2020 $0.8 $1.3 $0.3 $0.3 $0.2 $0.1 $2.9 $0.5 $0.9 $0.2 $0.2 $0.1 $0.0 $1.9

2021 $0.6 $1.0 $0.2 $0.2 $0.1 $0.1 $2.2 $0.4 $0.7 $0.1 $0.1 $0.1 $0.0 $1.4

2022 $0.4 $0.7 $0.1 $0.1 $0.1 $0.0 $1.5 $0.2 $0.4 $0.1 $0.1 $0.1 $0.0 $0.9

Exhibit 7‐29 provides similar results for 2015 installations.

Exhibit 7‐29. Gas‐to‐Electric Cross‐Fuel Heating DRIPE, $/MMBtu, 2015 gas efficiency installations

Winter or Space Heating Annual Baseload

CT MA ME NH RI VT ISO CT MA ME NH RI VT ISO

2015 $2.3 $4.0 $0.9 $0.9 $0.5 $0.2 $8.8 $1.3 $2.2 $0.5 $0.5 $0.3 $0.1 $4.8

2016 $8.1 $14.2 $3.1 $3.0 $1.9 $0.7 $31.1 $4.4 $7.8 $1.6 $1.6 $1.1 $0.4 $16.9

2017 $3.2 $5.6 $1.2 $1.2 $0.8 $0.3 $12.3 $1.9 $3.3 $0.7 $0.7 $0.5 $0.2 $7.1

2018 $2.4 $4.3 $0.9 $0.9 $0.6 $0.2 $9.4 $1.5 $2.6 $0.5 $0.5 $0.4 $0.1 $5.7

2019 $1.2 $2.1 $0.5 $0.4 $0.3 $0.1 $4.6 $0.8 $1.4 $0.3 $0.3 $0.2 $0.1 $3.0

2020 $0.8 $1.4 $0.3 $0.3 $0.2 $0.1 $3.0 $0.5 $0.9 $0.2 $0.2 $0.1 $0.0 $1.9

2021 $0.6 $1.0 $0.2 $0.2 $0.1 $0.1 $2.2 $0.4 $0.7 $0.1 $0.1 $0.1 $0.0 $1.4

2022 $0.4 $0.7 $0.1 $0.1 $0.1 $0.0 $1.5 $0.2 $0.4 $0.1 $0.1 $0.1 $0.0 $0.9

7.4.2 Effect of Electric Energy Conservation on Gas Supply Prices

From the data presented in the previous discussion, it appears that one MWh of reduction in annual

electric use would reduce annual gas use for electric generation in New England by 9.5 × 85% = 8.1

MMBtu. We thus impute to each MWh reduction in electric energy a DRIPE equal to 8 times the gas

supply.  

($0.632 × 10‐9

/MMBtu per MMBtu saved) × 8.1 MMBtu/MWh = $5.1 × 10‐9/MMBtu per MWh saved  

Effect of Electric Energy Conservation on End‐Use Gas Prices

Exhibit 7‐30 shows the results of multiplying the estimated supply price reduction per MWh of electric

conservation by the end‐use gas consumption in each state and the region to estimate the electric‐on‐

gas supply DRIPE effect.  
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐32  

Exhibit 7‐30. Annual Gas Price benefit per MWh saved

Coefficient   CT    MA    ME    NH    RI    VT   New

England

Gas End Use (quads)   0.1155 0.2559 0.0347 0.0222 0.0353 0.0085 0.4722

Electric‐Gas DRIPE $/MWh saved 5.103 $0.589 $1.306 $0.177 $0.113 $0.180 $0.043 $2.410

Effect of Electric Energy Conservation on Electric Price through Gas Supply Prices

The $5.1 × 10‐9

/MMBtu reduction in supply gas price per MWh saved would reduce electric market

prices by about  

$5.1 × 10‐9

/MMBtu  9.5 MMBtu/MWh  0.85 = $4.06  10‐8/MWh.

Exhibit 7‐31 shows the results of multiplying this supply price reduction per MWh of electric

conservation by the electric consumption exposed to the market in each state and the region (from

Exhibit 7‐26), adjusted by the cross‐fuel DRIPE estimates (in Exhibit 7‐26) to estimate the electric‐gas‐

electric DRIPE effect for 2014 installations.

Exhibit 7‐31. Annual Electric‐Gas‐Electric Price benefit per MWh saved, 2014 installations

CT MA ME NH RI VT ISO

2014 $0.24 $0.42 $0.09 $0.09 $0.06 $0.02 $0.91

2015 $0.96 $1.68 $0.35 $0.35 $0.23 $0.08 $3.63

2016 $1.07 $1.87 $0.39 $0.39 $0.26 $0.09 $4.06

2017 $1.09 $1.92 $0.40 $0.39 $0.26 $0.09 $4.16

2018 $1.00 $1.76 $0.36 $0.36 $0.24 $0.09 $3.81

2019 $0.73 $1.29 $0.27 $0.27 $0.18 $0.06 $2.79

2020 $0.47 $0.83 $0.17 $0.17 $0.11 $0.04 $1.78

2021 $0.35 $0.62 $0.13 $0.13 $0.08 $0.03 $1.34

2022 $0.23 $0.41 $0.08 $0.08 $0.06 $0.02 $0.89

Levelized

(2014‐2028) $0.43   $0.75   $0.16 $0.16 $0.10 $0.04 $1.63

Exhibit 7‐32 provides the results of the same computation for 2015 installations.
Synapse Energy Economics, Inc. – AESC 2013 Page 7‐33  

Exhibit 7‐32. Annual Electric‐Gas‐Electric Price benefit per MWh saved, 2015 installations

CT MA ME NH RI VT ISO

2015 $0.25 $0.43 $0.09 $0.09 $0.06 $0.02 $0.94

2016 $0.97 $1.70 $0.35 $0.35 $0.23 $0.08 $3.69

2017 $1.00 $1.76 $0.36 $0.36 $0.24 $0.08 $3.82

2018 $1.02 $1.79 $0.37 $0.37 $0.25 $0.09 $3.88

2019 $0.74 $1.31 $0.27 $0.27 $0.18 $0.06 $2.84

2020 $0.47 $0.84 $0.17 $0.17 $0.11 $0.04 $1.81

2021 $0.35 $0.63 $0.13 $0.13 $0.09 $0.03 $1.36

2022 $0.23 $0.42 $0.09 $0.09 $0.06 $0.02 $0.90

Levelized

(2014‐2028) $0.35   $0.62   $0.13   $0.13 $0.09 $0.03 $1.35
Synapse Energy Economics. Inc. — AESC 2013 Page 8‐1  

Chapter 8: Sensitivity Analyses

8.1 Introduction

For the gas High Price sensitivity case, wholesale natural gas prices are 23.2 percent higher at Henry Hub

through 2028 on a 15‐year levelized basis than the AESC 2013 Base Case. The change in Henry Hub

prices translate to an increase of 15.6 percent in the annual wholesale electric prices on a levelized

basis.  

For the gas Low Price sensitivity case, wholesale natural gas prices are 25.5 percent lower at Henry Hub

through 2028 on a 15‐year levelized basis than those used in the AESC 2013 Base Case. The change in

Henry Hub prices translate to a decrease of 16.3 percent in the annual wholesale electric prices on a

levelized basis.

For the RGGI Only scenario, wholesale electricity prices through 2028 on a 15‐year levelized basis are 9.6

percent lower than those used in the AESC 2013 Base Case.  

For the Northern Pass scenario, wholesale electricity prices through 2028 on a 15 year levelized basis are

2 percent lower in New Hampshire, but 1 percent higher at WCMA than those in the AESC 2013 Base

Case.  

8.1.1 Sensitivity of Wholesale Electric Energy Prices to Changes in Natural Gas Prices at

Henry Hub

As documented in previous chapters, natural gas prices have a large, direct impact on avoided electric

energy costs.  

The AESC natural gas High and Low Price cases are described in section 2.2.11. The High and Low Price

cases are based primarily on alternative assumptions about the EUR from the AESC 2013 Base Case. The

High Price case assumes the EUR is 50 percent lower than the EUR in the AESC 2013 Base Case and the

Low Price case assumes the EUR is 50 percent higher. In addition, the Low Price projections do not

include any adjustments for the economics of fracturing regulations and the AEO 2013 prices were used

through 2016. The High Price forecast adds an economic adjustment of $0.88 per MMBtu versus $0.33

per MMBtu in the AESC 2013 Base Case to the low EUR price.  

The Henry Hub prices under the AESC natural gas Base, Low, and High Price case are shown in columns

two and three of Exhibit 8‐1 and Exhibit 8‐2. The last column shows the impact on electricity prices using

the gas prices sensitivities as compared to the Base Case Henry Hub natural gas.  

   
Synapse Energy Economics, Inc. – AESC 2013 Page 8‐2  

Exhibit 8‐1. Henry Hub AESC 2013 Base Case and High Gas Case Prices (2013$/MMBtu)

Year Base NG

Price

High NG

Price

% Change

in NG

Price

% Change

in

Electricity

Price

2014 $4.20 $4.34 3% 2%

2015 $4.32 $4.50 4% 3%

2016 $4.18 $4.61 10% 5%

2017 $4.50 $5.09 13% 9%

2018 $4.77 $5.58 17% 12%

2019 $5.01 $6.10 22% 16%

2020 $5.34 $6.66 25% 17%

2021 $5.48 $6.92 26% 17%

2022 $5.77 $7.34 27% 19%

2023 $5.95 $7.66 29% 19%

2024 $6.07 $7.89 30% 20%

2025 $6.26 $8.18 31% 20%

2026 $6.41 $8.43 31% 20%

2027 $6.58 $8.69 32% 21%

2028 $6.69 $8.90 33% 22%

Levelized $5.39 $6.64 23.2% 15.6%

Exhibit 8‐2. Henry Hub AESC 2013 Base Case and Low Gas Case Prices (2013$/MMBtu)

Year

Base NG

Price

Low NG

Price

% Change

in NG

Price

% Change

in

Electricity

Price

2014 $4.20 $3.23 ‐23% ‐7%

2015 $4.32 $3.22 ‐25% ‐7%

2016 $4.18 $3.68 ‐12% ‐7%

2017 $4.50 $3.71 ‐18% ‐13%

2018 $4.77 $3.71 ‐22% ‐16%

2019 $5.01 $3.72 ‐26% ‐19%

2020 $5.34 $3.82 ‐28% ‐20%

2021 $5.48 $3.97 ‐28% ‐19%

2022 $5.77 $4.18 ‐27% ‐19%

2023 $5.95 $4.31 ‐28% ‐18%

2024 $6.07 $4.37 ‐28% ‐19%

2025 $6.26 $4.48 ‐28% ‐19%

2026 $6.41 $4.61 ‐28% ‐19%

2027 $6.58 $4.75 ‐28% ‐18%

2028 $6.69 $4.83 ‐28% ‐18%

Levelized $5.39   $4.01   ‐25.5% ‐16.3%

The gas prices in the sensitivity cases do not represent variations in actual market prices of gas (e.g.,

weekly, monthly, or even annual). Instead, the High Price case provides a set of gas prices that reflect
Synapse Energy Economics, Inc. – AESC 2013 Page 8‐3  

the range of upside uncertainty in gas prices in the long term. Our expectation is that any revised

forecasts of long‐term avoided Henry Hub gas costs made prior to the anticipated AESC 2015 update

would fall between the Low and High Price cases.

Exhibit 8‐3 shows the impacts of the High and Low Price case gas prices on New England wholesale

electric energy prices by costing period. The average 23.2 percent increase in the High Price case Henry

Hub natural gas price results in an average 15.6 percent increase in annual wholesale electric energy

prices. The average 25.3 percent decrease in the Low Price case Henry Hub natural gas price results in an

average 16.3 percent decrease in annual wholesale electric energy prices. The level of increase varies by

season and time period, but not dramatically.  

Exhibit 8‐3. Seasonal and Time Period Impacts of Henry Hub Price Changes

8.1.2 Sensitivity of Wholesale Electric‐Energy Prices to Changes in Carbon‐Dioxide‐

Allowance Prices

We tested the sensitivity of wholesale electric energy prices to a Low Case carbon forecast, shown in

Exhibit 8‐4 below. The RGGI carbon case provides a lower bound of CO2 allowance prices; we draw the

prices for this case from the “RGGI only” set of carbon dioxide allowance prices required under the AESC

2013 scope of work. Note that the RGGI price does not change in real terms after 2020; changes in the

RGGI requirements are quite possible, but beyond the scope of AESC 2013.  

   

Season Time of Day

High NG 

Price

Low NG 

Price

Winter Off-Peak 15.7% -17.1%

On-Peak 15.2% -15.8%

All-Hours 15.5% -16.4%

Summer Off-Peak 16.6% -17.0%

On-Peak 15.3% -15.2%

All-Hours 15.8% -16.0%

Annual All-Hours 15.6% -16.3%
Synapse Energy Economics, Inc. – AESC 2013 Page 8‐4  

Exhibit 8‐4. Carbon Dioxide Reference and RGGI Case Prices

Exhibit 8‐5 shows the annual CO2 price differences relative to the Base Case and their impacts on the

average annual wholesale energy prices. The average effect on energy prices is about $0.52/MWh on

average for each $1/ton change in CO2 prices.245

                                                            

245 The AESC 2013 results are quite close to the AESC 2011 calculated coefficient of $0.45/MWh on average for this effect, and

the AESC 2013 result is consistent with the average marginal price being set by a natural gas plant with a heat rate slightly

below 8,000 Btu/kWh.

Year Reference RGGI

2014 $4.22 $4.22

2015 $5.28 $5.28

2016 $6.33 $6.33

2017 $7.39 $7.39

2018 $8.44 $8.44

2019 $9.50 $9.50

2020 $20.30 $10.55

2021 $22.58 $10.55

2022 $24.87 $10.55

2023 $27.15 $10.55

2024 $29.44 $10.55

2025 $31.72 $10.55

2026 $34.00 $10.55

2027 $36.29 $10.55

2028 $38.57 $10.55

Levelized 

(2014-2028) $19.72 $8.97

CO2 (2013$/short ton)
Synapse Energy Economics, Inc. – AESC 2013 Page 8‐5  

Exhibit 8‐5. Energy Price Impacts of CO2 Price Changes(2013$)  

8.1.3 Sensitivity of Wholesale Electric‐Energy Prices to addition of Northern Pass Project

Exhibit 8‐6 shows the energy price impacts of the addition of the 1,200 MW Northern Pass project, a DC

transmission link that would bring hydroelectric power directly into New Hampshire. This project is

modeled with an installation date of Jan 1, 2018.

Electricity price effects are relatively modest mainly because additional transmission additions were not

included as part of this sensitivity run.

246 While prices in New Hampshire decrease 2 to 4 percent, prices

in the rest of New England remain flat or increase slightly, especially during summer peak periods. Our

model results indicate that transmission lines from New Hampshire to Boston, Vermont, and NEMA are

heavily loaded, operating at their full rated capacity 60 to 130 percent more of the time than in the Base

Case. We anticipate that changes in additional transmission additions would result in more widespread

electricity market price reductions than shown in this sensitivity run given the lack of additional

transmission upgrades.

                                                            

246 Our research in developing transmission assumption in Chapter 5 did not identify additional transmission projects that

would support additional changes resulting from the Northern Pass model run scenario.  

Year

CO2 Price 

Change 

($/ton)

Energy 

Price 

Change 

($/MWh)

AESC 2013 

Reference 

Case 

($/MWh)

AESC 2013 

RGGI 

Sensitivity 

($/MWh)

% 

Difference 

from 

Reference 

Case

2020 -$9.75 -$5.70 $59.95 $54.25 -9.5%

2021 -$12.03 -$6.54 $60.86 $54.32 -10.7%

2022 -$14.32 -$7.58 $63.12 $55.54 -12.0%

2023 -$16.60 -$8.74 $65.73 $57.00 -13.3%

2024 -$18.88 -$9.90 $67.66 $57.75 -14.6%

2025 -$21.17 -$10.67 $70.37 $59.70 -15.2%

2026 -$23.45 -$11.85 $72.42 $60.57 -16.4%

2027 -$25.73 -$12.86 $74.83 $61.97 -17.2%

2028 -$28.02 -$13.85 $76.95 $63.10 -18.0%

Average -$18.88 -$9.74 $43.49 $37.34 -14.1%

Ratio: $/MWh vs. $/ton 0.52

RGGI CO2 Price
Synapse Energy Economics, Inc. – AESC 2013 Page 8‐6  

Exhibit 8‐6. Energy Price Impacts of Northern Pass(2013$/MWh)

NH WCMA NH WCMA NH WCMA

2014 $46.72 $47.79 $46.71 $47.77 0% 0%

2015 $46.53 $47.79 $46.53 $47.77 0% 0%

2016 $44.84 $46.64 $44.84 $46.58 0% 0%

2017 $44.23 $46.51 $44.25 $46.44 0% 0%

2018 $47.08 $49.54 $45.42 $49.77 -4% 0%

2019 $49.89 $53.14 $48.00 $53.51 -4% 1%

2020 $57.65 $59.95 $56.09 $60.75 -3% 1%

2021 $58.66 $60.86 $57.35 $61.29 -2% 1%

2022 $61.12 $63.12 $60.14 $63.82 -2% 1%

2023 $63.80 $65.73 $62.72 $66.80 -2% 2%

2024 $65.69 $67.66 $64.18 $68.62 -2% 1%

2025 $68.05 $70.37 $66.31 $71.03 -3% 1%

2026 $69.95 $72.42 $68.26 $73.42 -2% 1%

2027 $72.47 $74.83 $70.42 $75.57 -3% 1%

2028 $74.29 $76.95 $71.88 $77.90 -3% 1%

Levelized $57.47 $59.62 $56.32 $60.11 -2% 1%

Reference Northern Pass % Change
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Troutman, Preston. 2013. Tennessee Gas Pipeline Company Presentation at Northeast Gas Association’s

Regional Market Trends Forum, April 30, 2013. Available at:

http://www.northeastgas.org/pdf/p_troutman_tennessee.pdf

U.S. Energy Information Administration (EIA). 1998. Analysis of S. 687, the Electric System Public Benefits

Protection Act of 1997. SR/OIAF/98‐01. Washington D.C.: Energy Information Administration.  

‐‐‐. 1999. Annual Energy Outlook 2000. DOE/EIA‐0383 (2000). Washington D.C.: Energy Information

Administration.

‐‐‐. 2001. Analysis of Strategies for Reducing Multiple Emissions from Electric Power Plants: Sulfur

Dioxide, Nitrogen Oxides, Carbon Dioxide, and Mercury and a Renewable Portfolio Standard.

SR/OIAF/2001‐03. Washington D.C.: Energy Information Administration.

‐‐‐. 2002. Impacts of a 10‐Percent Renewable Portfolio Standard. SR/OIAF/2002‐03. Washington D.C.:

Energy Information Administration.

‐‐‐. 2003. Analysis of a 10‐Percent Renewable Portfolio Standard. SR/OIAF/2003‐01. Washington D.C.:

Energy Information Administration.

‐‐‐. “Distillate Fuel Oil Consumption Estimates, 2011.” Available at:

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id=US.  

‐‐‐. April 2011. Annual Energy Outlook 2011: With Projections to 2035. U.S. Department of Energy.

Available at: http://en.openei.org/w/index.php?title=File:0383(2011).pdf&page=1  

‐‐‐. February 2, 2012. “Emissions allowance prices for SO2 and NOx remained low in 2011.” Available at:

http://www.eia.gov/todayinenergy/detail.cfm?id=4830
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‐‐‐. June 2012. Annual Energy Outlook 2012: With Projections to 2035. U.S. Department of Energy.

Available at: www.eia.gov/forecasts/aeo

‐‐‐. December 5, 2012. Annual Energy Outlook 2013 Early Release Overview. Available at:

http://www.eia.gov/forecasts/aeo/er/executive_summary.cfm.  

‐‐‐. April 2013. Annual Energy Outlook 2013: With Projections to 2040. U.S. Department of Energy.

Available at: www.eia.gov/forecasts/aeo

‐‐‐. April 9, 2013. EIA Short Term Energy Outlook.

‐‐‐. May 10, 2013 “Heating oil futures contract now uses ultra‐low sulfur diesel fuel.” Available at:

http://www.eia.gov/todayinenergy/detail.cfm?id=11211

U.S. Environmental Protection Agency (EPA). January 1995. “Emissions Factors & AP 42, Compilation of

Air Pollutant Emission Factors.” Vol. 1 Ed. 5, Chapter 1, External Combustion Sources. Available

at: http://www.epa.gov/ttnchie1/ap42/  

‐‐‐. 1999. “Stationary Point and Area Sources.” V. 1 of Compilation of Air Pollutant Emission Factors 5th

Ed. AP‐42. Triangle Park, N.C.: U.S. Environmental Protection Agency. (Section 1.3‐2)

‐‐‐. March 15, 1999. Technical background document for the report to congress on remaining wastes

from fossil fuel combustion: Potential damage cases.

http://www.epa.gov/osw/nonhaz/industrial/special/fossil/ffc2_397.pdf

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of the Clean Air Act.” Available: http://www.epa.gov/climatechange/endangerment

‐‐‐. 2010. Greenhouse Gas Emissions Reporting from the Petroleum and Natural Gas Industry:

Background Technical Support Document. Available at:

http://www.epa.gov/ghgreporting/documents/pdf/2010/Subpart‐W_TSD.pdf

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http://epa.gov/climatechange/emissions/usinventoryreport.html

‐‐‐. October 11, 2011. Steam Electric ELG Rulemaking. UMRA and Federalism Implications: Consultation

Meeting. http://water.epa.gov/scitech/wastetech/guide/upload/Steam‐Electric‐ELG‐

Rulemaking‐UMRA‐and‐Federalism‐Implications‐Consultation‐Meeting‐Presentation.pdf

‐‐‐. 2012a. “RSM‐based Benefit per Ton Estimates.” Available at:

http://www.epa.gov/oaqps001/benmap/bpt.html  

‐‐‐. 2012b. “Air Emissions.” Accessed on March 19, 2013. Available at:

http://www.epa.gov/cleanenergy/energy‐and‐you/affect/air‐emissions.html.  

‐‐‐. April 2012. “Regulatory Impact Analysis: Final new source performance standards and amendments

to the national emissions standards for hazardous air pollutants for the oil and natural gas

industry.” Office of Air and Radiation, Office of Air Quality Planning and Standards. Available:

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Industry: Fact Sheet.” Available at:

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‐‐‐. December 2012. Study of the Potential Impacts of Hydraulic Fracturing on Drinking Water Resources:

Progress Report. Available: http://epa.gov/hfstudy/. Accessed June 8, 2013.
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‐‐‐. Factsheet: EPA’s Final Air Toxics Standard Major and Area Source Boilers and Certain Incinerators

Overview of Rules and Impacts. Available at:

http://www.epa.gov/airquality/combustion/docs/overviewfsfinal.pdf. Accessed June 20, 2011.

‐‐‐. June 7, 2013. Proposed Effluent Guidelines for the Steam Electric Power Generating Category.

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electric/amendment.cfm

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development, and environmental and public health risks. GAO‐12‐732. Available:

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Today and the Future. Cambridge, Mass.: Union of Concerned Scientists.  

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Scientists.  

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Scientists.  

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Jobs and Save Consumers Money. Cambridge, Mass.: Union of Concerned Scientists.

United Illuminating Company. October 27, 2009. Avoided Transmission & Distribution Cost Study Report.

Black & Veatch.

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Hydraulic Fracturing in the Shale Plays. Reservoir Research Partners and Tudor Pickering Holt &

Co. Available at:

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pdf

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Wilson, R., P. Luckow, B. Biewald, F. Ackerman, and E. Hausman. October 2012. 2012 Carbon Dioxide

Price Forecast. Synapse Energy Economics. http://www.synapse‐

energy.com/Downloads/SynapseReport.2012‐10.0.2012‐CO2‐Forecast.A0035.pdf

Wiser, R., Bolinger, M. and M. St. Clair. 2005. Easing the Natural Gas Crisis: Reducing Natural Gas Prices

through Increased Deployment of Renewable Energy and Energy Efficiency. LBNL‐56756.

Berkeley, Calif.: Lawrence Berkeley National Laboratory.

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Worley, Parsons, and Schlumberger. 2011. Economic Assessment of Carbon Capture and Storage

Technologies. Supported by the Global CCS Institute. Available at:

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carbon‐capture‐and‐storage‐technologies‐2011‐update.pdf
Synapse Energy Economics. Inc. — AESC 2013 Appendix A Page A‐1  

Appendix A: Usage Instructions

Table of Exhibits

Exhibit A‐1. Appendix B Tables of Avoided Cost of Electricity.................................................................. A‐2

Exhibit A‐2. Delivery System Structure and Losses................................................................................. A‐12

Exhibit A‐3. Illustration of Alternative Approaches to Capturing Value from Reductions in Peak Demands

................................................................................................................................................................ A‐15

Exhibit A‐4. State Supply DRIPE Benefit (2013$ per MMBtu for installation in year 2014) ................... A‐17

Exhibit A‐5. 15‐year Levelized Gas‐Cross DRIPE for Heating Load 2014 Installation ($/MMBtu) .......... A‐17

1.1 Introduction

This appendix provides instructions on how to apply the Base Case avoided costs of electricity, how to

estimate avoided costs of electricity for both the High Gas and Low Gas Price sensitivity case, and how

to apply the Base Case avoided costs of natural gas.

1.2 Base Case Avoided Costs of Electricity

AESC 2013 provides detailed projections of avoided electricity costs for each New England state as well

as for specific regions within Connecticut and Massachusetts. These projections are provided as two‐ 

page tables in Appendix B. The Excel workbooks used to develop these tables are provided to Program

Administrators.  

Appendix B provides tables for the following reporting regions:
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐2  

Exhibit A‐1. Appendix B Tables of Avoided Cost of Electricity

The tables for each reporting region present avoided costs by year for the following ISO‐NE defined

costing periods:1

 Summer On‐Peak: The 16‐hour block 7 am–11 pm, Monday–Friday (except ISO

holidays), in the months of June–September (1,390 Hours, 15.9 percent of 8,760).2

 Summer Off‐Peak: All other hours–11 pm–7 am, Monday–Friday, weekends, and ISO

holidays in the months of June–September (1,530 Hours, 17.5 percent of 8,760).

 Winter On‐Peak: The 16‐hour block 7 am–11 pm, Monday–Friday (except ISO holidays),

in the eight months of January–May and October–December (2,781 Hours, 31.7 percent

of 8,760).

 Winter Off‐Peak: All other hours–11 pm‐7 am, Monday–Friday, all day on weekends,

and ISO holidays–in the months of January–May and October–December (3,059 Hours,

34.9 percent of 8,760)

The “all‐hours” avoided electricity cost for a given year, or set of years, is equal to the hour‐weighted

average of avoided costs for each costing period of that year.

                                                            

1 AESC 2013 follows the ISO‐NE defined on‐peak and off‐peak hours available at: http://www.iso‐

ne.com/support/training/glossary/index‐p5.html.  

2 ISO‐NE holidays are New Year’s Day, Memorial Day, July 4th , Labor Day, Thanksgiving Day, and Christmas.  

State Table 

Connecticut Statewide 

Norwalk/Stamford 

Southwest Connecticut, excluding Norwalk/Stamford 

Southwest Connecticut, including Norwalk/Stamford 

Connecticut excluding all of Southwest Connecticut 

Massachusetts Statewide 

NEMA (Northeast Massachusetts) 

Massachusetts excluding NEMA 

SEMA (Southeast Massachusetts) 

WCMA (West-Central Massachusetts) 

Maine Statewide 

New Hampshire Statewide 

Rhode Island Statewide 

Vermont Statewide 

Connecticut (nominal $) Statewide 

Norwalk/Stamford 

Southwest Connecticut, excluding Norwalk/Stamford 

Southwest Connecticut, including Norwalk/Stamford 

Connecticut excluding all of Southwest Connecticut 
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐3  

All‐hours avoided electricity cost = (15.9 percent *summer on‐peak) + (17.5 percent *summer off‐peak)

+ (31.7 * winter on‐peak) + (34.9 percent * winter off‐peak)

Page one of each reporting region table provides the following avoided cost components:

1. Avoided unit cost of electric energy;

2. Avoided unit cost of electric capacity by demand reduction bidding strategy;  

3. Intrastate energy DRIPE and capacity DRIPE for 2014 installations;

4. Intrastate energy DRIPE and capacity DRIPE for 2015 installations; and

5. Avoided non‐embedded costs.  

Page two of each reporting region table provides:

1. Wholesale avoided costs of electricity (energy and capacity);

2. Cross‐DRIPE;

3. Avoided REC costs to load;  

4. 2014 Rest‐of‐Pool Energy DRIPE values; and

5. 2015 Rest‐of‐Pool Energy DRIPE values.

Each table provides illustrative levelized values for each category of avoided cost at the bottom of each

cost column. These are computed using a real discount rate of 1.36 percent.

1.3 Worksheet Structure and Terminology

For each reporting region/zone there is a two‐page table of avoided electricity costs.  

1.3.1 Page One—Avoided Cost of Electricity Results

Reading from left to right, the structure of page one of each table is as follows:

User‐Defined Inputs

The tables have the following default values for the following three input assumptions:

1. Wholesale Risk Premium – 9 percent3

2. ISO Distribution Losses – 8 percent

3. Real Discount Rate – 1.36 percent

                                                            

3 The wholesale risk premium for Vermont is 11.1 percent per Vermont DPS.
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐4  

4. Percent of Capacity Bid into the FCM – 50 percent

Users may insert their own values for any or all of those input assumptions.  

Avoided Unit Cost of Electric Energy ($/kWh) (Columns a – d)

Avoided energy costs are presented by year for each of the four energy costing periods: Winter On‐

Peak, Winter Off‐Peak, Summer‐On Peak, and Summer Off‐Peak.4

  

The generalized avoided energy cost in each period is calculated as: (modeled avoided wholesale energy

cost + avoided renewable energy certificate cost) * (1 + wholesale risk premium).

Avoided Unit Cost of Electric Capacity ($/kW‐yr) (Columns e – g)

This section provides values for a PA to calculate the avoided capacity cost based on a simplified bidding

strategy consisting of x percent of demand reductions from measures in each year bid into the FCA for

that year and the remaining 1‐x percent not bid into any FCA. The default value for x is 50 percent. Users

can insert their own input for that value in the user‐defined inputs section of Table One. (See Chapter 5

for a discussion of energy efficiency and the capacity market).

The components of the avoided capacity cost are as follows:

 The Avoided Unit Cost of Capacity of a kW bid into the FCM in column e reflects an 8 percent

adjustment to reflect losses from the customer meter to the ISO‐NE delivery point.  

 The Avoided Unit Cost of Capacity in column f for avoided capacity not bid into an FCA reflects

upward adjustments for the wholesale risk premium, the reserve margin in that year, and also a

1.5 percent adjustment to reflect PTF losses. Because FCA auctions are set three years in

advance of the actual delivery year, avoided capacity not bid into a FCA will not impact ISO‐NE’s

determination of forecasted peak until 2018 for measures installed in 2014.

 The Weighted Average Capacity Value based on percent bid in column g is the weighted average

avoided capacity of column e and f reflecting an individual PA’s percent of capacity that is bid

into the Forward Capacity Market. The column presents a weighted average of 50 percent bid

default value that may be changed by PAs to reflect specific bidding strategies.  

                                                            

4 The avoided energy costs are computed for the aggregate load shape in each zone by costing period, and are applicable to

DSM programs reducing load roughly in proportion to existing load. Other resources, such as load management and

distributed generation, may have very different load shapes and significantly different avoided energy costs. Baseload

resources, such as combined‐heat‐and‐power (CHP) systems, would tend to have lower avoided costs per kWh. Peaking

resources, such as most non‐CHP distributed generation and load management, would tend to have higher avoided costs per

kWh.

  
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐5  

Under this approach the avoided capacity cost in each year is equal to the Weighted Average Capacity

Value in column g for the relevant year multiplied by the demand reduction in that year.

Demand‐Reduction‐Induced Price Effects (Columns h – q)  

Each table provides separate projections of intrastate energy DRIPE and capacity DRIPE for measures

implemented in 2014 and in 2015, respectively.  

PAs should use energy DRIPE values that reflect the relevant state regulations governing treatment of

energy DRIPE. For Massachusetts and Connecticut zones, the energy DRIPE values will be intrastate

values only. For the remaining four states, the energy DRIPE values should reflect both intrastate and

rest of pool values.  

The AESC 2013 capacity DRIPE values start in 2017 due to floor prices set through FCA 7 as described in

Chapter 5.  

Carbon Dioxide Avoided Externality Costs $/kWh (Columns r – u)

This section of the worksheet table provides estimates of CO2 non‐embedded values developed for this

study (values for RI are from the RGGI only scenario). CO2 non‐embedded values are presented by year

for each of the four energy costing periods.  

1.3.2 Page Two—Inputs to Avoided Cost Calculations

Reading from left to right, the structure of page two is as follows:

Wholesale Avoided Costs of Electricity Energy, $ per kWh (Columns v – y)

The wholesale electric energy prices are from the Market Analytics simulation runs described in the

description of the model results in Chapter 6. Values for RI are from the RGGI only scenario described in

Chapter 8, Sensitivity Analyses. Users should not normally need to use the input values directly, or

modify these values.

Electric‐gas‐electric Cross DRIPE (Columns z and aa)

The electric‐gas‐electric cross‐DRIPE benefits for installations years 2014 and 2015 are specific for each

state and described in Chapter 7.

Capacity, $ per kW‐year (Column ab and ac)

The wholesale electric capacity prices and reserve margin requirements are from the relevant Chapter 5

sections. Users should not normally need to use the input values directly or modify these values.
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐6  

Avoided REC Costs to Load $/kWh (Column ad)

The avoided REC costs are calculated based on REC prices and RPS requirements that are described in

detail in Chapter 5. Users should not normally need to use the input values directly or to modify these

values.

Rest‐of‐Pool Energy DRIPE Values $/kWh (Columns ae – al)

The rest‐of‐pool energy DRIPE values are calculated based on energy DRIPE factors described in detail in

Chapter 7. The Appendix B workbooks present both intrastate and rest of pool energy DRIPE values for

2014 and 2015 installations. Users should not normally need to use the input values directly or modify

these values.

1.4 Guide to Applying the Avoided Costs

Users have the ability to specify certain inputs as well as to choose which of the avoided cost

components to include in their analyses.

1.4.1 User‐Specified Inputs

The avoided cost results are based upon default values for three inputs that users can specify. They are

1) the wholesale risk premium of 9 percent (11.1 percent for Vermont), 2) the real discount rate of 1.36

percent, and 3) a percentage of capacity bid into the Forward Capacity Market of 50 percent. 5 The Excel

workbook is designed to allow Program Administrators to specify their preferred values for those three

inputs in the top left section of page one of each worksheet.  

If a user wishes to specify a different value for any of the inputs, the user should enter the new value

directly in the worksheet. The calculations in the worksheet are linked to these values and new avoided

costs will be calculated automatically.

Program administrators are responsible for developing and applying estimates of avoided transmission

and distribution costs for their own specific system that would be separate inputs to the values in the

provided tables. An application of avoided transmission and distribution costs is described in section

1.4.6.

1.4.2 Avoided Costs of Energy

Calculating the quantity reduction benefits of energy reductions in a given year requires an estimate of

losses from the ISO delivery points to the end use in addition to an estimate of the reduction at the

                                                            

5 For avoided capacity values, the Appendix B workbook includes ISO‐NE distribution loss factor of 8%. This value should not

need to be changed.  
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐7  

meter. Each PA should obtain or calculate the losses applicable to its specific system as discussed in

section 1.7.1.

These avoided costs should be estimated as follows:

 Reduction in winter peak energy at the end use  

× winter peak energy losses from the ISO delivery points to the end use

× the Winter Peak Energy value for that year by costing period

 Reduction in winter off‐peak energy at the end use  

× winter off‐peak energy losses from the ISO delivery points to the end use

× the Winter Off‐Peak Energy value for that year by costing period

 Reduction in summer peak energy at the end use  

× summer peak energy losses from the ISO delivery points to the end use

× the Summer Peak Energy value for that year by costing period

 Reduction in summer off‐peak energy at the end use  

× summer off‐peak energy losses from the ISO delivery points to the end use

× the Summer Off‐Peak Energy value for that year by costing period

1.4.3 Capacity Costs Avoided by Reductions in Peak Demand

The quantity benefit of a reduction in peak demand in a given year will depend upon the approach the

PA has taken and/or will take towards bidding the reduction in demand from the efficiency program in

that year into the applicable FCAs. As discussed in Chapter 5, a PA may achieve avoided capacity costs

from reductions in peak demand through a range of approaches.  

A PA will bid some percent of demand reduction into Forward Capacity Market, and withhold the

remaining percent of demand reduction since there are issues of timing and funding that may not allow

a PA to bid the full quantity of demand reduction with confidence. A PA would therefore obtain a

combination of the value of the capacity that is bid into the FCM (highest value) as described in section

1.9 (below) and the market capacity value of a reduction in peak load (lowest value) as also described in

section 1.9 based on the percent of capacity that is bid into the FCM.

Following are descriptions of how a PA can calculate the avoided cost of reductions in peak demand for

the two extreme approaches and the simplified user‐specified bid strategy.

Value of 100 Percent Bid of Demand Reduction from First Program Year into the First Relevant FCA

(Column e)

A PA will obtain the highest benefit for the reductions in peak demand from an energy efficiency

program by bidding the full anticipated reduction into the FCA for the first power year in which that

program would produce reductions. Thus, a PA responsible for an efficiency program that is expected to

start January 2014 would have had to have bid 100 percent of the anticipated reduction in demand from

that program into FCA 5, which was held in 2011 for the power year starting June 1, 2014. There is some

financial risk associated with bidding in advance, in particular the potential a regulator may not approve
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐8  

the anticipated program budget and/or the possibility the program may fail to produce the anticipated

level of demand reductions.

The benefit of a reduction in peak demand from either an On‐Peak or a Seasonal Peak resource in a

given year starting 2014 is estimated as the result of:

Average kW reduction at the meter for the relevant period in a given year

× the Avoided Unit Cost of Capacity bid if a kW bid into the FCM for that year, which

incorporates the market‐clearing price in the forward capacity market and an ISO‐NE loss factor

of 8 percent.

If the benefits of demand reductions are to include capacity DRIPE, the benefits calculated above should

be increased by the estimate of capacity DRIPE allowed under the regulatory framework applicable to

that screening zone as follows:

Average kW reduction at the meter bid into FCA for given year

× capacity DRIPE for that year

Value of Zero Percent Bid of Demand Reduction into Any FCA (column f)

For an efficiency program that produces reductions starting in 2014, there is no benefit of a reduction in

peak demand until 2018, at which point the annual benefit is calculated as follows:

kW reduction at the meter during system peak in a given year× summer peak‐hour losses from the

ISO delivery points to the end use

× the Avoided Unit Cost of Capacity for that year, which is the FCA price for that year adjusted

upward by the reserve margin that ISO‐NE requires for that year, distribution losses (user

defined), by the PTF losses, and the wholesale risk premium.

Value of 50 Percent Bid of Demand Reduction into FCM (Column g)  

The column reflects a 50 percent weighted average of demand reduction into Forward Capacity Market.

A PA would therefore obtain 50 percent of the value of the capacity that is bid into the FCM (highest

value) as described in section 1.9 and 50 percent of the market capacity value of a reduction in peak

load (lowest value) based on the default percentage.  

1.4.4 DRIPE

The provided workbook tables include energy and capacity DRIPE values based on installation year 2014

and 2015.

Capacity DRIPE  

The price benefits of demand reductions are capacity DRIPE. A PA can estimate capacity DRIPE for 2014

and 2015 installations:
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐9  

kW reduction at the meter during system peak in a given year

× summer peak‐hour losses from the ISO delivery points to the end use

× capacity DRIPE for that year

Avoided Cost of Energy DRIPE

The price benefits of energy reductions are energy DRIPE. A PA can estimate energy DRIPE for 2014 and

2015 installations:

 Reduction in annual winter on peak energy at the end use  

× winter peak energy losses from ISO delivery to the end use

× the Winter On‐Peak Energy DRIPE;

 Reduction in annual winter off‐peak energy at the end use  

× winter off‐peak energy losses from ISO delivery to the end use

× the Winter Off‐Peak Energy DRIPE;

 Reduction in annual summer on peak energy at the end use  

× summer peak energy losses from ISO delivery to the end use

× the Summer On‐Peak Energy DRIPE;

 Reduction in annual summer off‐peak energy at the end use  

× summer off‐peak energy losses from ISO delivery to the end use

× the Summer Off‐Peak Energy DRIPE;

Cross DRIPE

A reduction in the quantity of electricity reduces gas consumption, which reduces electric prices. A PA

can estimate the electric‐gas‐electric DRIPE for 2014 and 2015 installations:

kW reduction at the meter during system peak in a given year

× electric‐gas‐electric DRIPE for that year

1.4.5 Avoided Non‐Embedded Cost of Carbon  

The non‐embedded carbon costs can be calculated as follows:

 Reduction in winter peak energy at the end use  

× winter peak energy losses from the ISO delivery points to the end use

× the CO2 Externality Winter On Peak Energy value for that year,

 Reduction in winter off‐peak energy at the end use  

× winter off‐peak energy losses from the ISO delivery points to the end use

× the CO2 Externality Winter Off‐Peak Energy value for that year,
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐10  

 Reduction in summer peak energy at the end use  

× summer peak energy losses from the ISO delivery points to the end use

× the CO2 Externality Summer On‐Peak Energy value for that year,

 Reduction in summer off‐peak energy at the end use  

× summer off‐peak energy losses from the ISO delivery points to the end use

× the CO2 Externality Summer Off‐Peak Energy value for that year

1.4.6 Local T&D Capacity Costs Avoided by Reductions in Peak Demand

Although not part of the provided tables, the benefits of peak demand reductions of avoided local

transmission and distribution costs, which should be based upon specific PA information, can be

calculated as follows:

 Reduction in the peak demand used in estimating avoided transmission and distribution

costs at the end use

 × the utility‐specific estimate of avoided T&D costs in $/kW‐year.

6

1.5 Levelization Calculations

Illustrative levelized costs for each of the direct avoided costs are presented along the bottom of each

table. These values are calculated for three periods (2014‐2023, 2014‐2028, and 2014‐2043), using a

1.36 percent real discount rate assumed throughout this project.

For levelization calculations outside the three periods documented in the workbook, the following

inputs are required:

 The real discount rate of 1.36 percent or other user specified discount rate

 The number or periods over the levelizing time frame. For instance, the period 2014‐

2023 contains 10 periods

 The avoided costs within the levelizing period

The Excel formula used to calculate levelized values in the workbook is:

Present Value PMT(Discount_ Rate,Period,(NPV(Discount_ Rate, Annual_ costs _ within_ period)

                                                            

6 Most demand‐response and load‐management programs will not avoid transmission and distribution costs, since they are as

likely to shift local loads to new hours as to reduce local peak load.
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐11  

1.6 Converting Constant 2013 Dollars to Nominal Dollars

Unless specifically noted, all dollar values in AESC 2013 are presented in 2013 constant dollars. To

convert constant dollars into nominal (current) dollars, a user would follow the formula:

2013$

Constant Value

Nominal Value 2013$

ConversionFactorto 

For instance, in order to convert an AESC 2013 $1 in 2014 into nominal 2014 dollars, one would use the

AESC 2014 conversion factor from 2014 to 2013 of 0.98. Inserting the conversion factor into the

equation above (Nominal Value2014 = ($12013$/0.98)) results in a value of $1.02 in nominal dollars.

The AESC 2013 inflator and deflator conversion factors are presented in Appendix E.

1.7 Comparisons to AESC 2011 Reference Case Avoided Costs of Electricity

A PA can prepare a comparison of the 15‐year levelized avoided costs of electricity from AESC 2013 for a

given reporting location and costing period to the corresponding AESC 2011 results, such as the

comparison presented in Exhibit 1‐2, as follows:

 Identify the relevant reporting location and costing period

 For the relevant reporting location and costing period, obtain the yearly values of each

component from AESC 2011 Appendix B. The potential components are avoided energy

costs, avoided capacity costs (by type of bidding strategy), energy DRIPE, capacity DRIPE

and carbon externality.

 Convert the AESC 2011 yearly values for each component from $2011 to $2013

 Calculate the 15‐year levelized values of each AESC 2011 component (AESC 2011 uses a

default discount rate of 2.46 percent)

 For the relevant reporting location and costing period, obtain the fifteen year values of

each component from AESC 2013 Appendix B.  

1.8 Utility‐Specific Costs to be Added/Considered by Program Administrators

Not Included in Worksheets

This section details additional inputs that are not specifically included in the worksheet and not part of

the AESC 2013 scope of work, but should be considered by program administrators.

1.8.1 Losses between the ISO Delivery Point and the End Use

The avoided energy and capacity costs and the estimates of DRIPE include energy and capacity losses on

the ISO‐administered PTF, from the generator to the delivery points at which the PTF system connects to

local non‐PTF transmission or to distribution substations.  
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐12  

The presented values do not include the following losses:

 Losses over the non‐PTF transmission substations and lines to distribution substations;

 Losses in distribution substations;

 Losses from the distribution substations to the line transformers on primary feeders and

laterals;7

 Losses from the line transformers over the secondary lines and services to the customer

meter;8

 Losses from the customer meter to the end use.

See Exhibit A‐2, which schematically illustrates the many types of losses on transmission and distribution

systems highlighted in the list above.

Exhibit A‐2. Delivery System Structure and Losses

In most cases, DSM program administrators measure demand savings from DSM programs at the end

use. To be more comprehensive, the program administrator should estimate the losses from delivery

points to the end uses. For example, if the energy delivered to the utility at the PTF is a, losses are b, and

the customer received energy is c,

                                                            

7 In some cases, this may involve multiple stages of transformers and distribution, as (for example) power is transformed from

115 kV transmission to 34 kV primary distribution and then to 14 kV primary distribution and then to 4 kV primary

distribution, to which the line transformer is connected.

8 Some customers receive their power from the utility at primary voltage. Since virtually all electricity is used at secondary

voltages, these customers generally have line transformers on the customer side of the meter and secondary distribution

within the customer facility.

Generator

Step-up 

Transformer

ISO 

Delivery 

Point

Primary-toSecondary 

Transformer Customer

Utility 

Substations

Utility-administered

transmission or 

sub-transmission 

Primary

lines 

Secondary 

distribution 

Transmission Distribution 

Losses included in 

AESC avoided costs 

Losses to be added by 

program administrator 

ISO-administered 

PTF transmission 
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐13  

 Losses as a fraction of deliveries to the utility are b ÷ a,

 Losses as a fraction of deliveries to customers are b ÷ c.

Hence, each kilowatt or kilowatt‐hour saved at the end use saves 1 + b⁄c. The program administrator

should estimate that ratio and multiply the end‐use savings or benefits by that loss ratio. Loss ratios will

be generally higher for higher‐load periods than lower‐load periods, since losses in wires (both within

transformers and in lines) vary with the square of the load, for a given voltage and conductor type.

If the change in load does not change the capacity of the transmission and distribution system, then the

losses should be computed as marginal losses, which are roughly twice the percentage as average line

losses for the same load level.9 Energy savings and/or growth do not generally result in changing the

wire sizes. Hence, for energy avoided costs, losses are estimated on a marginal basis, so a, b, and c

above are increments or derivatives, rather than total load values.

If the change in load results in a proportional change in transmission and distribution capacity, losses

should be computed as the average losses for that load level. If the program administrator treats all

load‐carrying parts of the transmission and distribution as avoidable and varying with peak load, then

only average losses should be applied to avoided capacity costs.

1.9 Energy Efficiency Programs and the Capacity Market

An energy efficiency program that produces a reduction in peak demand has the ability to avoid the

wholesale capacity cost associated with that reduction. The capacity‐cost amount that a particular

reduction in peak demand will avoid in a given year will depend upon the approach that the program

administrator responsible for that energy efficiency program takes towards bidding all, or some, of that

reduction into the applicable FCAs.

A program administrator (PA) can choose an approach that ranges between bidding 100 percent of the

anticipated demand reduction from the program into the relevant FCAs to bidding zero percent of the

anticipated reduction into any FCA.

 A PA that wishes to bid 100 percent of the anticipated demand reduction from the

program into the relevant FCA has to do so when that FCA is conducted, which can be

up to three years in advance of the program implementation year. For example, a PA

responsible for an efficiency program that will be implemented starting January 2014

would have had to have bid 100 percent of the forecast demand reduction for June

2014 onwards from that program into FCA 5, which was held in 2011. Since a bid is a

firm financial commitment, there is an associated financial risk if the PA is unable to

                                                            

9 In this sense, “line losses” does not include the no‐load losses that result from eddy currents in the cores of transformers.

These are often called “iron” losses (since transformer cores were historically made of iron), in contrast to the load‐related

“copper” losses of the lines and transformer windings.
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐14  

actually deliver the full demand reduction for whatever reason. The value of this

approach is the compensation paid by ISO‐NE, i.e., the quantity of peak reduction each

year times the FCA price for the corresponding year.

 If a PA does not bid any of the anticipated demand reduction into any FCA, the program

can still avoid some capacity costs if it has a measure life longer than three years.10

Under this approach, a PA responsible for an efficiency program starting January 2014

simply implements that program. The customers’ contribution to the ISO peak load,

whenever that occurs in the summer of 2014, would be lower due to the program. This

PA’s customers would see some benefit from a lower capacity share starting in June

2015 (the following year). The reduced capacity requirement will reduce the capacity

acquired in future FCAs, starting as early as the reconfiguration auctions for the power

year starting in June 2015 and affecting all the auctions for the power years from June

2018 onward; the entire region will benefit from the reduction of capacity purchases.

Exhibit A‐3 below illustrates the various approaches that a program administrator could choose for

avoiding wholesale capacity costs via a hypothetical energy efficiency measure that is implemented in

2012 and produces a 100 kW reduction for a five year period, 2014 to 2018. In this example, the PA

considers three approaches.

The first approach is to bid 100 percent of the projected reduction, 100 kW, into each of the relevant

FCAs. Under this approach the reduction avoids capacity costs roughly equals to its revenues from the

FCM each year, i.e., l to 100 kW times the FCA price in each of the five years, 2014 through 2018.11

However, the PA would have had to bid that 100‐kW reduction, scheduled to start in 2014, into each

FCA from FCA 5 onward.

The second approach is to bid none of the projected reductions into any FCA. Under this approach, the

reduction avoids capacity costs equal to the value of the reduction in installed capacity it causes in 2018.

That value is 100 kW increased by the reserve margin (17.2 percent for illustrative purposes) in 2018 and

multiplied by the FCA price in 2018. The avoided capacity cost is limited to the impact in 2018 because

ISO‐NE sets the ICR to be acquired in each power year three years in advance of that year. Thus, in this

approach, ISO‐NE would first see the 100 kW reduction as a lower actual peak load in 2014. However,

2018 is the earliest power year for which ISO‐NE could reflect the actual reduction in 2014 because, by

July 2015 ISO‐NE will have forecast peak load for 2018, set the ICR for 2018, and run the FCA for 2018.

The third illustrated approach is to bid 50 percent of the projected reduction, 50 kW, into each of the

relevant FCAs.

                                                            

10 In many cases, the PA is a utility; in other cases it is a state agency or other entity. In any case, the reduction in load benefits

the customers served by the PA, whether they pay for generation supply through a utility standard‐offer supply, an

aggregator, or a competitive supplier.

11 The price paid to a capacity resource in any year can vary from the price paid by load‐serving entities by various factors,

including PER deductions, availability penalties, multi‐year prices for new resources, local reliability costs, etc.
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐15  

Other approaches, not illustrated in Exhibit A‐3, would include bidding an increasing percentage of the

2014 load reduction into FCA 5 and future auctions, as the PA becomes more confident in its estimates

of the demonstrable savings.

Exhibit A‐3. Illustration of Alternative Approaches to Capturing Value from Reductions in Peak Demands  

Hypothetical measure installed in 2012, reduces peak by 100 kw for 5 years 

 

ISO-NE sets NICR and 

Conducts FCA 

Example 1—PA bids 

100% of expected 

demand reduction into 

each corresponding 

FCA 

Example 2—PA bids 

zero expected demand 

reduction into each 

corresponding FCA 

Example 3—PA bids 

50% of expected 

demand reduction into 

each corresponding 

FCA 

FCA 

# 

Calendar 

year 

FCA for 

power 

year 

Starting 

Reduction 

Bid into 

FCA 

Impact of 

Reduction 

on NICR 

set for 

power 

year 

Reduction 

Bid into 

FCA 

Impact of 

Reduction 

on NICR 

set for 

power 

year 

Reduction 

Bid into 

FCA 

Impact of 

Reduction 

on NICR 

set for 

power 

year 

 kw kw kw kw kw kw 

5 2011 6/1/2014 100 0 50 

6 2012 6/1/2015 100 0 50 

7 2013 6/1/2016 100 0 50 

8 2014 6/1/2017 100 0 0 0 50 0 

9 2015 6/1/2018 100 0 0 0 50 0 

10 2016 6/1/2019 0 0 0 0 0 

11 2017 6/1/2020 0 0 0 0 0 

12 2018 6/1/2021 0 0 117 0 58.6 

1.10 Sensitivity Case Avoided Costs of Electricity

Chapter 8 provides avoided wholesale electric energy costs for a High and Low Gas Price sensitivity case.

Calculating the complete avoided cost of electricity under each of those sensitivity cases is not included

in the AESC 2013 scope of work. However, a PA could use the results from those sensitivity cases to

develop approximate estimates of the avoided costs of electricity for either or both sensitivity cases.  

The estimates developed through the approach described below will be approximate because they will

not reflect the changes in various components, relative to Base Case values, that would occur with a

change in wholesale electric energy costs. For example, an increase in wholesale electric energy costs

under the High Gas Price would cause a decrease in the REC cost component.

1.10.1 High Gas Price Sensitivity Case

A PA could develop an approximate estimate of the 15‐year levelized avoided costs of electricity for the

High Gas Price sensitivity case for a given reporting location by multiplying the wholesale avoided costs

of electric energy for that location, on page two of the relevant Appendix B workbook, in each of the
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐16  

columns v, w, x, and y, by 1.156 for each of the years 2014 through 2028. (The factor of 1.156 is the

annual 15.6 percent increase reported in Chapter 8 on an annual basis).12

1.10.2 Low Gas Price Sensitivity Case  

A PA could develop an approximate estimate of the 15‐year levelized avoided costs of electricity for the

Low Gas Price sensitivity case for a given reporting location by multiplying the wholesale avoided costs

of electric energy for that location, on page two of the relevant Appendix B workbook, in each of the

columns v, w, x, and y, by 0.837 for each of the years 2014 through 2028. (The factor of 0.837 is the 16.3

percent decrease reported in Chapter 8 on an annual basis).13

1.11 Guide to Applying the Avoided Natural Gas Costs

The avoided cost for each end use by sector and the retail sector is the sum of the avoided cost of the

gas sent out by the LDC and the avoidable distribution cost, called the avoidable LDC margin, applicable

from the city gate to the burner tip for some LDCs. Other LDCs assume they will not avoid any

distribution costs due to reductions in gas use from efficiency measures. For the LDCs with no avoided

distribution cost, the avoided cost of gas by end use is their avoided cost of gas delivered to their city‐

gate. Users will need to determine if the LDC has avoidable LDC margins or not.  

Appendix D provides by end use of the value streams of avoided natural gas costs for both avoidable

margins and no avoidable margins. These columns refer to 1) non‐heating, 2) heating, and 3) all by

sector.

Non‐heating value streams apply to year‐round end uses, such as hot water, where usage is generally

constant over the year.  

Heating value streams apply to heating end uses where usage is high during winter months.  

For each program and/or measure, users should choose the appropriate value stream to determine the

avoided cost benefit stream in evaluating cost‐effectiveness.

Natural Gas “Own” DRIPE

As discussed in Section 7.3.1, a reduction in the quantity of gas used by retail gas customers reduces the

demand for gas in producing regions and therefore reduce the market price for that gas supply.  

                                                            

12 Exhibit 7‐2 provides the impact by costing period. Using the costing period values provides a more precise approximation

that accounts for seasonal differences.

13 Exhibit 7‐2 provides the impact by costing period. Using the costing period values provides a more precise approximation

that accounts for seasonal differences.
Synapse Energy Economics., Inc. — AESC 2013 Appendix A    Page A‐17  

Exhibit A‐4 presents the natural gas supply DRIPE for each state and the annual benefit for New England

gas consumers based on our analysis of AEO 2012 demand scenarios.  

Exhibit A‐4. State Supply DRIPE Benefit (2013$ per MMBtu for installation in year 2014)

CT MA ME NH RI VT New England

2014 $0.039 $0.085 $0.012 $0.007 $0.012 $0.003 $0.157

2015 $0.054 $0.119 $0.016 $0.010 $0.016 $0.004 $0.220

2016+ $0.077 $0.171 $0.023 $0.015 $0.024 $0.006 $0.315

          

Notes:        

Supply DRIPE benefit stream extends for gas efficiency measure life

Based on Exhibit: Supply DRIPE Benefit per Annual MMBtu Load Reduction by State

LDC gas supply hedge estimated at 50% Year 1, 30% Year 2, 0% Year 3

As discussed in detail in Chapter 7, we do not expect to see any significant decay in these natural gas

supply DRIPE values.  

Natural Gas Cross‐Fuel DRIPE

A reduction in gas use by retail gas customers reduces the gas supply and gas transportation

components of gas costs incurred by electric generators.  

Exhibit A‐5 summarizes the own‐state and ISO‐wide gas cross‐fuel DRIPE values for 2014 installations.

These values vary based on electric usage and are benefits accrued to gas programs for reducing natural

gas prices for electric generation as a result of natural gas efficiency. For illustrative purposes, we have

included the 15‐year levelized values in the exhibit.  

Exhibit A‐5. 15‐year Levelized Gas‐Cross DRIPE for Heating Load 2014 Installation ($/MMBtu)

   State Winter Heating DRIPE     

   CT MA ME NH RI VT ISO

2014 $3.04 $5.31 $1.14 $1.13 $0.73 $0.20 $11.55

2015 $8.92 $15.58 $3.34 $3.29 $2.11 $0.75 $33.99

2016 $8.94 $15.60 $3.36 $3.30 $2.12 $0.79 $34.11

2017 $3.49 $6.13 $1.31 $1.29 $0.83 $0.31 $13.36

2018 $2.41 $4.24 $0.90 $0.89 $0.57 $0.22 $9.24

2019 $1.19 $2.10 $0.45 $0.44 $0.28 $0.11 $4.56

2020 $0.76 $1.34 $0.28 $0.28 $0.18 $0.07 $2.92

2021 $0.57 $1.01 $0.21 $0.21 $0.14 $0.05 $2.19

2022 $0.38 $0.67 $0.14 $0.14 $0.09 $0.04 $1.46

Levelized $2.11   $3.69   $0.79   $0.78 $0.50 $0.18 $8.06
Synapse Energy Economics. Inc. — AESC 2013 Appendix B Page B‐1  

Appendix B: Avoided Electricity Cost Results

Table of Exhibits

Avoided Cost of Electricity (2013$) Results: CT .........................................................................................B‐3

Inputs to Avoided Cost Calculations (2013$): CT.......................................................................................B‐4

Avoided Cost of Electricity (2013$) Results: CT‐NS....................................................................................B‐5

Inputs to Avoided Cost Calculations (2013$): CT‐NS .................................................................................B‐6

Avoided Cost of Electricity (2013$) Results: CT‐R......................................................................................B‐7

Inputs to Avoided Cost Calculations (2013$): CT‐R ...................................................................................B‐8

Avoided Cost of Electricity (2013$) Results: CT‐SWe.................................................................................B‐9

Inputs to Avoided Cost Calculations (2013$): CT‐SWe ............................................................................B‐10

Avoided Cost of Electricity (2013$) Results: CT‐SWi................................................................................B‐11

Inputs to Avoided Cost Calculations (2013$): CT‐SWi .............................................................................B‐12

Avoided Cost of Electricity (2013$) Results: MA .....................................................................................B‐13

Inputs to Avoided Cost Calculations (2013$): MA ...................................................................................B‐14

Avoided Cost of Electricity (2013$) Results: MA‐NEMA..........................................................................B‐15

Inputs to Avoided Cost Calculations (2013$): MA‐NEMA........................................................................B‐16

Avoided Cost of Electricity (2013$) Results: MA‐R ..................................................................................B‐17

Inputs to Avoided Cost Calculations (2013$): MA‐R................................................................................B‐18

Avoided Cost of Electricity (2013$) Results: MA‐SEMA...........................................................................B‐19

Inputs to Avoided Cost Calculations (2013$): MA‐SEMA ........................................................................B‐20

Avoided Cost of Electricity (2013$) Results: MA‐WCMA.........................................................................B‐21

Inputs to Avoided Cost Calculations (2013$): MA‐WCMA.......................................................................B‐22

Avoided Cost of Electricity (2013$) Results: ME......................................................................................B‐23

Inputs to Avoided Cost Calculations (2013$): ME ...................................................................................B‐24

Avoided Cost of Electricity (2013$) Results: NH ......................................................................................B‐25

Inputs to Avoided Cost Calculations (2013$): NH....................................................................................B‐26

Avoided Cost of Electricity (2013$) Results: RI ........................................................................................B‐27

Inputs to Avoided Cost Calculations (2013$): RI......................................................................................B‐28

Avoided Cost of Electricity (2013$) Results: VT .......................................................................................B‐29
Synapse Energy Economics. Inc. — AESC 2013 Appendix B Page B‐2

Inputs to Avoided Cost Calculations (2013$): VT.....................................................................................B‐30

Avoided Cost of Electricity (Nominal $) Results: CT.................................................................................B‐31

Inputs to Avoided Cost Calculations (Nominal $): CT ..............................................................................B‐32

Avoided Cost of Electricity (Nominal $) Results: CT‐NS ...........................................................................B‐33

Inputs to Avoided Cost Calculations (Nominal $): CT‐NS.........................................................................B‐34

Avoided Cost of Electricity (Nominal $) Results: CT‐R .............................................................................B‐35

Inputs to Avoided Cost Calculations (Nominal $): CT‐R...........................................................................B‐36

Avoided Cost of Electricity (Nominal $) Results: CT‐SWe ........................................................................B‐37

Inputs to Avoided Cost Calculations (Nominal $): CT‐SWe......................................................................B‐38

Avoided Cost of Electricity (Nominal $) Results: CT‐SWi .........................................................................B‐39

Inputs to Avoided Cost Calculations (Nominal $): CT‐SWi.......................................................................B‐40
Appendix B: CT

Table One: Avoided Cost of Electricity (2013 $) Results : CT 

State CT Connecticut (Statewide)

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.068 0.059 0.057 0.050 0.04

2014 0.066 0.058 0.054 0.048 40.82 0.00 20.41 0.005 0.006 0.004 0.005 0.00 0.04

2015 0.065 0.058 0.056 0.048 42.77 0.00 21.38 0.019 0.024 0.016 0.019 0.00 0.005 0.006 0.004 0.005 0.00 0.04

2016 0.062 0.056 0.056 0.047 38.49 0.00 19.25 0.020 0.025 0.018 0.020 0.00 0.018 0.023 0.016 0.019 0.00 0.04

2017 0.059 0.052 0.061 0.049 42.77 0.00 21.38 0.020 0.024 0.020 0.022 60.78 0.018 0.022 0.019 0.021 0.00 0.04

2018 0.062 0.054 0.065 0.051 48.21 62.51 55.36 0.019 0.023 0.020 0.022 51.50 0.019 0.024 0.020 0.022 61.79 0.042019 0.067 0.058 0.070 0.054 53.27 69.07 61.17 0.016 0.020 0.017 0.019 41.69 0.017 0.021 0.018 0.019 52.11 0.042020 0.076 0.067 0.075 0.062 98.37 127.55 112.96 0.014 0.018 0.014 0.017 32.07 0.015 0.018 0.015 0.017 42.76 0.032021 0.076 0.068 0.078 0.063 119.75 155.27 137.51 0.011 0.013 0.011 0.012 21.64 0.011 0.014 0.011 0.013 32.47 0.032022 0.079 0.071 0.078 0.066 119.75 155.27 137.51 0.007 0.009 0.007 0.008 16.45 0.007 0.009 0.007 0.008 21.93 0.032023 0.081 0.073 0.083 0.068 119.75 155.27 137.51 0.004 0.004 0.004 0.004 11.11 0.004 0.000 0.004 0.000 16.66 0.032024 0.084 0.075 0.086 0.070 119.75 155.27 137.51 0.000 0.000 0.000 0.000 5.62 0.000 0.000 0.000 0.000 11.25 0.032025 0.089 0.080 0.092 0.074 119.75 155.27 137.51 0.000 0.000 0.000 0.000 5.74 0.03

2026 0.091 0.082 0.095 0.077 119.75 155.27 137.51 0.03

2027 0.095 0.084 0.098 0.079 119.75 155.27 137.51 0.03

2028 0.098 0.086 0.100 0.080 119.75 155.27 137.51 0.03

2029 0.101 0.088 0.103 0.083 119.75 155.27 137.51 0.02

2030 0.105 0.090 0.106 0.085 119.75 155.27 137.51 0.02

2031 0.108 0.093 0.110 0.087 119.75 155.27 137.51 0.02

2032 0.112 0.095 0.114 0.090 119.75 155.27 137.51 0.02

2033 0.116 0.098 0.118 0.092 119.75 155.27 137.51 0.02

2034 0.120 0.100 0.121 0.095 119.75 155.27 137.51 0.02

2035 0.124 0.103 0.126 0.098 119.75 155.27 137.51 0.02

2036 0.128 0.106 0.130 0.101 119.75 155.27 137.51 0.02

2037 0.132 0.109 0.134 0.103 119.75 155.27 137.51 0.02

2038 0.137 0.112 0.139 0.106 119.75 155.27 137.51 0.02

2039 0.141 0.115 0.143 0.109 119.75 155.27 137.51 0.02

2040 0.146 0.118 0.148 0.112 119.75 155.27 137.51 0.02

2041 0.151 0.121 0.153 0.116 119.75 155.27 137.51 0.02

2042 0.156 0.125 0.159 0.119 119.75 155.27 137.51 0.02

2043 0.162 0.128 0.164 0.122 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.069 0.061 0.067 0.055 71.17 70.03 70.60 0.014 0.017 0.013 0.015 23.36 0.012 0.014 0.012 0.013 22.67 0.0415 years (2014-2028) 0.076 0.067 0.075 0.062 86.28 96.55 91.42 0.009 0.012 0.009 0.010 16.45 0.008 0.010 0.008 0.009 16.72 0.0330 years (2014-2043) 0.100 0.085 0.100 0.079 101.33 122.95 112.14 0.005 0.006 0.005 0.006 9.06 0.004 0.005 0.004 0.005 9.20 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: CT

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.056 0.048 0.046 0.040 35.40 17.2% 0.00632014 0.055 0.048 0.044 0.038 37.80 17.2% 0.0054 0.007 0.004 0.006 0.0032015 0.055 0.048 0.046 0.039 0.001 0.000 39.60 17.2% 0.0051 0.028 0.015 0.023 0.012 0.007 0.004 0.002016 0.053 0.047 0.046 0.038 0.001 0.001 35.64 17.2% 0.0046 0.029 0.016 0.026 0.013 0.027 0.015 0.022017 0.051 0.044 0.052 0.041 0.001 0.001 39.60 17.2% 0.0039 0.029 0.015 0.029 0.014 0.026 0.014 0.022018 0.053 0.047 0.056 0.044 0.001 0.001 44.64 17.2% 0.0030 0.028 0.015 0.029 0.014 0.028 0.015 0.032019 0.058 0.050 0.061 0.046 0.001 0.001 49.32 17.2% 0.0035 0.024 0.013 0.025 0.012 0.025 0.013 0.022020 0.066 0.057 0.065 0.053 0.000 0.000 91.08 17.2% 0.0040 0.021 0.011 0.021 0.011 0.022 0.012 0.022021 0.065 0.058 0.068 0.054 0.000 0.000 110.88 17.2% 0.0040 0.016 0.009 0.016 0.008 0.016 0.009 0.012022 0.069 0.061 0.067 0.056 0.000 0.000 110.88 17.2% 0.0039 0.011 0.006 0.010 0.005 0.011 0.006 0.012023 0.071 0.063 0.072 0.058 110.88 17.2% 0.0037 0.005 0.003 0.005 0.003 0.006 0.000 0.002024 0.073 0.065 0.074 0.060 110.88 17.2% 0.0045 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.076 0.067 0.078 0.062 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.078 0.069 0.081 0.064 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.081 0.071 0.084 0.066 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.084 0.073 0.085 0.068 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.087 0.075 0.089 0.070 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.090 0.077 0.092 0.072 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.093 0.079 0.095 0.074 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.097 0.081 0.098 0.077 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.100 0.084 0.102 0.079 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.104 0.086 0.105 0.081 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.108 0.089 0.109 0.084 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.111 0.091 0.113 0.086 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.115 0.094 0.117 0.089 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.119 0.097 0.121 0.092 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.124 0.099 0.126 0.094 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.128 0.102 0.130 0.097 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.133 0.105 0.135 0.100 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.138 0.108 0.140 0.103 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.142 0.112 0.145 0.106 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.059 0.052 0.057 0.046 0.001 0.001 65.89 0.004 0.020 0.011 0.019 0.010 0.017 0.009 0.01

15 years (2014-2028) 0.065 0.057 0.065 0.052 0.000 0.000 79.89 0.005 0.014 0.007 0.013 0.007 0.012 0.006 0.01

30 years (2014-2043) 0.086 0.073 0.087 0.067 0.000 0.000 93.82 0.005 0.008 0.004 0.007 0.004 0.006 0.003 0.00

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-NS

Table One: Avoided Cost of Electricity (2013 $) Results : CT-NS 

State CT Norwalk/Stamford

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.069 0.059 0.057 0.050 0.04

2014 0.067 0.059 0.055 0.048 40.82 0.00 20.41 0.005 0.006 0.004 0.005 0.00 0.04

2015 0.066 0.059 0.056 0.048 42.77 0.00 21.38 0.019 0.024 0.016 0.019 0.00 0.005 0.006 0.004 0.005 0.00 0.04

2016 0.063 0.056 0.056 0.047 38.49 0.00 19.25 0.020 0.025 0.018 0.020 0.00 0.018 0.023 0.016 0.019 0.00 0.04

2017 0.060 0.053 0.062 0.049 42.77 0.00 21.38 0.020 0.024 0.020 0.022 60.78 0.018 0.022 0.019 0.021 0.00 0.04

2018 0.062 0.055 0.065 0.051 48.21 62.51 55.36 0.019 0.023 0.020 0.022 51.50 0.019 0.024 0.020 0.022 61.79 0.042019 0.067 0.059 0.071 0.055 53.27 69.07 61.17 0.016 0.020 0.017 0.019 41.69 0.017 0.021 0.018 0.019 52.11 0.042020 0.077 0.067 0.076 0.063 98.37 127.55 112.96 0.014 0.018 0.014 0.017 32.07 0.015 0.018 0.015 0.017 42.76 0.032021 0.076 0.069 0.079 0.064 119.75 155.27 137.51 0.011 0.013 0.011 0.012 21.64 0.011 0.014 0.011 0.013 32.47 0.032022 0.080 0.071 0.078 0.066 119.75 155.27 137.51 0.007 0.009 0.007 0.008 16.45 0.007 0.009 0.007 0.008 21.93 0.032023 0.082 0.073 0.083 0.068 119.75 155.27 137.51 0.004 0.004 0.004 0.004 11.11 0.004 0.000 0.004 0.000 16.66 0.032024 0.085 0.076 0.087 0.071 119.75 155.27 137.51 0.000 0.000 0.000 0.000 5.62 0.000 0.000 0.000 0.000 11.25 0.032025 0.090 0.080 0.093 0.075 119.75 155.27 137.51 0.000 0.000 0.000 0.000 5.74 0.03

2026 0.092 0.083 0.096 0.077 119.75 155.27 137.51 0.03

2027 0.096 0.084 0.099 0.079 119.75 155.27 137.51 0.03

2028 0.099 0.087 0.101 0.081 119.75 155.27 137.51 0.03

2029 0.102 0.089 0.104 0.084 119.75 155.27 137.51 0.02

2030 0.106 0.091 0.108 0.086 119.75 155.27 137.51 0.02

2031 0.109 0.094 0.111 0.088 119.75 155.27 137.51 0.02

2032 0.113 0.096 0.115 0.091 119.75 155.27 137.51 0.02

2033 0.117 0.099 0.119 0.093 119.75 155.27 137.51 0.02

2034 0.121 0.101 0.123 0.096 119.75 155.27 137.51 0.02

2035 0.125 0.104 0.127 0.099 119.75 155.27 137.51 0.02

2036 0.129 0.107 0.131 0.102 119.75 155.27 137.51 0.02

2037 0.134 0.110 0.136 0.104 119.75 155.27 137.51 0.02

2038 0.138 0.113 0.140 0.107 119.75 155.27 137.51 0.02

2039 0.143 0.116 0.145 0.110 119.75 155.27 137.51 0.02

2040 0.148 0.119 0.150 0.114 119.75 155.27 137.51 0.02

2041 0.153 0.122 0.155 0.117 119.75 155.27 137.51 0.02

2042 0.158 0.126 0.160 0.120 119.75 155.27 137.51 0.02

2043 0.163 0.129 0.166 0.124 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.070 0.062 0.068 0.056 71.17 70.03 70.60 0.014 0.017 0.013 0.015 23.36 0.012 0.014 0.012 0.013 22.67 0.0415 years (2014-2028) 0.077 0.068 0.076 0.062 86.28 96.55 91.42 0.009 0.012 0.009 0.010 16.45 0.008 0.010 0.008 0.009 16.72 0.0330 years (2014-2043) 0.100 0.086 0.101 0.080 101.33 122.95 112.14 0.005 0.006 0.005 0.006 9.06 0.004 0.005 0.004 0.005 9.20 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-NS

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: CT-NS

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.057 0.048 0.046 0.040 35.40 17.2% 0.00632014 0.056 0.049 0.045 0.039 37.80 17.2% 0.0054 0.007 0.004 0.006 0.0032015 0.055 0.049 0.046 0.039 0.001 0.000 39.60 17.2% 0.0051 0.028 0.015 0.023 0.012 0.007 0.004 0.002016 0.053 0.047 0.047 0.039 0.001 0.001 35.64 17.2% 0.0046 0.029 0.016 0.026 0.013 0.027 0.015 0.022017 0.051 0.045 0.053 0.041 0.001 0.001 39.60 17.2% 0.0039 0.029 0.015 0.029 0.014 0.026 0.014 0.022018 0.054 0.047 0.057 0.044 0.001 0.001 44.64 17.2% 0.0030 0.028 0.015 0.029 0.014 0.028 0.015 0.032019 0.058 0.051 0.061 0.047 0.001 0.001 49.32 17.2% 0.0035 0.024 0.013 0.025 0.012 0.025 0.013 0.022020 0.066 0.058 0.066 0.054 0.000 0.000 91.08 17.2% 0.0040 0.021 0.011 0.021 0.011 0.022 0.012 0.022021 0.066 0.059 0.068 0.055 0.000 0.000 110.88 17.2% 0.0040 0.016 0.009 0.016 0.008 0.016 0.009 0.012022 0.069 0.062 0.068 0.057 0.000 0.000 110.88 17.2% 0.0039 0.011 0.006 0.010 0.005 0.011 0.006 0.012023 0.072 0.064 0.073 0.059 110.88 17.2% 0.0037 0.005 0.003 0.005 0.003 0.006 0.000 0.002024 0.074 0.065 0.075 0.061 110.88 17.2% 0.0045 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.077 0.068 0.079 0.063 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.079 0.070 0.082 0.065 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.082 0.071 0.085 0.067 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.085 0.073 0.086 0.069 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.088 0.076 0.089 0.071 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.091 0.078 0.093 0.073 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.094 0.080 0.096 0.075 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.098 0.082 0.099 0.077 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.101 0.085 0.103 0.080 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.105 0.087 0.107 0.082 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.109 0.090 0.110 0.085 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.113 0.092 0.114 0.087 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.117 0.095 0.118 0.090 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.121 0.098 0.123 0.093 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.125 0.101 0.127 0.095 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.130 0.103 0.132 0.098 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.134 0.106 0.136 0.101 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.139 0.110 0.141 0.104 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.144 0.113 0.146 0.108 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.060 0.053 0.058 0.047 0.001 0.001 65.89 0.004 0.020 0.011 0.019 0.010 0.017 0.009 0.01

15 years (2014-2028) 0.066 0.058 0.065 0.052 0.000 0.000 79.89 0.005 0.014 0.007 0.013 0.007 0.012 0.006 0.01

30 years (2014-2043) 0.087 0.073 0.087 0.068 0.000 0.000 93.82 0.005 0.008 0.004 0.007 0.004 0.006 0.003 0.00

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-R

Table One: Avoided Cost of Electricity (2013 $) Results : CT-R 

State CT Rest of Connecticut (excluding all of Southwes

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.068 0.058 0.056 0.050 0.04

2014 0.066 0.058 0.054 0.047 40.82 0.00 20.41 0.005 0.006 0.004 0.005 0.00 0.04

2015 0.064 0.057 0.055 0.047 42.77 0.00 21.38 0.019 0.024 0.016 0.019 0.00 0.005 0.006 0.004 0.005 0.00 0.04

2016 0.062 0.055 0.055 0.046 38.49 0.00 19.25 0.020 0.025 0.018 0.020 0.00 0.018 0.023 0.016 0.019 0.00 0.04

2017 0.059 0.052 0.060 0.048 42.77 0.00 21.38 0.020 0.024 0.020 0.022 60.78 0.018 0.022 0.019 0.021 0.00 0.04

2018 0.061 0.054 0.064 0.050 48.21 62.51 55.36 0.019 0.023 0.020 0.022 51.50 0.019 0.024 0.020 0.022 61.79 0.042019 0.066 0.058 0.069 0.054 53.27 69.07 61.17 0.016 0.020 0.017 0.019 41.69 0.017 0.021 0.018 0.019 52.11 0.042020 0.075 0.066 0.074 0.062 98.37 127.55 112.96 0.014 0.018 0.014 0.017 32.07 0.015 0.018 0.015 0.017 42.76 0.032021 0.075 0.067 0.077 0.063 119.75 155.27 137.51 0.011 0.013 0.011 0.012 21.64 0.011 0.014 0.011 0.013 32.47 0.032022 0.078 0.070 0.077 0.065 119.75 155.27 137.51 0.007 0.009 0.007 0.008 16.45 0.007 0.009 0.007 0.008 21.93 0.032023 0.081 0.072 0.082 0.067 119.75 155.27 137.51 0.004 0.004 0.004 0.004 11.11 0.004 0.000 0.004 0.000 16.66 0.032024 0.084 0.075 0.085 0.070 119.75 155.27 137.51 0.000 0.000 0.000 0.000 5.62 0.000 0.000 0.000 0.000 11.25 0.032025 0.089 0.079 0.091 0.074 119.75 155.27 137.51 0.000 0.000 0.000 0.000 5.74 0.03

2026 0.091 0.081 0.094 0.076 119.75 155.27 137.51 0.03

2027 0.094 0.083 0.097 0.078 119.75 155.27 137.51 0.03

2028 0.097 0.085 0.099 0.080 119.75 155.27 137.51 0.03

2029 0.100 0.087 0.102 0.082 119.75 155.27 137.51 0.02

2030 0.104 0.089 0.105 0.084 119.75 155.27 137.51 0.02

2031 0.107 0.092 0.109 0.087 119.75 155.27 137.51 0.02

2032 0.111 0.094 0.113 0.089 119.75 155.27 137.51 0.02

2033 0.115 0.097 0.116 0.092 119.75 155.27 137.51 0.02

2034 0.118 0.100 0.120 0.094 119.75 155.27 137.51 0.02

2035 0.122 0.102 0.124 0.097 119.75 155.27 137.51 0.02

2036 0.127 0.105 0.129 0.100 119.75 155.27 137.51 0.02

2037 0.131 0.108 0.133 0.102 119.75 155.27 137.51 0.02

2038 0.135 0.111 0.137 0.105 119.75 155.27 137.51 0.02

2039 0.140 0.114 0.142 0.108 119.75 155.27 137.51 0.02

2040 0.145 0.117 0.147 0.111 119.75 155.27 137.51 0.02

2041 0.150 0.120 0.152 0.115 119.75 155.27 137.51 0.02

2042 0.155 0.123 0.157 0.118 119.75 155.27 137.51 0.02

2043 0.160 0.127 0.162 0.121 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.068 0.061 0.066 0.055 71.17 70.03 70.60 0.014 0.017 0.013 0.015 23.36 0.012 0.014 0.012 0.013 22.67 0.0415 years (2014-2028) 0.075 0.067 0.075 0.061 86.28 96.55 91.42 0.009 0.012 0.009 0.010 16.45 0.008 0.010 0.008 0.009 16.72 0.0330 years (2014-2043) 0.099 0.084 0.099 0.078 101.33 122.95 112.14 0.005 0.006 0.005 0.006 9.06 0.004 0.005 0.004 0.005 9.20 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-R

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: CT-R

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.056 0.047 0.045 0.039 35.40 17.2% 0.00632014 0.055 0.048 0.044 0.038 37.80 17.2% 0.0054 0.007 0.004 0.006 0.0032015 0.054 0.048 0.045 0.038 0.001 0.000 39.60 17.2% 0.0051 0.028 0.015 0.023 0.012 0.007 0.004 0.002016 0.052 0.046 0.046 0.038 0.001 0.001 35.64 17.2% 0.0046 0.029 0.016 0.026 0.013 0.027 0.015 0.022017 0.050 0.044 0.051 0.040 0.001 0.001 39.60 17.2% 0.0039 0.029 0.015 0.029 0.014 0.026 0.014 0.022018 0.053 0.046 0.056 0.043 0.001 0.001 44.64 17.2% 0.0030 0.028 0.015 0.029 0.014 0.028 0.015 0.032019 0.057 0.050 0.060 0.046 0.001 0.001 49.32 17.2% 0.0035 0.024 0.013 0.025 0.012 0.025 0.013 0.022020 0.065 0.057 0.064 0.052 0.000 0.000 91.08 17.2% 0.0040 0.021 0.011 0.021 0.011 0.022 0.012 0.022021 0.065 0.058 0.067 0.053 0.000 0.000 110.88 17.2% 0.0040 0.016 0.009 0.016 0.008 0.016 0.009 0.012022 0.068 0.060 0.067 0.056 0.000 0.000 110.88 17.2% 0.0039 0.011 0.006 0.010 0.005 0.011 0.006 0.012023 0.070 0.062 0.071 0.058 110.88 17.2% 0.0037 0.005 0.003 0.005 0.003 0.006 0.000 0.002024 0.072 0.064 0.074 0.060 110.88 17.2% 0.0045 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.075 0.066 0.077 0.062 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.077 0.068 0.080 0.064 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.080 0.070 0.083 0.065 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.083 0.072 0.085 0.067 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.086 0.074 0.088 0.069 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.089 0.076 0.091 0.071 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.092 0.078 0.094 0.073 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.096 0.081 0.097 0.076 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.099 0.083 0.101 0.078 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.103 0.085 0.104 0.080 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.106 0.088 0.108 0.083 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.110 0.090 0.112 0.085 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.114 0.093 0.116 0.088 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.118 0.096 0.120 0.091 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.122 0.098 0.124 0.093 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.127 0.101 0.129 0.096 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.131 0.104 0.133 0.099 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.136 0.107 0.138 0.102 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.141 0.110 0.143 0.105 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.059 0.052 0.057 0.046 0.001 0.001 65.89 0.004 0.020 0.011 0.019 0.010 0.017 0.009 0.01

15 years (2014-2028) 0.064 0.057 0.064 0.051 0.000 0.000 79.89 0.005 0.014 0.007 0.013 0.007 0.012 0.006 0.01

30 years (2014-2043) 0.085 0.072 0.086 0.067 0.000 0.000 93.82 0.005 0.008 0.004 0.007 0.004 0.006 0.003 0.00

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-SWe

Table One: Avoided Cost of Electricity (2013 $) Results : CT-SWe 

State CT Southwest Connecticut, excluding Norwalk/Sta

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.069 0.059 0.057 0.050 0.04

2014 0.067 0.059 0.055 0.048 40.82 0.00 20.41 0.005 0.006 0.004 0.005 0.00 0.04

2015 0.066 0.058 0.056 0.048 42.77 0.00 21.38 0.019 0.024 0.016 0.019 0.00 0.005 0.006 0.004 0.005 0.00 0.04

2016 0.063 0.056 0.056 0.047 38.49 0.00 19.25 0.020 0.025 0.018 0.020 0.00 0.018 0.023 0.016 0.019 0.00 0.04

2017 0.060 0.053 0.062 0.049 42.77 0.00 21.38 0.020 0.024 0.020 0.022 60.78 0.018 0.022 0.019 0.021 0.00 0.04

2018 0.062 0.055 0.065 0.051 48.21 62.51 55.36 0.019 0.023 0.020 0.022 51.50 0.019 0.024 0.020 0.022 61.79 0.042019 0.067 0.059 0.071 0.055 53.27 69.07 61.17 0.016 0.020 0.017 0.019 41.69 0.017 0.021 0.018 0.019 52.11 0.042020 0.077 0.067 0.076 0.063 98.37 127.55 112.96 0.014 0.018 0.014 0.017 32.07 0.015 0.018 0.015 0.017 42.76 0.032021 0.076 0.069 0.079 0.064 119.75 155.27 137.51 0.011 0.013 0.011 0.012 21.64 0.011 0.014 0.011 0.013 32.47 0.032022 0.080 0.071 0.078 0.066 119.75 155.27 137.51 0.007 0.009 0.007 0.008 16.45 0.007 0.009 0.007 0.008 21.93 0.032023 0.082 0.073 0.083 0.068 119.75 155.27 137.51 0.004 0.004 0.004 0.004 11.11 0.004 0.000 0.004 0.000 16.66 0.032024 0.085 0.076 0.087 0.071 119.75 155.27 137.51 0.000 0.000 0.000 0.000 5.62 0.000 0.000 0.000 0.000 11.25 0.032025 0.090 0.080 0.093 0.075 119.75 155.27 137.51 0.000 0.000 0.000 0.000 5.74 0.03

2026 0.092 0.082 0.095 0.077 119.75 155.27 137.51 0.03

2027 0.095 0.084 0.099 0.079 119.75 155.27 137.51 0.03

2028 0.099 0.086 0.101 0.081 119.75 155.27 137.51 0.03

2029 0.102 0.089 0.104 0.083 119.75 155.27 137.51 0.02

2030 0.106 0.091 0.107 0.086 119.75 155.27 137.51 0.02

2031 0.109 0.094 0.111 0.088 119.75 155.27 137.51 0.02

2032 0.113 0.096 0.115 0.091 119.75 155.27 137.51 0.02

2033 0.117 0.099 0.119 0.093 119.75 155.27 137.51 0.02

2034 0.121 0.101 0.123 0.096 119.75 155.27 137.51 0.02

2035 0.125 0.104 0.127 0.099 119.75 155.27 137.51 0.02

2036 0.129 0.107 0.131 0.101 119.75 155.27 137.51 0.02

2037 0.133 0.110 0.135 0.104 119.75 155.27 137.51 0.02

2038 0.138 0.113 0.140 0.107 119.75 155.27 137.51 0.02

2039 0.143 0.116 0.145 0.110 119.75 155.27 137.51 0.02

2040 0.148 0.119 0.150 0.113 119.75 155.27 137.51 0.02

2041 0.153 0.122 0.155 0.117 119.75 155.27 137.51 0.02

2042 0.158 0.126 0.160 0.120 119.75 155.27 137.51 0.02

2043 0.163 0.129 0.165 0.124 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.070 0.062 0.068 0.056 71.17 70.03 70.60 0.014 0.017 0.013 0.015 23.36 0.012 0.014 0.012 0.013 22.67 0.0415 years (2014-2028) 0.077 0.068 0.076 0.062 86.28 96.55 91.42 0.009 0.012 0.009 0.010 16.45 0.008 0.010 0.008 0.009 16.72 0.0330 years (2014-2043) 0.100 0.086 0.101 0.080 101.33 122.95 112.14 0.005 0.006 0.005 0.006 9.06 0.004 0.005 0.004 0.005 9.20 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-SWe

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: CT-SWe

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.057 0.048 0.046 0.040 35.40 17.2% 0.00632014 0.056 0.049 0.045 0.039 37.80 17.2% 0.0054 0.007 0.004 0.006 0.0032015 0.055 0.049 0.046 0.039 0.001 0.000 39.60 17.2% 0.0051 0.028 0.015 0.023 0.012 0.007 0.004 0.002016 0.053 0.047 0.047 0.039 0.001 0.001 35.64 17.2% 0.0046 0.029 0.016 0.026 0.013 0.027 0.015 0.022017 0.051 0.045 0.053 0.041 0.001 0.001 39.60 17.2% 0.0039 0.029 0.015 0.029 0.014 0.026 0.014 0.022018 0.054 0.047 0.057 0.044 0.001 0.001 44.64 17.2% 0.0030 0.028 0.015 0.029 0.014 0.028 0.015 0.032019 0.058 0.051 0.061 0.047 0.001 0.001 49.32 17.2% 0.0035 0.024 0.013 0.025 0.012 0.025 0.013 0.022020 0.066 0.058 0.066 0.054 0.000 0.000 91.08 17.2% 0.0040 0.021 0.011 0.021 0.011 0.022 0.012 0.022021 0.066 0.059 0.068 0.055 0.000 0.000 110.88 17.2% 0.0040 0.016 0.009 0.016 0.008 0.016 0.009 0.012022 0.069 0.062 0.068 0.057 0.000 0.000 110.88 17.2% 0.0039 0.011 0.006 0.010 0.005 0.011 0.006 0.012023 0.072 0.063 0.073 0.059 110.88 17.2% 0.0037 0.005 0.003 0.005 0.003 0.006 0.000 0.002024 0.074 0.065 0.075 0.061 110.88 17.2% 0.0045 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.077 0.068 0.079 0.063 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.079 0.070 0.082 0.065 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.082 0.071 0.085 0.067 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.085 0.073 0.086 0.068 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.088 0.075 0.089 0.071 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.091 0.078 0.093 0.073 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.094 0.080 0.096 0.075 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.098 0.082 0.099 0.077 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.101 0.085 0.103 0.080 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.105 0.087 0.106 0.082 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.109 0.090 0.110 0.085 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.112 0.092 0.114 0.087 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.116 0.095 0.118 0.090 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.121 0.098 0.122 0.092 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.125 0.100 0.127 0.095 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.129 0.103 0.131 0.098 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.134 0.106 0.136 0.101 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.139 0.109 0.141 0.104 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.144 0.113 0.146 0.107 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.060 0.053 0.058 0.047 0.001 0.001 65.89 0.004 0.020 0.011 0.019 0.010 0.017 0.009 0.01

15 years (2014-2028) 0.066 0.058 0.065 0.052 0.000 0.000 79.89 0.005 0.014 0.007 0.013 0.007 0.012 0.006 0.01

30 years (2014-2043) 0.087 0.073 0.087 0.068 0.000 0.000 93.82 0.005 0.008 0.004 0.007 0.004 0.006 0.003 0.00

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-SWi

Table One: Avoided Cost of Electricity (2013 $) Results : CT-SWi 

State CT Southwest Connecticut, including Norwalk/Sta

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.069 0.059 0.057 0.050 0.04

2014 0.067 0.059 0.055 0.048 40.82 0.00 20.41 0.005 0.006 0.004 0.005 0.00 0.04

2015 0.066 0.058 0.056 0.048 42.77 0.00 21.38 0.019 0.024 0.016 0.019 0.00 0.005 0.006 0.004 0.005 0.00 0.04

2016 0.063 0.056 0.056 0.047 38.49 0.00 19.25 0.020 0.025 0.018 0.020 0.00 0.018 0.023 0.016 0.019 0.00 0.04

2017 0.060 0.053 0.062 0.049 42.77 0.00 21.38 0.020 0.024 0.020 0.022 60.78 0.018 0.022 0.019 0.021 0.00 0.04

2018 0.062 0.055 0.065 0.051 48.21 62.51 55.36 0.019 0.023 0.020 0.022 51.50 0.019 0.024 0.020 0.022 61.79 0.042019 0.067 0.059 0.071 0.055 53.27 69.07 61.17 0.016 0.020 0.017 0.019 41.69 0.017 0.021 0.018 0.019 52.11 0.042020 0.077 0.067 0.076 0.063 98.37 127.55 112.96 0.014 0.018 0.014 0.017 32.07 0.015 0.018 0.015 0.017 42.76 0.032021 0.076 0.069 0.079 0.064 119.75 155.27 137.51 0.011 0.013 0.011 0.012 21.64 0.011 0.014 0.011 0.013 32.47 0.032022 0.080 0.071 0.078 0.066 119.75 155.27 137.51 0.007 0.009 0.007 0.008 16.45 0.007 0.009 0.007 0.008 21.93 0.032023 0.082 0.073 0.083 0.068 119.75 155.27 137.51 0.004 0.004 0.004 0.004 11.11 0.004 0.000 0.004 0.000 16.66 0.032024 0.085 0.076 0.087 0.071 119.75 155.27 137.51 0.000 0.000 0.000 0.000 5.62 0.000 0.000 0.000 0.000 11.25 0.032025 0.090 0.080 0.093 0.075 119.75 155.27 137.51 0.000 0.000 0.000 0.000 5.74 0.03

2026 0.092 0.082 0.095 0.077 119.75 155.27 137.51 0.03

2027 0.096 0.084 0.099 0.079 119.75 155.27 137.51 0.03

2028 0.099 0.086 0.101 0.081 119.75 155.27 137.51 0.03

2029 0.102 0.089 0.104 0.083 119.75 155.27 137.51 0.02

2030 0.106 0.091 0.107 0.086 119.75 155.27 137.51 0.02

2031 0.109 0.094 0.111 0.088 119.75 155.27 137.51 0.02

2032 0.113 0.096 0.115 0.091 119.75 155.27 137.51 0.02

2033 0.117 0.099 0.119 0.093 119.75 155.27 137.51 0.02

2034 0.121 0.101 0.123 0.096 119.75 155.27 137.51 0.02

2035 0.125 0.104 0.127 0.099 119.75 155.27 137.51 0.02

2036 0.129 0.107 0.131 0.101 119.75 155.27 137.51 0.02

2037 0.133 0.110 0.135 0.104 119.75 155.27 137.51 0.02

2038 0.138 0.113 0.140 0.107 119.75 155.27 137.51 0.02

2039 0.143 0.116 0.145 0.110 119.75 155.27 137.51 0.02

2040 0.148 0.119 0.150 0.114 119.75 155.27 137.51 0.02

2041 0.153 0.122 0.155 0.117 119.75 155.27 137.51 0.02

2042 0.158 0.126 0.160 0.120 119.75 155.27 137.51 0.02

2043 0.163 0.129 0.166 0.124 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.070 0.062 0.068 0.056 71.17 70.03 70.60 0.014 0.017 0.013 0.015 23.36 0.012 0.014 0.012 0.013 22.67 0.0415 years (2014-2028) 0.077 0.068 0.076 0.062 86.28 96.55 91.42 0.009 0.012 0.009 0.010 16.45 0.008 0.010 0.008 0.009 16.72 0.0330 years (2014-2043) 0.100 0.086 0.101 0.080 101.33 122.95 112.14 0.005 0.006 0.005 0.006 9.06 0.004 0.005 0.004 0.005 9.20 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-SWi

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: CT-SWi

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.057 0.048 0.046 0.040 35.40 17.2% 0.00632014 0.056 0.049 0.045 0.039 37.80 17.2% 0.0054 0.007 0.004 0.006 0.0032015 0.055 0.049 0.046 0.039 0.001 0.000 39.60 17.2% 0.0051 0.028 0.015 0.023 0.012 0.007 0.004 0.002016 0.053 0.047 0.047 0.039 0.001 0.001 35.64 17.2% 0.0046 0.029 0.016 0.026 0.013 0.027 0.015 0.022017 0.051 0.045 0.053 0.041 0.001 0.001 39.60 17.2% 0.0039 0.029 0.015 0.029 0.014 0.026 0.014 0.022018 0.054 0.047 0.057 0.044 0.001 0.001 44.64 17.2% 0.0030 0.028 0.015 0.029 0.014 0.028 0.015 0.032019 0.058 0.051 0.061 0.047 0.001 0.001 49.32 17.2% 0.0035 0.024 0.013 0.025 0.012 0.025 0.013 0.022020 0.066 0.058 0.066 0.054 0.000 0.000 91.08 17.2% 0.0040 0.021 0.011 0.021 0.011 0.022 0.012 0.022021 0.066 0.059 0.068 0.055 0.000 0.000 110.88 17.2% 0.0040 0.016 0.009 0.016 0.008 0.016 0.009 0.012022 0.069 0.062 0.068 0.057 0.000 0.000 110.88 17.2% 0.0039 0.011 0.006 0.010 0.005 0.011 0.006 0.012023 0.072 0.064 0.073 0.059 110.88 17.2% 0.0037 0.005 0.003 0.005 0.003 0.006 0.000 0.002024 0.074 0.065 0.075 0.061 110.88 17.2% 0.0045 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.077 0.068 0.079 0.063 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.079 0.070 0.082 0.065 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.082 0.071 0.085 0.067 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.085 0.073 0.086 0.069 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.088 0.075 0.089 0.071 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.091 0.078 0.093 0.073 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.094 0.080 0.096 0.075 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.098 0.082 0.099 0.077 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.101 0.085 0.103 0.080 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.105 0.087 0.107 0.082 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.109 0.090 0.110 0.085 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.112 0.092 0.114 0.087 110.88 17.2% 0.0060 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.116 0.095 0.118 0.090 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.121 0.098 0.123 0.092 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.125 0.100 0.127 0.095 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.129 0.103 0.131 0.098 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.134 0.106 0.136 0.101 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.139 0.109 0.141 0.104 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.144 0.113 0.146 0.107 110.88 17.2% 0.0059 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.060 0.053 0.058 0.047 0.001 0.001 65.89 0.004 0.020 0.011 0.019 0.010 0.017 0.009 0.01

15 years (2014-2028) 0.066 0.058 0.065 0.052 0.000 0.000 79.89 0.005 0.014 0.007 0.013 0.007 0.012 0.006 0.01

30 years (2014-2043) 0.087 0.073 0.087 0.068 0.000 0.000 93.82 0.005 0.008 0.004 0.007 0.004 0.006 0.003 0.00

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: MA

Table One: Avoided Cost of Electricity (2013 $) Results : MA 

State MA Massachusetts (Statewide)

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.069 0.060 0.057 0.050 0.04

2014 0.068 0.060 0.056 0.049 40.82 0.00 20.41 0.015 0.006 0.012 0.005 0.00 0.04

2015 0.066 0.059 0.056 0.048 42.77 0.00 21.38 0.058 0.023 0.049 0.018 0.00 0.015 0.006 0.013 0.005 0.00 0.04

2016 0.062 0.056 0.056 0.047 38.49 0.00 19.25 0.060 0.024 0.053 0.019 0.00 0.055 0.022 0.049 0.018 0.00 0.04

2017 0.059 0.052 0.060 0.048 42.77 0.00 21.38 0.059 0.023 0.061 0.021 123.00 0.055 0.021 0.056 0.020 0.00 0.04

2018 0.061 0.054 0.064 0.050 48.21 62.51 55.36 0.057 0.022 0.060 0.021 104.80 0.059 0.023 0.062 0.021 125.76 0.042019 0.065 0.058 0.069 0.053 53.27 69.07 61.17 0.050 0.019 0.052 0.018 85.08 0.051 0.020 0.053 0.018 106.35 0.042020 0.074 0.066 0.074 0.061 98.37 127.55 112.96 0.044 0.017 0.043 0.016 65.01 0.045 0.017 0.044 0.016 86.68 0.032021 0.075 0.067 0.077 0.062 119.75 155.27 137.51 0.032 0.013 0.033 0.012 44.09 0.033 0.013 0.034 0.012 66.13 0.032022 0.078 0.070 0.077 0.065 119.75 155.27 137.51 0.022 0.009 0.021 0.008 33.63 0.022 0.009 0.022 0.008 44.84 0.032023 0.081 0.073 0.082 0.068 119.75 155.27 137.51 0.011 0.004 0.011 0.004 22.77 0.011 0.000 0.011 0.000 34.16 0.032024 0.084 0.076 0.086 0.071 119.75 155.27 137.51 0.000 0.000 0.000 0.000 11.57 0.000 0.000 0.000 0.000 23.14 0.032025 0.090 0.080 0.092 0.075 119.75 155.27 137.51 0.000 0.000 0.000 0.000 11.75 0.03

2026 0.092 0.083 0.095 0.078 119.75 155.27 137.51 0.03

2027 0.096 0.086 0.099 0.080 119.75 155.27 137.51 0.03

2028 0.100 0.088 0.101 0.082 119.75 155.27 137.51 0.03

2029 0.103 0.091 0.105 0.085 119.75 155.27 137.51 0.02

2030 0.107 0.093 0.109 0.088 119.75 155.27 137.51 0.02

2031 0.110 0.096 0.112 0.090 119.75 155.27 137.51 0.02

2032 0.114 0.098 0.116 0.092 119.75 155.27 137.51 0.02

2033 0.118 0.101 0.120 0.095 119.75 155.27 137.51 0.02

2034 0.122 0.104 0.124 0.098 119.75 155.27 137.51 0.02

2035 0.126 0.106 0.128 0.100 119.75 155.27 137.51 0.02

2036 0.130 0.109 0.133 0.103 119.75 155.27 137.51 0.02

2037 0.135 0.112 0.137 0.106 119.75 155.27 137.51 0.02

2038 0.139 0.115 0.142 0.109 119.75 155.27 137.51 0.02

2039 0.144 0.118 0.147 0.112 119.75 155.27 137.51 0.02

2040 0.149 0.121 0.152 0.115 119.75 155.27 137.51 0.02

2041 0.154 0.125 0.157 0.119 119.75 155.27 137.51 0.02

2042 0.159 0.128 0.162 0.122 119.75 155.27 137.51 0.02

2043 0.165 0.132 0.168 0.126 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.069 0.061 0.067 0.055 71.17 70.03 70.60 0.041 0.016 0.040 0.014 47.50 0.035 0.013 0.035 0.012 46.19 0.0415 years (2014-2028) 0.076 0.068 0.075 0.062 86.28 96.55 91.42 0.028 0.011 0.027 0.010 33.46 0.024 0.009 0.024 0.008 34.07 0.0330 years (2014-2043) 0.100 0.086 0.101 0.081 101.33 122.95 112.14 0.016 0.006 0.015 0.005 18.42 0.013 0.005 0.013 0.005 18.75 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: MA

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: MA

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.056 0.047 0.045 0.039 35.40 17.2% 0.00732014 0.055 0.048 0.044 0.038 37.80 17.2% 0.0075 0.017 0.005 0.013 0.0042015 0.054 0.048 0.045 0.038 0.002 0.000 39.60 17.2% 0.0067 0.063 0.018 0.053 0.014 0.016 0.005 0.012016 0.051 0.045 0.045 0.037 0.002 0.002 35.64 17.2% 0.0060 0.066 0.019 0.058 0.015 0.061 0.017 0.052017 0.049 0.043 0.050 0.039 0.002 0.002 39.60 17.2% 0.0050 0.065 0.018 0.067 0.017 0.060 0.017 0.062018 0.052 0.046 0.055 0.042 0.002 0.002 44.64 17.2% 0.0039 0.063 0.018 0.066 0.017 0.064 0.018 0.062019 0.056 0.049 0.059 0.045 0.001 0.001 49.32 17.2% 0.0043 0.055 0.015 0.057 0.014 0.056 0.016 0.052020 0.063 0.056 0.063 0.051 0.001 0.001 91.08 17.2% 0.0048 0.048 0.014 0.048 0.013 0.049 0.014 0.042021 0.063 0.057 0.065 0.052 0.001 0.001 110.88 17.2% 0.0050 0.035 0.010 0.036 0.009 0.036 0.010 0.032022 0.066 0.059 0.065 0.055 0.000 0.000 110.88 17.2% 0.0052 0.024 0.007 0.023 0.006 0.025 0.007 0.022023 0.069 0.061 0.070 0.057 110.88 17.2% 0.0053 0.012 0.003 0.012 0.003 0.012 0.000 0.012024 0.071 0.063 0.072 0.059 110.88 17.2% 0.0063 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.074 0.066 0.076 0.061 110.88 17.2% 0.0081 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.076 0.068 0.079 0.063 110.88 17.2% 0.0085 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.079 0.070 0.082 0.064 110.88 17.2% 0.0088 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.082 0.071 0.084 0.066 110.88 17.2% 0.0092 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.085 0.074 0.087 0.068 110.88 17.2% 0.0095 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.088 0.076 0.090 0.071 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.092 0.078 0.093 0.073 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.095 0.080 0.097 0.075 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.098 0.083 0.100 0.077 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.102 0.085 0.104 0.080 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.106 0.088 0.108 0.082 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.110 0.090 0.112 0.085 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.114 0.093 0.116 0.088 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.118 0.096 0.121 0.091 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.122 0.099 0.125 0.093 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.127 0.102 0.130 0.096 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.132 0.105 0.135 0.099 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.136 0.108 0.140 0.103 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.141 0.111 0.145 0.106 110.88 17.2% 0.0095 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.058 0.051 0.056 0.045 0.001 0.001 65.89 0.005 0.045 0.013 0.044 0.011 0.038 0.011 0.03

15 years (2014-2028) 0.063 0.056 0.063 0.050 0.001 0.001 79.89 0.006 0.031 0.009 0.030 0.008 0.026 0.007 0.02

30 years (2014-2043) 0.084 0.072 0.085 0.066 0.000 0.000 93.82 0.008 0.017 0.005 0.017 0.004 0.015 0.004 0.01

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: MA-NEMA

Table One: Avoided Cost of Electricity (2013 $) Results : MA-NEMA 

State MA NEMA (Northeast Massachusetts)

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.068 0.059 0.056 0.050 0.04

2014 0.067 0.060 0.055 0.049 40.82 0.00 20.41 0.015 0.006 0.012 0.005 0.00 0.04

2015 0.065 0.059 0.056 0.048 42.77 0.00 21.38 0.058 0.023 0.049 0.018 0.00 0.015 0.006 0.013 0.005 0.00 0.04

2016 0.062 0.056 0.055 0.047 38.49 0.00 19.25 0.060 0.024 0.053 0.019 0.00 0.055 0.022 0.049 0.018 0.00 0.04

2017 0.058 0.052 0.060 0.048 42.77 0.00 21.38 0.059 0.023 0.061 0.021 123.00 0.055 0.021 0.056 0.020 0.00 0.04

2018 0.060 0.054 0.063 0.050 48.21 62.51 55.36 0.057 0.022 0.060 0.021 104.80 0.059 0.023 0.062 0.021 125.76 0.042019 0.065 0.058 0.068 0.053 53.27 69.07 61.17 0.050 0.019 0.052 0.018 85.08 0.051 0.020 0.053 0.018 106.35 0.042020 0.074 0.066 0.073 0.061 98.37 127.55 112.96 0.044 0.017 0.043 0.016 65.01 0.045 0.017 0.044 0.016 86.68 0.032021 0.074 0.067 0.076 0.062 119.75 155.27 137.51 0.032 0.013 0.033 0.012 44.09 0.033 0.013 0.034 0.012 66.13 0.032022 0.078 0.070 0.076 0.065 119.75 155.27 137.51 0.022 0.009 0.021 0.008 33.63 0.022 0.009 0.022 0.008 44.84 0.032023 0.081 0.072 0.081 0.068 119.75 155.27 137.51 0.011 0.004 0.011 0.004 22.77 0.011 0.000 0.011 0.000 34.16 0.032024 0.084 0.076 0.085 0.070 119.75 155.27 137.51 0.000 0.000 0.000 0.000 11.57 0.000 0.000 0.000 0.000 23.14 0.032025 0.089 0.080 0.091 0.075 119.75 155.27 137.51 0.000 0.000 0.000 0.000 11.75 0.03

2026 0.091 0.082 0.095 0.077 119.75 155.27 137.51 0.03

2027 0.095 0.085 0.098 0.080 119.75 155.27 137.51 0.03

2028 0.099 0.087 0.100 0.082 119.75 155.27 137.51 0.03

2029 0.103 0.090 0.104 0.085 119.75 155.27 137.51 0.02

2030 0.106 0.092 0.108 0.087 119.75 155.27 137.51 0.02

2031 0.110 0.095 0.111 0.090 119.75 155.27 137.51 0.02

2032 0.113 0.097 0.115 0.092 119.75 155.27 137.51 0.02

2033 0.117 0.100 0.119 0.095 119.75 155.27 137.51 0.02

2034 0.121 0.102 0.123 0.097 119.75 155.27 137.51 0.02

2035 0.125 0.105 0.127 0.100 119.75 155.27 137.51 0.02

2036 0.129 0.108 0.132 0.103 119.75 155.27 137.51 0.02

2037 0.134 0.111 0.136 0.106 119.75 155.27 137.51 0.02

2038 0.138 0.114 0.141 0.109 119.75 155.27 137.51 0.02

2039 0.143 0.117 0.145 0.112 119.75 155.27 137.51 0.02

2040 0.148 0.120 0.150 0.115 119.75 155.27 137.51 0.02

2041 0.153 0.123 0.156 0.118 119.75 155.27 137.51 0.02

2042 0.158 0.126 0.161 0.122 119.75 155.27 137.51 0.02

2043 0.163 0.130 0.167 0.125 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.068 0.061 0.066 0.055 71.17 70.03 70.60 0.041 0.016 0.040 0.014 47.50 0.035 0.013 0.035 0.012 46.19 0.0415 years (2014-2028) 0.075 0.068 0.075 0.062 86.28 96.55 91.42 0.028 0.011 0.027 0.010 33.46 0.024 0.009 0.024 0.008 34.07 0.0330 years (2014-2043) 0.100 0.086 0.100 0.080 101.33 122.95 112.14 0.016 0.006 0.015 0.005 18.42 0.013 0.005 0.013 0.005 18.75 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: MA-NEMA

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: MA-NEMA

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.055 0.047 0.044 0.038 35.40 17.2% 0.00732014 0.054 0.047 0.043 0.037 37.80 17.2% 0.0075 0.017 0.005 0.013 0.0042015 0.053 0.047 0.044 0.037 0.002 0.000 39.60 17.2% 0.0067 0.063 0.018 0.053 0.014 0.016 0.005 0.012016 0.051 0.045 0.045 0.037 0.002 0.002 35.64 17.2% 0.0060 0.066 0.019 0.058 0.015 0.061 0.017 0.052017 0.049 0.043 0.050 0.039 0.002 0.002 39.60 17.2% 0.0050 0.065 0.018 0.067 0.017 0.060 0.017 0.062018 0.051 0.045 0.054 0.042 0.002 0.002 44.64 17.2% 0.0039 0.063 0.018 0.066 0.017 0.064 0.018 0.062019 0.055 0.048 0.058 0.044 0.001 0.001 49.32 17.2% 0.0043 0.055 0.015 0.057 0.014 0.056 0.016 0.052020 0.063 0.055 0.062 0.051 0.001 0.001 91.08 17.2% 0.0048 0.048 0.014 0.048 0.013 0.049 0.014 0.042021 0.063 0.057 0.065 0.052 0.001 0.001 110.88 17.2% 0.0050 0.035 0.010 0.036 0.009 0.036 0.010 0.032022 0.066 0.059 0.065 0.054 0.000 0.000 110.88 17.2% 0.0052 0.024 0.007 0.023 0.006 0.025 0.007 0.022023 0.069 0.061 0.069 0.057 110.88 17.2% 0.0053 0.012 0.003 0.012 0.003 0.012 0.000 0.012024 0.071 0.063 0.072 0.058 110.88 17.2% 0.0063 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.074 0.065 0.075 0.060 110.88 17.2% 0.0081 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.075 0.067 0.078 0.062 110.88 17.2% 0.0085 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.078 0.069 0.081 0.064 110.88 17.2% 0.0088 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.082 0.071 0.083 0.066 110.88 17.2% 0.0092 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.085 0.073 0.086 0.068 110.88 17.2% 0.0095 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.088 0.075 0.089 0.070 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.091 0.077 0.092 0.072 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.094 0.080 0.096 0.075 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.098 0.082 0.099 0.077 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.101 0.084 0.103 0.079 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.105 0.087 0.107 0.082 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.109 0.089 0.111 0.085 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.113 0.092 0.115 0.087 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.117 0.095 0.119 0.090 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.121 0.097 0.124 0.093 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.126 0.100 0.128 0.096 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.130 0.103 0.133 0.099 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.135 0.106 0.138 0.102 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.140 0.109 0.143 0.105 110.88 17.2% 0.0095 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.057 0.051 0.055 0.045 0.001 0.001 65.89 0.005 0.045 0.013 0.044 0.011 0.038 0.011 0.03

15 years (2014-2028) 0.063 0.056 0.062 0.050 0.001 0.001 79.89 0.006 0.031 0.009 0.030 0.008 0.026 0.007 0.02

30 years (2014-2043) 0.084 0.071 0.084 0.066 0.000 0.000 93.82 0.008 0.017 0.005 0.017 0.004 0.015 0.004 0.01

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: MA-R

Table One: Avoided Cost of Electricity (2013 $) Results : MA-R 

State MA Rest of Massachusetts (Massachusetts exclud

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.069 0.060 0.057 0.050 0.04

2014 0.068 0.061 0.056 0.049 40.82 0.00 20.41 0.015 0.006 0.012 0.005 0.00 0.04

2015 0.066 0.060 0.056 0.048 42.77 0.00 21.38 0.058 0.023 0.049 0.018 0.00 0.015 0.006 0.013 0.005 0.00 0.04

2016 0.062 0.056 0.056 0.047 38.49 0.00 19.25 0.060 0.024 0.053 0.019 0.00 0.055 0.022 0.049 0.018 0.00 0.04

2017 0.059 0.052 0.061 0.048 42.77 0.00 21.38 0.059 0.023 0.061 0.021 123.00 0.055 0.021 0.056 0.020 0.00 0.04

2018 0.061 0.054 0.064 0.050 48.21 62.51 55.36 0.057 0.022 0.060 0.021 104.80 0.059 0.023 0.062 0.021 125.76 0.042019 0.065 0.058 0.069 0.053 53.27 69.07 61.17 0.050 0.019 0.052 0.018 85.08 0.051 0.020 0.053 0.018 106.35 0.042020 0.074 0.066 0.074 0.061 98.37 127.55 112.96 0.044 0.017 0.043 0.016 65.01 0.045 0.017 0.044 0.016 86.68 0.032021 0.075 0.067 0.077 0.062 119.75 155.27 137.51 0.032 0.013 0.033 0.012 44.09 0.033 0.013 0.034 0.012 66.13 0.032022 0.078 0.070 0.077 0.065 119.75 155.27 137.51 0.022 0.009 0.021 0.008 33.63 0.022 0.009 0.022 0.008 44.84 0.032023 0.081 0.073 0.082 0.068 119.75 155.27 137.51 0.011 0.004 0.011 0.004 22.77 0.011 0.000 0.011 0.000 34.16 0.032024 0.084 0.076 0.086 0.071 119.75 155.27 137.51 0.000 0.000 0.000 0.000 11.57 0.000 0.000 0.000 0.000 23.14 0.032025 0.090 0.081 0.092 0.075 119.75 155.27 137.51 0.000 0.000 0.000 0.000 11.75 0.03

2026 0.092 0.083 0.095 0.078 119.75 155.27 137.51 0.03

2027 0.096 0.086 0.099 0.080 119.75 155.27 137.51 0.03

2028 0.100 0.088 0.101 0.082 119.75 155.27 137.51 0.03

2029 0.103 0.091 0.105 0.085 119.75 155.27 137.51 0.02

2030 0.107 0.093 0.109 0.088 119.75 155.27 137.51 0.02

2031 0.111 0.096 0.113 0.090 119.75 155.27 137.51 0.02

2032 0.114 0.098 0.116 0.093 119.75 155.27 137.51 0.02

2033 0.118 0.101 0.120 0.095 119.75 155.27 137.51 0.02

2034 0.122 0.104 0.124 0.098 119.75 155.27 137.51 0.02

2035 0.126 0.107 0.129 0.101 119.75 155.27 137.51 0.02

2036 0.130 0.109 0.133 0.103 119.75 155.27 137.51 0.02

2037 0.135 0.112 0.137 0.106 119.75 155.27 137.51 0.02

2038 0.139 0.115 0.142 0.109 119.75 155.27 137.51 0.02

2039 0.144 0.118 0.147 0.112 119.75 155.27 137.51 0.02

2040 0.149 0.122 0.152 0.116 119.75 155.27 137.51 0.02

2041 0.154 0.125 0.157 0.119 119.75 155.27 137.51 0.02

2042 0.159 0.128 0.163 0.122 119.75 155.27 137.51 0.02

2043 0.165 0.132 0.168 0.126 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.069 0.061 0.067 0.055 71.17 70.03 70.60 0.041 0.016 0.040 0.014 47.50 0.035 0.013 0.035 0.012 46.19 0.0415 years (2014-2028) 0.076 0.068 0.075 0.062 86.28 96.55 91.42 0.028 0.011 0.027 0.010 33.46 0.024 0.009 0.024 0.008 34.07 0.0330 years (2014-2043) 0.101 0.087 0.101 0.081 101.33 122.95 112.14 0.016 0.006 0.015 0.005 18.42 0.013 0.005 0.013 0.005 18.75 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: MA-R

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: MA-R

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.056 0.048 0.045 0.039 35.40 17.2% 0.00732014 0.055 0.048 0.044 0.038 37.80 17.2% 0.0075 0.017 0.005 0.013 0.0042015 0.054 0.048 0.045 0.038 0.002 0.000 39.60 17.2% 0.0067 0.063 0.018 0.053 0.014 0.016 0.005 0.012016 0.051 0.046 0.045 0.037 0.002 0.002 35.64 17.2% 0.0060 0.066 0.019 0.058 0.015 0.061 0.017 0.052017 0.049 0.043 0.051 0.039 0.002 0.002 39.60 17.2% 0.0050 0.065 0.018 0.067 0.017 0.060 0.017 0.062018 0.052 0.046 0.055 0.042 0.002 0.002 44.64 17.2% 0.0039 0.063 0.018 0.066 0.017 0.064 0.018 0.062019 0.056 0.049 0.059 0.045 0.001 0.001 49.32 17.2% 0.0043 0.055 0.015 0.057 0.014 0.056 0.016 0.052020 0.063 0.056 0.063 0.051 0.001 0.001 91.08 17.2% 0.0048 0.048 0.014 0.048 0.013 0.049 0.014 0.042021 0.063 0.057 0.066 0.052 0.001 0.001 110.88 17.2% 0.0050 0.035 0.010 0.036 0.009 0.036 0.010 0.032022 0.067 0.059 0.065 0.055 0.000 0.000 110.88 17.2% 0.0052 0.024 0.007 0.023 0.006 0.025 0.007 0.022023 0.069 0.061 0.070 0.057 110.88 17.2% 0.0053 0.012 0.003 0.012 0.003 0.012 0.000 0.012024 0.071 0.064 0.072 0.059 110.88 17.2% 0.0063 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.074 0.066 0.076 0.061 110.88 17.2% 0.0081 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.076 0.068 0.079 0.063 110.88 17.2% 0.0085 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.079 0.070 0.082 0.065 110.88 17.2% 0.0088 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.082 0.072 0.084 0.066 110.88 17.2% 0.0092 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.085 0.074 0.087 0.068 110.88 17.2% 0.0095 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.088 0.076 0.090 0.071 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.092 0.078 0.094 0.073 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.095 0.081 0.097 0.075 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.099 0.083 0.101 0.078 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.102 0.085 0.104 0.080 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.106 0.088 0.108 0.083 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.110 0.091 0.112 0.085 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.114 0.093 0.116 0.088 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.118 0.096 0.121 0.091 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.123 0.099 0.125 0.094 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.127 0.102 0.130 0.096 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.132 0.105 0.135 0.100 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.137 0.108 0.140 0.103 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.142 0.112 0.145 0.106 110.88 17.2% 0.0095 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.058 0.051 0.056 0.045 0.001 0.001 65.89 0.005 0.045 0.013 0.044 0.011 0.038 0.011 0.03

15 years (2014-2028) 0.063 0.056 0.063 0.051 0.001 0.001 79.89 0.006 0.031 0.009 0.030 0.008 0.026 0.007 0.02

30 years (2014-2043) 0.085 0.072 0.085 0.066 0.000 0.000 93.82 0.008 0.017 0.005 0.017 0.004 0.015 0.004 0.01

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: MA-SEMA

Table One: Avoided Cost of Electricity (2013 $) Results : MA-SEMA 

State MA SEMA (Southeast Massachusetts)

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.068 0.059 0.055 0.049 0.04

2014 0.067 0.060 0.054 0.048 40.82 0.00 20.41 0.015 0.006 0.012 0.005 0.00 0.04

2015 0.065 0.059 0.055 0.047 42.77 0.00 21.38 0.058 0.023 0.049 0.018 0.00 0.015 0.006 0.013 0.005 0.00 0.04

2016 0.061 0.055 0.054 0.046 38.49 0.00 19.25 0.060 0.024 0.053 0.019 0.00 0.055 0.022 0.049 0.018 0.00 0.04

2017 0.057 0.051 0.059 0.047 42.77 0.00 21.38 0.059 0.023 0.061 0.021 123.00 0.055 0.021 0.056 0.020 0.00 0.04

2018 0.059 0.053 0.062 0.049 48.21 62.51 55.36 0.057 0.022 0.060 0.021 104.80 0.059 0.023 0.062 0.021 125.76 0.042019 0.064 0.057 0.067 0.052 53.27 69.07 61.17 0.050 0.019 0.052 0.018 85.08 0.051 0.020 0.053 0.018 106.35 0.042020 0.072 0.064 0.072 0.060 98.37 127.55 112.96 0.044 0.017 0.043 0.016 65.01 0.045 0.017 0.044 0.016 86.68 0.032021 0.073 0.066 0.075 0.061 119.75 155.27 137.51 0.032 0.013 0.033 0.012 44.09 0.033 0.013 0.034 0.012 66.13 0.032022 0.076 0.069 0.075 0.064 119.75 155.27 137.51 0.022 0.009 0.021 0.008 33.63 0.022 0.009 0.022 0.008 44.84 0.032023 0.079 0.071 0.080 0.066 119.75 155.27 137.51 0.011 0.004 0.011 0.004 22.77 0.011 0.000 0.011 0.000 34.16 0.032024 0.083 0.075 0.084 0.069 119.75 155.27 137.51 0.000 0.000 0.000 0.000 11.57 0.000 0.000 0.000 0.000 23.14 0.032025 0.088 0.079 0.090 0.074 119.75 155.27 137.51 0.000 0.000 0.000 0.000 11.75 0.03

2026 0.090 0.082 0.093 0.076 119.75 155.27 137.51 0.03

2027 0.094 0.085 0.097 0.078 119.75 155.27 137.51 0.03

2028 0.098 0.087 0.099 0.081 119.75 155.27 137.51 0.03

2029 0.101 0.090 0.103 0.084 119.75 155.27 137.51 0.02

2030 0.105 0.092 0.107 0.086 119.75 155.27 137.51 0.02

2031 0.109 0.095 0.110 0.089 119.75 155.27 137.51 0.02

2032 0.112 0.098 0.114 0.091 119.75 155.27 137.51 0.02

2033 0.116 0.100 0.118 0.094 119.75 155.27 137.51 0.02

2034 0.120 0.103 0.122 0.096 119.75 155.27 137.51 0.02

2035 0.124 0.106 0.126 0.099 119.75 155.27 137.51 0.02

2036 0.128 0.109 0.131 0.102 119.75 155.27 137.51 0.02

2037 0.133 0.112 0.135 0.105 119.75 155.27 137.51 0.02

2038 0.137 0.115 0.140 0.108 119.75 155.27 137.51 0.02

2039 0.142 0.119 0.145 0.111 119.75 155.27 137.51 0.02

2040 0.147 0.122 0.150 0.114 119.75 155.27 137.51 0.02

2041 0.152 0.126 0.155 0.117 119.75 155.27 137.51 0.02

2042 0.157 0.129 0.161 0.121 119.75 155.27 137.51 0.02

2043 0.162 0.133 0.166 0.124 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.067 0.060 0.065 0.054 71.17 70.03 70.60 0.041 0.016 0.040 0.014 47.50 0.035 0.013 0.035 0.012 46.19 0.0415 years (2014-2028) 0.074 0.067 0.074 0.061 86.28 96.55 91.42 0.028 0.011 0.027 0.010 33.46 0.024 0.009 0.024 0.008 34.07 0.0330 years (2014-2043) 0.099 0.086 0.099 0.079 101.33 122.95 112.14 0.016 0.006 0.015 0.005 18.42 0.013 0.005 0.013 0.005 18.75 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: MA-SEMA

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: MA-SEMA

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.055 0.047 0.043 0.038 35.40 17.2% 0.00732014 0.054 0.047 0.042 0.037 37.80 17.2% 0.0075 0.017 0.005 0.013 0.0042015 0.053 0.047 0.043 0.037 0.002 0.000 39.60 17.2% 0.0067 0.063 0.018 0.053 0.014 0.016 0.005 0.012016 0.050 0.045 0.044 0.036 0.002 0.002 35.64 17.2% 0.0060 0.066 0.019 0.058 0.015 0.061 0.017 0.052017 0.048 0.042 0.049 0.038 0.002 0.002 39.60 17.2% 0.0050 0.065 0.018 0.067 0.017 0.060 0.017 0.062018 0.050 0.045 0.053 0.041 0.002 0.002 44.64 17.2% 0.0039 0.063 0.018 0.066 0.017 0.064 0.018 0.062019 0.054 0.048 0.057 0.043 0.001 0.001 49.32 17.2% 0.0043 0.055 0.015 0.057 0.014 0.056 0.016 0.052020 0.062 0.054 0.061 0.050 0.001 0.001 91.08 17.2% 0.0048 0.048 0.014 0.048 0.013 0.049 0.014 0.042021 0.062 0.056 0.064 0.051 0.001 0.001 110.88 17.2% 0.0050 0.035 0.010 0.036 0.009 0.036 0.010 0.032022 0.065 0.058 0.063 0.053 0.000 0.000 110.88 17.2% 0.0052 0.024 0.007 0.023 0.006 0.025 0.007 0.022023 0.067 0.060 0.068 0.056 110.88 17.2% 0.0053 0.012 0.003 0.012 0.003 0.012 0.000 0.012024 0.070 0.062 0.071 0.057 110.88 17.2% 0.0063 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.072 0.065 0.074 0.060 110.88 17.2% 0.0081 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.074 0.066 0.077 0.061 110.88 17.2% 0.0085 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.077 0.069 0.080 0.063 110.88 17.2% 0.0088 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.081 0.070 0.082 0.065 110.88 17.2% 0.0092 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.084 0.073 0.085 0.067 110.88 17.2% 0.0095 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.087 0.075 0.088 0.069 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.090 0.077 0.092 0.071 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.093 0.080 0.095 0.074 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.097 0.082 0.099 0.076 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.100 0.085 0.102 0.079 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.104 0.088 0.106 0.081 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.108 0.090 0.110 0.084 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.112 0.093 0.114 0.086 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.116 0.096 0.119 0.089 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.120 0.099 0.123 0.092 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.125 0.102 0.128 0.095 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.130 0.106 0.133 0.098 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.134 0.109 0.138 0.101 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.140 0.112 0.143 0.104 110.88 17.2% 0.0095 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.056 0.050 0.054 0.044 0.001 0.001 65.89 0.005 0.045 0.013 0.044 0.011 0.038 0.011 0.03

15 years (2014-2028) 0.062 0.055 0.061 0.049 0.001 0.001 79.89 0.006 0.031 0.009 0.030 0.008 0.026 0.007 0.02

30 years (2014-2043) 0.083 0.071 0.083 0.065 0.000 0.000 93.82 0.008 0.017 0.005 0.017 0.004 0.015 0.004 0.01

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: MA-WCMA

Table One: Avoided Cost of Electricity (2013 $) Results : MA-WCMA 

State MA WCMA (West-Central Massachusetts)

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.069 0.060 0.057 0.051 0.04

2014 0.068 0.060 0.056 0.050 40.82 0.00 20.41 0.015 0.006 0.012 0.005 0.00 0.04

2015 0.066 0.059 0.057 0.049 42.77 0.00 21.38 0.058 0.023 0.049 0.018 0.00 0.015 0.006 0.013 0.005 0.00 0.04

2016 0.063 0.057 0.056 0.048 38.49 0.00 19.25 0.060 0.024 0.053 0.019 0.00 0.055 0.022 0.049 0.018 0.00 0.04

2017 0.060 0.053 0.061 0.049 42.77 0.00 21.38 0.059 0.023 0.061 0.021 123.00 0.055 0.021 0.056 0.020 0.00 0.04

2018 0.062 0.055 0.065 0.051 48.21 62.51 55.36 0.057 0.022 0.060 0.021 104.80 0.059 0.023 0.062 0.021 125.76 0.042019 0.067 0.059 0.070 0.055 53.27 69.07 61.17 0.050 0.019 0.052 0.018 85.08 0.051 0.020 0.053 0.018 106.35 0.042020 0.076 0.067 0.075 0.062 98.37 127.55 112.96 0.044 0.017 0.043 0.016 65.01 0.045 0.017 0.044 0.016 86.68 0.032021 0.076 0.068 0.078 0.064 119.75 155.27 137.51 0.032 0.013 0.033 0.012 44.09 0.033 0.013 0.034 0.012 66.13 0.032022 0.080 0.071 0.078 0.066 119.75 155.27 137.51 0.022 0.009 0.021 0.008 33.63 0.022 0.009 0.022 0.008 44.84 0.032023 0.082 0.074 0.083 0.069 119.75 155.27 137.51 0.011 0.004 0.011 0.004 22.77 0.011 0.000 0.011 0.000 34.16 0.032024 0.085 0.077 0.087 0.072 119.75 155.27 137.51 0.000 0.000 0.000 0.000 11.57 0.000 0.000 0.000 0.000 23.14 0.032025 0.091 0.081 0.093 0.076 119.75 155.27 137.51 0.000 0.000 0.000 0.000 11.75 0.03

2026 0.093 0.084 0.096 0.079 119.75 155.27 137.51 0.03

2027 0.097 0.086 0.100 0.081 119.75 155.27 137.51 0.03

2028 0.101 0.088 0.102 0.083 119.75 155.27 137.51 0.03

2029 0.104 0.091 0.106 0.086 119.75 155.27 137.51 0.02

2030 0.108 0.094 0.109 0.089 119.75 155.27 137.51 0.02

2031 0.111 0.096 0.113 0.091 119.75 155.27 137.51 0.02

2032 0.115 0.098 0.117 0.093 119.75 155.27 137.51 0.02

2033 0.119 0.101 0.120 0.096 119.75 155.27 137.51 0.02

2034 0.122 0.103 0.124 0.099 119.75 155.27 137.51 0.02

2035 0.126 0.106 0.128 0.101 119.75 155.27 137.51 0.02

2036 0.130 0.109 0.132 0.104 119.75 155.27 137.51 0.02

2037 0.135 0.112 0.137 0.107 119.75 155.27 137.51 0.02

2038 0.139 0.114 0.141 0.110 119.75 155.27 137.51 0.02

2039 0.144 0.117 0.146 0.113 119.75 155.27 137.51 0.02

2040 0.148 0.120 0.151 0.116 119.75 155.27 137.51 0.02

2041 0.153 0.123 0.156 0.119 119.75 155.27 137.51 0.02

2042 0.158 0.127 0.161 0.122 119.75 155.27 137.51 0.02

2043 0.164 0.130 0.166 0.126 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.070 0.062 0.068 0.056 71.17 70.03 70.60 0.041 0.016 0.040 0.014 47.50 0.035 0.013 0.035 0.012 46.19 0.0415 years (2014-2028) 0.077 0.069 0.076 0.063 86.28 96.55 91.42 0.028 0.011 0.027 0.010 33.46 0.024 0.009 0.024 0.008 34.07 0.0330 years (2014-2043) 0.101 0.087 0.102 0.081 101.33 122.95 112.14 0.016 0.006 0.015 0.005 18.42 0.013 0.005 0.013 0.005 18.75 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: MA-WCMA

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: MA-WCMA

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.056 0.047 0.045 0.039 35.40 17.2% 0.00732014 0.055 0.048 0.044 0.038 37.80 17.2% 0.0075 0.017 0.005 0.013 0.0042015 0.054 0.048 0.045 0.038 0.002 0.000 39.60 17.2% 0.0067 0.063 0.018 0.053 0.014 0.016 0.005 0.012016 0.052 0.046 0.046 0.038 0.002 0.002 35.64 17.2% 0.0060 0.066 0.019 0.058 0.015 0.061 0.017 0.052017 0.050 0.044 0.051 0.040 0.002 0.002 39.60 17.2% 0.0050 0.065 0.018 0.067 0.017 0.060 0.017 0.062018 0.053 0.046 0.056 0.043 0.002 0.002 44.64 17.2% 0.0039 0.063 0.018 0.066 0.017 0.064 0.018 0.062019 0.057 0.050 0.060 0.046 0.001 0.001 49.32 17.2% 0.0043 0.055 0.015 0.057 0.014 0.056 0.016 0.052020 0.065 0.057 0.064 0.053 0.001 0.001 91.08 17.2% 0.0048 0.048 0.014 0.048 0.013 0.049 0.014 0.042021 0.065 0.058 0.067 0.054 0.001 0.001 110.88 17.2% 0.0050 0.035 0.010 0.036 0.009 0.036 0.010 0.032022 0.068 0.060 0.067 0.056 0.000 0.000 110.88 17.2% 0.0052 0.024 0.007 0.023 0.006 0.025 0.007 0.022023 0.070 0.062 0.071 0.058 110.88 17.2% 0.0053 0.012 0.003 0.012 0.003 0.012 0.000 0.012024 0.072 0.064 0.073 0.060 110.88 17.2% 0.0063 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.075 0.066 0.077 0.062 110.88 17.2% 0.0081 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.077 0.068 0.080 0.064 110.88 17.2% 0.0085 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.080 0.070 0.083 0.066 110.88 17.2% 0.0088 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.083 0.072 0.084 0.067 110.88 17.2% 0.0092 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.086 0.074 0.087 0.069 110.88 17.2% 0.0095 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.089 0.076 0.091 0.072 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.092 0.078 0.094 0.074 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.096 0.080 0.097 0.076 110.88 17.2% 0.0098 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.099 0.083 0.101 0.078 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.103 0.085 0.104 0.081 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.106 0.088 0.108 0.083 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.110 0.090 0.112 0.086 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.114 0.093 0.116 0.088 110.88 17.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.118 0.095 0.120 0.091 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.122 0.098 0.124 0.094 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.127 0.101 0.129 0.097 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.131 0.104 0.133 0.100 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.136 0.107 0.138 0.103 110.88 17.2% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.141 0.110 0.143 0.106 110.88 17.2% 0.0095 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.059 0.052 0.057 0.046 0.001 0.001 65.89 0.005 0.045 0.013 0.044 0.011 0.038 0.011 0.03

15 years (2014-2028) 0.064 0.057 0.064 0.052 0.001 0.001 79.89 0.006 0.031 0.009 0.030 0.008 0.026 0.007 0.02

30 years (2014-2043) 0.085 0.072 0.085 0.067 0.000 0.000 93.82 0.008 0.017 0.005 0.017 0.004 0.015 0.004 0.01

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: ME

Table One: Avoided Cost of Electricity (2013 $) Results : ME 

State ME Maine

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.056 0.048 0.045 0.040 0.04

2014 0.055 0.049 0.044 0.039 40.82 0.00 20.41 0.002 0.001 0.001 0.001 0.00 0.04

2015 0.054 0.049 0.045 0.039 42.77 0.00 21.38 0.007 0.003 0.006 0.003 0.00 0.002 0.001 0.001 0.001 0.00 0.04

2016 0.051 0.047 0.046 0.038 38.49 0.00 19.25 0.007 0.003 0.006 0.003 0.00 0.006 0.003 0.006 0.002 0.00 0.04

2017 0.049 0.044 0.049 0.040 42.77 0.00 21.38 0.007 0.003 0.007 0.003 20.26 0.006 0.003 0.007 0.003 0.00 0.04

2018 0.051 0.046 0.052 0.043 48.21 62.51 55.36 0.007 0.003 0.007 0.003 17.14 0.007 0.003 0.007 0.003 20.57 0.042019 0.054 0.049 0.055 0.045 53.27 69.07 61.17 0.006 0.003 0.006 0.002 13.88 0.006 0.003 0.006 0.002 17.35 0.042020 0.063 0.057 0.063 0.052 98.37 127.55 112.96 0.005 0.002 0.005 0.002 10.54 0.005 0.002 0.005 0.002 14.05 0.032021 0.064 0.058 0.064 0.054 119.75 155.27 137.51 0.004 0.002 0.004 0.002 7.12 0.004 0.002 0.004 0.002 10.67 0.032022 0.067 0.061 0.066 0.056 119.75 155.27 137.51 0.002 0.001 0.002 0.001 5.41 0.003 0.001 0.002 0.001 7.21 0.032023 0.070 0.063 0.070 0.059 119.75 155.27 137.51 0.001 0.001 0.001 0.001 3.65 0.001 0.000 0.001 0.000 5.48 0.032024 0.072 0.066 0.072 0.061 119.75 155.27 137.51 0.000 0.000 0.000 0.000 1.85 0.000 0.000 0.000 0.000 3.70 0.032025 0.075 0.068 0.075 0.064 119.75 155.27 137.51 0.000 0.000 0.000 0.000 1.87 0.03

2026 0.077 0.070 0.078 0.065 119.75 155.27 137.51 0.03

2027 0.080 0.072 0.081 0.067 119.75 155.27 137.51 0.03

2028 0.081 0.074 0.083 0.069 119.75 155.27 137.51 0.03

2029 0.083 0.076 0.086 0.071 119.75 155.27 137.51 0.02

2030 0.085 0.078 0.089 0.073 119.75 155.27 137.51 0.02

2031 0.088 0.080 0.091 0.075 119.75 155.27 137.51 0.02

2032 0.090 0.082 0.095 0.078 119.75 155.27 137.51 0.02

2033 0.093 0.084 0.098 0.080 119.75 155.27 137.51 0.02

2034 0.095 0.087 0.101 0.082 119.75 155.27 137.51 0.02

2035 0.098 0.089 0.104 0.085 119.75 155.27 137.51 0.02

2036 0.100 0.091 0.108 0.087 119.75 155.27 137.51 0.02

2037 0.103 0.094 0.111 0.090 119.75 155.27 137.51 0.02

2038 0.106 0.097 0.115 0.093 119.75 155.27 137.51 0.02

2039 0.109 0.099 0.119 0.096 119.75 155.27 137.51 0.02

2040 0.112 0.102 0.123 0.099 119.75 155.27 137.51 0.02

2041 0.115 0.105 0.127 0.102 119.75 155.27 137.51 0.02

2042 0.118 0.108 0.131 0.105 119.75 155.27 137.51 0.02

2043 0.121 0.111 0.136 0.108 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.058 0.052 0.055 0.046 71.17 70.03 70.60 0.005 0.002 0.005 0.002 7.75 0.004 0.002 0.004 0.002 7.50 0.0415 years (2014-2028) 0.064 0.058 0.062 0.052 86.28 96.55 91.42 0.003 0.002 0.003 0.001 5.45 0.003 0.001 0.003 0.001 5.53 0.0330 years (2014-2043) 0.080 0.073 0.083 0.068 101.33 122.95 112.14 0.002 0.001 0.002 0.001 3.00 0.002 0.001 0.002 0.001 3.04 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: ME

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: ME

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.050 0.043 0.040 0.035 35.40 17.2% 0.00132014 0.049 0.043 0.039 0.034 37.80 17.2% 0.0016 0.010 0.003 0.008 0.0032015 0.048 0.043 0.040 0.034 0.000 0.000 39.60 17.2% 0.0015 0.037 0.013 0.031 0.010 0.010 0.003 0.002016 0.045 0.041 0.041 0.034 0.000 0.000 35.64 17.2% 0.0014 0.038 0.013 0.034 0.011 0.035 0.012 0.032017 0.044 0.039 0.043 0.036 0.000 0.000 39.60 17.2% 0.0013 0.038 0.013 0.039 0.012 0.035 0.012 0.032018 0.046 0.042 0.047 0.038 0.000 0.000 44.64 17.2% 0.0010 0.036 0.012 0.038 0.011 0.037 0.013 0.032019 0.049 0.044 0.049 0.040 0.000 0.000 49.32 17.2% 0.0011 0.032 0.011 0.033 0.010 0.032 0.011 0.032020 0.057 0.051 0.056 0.047 0.000 0.000 91.08 17.2% 0.0013 0.028 0.009 0.028 0.009 0.029 0.010 0.022021 0.057 0.052 0.057 0.048 0.000 0.000 110.88 17.2% 0.0015 0.020 0.007 0.021 0.007 0.021 0.007 0.022022 0.060 0.054 0.059 0.050 0.000 0.000 110.88 17.2% 0.0017 0.014 0.005 0.014 0.004 0.014 0.005 0.012023 0.062 0.056 0.062 0.052 110.88 17.2% 0.0020 0.007 0.002 0.007 0.002 0.007 0.000 0.002024 0.064 0.058 0.064 0.053 110.88 17.2% 0.0024 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.066 0.060 0.066 0.056 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.068 0.062 0.069 0.057 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.070 0.063 0.071 0.059 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.072 0.065 0.073 0.060 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.074 0.067 0.076 0.062 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.076 0.068 0.079 0.064 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.078 0.070 0.081 0.066 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.080 0.072 0.084 0.068 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.082 0.075 0.087 0.071 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.084 0.077 0.090 0.073 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.087 0.079 0.093 0.075 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.089 0.081 0.096 0.078 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.092 0.083 0.099 0.080 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.094 0.086 0.103 0.082 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.097 0.088 0.106 0.085 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.100 0.091 0.110 0.088 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.103 0.093 0.114 0.091 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.105 0.096 0.118 0.093 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.108 0.099 0.122 0.096 110.88 17.2% 0.0027 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.051 0.046 0.049 0.041 0.000 0.000 65.89 0.001 0.026 0.009 0.025 0.008 0.022 0.007 0.02

15 years (2014-2028) 0.057 0.051 0.055 0.046 0.000 0.000 79.89 0.002 0.018 0.006 0.017 0.005 0.015 0.005 0.01

30 years (2014-2043) 0.071 0.065 0.074 0.060 0.000 0.000 93.82 0.002 0.010 0.003 0.010 0.003 0.008 0.003 0.00

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: NH

Table One: Avoided Cost of Electricity (2013 $) Results : NH 

State NH New Hampshire

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.063 0.054 0.051 0.045 0.04

2014 0.064 0.057 0.052 0.046 40.82 0.00 20.41 0.004 0.003 0.003 0.002 0.00 0.04

2015 0.063 0.057 0.053 0.047 42.77 0.00 21.38 0.016 0.011 0.016 0.009 0.00 0.004 0.003 0.004 0.002 0.00 0.04

2016 0.059 0.055 0.054 0.046 38.49 0.00 19.25 0.017 0.011 0.017 0.009 0.00 0.015 0.010 0.015 0.008 0.00 0.04

2017 0.056 0.051 0.056 0.047 42.77 0.00 21.38 0.016 0.011 0.016 0.010 25.09 0.015 0.010 0.015 0.009 0.00 0.04

2018 0.058 0.053 0.059 0.049 48.21 62.51 55.36 0.016 0.011 0.016 0.010 21.26 0.016 0.011 0.016 0.010 25.52 0.042019 0.061 0.056 0.062 0.052 53.27 69.07 61.17 0.014 0.009 0.014 0.008 17.34 0.014 0.009 0.014 0.009 21.67 0.042020 0.071 0.064 0.071 0.060 98.37 127.55 112.96 0.013 0.008 0.013 0.008 13.21 0.013 0.009 0.013 0.008 17.61 0.032021 0.072 0.066 0.073 0.061 119.75 155.27 137.51 0.009 0.006 0.009 0.006 8.93 0.010 0.006 0.010 0.006 13.40 0.032022 0.076 0.069 0.075 0.064 119.75 155.27 137.51 0.006 0.004 0.006 0.004 6.81 0.007 0.004 0.007 0.004 9.08 0.032023 0.079 0.071 0.079 0.066 119.75 155.27 137.51 0.003 0.002 0.003 0.002 4.62 0.003 0.000 0.003 0.000 6.92 0.032024 0.082 0.074 0.083 0.069 119.75 155.27 137.51 0.000 0.000 0.000 0.000 2.35 0.000 0.000 0.000 0.000 4.70 0.032025 0.086 0.078 0.088 0.073 119.75 155.27 137.51 0.000 0.000 0.000 0.000 2.39 0.03

2026 0.088 0.081 0.091 0.076 119.75 155.27 137.51 0.03

2027 0.092 0.083 0.093 0.077 119.75 155.27 137.51 0.03

2028 0.093 0.084 0.096 0.079 119.75 155.27 137.51 0.03

2029 0.096 0.087 0.099 0.082 119.75 155.27 137.51 0.02

2030 0.099 0.089 0.103 0.084 119.75 155.27 137.51 0.02

2031 0.102 0.091 0.106 0.086 119.75 155.27 137.51 0.02

2032 0.105 0.094 0.109 0.089 119.75 155.27 137.51 0.02

2033 0.108 0.096 0.113 0.091 119.75 155.27 137.51 0.02

2034 0.111 0.099 0.117 0.094 119.75 155.27 137.51 0.02

2035 0.114 0.101 0.120 0.097 119.75 155.27 137.51 0.02

2036 0.117 0.104 0.124 0.100 119.75 155.27 137.51 0.02

2037 0.121 0.107 0.129 0.102 119.75 155.27 137.51 0.02

2038 0.124 0.110 0.133 0.105 119.75 155.27 137.51 0.02

2039 0.128 0.113 0.137 0.108 119.75 155.27 137.51 0.02

2040 0.132 0.116 0.142 0.112 119.75 155.27 137.51 0.02

2041 0.136 0.119 0.146 0.115 119.75 155.27 137.51 0.02

2042 0.140 0.122 0.151 0.118 119.75 155.27 137.51 0.02

2043 0.144 0.125 0.156 0.122 119.75 155.27 137.51 0.02

Levelized Costs10 years (2014-2023) 0.066 0.060 0.063 0.053 71.17 70.03 70.60 0.012 0.008 0.012 0.007 9.66 0.010 0.006 0.010 0.006 9.38 0.0415 years (2014-2028) 0.073 0.066 0.071 0.060 86.28 96.55 91.42 0.008 0.005 0.008 0.005 6.80 0.007 0.004 0.007 0.004 6.92 0.0330 years (2014-2043) 0.093 0.083 0.095 0.078 101.33 122.95 112.14 0.004 0.003 0.004 0.003 3.75 0.004 0.002 0.004 0.002 3.81 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: NH

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: NH

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.054 0.047 0.043 0.038 35.40 17.2% 0.00322014 0.053 0.047 0.042 0.037 37.80 17.2% 0.0054 0.011 0.004 0.009 0.0032015 0.052 0.047 0.043 0.037 0.000 0.000 39.60 17.2% 0.0056 0.041 0.015 0.034 0.012 0.011 0.004 0.002016 0.049 0.045 0.044 0.037 0.000 0.000 35.64 17.2% 0.0053 0.042 0.015 0.037 0.013 0.039 0.014 0.032017 0.047 0.042 0.047 0.039 0.000 0.000 39.60 17.2% 0.0042 0.041 0.015 0.043 0.014 0.038 0.014 0.032018 0.050 0.045 0.051 0.042 0.000 0.000 44.64 17.2% 0.0030 0.040 0.014 0.042 0.013 0.041 0.015 0.042019 0.053 0.048 0.054 0.044 0.000 0.000 49.32 17.2% 0.0034 0.035 0.012 0.037 0.011 0.036 0.013 0.032020 0.062 0.055 0.061 0.051 0.000 0.000 91.08 17.2% 0.0038 0.031 0.011 0.030 0.010 0.031 0.011 0.032021 0.062 0.056 0.063 0.052 0.000 0.000 110.88 17.2% 0.0040 0.022 0.008 0.023 0.008 0.023 0.008 0.022022 0.065 0.059 0.064 0.054 0.000 0.000 110.88 17.2% 0.0042 0.015 0.006 0.015 0.005 0.016 0.006 0.012023 0.068 0.061 0.069 0.057 110.88 17.2% 0.0042 0.008 0.003 0.008 0.003 0.008 0.000 0.002024 0.070 0.063 0.071 0.058 110.88 17.2% 0.0052 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.072 0.065 0.073 0.060 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.074 0.067 0.076 0.062 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.077 0.069 0.079 0.064 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.079 0.070 0.081 0.066 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.081 0.073 0.084 0.068 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.084 0.075 0.087 0.070 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.086 0.077 0.090 0.072 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.089 0.079 0.093 0.075 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.092 0.081 0.097 0.077 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.095 0.084 0.100 0.079 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.098 0.086 0.104 0.082 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.101 0.089 0.107 0.084 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.104 0.091 0.111 0.087 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.107 0.094 0.115 0.090 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.110 0.096 0.119 0.093 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.114 0.099 0.123 0.095 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.117 0.102 0.127 0.098 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.121 0.105 0.132 0.102 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.125 0.108 0.137 0.105 110.88 17.2% 0.0070 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.056 0.050 0.054 0.045 0.000 0.000 65.89 0.004 0.029 0.010 0.028 0.009 0.025 0.009 0.02

15 years (2014-2028) 0.062 0.055 0.061 0.050 0.000 0.000 79.89 0.005 0.020 0.007 0.019 0.006 0.017 0.006 0.01

30 years (2014-2043) 0.079 0.070 0.082 0.066 0.000 0.000 93.82 0.006 0.011 0.004 0.011 0.003 0.009 0.003 0.00

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: RI

Table One: Avoided Cost of Electricity (2013 $) Results : RI 

State RI Rhode Island

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.063 0.054 0.051 0.045 0.04

2014 0.062 0.055 0.050 0.044 40.82 0.00 20.41 0.006 0.002 0.005 0.001 0.00 0.04

2015 0.061 0.055 0.051 0.044 42.77 0.00 21.38 0.023 0.007 0.019 0.006 0.00 0.006 0.002 0.005 0.001 0.00 0.04

2016 0.057 0.052 0.051 0.043 38.49 0.00 19.25 0.024 0.007 0.021 0.006 0.00 0.022 0.007 0.019 0.005 0.00 0.04

2017 0.054 0.048 0.055 0.044 42.77 0.00 21.38 0.023 0.007 0.024 0.006 18.57 0.022 0.006 0.022 0.006 0.00 0.04

2018 0.056 0.050 0.059 0.046 48.21 62.51 55.36 0.023 0.007 0.024 0.006 15.75 0.023 0.007 0.024 0.006 18.90 0.042019 0.060 0.054 0.063 0.049 53.27 69.07 61.17 0.020 0.006 0.021 0.005 12.79 0.020 0.006 0.021 0.006 15.99 0.042020 0.063 0.057 0.061 0.052 98.37 127.55 112.96 0.015 0.005 0.015 0.004 9.73 0.016 0.005 0.015 0.004 12.98 0.042021 0.063 0.057 0.063 0.052 119.75 155.27 137.51 0.011 0.003 0.011 0.003 6.58 0.011 0.004 0.012 0.003 9.87 0.042022 0.064 0.058 0.062 0.053 119.75 155.27 137.51 0.007 0.002 0.007 0.002 5.01 0.008 0.002 0.007 0.002 6.68 0.042023 0.066 0.060 0.065 0.055 119.75 155.27 137.51 0.004 0.001 0.004 0.001 3.39 0.004 0.001 0.004 0.001 5.09 0.042024 0.068 0.062 0.067 0.056 119.75 155.27 137.51 0.000 0.000 0.000 0.000 1.72 0.000 0.000 0.000 0.000 3.44 0.042025 0.071 0.064 0.071 0.058 119.75 155.27 137.51 0.000 0.000 0.000 0.000 1.75 0.04

2026 0.072 0.065 0.072 0.059 119.75 155.27 137.51 0.04

2027 0.074 0.066 0.074 0.060 119.75 155.27 137.51 0.04

2028 0.077 0.068 0.075 0.061 119.75 155.27 137.51 0.04

2029 0.079 0.069 0.077 0.062 119.75 155.27 137.51 0.04

2030 0.081 0.070 0.079 0.063 119.75 155.27 137.51 0.04

2031 0.083 0.072 0.081 0.064 119.75 155.27 137.51 0.04

2032 0.085 0.073 0.083 0.065 119.75 155.27 137.51 0.04

2033 0.087 0.075 0.085 0.066 119.75 155.27 137.51 0.04

2034 0.090 0.076 0.087 0.068 119.75 155.27 137.51 0.04

2035 0.092 0.078 0.090 0.069 119.75 155.27 137.51 0.04

2036 0.095 0.079 0.092 0.070 119.75 155.27 137.51 0.04

2037 0.097 0.081 0.094 0.071 119.75 155.27 137.51 0.04

2038 0.100 0.083 0.097 0.073 119.75 155.27 137.51 0.04

2039 0.103 0.084 0.099 0.074 119.75 155.27 137.51 0.04

2040 0.105 0.086 0.102 0.075 119.75 155.27 137.51 0.04

2041 0.108 0.088 0.105 0.077 119.75 155.27 137.51 0.04

2042 0.111 0.090 0.107 0.078 119.75 155.27 137.51 0.04

2043 0.114 0.092 0.110 0.080 119.75 155.27 137.51 0.04

Levelized Costs10 years (2014-2023) 0.061 0.054 0.058 0.048 71.17 70.03 70.60 0.016 0.005 0.015 0.004 7.13 0.013 0.004 0.013 0.004 6.92 0.0415 years (2014-2028) 0.064 0.058 0.062 0.051 86.28 96.55 91.42 0.011 0.003 0.010 0.003 5.02 0.009 0.003 0.009 0.002 5.10 0.0430 years (2014-2043) 0.078 0.067 0.076 0.060 101.33 122.95 112.14 0.006 0.002 0.006 0.002 2.77 0.005 0.002 0.005 0.001 2.81 0.04

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: RI

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: RI

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.054 0.046 0.043 0.037 35.40 17.2% 0.00382014 0.053 0.046 0.042 0.036 37.80 17.2% 0.0041 0.010 0.004 0.008 0.0032015 0.052 0.046 0.043 0.036 0.000 0.000 39.60 17.2% 0.0040 0.040 0.014 0.033 0.011 0.010 0.004 0.002016 0.048 0.044 0.043 0.035 0.000 0.000 35.64 17.2% 0.0037 0.042 0.014 0.037 0.012 0.038 0.013 0.032017 0.046 0.041 0.048 0.038 0.000 0.000 39.60 17.2% 0.0031 0.041 0.014 0.042 0.013 0.038 0.013 0.032018 0.049 0.044 0.052 0.040 0.000 0.000 44.64 17.2% 0.0021 0.039 0.014 0.042 0.013 0.040 0.014 0.042019 0.053 0.047 0.055 0.043 0.000 0.000 49.32 17.2% 0.0026 0.034 0.012 0.036 0.011 0.035 0.012 0.032020 0.055 0.049 0.053 0.045 0.000 0.000 91.08 17.2% 0.0028 0.030 0.010 0.026 0.009 0.031 0.011 0.022021 0.055 0.049 0.055 0.045 0.000 0.000 110.88 17.2% 0.0028 0.022 0.008 0.020 0.006 0.023 0.008 0.022022 0.056 0.051 0.054 0.046 0.000 0.000 110.88 17.2% 0.0027 0.015 0.005 0.013 0.004 0.015 0.005 0.012023 0.058 0.052 0.057 0.047 110.88 17.2% 0.0032 0.008 0.003 0.006 0.002 0.008 0.003 0.002024 0.059 0.053 0.058 0.048 110.88 17.2% 0.0037 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.061 0.055 0.061 0.049 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.062 0.055 0.062 0.050 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.064 0.057 0.064 0.051 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.066 0.058 0.064 0.052 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.068 0.059 0.066 0.053 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.070 0.060 0.068 0.054 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.072 0.062 0.070 0.055 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.074 0.063 0.072 0.056 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.076 0.064 0.074 0.057 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.078 0.066 0.076 0.058 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.080 0.067 0.078 0.059 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.083 0.069 0.080 0.060 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.085 0.070 0.082 0.061 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.087 0.072 0.085 0.062 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.090 0.073 0.087 0.064 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.092 0.075 0.089 0.065 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.095 0.076 0.092 0.066 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.098 0.078 0.094 0.067 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.100 0.080 0.097 0.069 110.88 17.2% 0.0043 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.052 0.047 0.050 0.041 0.000 0.000 65.89 0.003 0.028 0.010 0.027 0.008 0.024 0.008 0.02

15 years (2014-2028) 0.055 0.049 0.054 0.044 0.000 0.000 79.89 0.003 0.020 0.007 0.018 0.006 0.017 0.006 0.01

30 years (2014-2043) 0.068 0.058 0.065 0.051 0.000 0.000 93.82 0.004 0.011 0.004 0.010 0.003 0.009 0.003 0.00

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: VT

Table One: Avoided Cost of Electricity (2013 $) Results : VT 

State VT Vermont

User-defined InputsWholesale Risk Premium (WRP) 11.10%Distribution Losses 8.00%

Real Discount Rate 1.36%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.062 0.052 0.050 0.044 0.04

2014 0.061 0.053 0.049 0.042 40.82 0.00 20.41 0.000 0.000 0.000 0.000 0.00 0.04

2015 0.060 0.053 0.050 0.043 42.77 0.00 21.38 0.001 0.001 0.001 0.001 0.00 0.000 0.000 0.000 0.000 0.00 0.04

2016 0.058 0.051 0.051 0.042 38.49 0.00 19.25 0.002 0.001 0.002 0.001 0.00 0.001 0.001 0.001 0.001 0.00 0.04

2017 0.055 0.049 0.057 0.045 42.77 0.00 21.38 0.002 0.001 0.002 0.001 3.80 0.001 0.001 0.001 0.001 0.00 0.04

2018 0.059 0.052 0.062 0.048 48.21 63.72 55.96 0.002 0.001 0.002 0.001 3.33 0.002 0.001 0.002 0.001 4.00 0.042019 0.063 0.055 0.067 0.051 53.27 70.40 61.83 0.001 0.001 0.001 0.001 2.66 0.001 0.001 0.001 0.001 3.32 0.042020 0.072 0.063 0.071 0.058 98.37 130.00 114.18 0.001 0.001 0.001 0.001 2.08 0.001 0.001 0.001 0.001 2.78 0.032021 0.072 0.064 0.074 0.059 119.75 158.26 139.01 0.001 0.000 0.001 0.000 1.53 0.001 0.000 0.001 0.000 2.29 0.032022 0.075 0.067 0.074 0.062 119.75 158.26 139.01 0.001 0.000 0.001 0.000 1.22 0.001 0.000 0.001 0.000 1.63 0.032023 0.078 0.069 0.079 0.065 119.75 158.26 139.01 0.000 0.000 0.000 0.000 0.84 0.000 0.000 0.000 0.000 1.26 0.032024 0.080 0.071 0.082 0.066 119.75 158.26 139.01 0.000 0.000 0.000 0.000 0.43 0.000 0.000 0.000 0.000 0.87 0.032025 0.084 0.074 0.086 0.069 119.75 158.26 139.01 0.000 0.000 0.000 0.000 0.45 0.03

2026 0.086 0.076 0.089 0.071 119.75 158.26 139.01 0.03

2027 0.089 0.078 0.092 0.073 119.75 158.26 139.01 0.03

2028 0.092 0.080 0.094 0.075 119.75 158.26 139.01 0.03

2029 0.096 0.082 0.097 0.077 119.75 158.26 139.01 0.02

2030 0.099 0.085 0.101 0.080 119.75 158.26 139.01 0.02

2031 0.103 0.087 0.104 0.082 119.75 158.26 139.01 0.02

2032 0.106 0.089 0.108 0.085 119.75 158.26 139.01 0.02

2033 0.110 0.092 0.112 0.087 119.75 158.26 139.01 0.02

2034 0.114 0.095 0.116 0.090 119.75 158.26 139.01 0.02

2035 0.118 0.097 0.120 0.093 119.75 158.26 139.01 0.02

2036 0.122 0.100 0.125 0.096 119.75 158.26 139.01 0.02

2037 0.127 0.103 0.129 0.099 119.75 158.26 139.01 0.02

2038 0.131 0.106 0.134 0.102 119.75 158.26 139.01 0.02

2039 0.136 0.109 0.138 0.105 119.75 158.26 139.01 0.02

2040 0.141 0.112 0.143 0.108 119.75 158.26 139.01 0.02

2041 0.146 0.115 0.149 0.111 119.75 158.26 139.01 0.02

2042 0.151 0.119 0.154 0.115 119.75 158.26 139.01 0.02

2043 0.156 0.122 0.159 0.118 119.75 158.26 139.01 0.02

Levelized Costs10 years (2014-2023) 0.065 0.057 0.063 0.051 71.17 71.38 71.27 0.001 0.001 0.001 0.000 1.53 0.001 0.000 0.001 0.000 1.52 0.0415 years (2014-2028) 0.072 0.063 0.071 0.057 86.28 98.41 92.35 0.001 0.000 0.001 0.000 1.08 0.001 0.000 0.001 0.000 1.13 0.0330 years (2014-2043) 0.094 0.080 0.095 0.075 101.33 125.32 113.32 0.000 0.000 0.000 0.000 0.60 0.000 0.000 0.000 0.000 0.62 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: VT

Table Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: VT

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.056 0.047 0.045 0.039 35.40 17.2% 0.00002014 0.055 0.048 0.044 0.038 37.80 17.2% 0.0000 0.011 0.004 0.009 0.0032015 0.054 0.048 0.045 0.038 0.000 0.000 39.60 17.2% 0.0000 0.041 0.015 0.034 0.012 0.011 0.004 0.002016 0.052 0.046 0.046 0.038 0.000 0.000 35.64 17.2% 0.0000 0.042 0.015 0.037 0.012 0.039 0.014 0.032017 0.050 0.044 0.051 0.040 0.000 0.000 39.60 17.2% 0.0000 0.041 0.015 0.043 0.014 0.038 0.014 0.032018 0.053 0.046 0.056 0.043 0.000 0.000 44.64 17.2% 0.0000 0.040 0.014 0.042 0.013 0.041 0.015 0.042019 0.057 0.050 0.060 0.046 0.000 0.000 49.32 17.2% 0.0000 0.035 0.012 0.037 0.011 0.036 0.013 0.032020 0.065 0.057 0.064 0.053 0.000 0.000 91.08 17.2% 0.0000 0.031 0.011 0.031 0.010 0.032 0.011 0.032021 0.065 0.058 0.067 0.054 0.000 0.000 110.88 17.2% 0.0000 0.022 0.008 0.023 0.008 0.023 0.008 0.022022 0.068 0.060 0.067 0.056 0.000 0.000 110.88 17.2% 0.0000 0.015 0.005 0.015 0.005 0.016 0.006 0.012023 0.070 0.062 0.071 0.058 110.88 17.2% 0.0000 0.008 0.003 0.008 0.003 0.008 0.000 0.002024 0.072 0.064 0.073 0.060 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.075 0.066 0.077 0.062 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.077 0.068 0.080 0.064 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.080 0.070 0.083 0.066 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.083 0.072 0.085 0.067 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.086 0.074 0.088 0.070 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.089 0.076 0.091 0.072 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.092 0.078 0.094 0.074 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.096 0.081 0.097 0.076 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.099 0.083 0.101 0.079 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.103 0.085 0.105 0.081 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.106 0.088 0.108 0.083 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.110 0.090 0.112 0.086 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.114 0.093 0.116 0.089 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.118 0.095 0.120 0.091 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.122 0.098 0.125 0.094 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.127 0.101 0.129 0.097 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.131 0.104 0.134 0.100 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.136 0.107 0.139 0.103 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.141 0.110 0.144 0.106 110.88 17.2% 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.059 0.052 0.057 0.046 0.000 0.000 65.89 0.000 0.029 0.010 0.028 0.009 0.025 0.008 0.02

15 years (2014-2028) 0.064 0.057 0.064 0.052 0.000 0.000 79.89 0.000 0.020 0.007 0.019 0.006 0.017 0.006 0.01

30 years (2014-2043) 0.085 0.072 0.086 0.067 0.000 0.000 93.82 0.000 0.011 0.004 0.011 0.003 0.009 0.003 0.00

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT_Nominal

Table One: Avoided Cost of Electricity (Nominal $) Results : CT 

State CT Connecticut (Statewide)

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%Nominal Discount Rate 3.39%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.068 0.059 0.057 0.050 0.04

2014 0.067 0.060 0.056 0.049 41.64 0.00 20.82 0.005 0.006 0.004 0.005 0.00 0.042015 0.068 0.060 0.058 0.050 44.50 0.00 22.25 0.020 0.025 0.017 0.020 0.00 0.005 0.006 0.004 0.005 0.00 0.042016 0.066 0.059 0.059 0.050 40.85 0.00 20.42 0.021 0.026 0.019 0.022 0.00 0.020 0.024 0.017 0.020 0.00 0.042017 0.064 0.057 0.066 0.053 46.29 0.00 23.15 0.021 0.026 0.022 0.024 65.79 0.020 0.024 0.020 0.022 0.00 0.042018 0.068 0.060 0.071 0.056 53.23 69.02 61.12 0.021 0.026 0.022 0.024 56.85 0.021 0.026 0.023 0.025 68.23 0.042019 0.075 0.066 0.079 0.061 59.99 77.78 68.88 0.019 0.023 0.019 0.021 46.95 0.019 0.023 0.020 0.021 58.68 0.042020 0.087 0.077 0.086 0.071 112.99 146.51 129.75 0.017 0.020 0.016 0.019 36.84 0.017 0.021 0.017 0.019 49.12 0.042021 0.089 0.080 0.092 0.074 140.31 181.93 161.12 0.012 0.016 0.013 0.014 25.36 0.013 0.016 0.013 0.015 38.04 0.042022 0.094 0.084 0.093 0.078 143.11 185.57 164.34 0.009 0.011 0.008 0.010 19.66 0.009 0.011 0.009 0.010 26.21 0.042023 0.099 0.088 0.101 0.083 145.98 189.28 167.63 0.004 0.005 0.004 0.005 13.54 0.004 0.000 0.005 0.000 20.31 0.042024 0.105 0.094 0.107 0.088 148.89 193.06 170.98 0.000 0.000 0.000 0.000 6.99 0.000 0.000 0.000 0.000 13.98 0.042025 0.113 0.101 0.116 0.094 151.87 196.92 174.40 0.000 0.000 0.000 0.000 7.27 0.04

2026 0.118 0.106 0.122 0.099 154.91 200.86 177.89 0.04

2027 0.125 0.110 0.129 0.104 158.01 204.88 181.44 0.04

2028 0.132 0.115 0.134 0.108 161.17 208.98 185.07 0.04

2029 0.139 0.121 0.141 0.114 164.39 213.16 188.77 0.03

2030 0.147 0.127 0.149 0.119 167.68 217.42 192.55 0.03

2031 0.155 0.132 0.157 0.125 171.03 221.77 196.40 0.03

2032 0.163 0.139 0.166 0.131 174.45 226.20 200.33 0.04

2033 0.172 0.145 0.175 0.137 177.94 230.73 204.34 0.04

2034 0.181 0.152 0.184 0.144 181.50 235.34 208.42 0.04

2035 0.191 0.160 0.194 0.151 185.13 240.05 212.59 0.04

2036 0.202 0.167 0.205 0.159 188.83 244.85 216.84 0.04

2037 0.213 0.175 0.216 0.166 192.61 249.75 221.18 0.04

2038 0.224 0.183 0.228 0.174 196.46 254.74 225.60 0.04

2039 0.237 0.192 0.240 0.183 200.39 259.84 230.11 0.04

2040 0.250 0.201 0.253 0.192 204.40 265.03 234.72 0.04

2041 0.263 0.211 0.267 0.201 208.49 270.33 239.41 0.04

2042 0.278 0.221 0.282 0.211 212.66 275.74 244.20 0.04

2043 0.293 0.232 0.297 0.222 216.91 281.26 249.08 0.05

Levelized Costs10 years (2014-2023) 0.069 0.061 0.067 0.055 71.17 70.03 70.60 0.014 0.017 0.013 0.015 23.36 0.012 0.014 0.012 0.013 23.13 0.0415 years (2014-2028) 0.076 0.067 0.075 0.062 86.28 96.55 91.42 0.009 0.012 0.009 0.010 16.45 0.008 0.010 0.008 0.009 17.05 0.0330 years (2014-2043) 0.100 0.085 0.100 0.079 101.33 122.95 112.14 0.005 0.006 0.005 0.006 9.06 0.004 0.005 0.004 0.005 9.39 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)

2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT_Nominal

TTable Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: CT

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.056 0.048 0.046 0.040 35.40 17.2% 0.00632014 0.056 0.049 0.045 0.039 38.56 17.5% 0.0056 0.007 0.004 0.006 0.0032015 0.057 0.050 0.048 0.040 0.001 0.000 41.20 17.9% 0.0053 0.029 0.016 0.024 0.013 0.008 0.004 0.002016 0.056 0.049 0.049 0.041 0.001 0.001 37.82 18.3% 0.0049 0.031 0.017 0.027 0.014 0.028 0.015 0.022017 0.055 0.048 0.056 0.044 0.001 0.001 42.86 18.6% 0.0042 0.031 0.017 0.032 0.015 0.029 0.015 0.032018 0.059 0.052 0.062 0.048 0.001 0.001 49.29 19.0% 0.0034 0.031 0.017 0.032 0.015 0.031 0.017 0.032019 0.065 0.056 0.068 0.052 0.001 0.001 55.54 19.4% 0.0039 0.027 0.014 0.029 0.013 0.028 0.015 0.022020 0.075 0.066 0.075 0.061 0.001 0.001 104.62 19.8% 0.0046 0.025 0.013 0.024 0.012 0.025 0.013 0.022021 0.077 0.068 0.079 0.063 0.000 0.000 129.91 20.2% 0.0047 0.018 0.010 0.019 0.009 0.019 0.010 0.012022 0.082 0.073 0.080 0.067 0.000 0.000 132.51 20.6% 0.0046 0.013 0.007 0.012 0.006 0.013 0.007 0.012023 0.087 0.077 0.088 0.071 135.16 21.0% 0.0045 0.007 0.004 0.007 0.003 0.007 0.000 0.002024 0.091 0.081 0.092 0.075 137.87 21.4% 0.0056 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.096 0.085 0.099 0.079 140.62 21.8% 0.0076 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.101 0.089 0.105 0.083 143.44 22.3% 0.0078 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.107 0.093 0.111 0.087 146.30 22.7% 0.0079 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.113 0.098 0.115 0.091 149.23 23.1% 0.0081 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.120 0.103 0.122 0.096 152.21 23.6% 0.0082 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.126 0.108 0.128 0.101 155.26 24.1% 0.0084 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.133 0.113 0.136 0.106 158.36 24.6% 0.0085 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.141 0.119 0.143 0.111 161.53 25.1% 0.0087 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.149 0.124 0.151 0.117 164.76 25.6% 0.0089 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.157 0.131 0.160 0.123 168.06 26.1% 0.0090 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.166 0.137 0.169 0.129 171.42 26.6% 0.0092 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.176 0.144 0.178 0.136 174.85 27.1% 0.0094 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.186 0.151 0.188 0.143 178.34 27.7% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.196 0.159 0.199 0.150 181.91 28.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.207 0.166 0.210 0.158 185.55 28.8% 0.0099 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.219 0.175 0.222 0.166 189.26 29.4% 0.0101 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.231 0.183 0.235 0.175 193.04 29.9% 0.0103 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.244 0.192 0.248 0.183 196.91 30.5% 0.0105 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.258 0.202 0.262 0.193 200.84 31.2% 0.0107 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.059 0.052 0.057 0.046 0.001 0.001 65.89 0.004 0.020 0.011 0.019 0.010 0.017 0.009 0.0115 years (2014-2028) 0.065 0.057 0.065 0.052 0.000 0.000 79.89 0.005 0.014 0.007 0.013 0.007 0.012 0.006 0.01

30 years (2014-2043) 0.086 0.073 0.087 0.067 0.000 0.000 93.82 0.005 0.008 0.004 0.007 0.004 0.007 0.003 0.00

NOTES: General All Avoided Costs are in Nominal Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-NS_Nominal

Table One: Avoided Cost of Electricity (Nominal $) Results : CT-NS 

State CT Norwalk/Stamford

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%Nominal Discount Rate 3.39%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.069 0.059 0.057 0.050 0.04

2014 0.068 0.060 0.056 0.049 41.64 0.00 20.82 0.005 0.006 0.004 0.005 0.00 0.042015 0.068 0.061 0.058 0.050 44.50 0.00 22.25 0.020 0.025 0.017 0.020 0.00 0.005 0.006 0.004 0.005 0.00 0.042016 0.067 0.060 0.060 0.050 40.85 0.00 20.42 0.021 0.026 0.019 0.022 0.00 0.020 0.024 0.017 0.020 0.00 0.042017 0.065 0.057 0.067 0.053 46.29 0.00 23.15 0.021 0.026 0.022 0.024 65.79 0.020 0.024 0.020 0.022 0.00 0.042018 0.069 0.061 0.072 0.057 53.23 69.02 61.12 0.021 0.026 0.022 0.024 56.85 0.021 0.026 0.023 0.025 68.23 0.042019 0.076 0.066 0.080 0.062 59.99 77.78 68.88 0.019 0.023 0.019 0.021 46.95 0.019 0.023 0.020 0.021 58.68 0.042020 0.088 0.077 0.087 0.072 112.99 146.51 129.75 0.017 0.020 0.016 0.019 36.84 0.017 0.021 0.017 0.019 49.12 0.042021 0.089 0.080 0.092 0.075 140.31 181.93 161.12 0.012 0.016 0.013 0.014 25.36 0.013 0.016 0.013 0.015 38.04 0.042022 0.095 0.085 0.094 0.079 143.11 185.57 164.34 0.009 0.011 0.008 0.010 19.66 0.009 0.011 0.009 0.010 26.21 0.042023 0.100 0.089 0.102 0.083 145.98 189.28 167.63 0.004 0.005 0.004 0.005 13.54 0.004 0.000 0.005 0.000 20.31 0.042024 0.106 0.095 0.108 0.088 148.89 193.06 170.98 0.000 0.000 0.000 0.000 6.99 0.000 0.000 0.000 0.000 13.98 0.042025 0.115 0.102 0.117 0.095 151.87 196.92 174.40 0.000 0.000 0.000 0.000 7.27 0.04

2026 0.120 0.107 0.124 0.100 154.91 200.86 177.89 0.04

2027 0.126 0.111 0.131 0.105 158.01 204.88 181.44 0.04

2028 0.133 0.116 0.136 0.109 161.17 208.98 185.07 0.04

2029 0.141 0.122 0.143 0.115 164.39 213.16 188.77 0.03

2030 0.148 0.128 0.151 0.120 167.68 217.42 192.55 0.03

2031 0.156 0.134 0.159 0.126 171.03 221.77 196.40 0.03

2032 0.165 0.140 0.167 0.132 174.45 226.20 200.33 0.04

2033 0.174 0.147 0.176 0.139 177.94 230.73 204.34 0.04

2034 0.183 0.154 0.186 0.145 181.50 235.34 208.42 0.04

2035 0.193 0.161 0.196 0.153 185.13 240.05 212.59 0.04

2036 0.204 0.169 0.207 0.160 188.83 244.85 216.84 0.04

2037 0.215 0.177 0.218 0.168 192.61 249.75 221.18 0.04

2038 0.227 0.185 0.230 0.176 196.46 254.74 225.60 0.04

2039 0.239 0.194 0.242 0.185 200.39 259.84 230.11 0.04

2040 0.252 0.203 0.256 0.194 204.40 265.03 234.72 0.04

2041 0.266 0.213 0.270 0.203 208.49 270.33 239.41 0.04

2042 0.280 0.223 0.284 0.213 212.66 275.74 244.20 0.04

2043 0.296 0.234 0.300 0.224 216.91 281.26 249.08 0.05

Levelized Costs10 years (2014-2023) 0.070 0.062 0.068 0.056 71.17 70.03 70.60 0.014 0.017 0.013 0.015 23.36 0.012 0.014 0.012 0.013 23.13 0.0415 years (2014-2028) 0.077 0.068 0.076 0.062 86.28 96.55 91.42 0.009 0.012 0.009 0.010 16.45 0.008 0.010 0.008 0.009 17.05 0.0330 years (2014-2043) 0.100 0.086 0.101 0.080 101.33 122.95 112.14 0.005 0.006 0.005 0.006 9.06 0.004 0.005 0.004 0.005 9.39 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)

2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-NS_Nominal

TTable Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: CT-NS

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.057 0.048 0.046 0.040 35.40 17.2% 0.00632014 0.057 0.050 0.046 0.040 38.56 17.5% 0.0056 0.007 0.004 0.006 0.0032015 0.057 0.051 0.048 0.041 0.001 0.000 41.20 17.9% 0.0053 0.029 0.016 0.024 0.013 0.008 0.004 0.002016 0.056 0.050 0.050 0.041 0.001 0.001 37.82 18.3% 0.0049 0.031 0.017 0.027 0.014 0.028 0.015 0.022017 0.055 0.048 0.057 0.045 0.001 0.001 42.86 18.6% 0.0042 0.031 0.017 0.032 0.015 0.029 0.015 0.032018 0.060 0.052 0.063 0.049 0.001 0.001 49.29 19.0% 0.0034 0.031 0.017 0.032 0.015 0.031 0.017 0.032019 0.066 0.057 0.069 0.053 0.001 0.001 55.54 19.4% 0.0039 0.027 0.014 0.029 0.013 0.028 0.015 0.022020 0.076 0.066 0.075 0.062 0.001 0.001 104.62 19.8% 0.0046 0.025 0.013 0.024 0.012 0.025 0.013 0.022021 0.077 0.069 0.080 0.064 0.000 0.000 129.91 20.2% 0.0047 0.018 0.010 0.019 0.009 0.019 0.010 0.012022 0.083 0.074 0.081 0.068 0.000 0.000 132.51 20.6% 0.0046 0.013 0.007 0.012 0.006 0.013 0.007 0.012023 0.088 0.077 0.089 0.072 135.16 21.0% 0.0045 0.007 0.004 0.007 0.003 0.007 0.000 0.002024 0.092 0.081 0.093 0.076 137.87 21.4% 0.0056 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.097 0.086 0.100 0.080 140.62 21.8% 0.0076 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.102 0.090 0.106 0.084 143.44 22.3% 0.0078 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.108 0.094 0.112 0.088 146.30 22.7% 0.0079 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.114 0.099 0.116 0.092 149.23 23.1% 0.0081 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.121 0.104 0.123 0.097 152.21 23.6% 0.0082 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.128 0.109 0.130 0.102 155.26 24.1% 0.0084 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.135 0.114 0.137 0.107 158.36 24.6% 0.0085 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.142 0.120 0.145 0.113 161.53 25.1% 0.0087 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.150 0.126 0.153 0.118 164.76 25.6% 0.0089 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.159 0.132 0.162 0.124 168.06 26.1% 0.0090 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.168 0.139 0.171 0.131 171.42 26.6% 0.0092 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.177 0.145 0.180 0.137 174.85 27.1% 0.0094 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.187 0.153 0.190 0.144 178.34 27.7% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.198 0.160 0.201 0.152 181.91 28.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.209 0.168 0.212 0.160 185.55 28.8% 0.0099 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.221 0.177 0.224 0.168 189.26 29.4% 0.0101 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.234 0.185 0.237 0.176 193.04 29.9% 0.0103 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.247 0.194 0.250 0.185 196.91 30.5% 0.0105 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.261 0.204 0.265 0.195 200.84 31.2% 0.0107 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.060 0.053 0.058 0.047 0.001 0.001 65.89 0.004 0.020 0.011 0.019 0.010 0.017 0.009 0.0115 years (2014-2028) 0.066 0.058 0.065 0.052 0.000 0.000 79.89 0.005 0.014 0.007 0.013 0.007 0.012 0.006 0.01

30 years (2014-2043) 0.087 0.073 0.087 0.068 0.000 0.000 93.82 0.005 0.008 0.004 0.007 0.004 0.007 0.003 0.00

NOTES: General All Avoided Costs are in Nominal Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-R_Nominal

Table One: Avoided Cost of Electricity (Nominal $) Results : CT-R 

State CT Rest of Connecticut (excluding all of Southwes

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%Nominal Discount Rate 3.39%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.068 0.058 0.056 0.050 0.04

2014 0.067 0.059 0.055 0.048 41.64 0.00 20.82 0.005 0.006 0.004 0.005 0.00 0.042015 0.067 0.060 0.057 0.049 44.50 0.00 22.25 0.020 0.025 0.017 0.020 0.00 0.005 0.006 0.004 0.005 0.00 0.042016 0.065 0.059 0.058 0.049 40.85 0.00 20.42 0.021 0.026 0.019 0.022 0.00 0.020 0.024 0.017 0.020 0.00 0.042017 0.064 0.056 0.065 0.052 46.29 0.00 23.15 0.021 0.026 0.022 0.024 65.79 0.020 0.024 0.020 0.022 0.00 0.042018 0.067 0.060 0.071 0.055 53.23 69.02 61.12 0.021 0.026 0.022 0.024 56.85 0.021 0.026 0.023 0.025 68.23 0.042019 0.074 0.065 0.078 0.060 59.99 77.78 68.88 0.019 0.023 0.019 0.021 46.95 0.019 0.023 0.020 0.021 58.68 0.042020 0.086 0.076 0.086 0.071 112.99 146.51 129.75 0.017 0.020 0.016 0.019 36.84 0.017 0.021 0.017 0.019 49.12 0.042021 0.088 0.079 0.091 0.073 140.31 181.93 161.12 0.012 0.016 0.013 0.014 25.36 0.013 0.016 0.013 0.015 38.04 0.042022 0.094 0.084 0.092 0.078 143.11 185.57 164.34 0.009 0.011 0.008 0.010 19.66 0.009 0.011 0.009 0.010 26.21 0.042023 0.098 0.088 0.100 0.082 145.98 189.28 167.63 0.004 0.005 0.004 0.005 13.54 0.004 0.000 0.005 0.000 20.31 0.042024 0.104 0.093 0.106 0.087 148.89 193.06 170.98 0.000 0.000 0.000 0.000 6.99 0.000 0.000 0.000 0.000 13.98 0.042025 0.112 0.100 0.115 0.093 151.87 196.92 174.40 0.000 0.000 0.000 0.000 7.27 0.04

2026 0.117 0.105 0.121 0.098 154.91 200.86 177.89 0.04

2027 0.124 0.109 0.128 0.103 158.01 204.88 181.44 0.04

2028 0.131 0.114 0.133 0.107 161.17 208.98 185.07 0.04

2029 0.138 0.120 0.140 0.113 164.39 213.16 188.77 0.03

2030 0.145 0.125 0.148 0.118 167.68 217.42 192.55 0.03

2031 0.153 0.131 0.156 0.124 171.03 221.77 196.40 0.03

2032 0.162 0.137 0.164 0.130 174.45 226.20 200.33 0.04

2033 0.170 0.144 0.173 0.136 177.94 230.73 204.34 0.04

2034 0.180 0.151 0.182 0.143 181.50 235.34 208.42 0.04

2035 0.189 0.158 0.192 0.150 185.13 240.05 212.59 0.04

2036 0.200 0.166 0.203 0.157 188.83 244.85 216.84 0.04

2037 0.211 0.173 0.214 0.165 192.61 249.75 221.18 0.04

2038 0.222 0.182 0.225 0.173 196.46 254.74 225.60 0.04

2039 0.234 0.190 0.238 0.181 200.39 259.84 230.11 0.04

2040 0.247 0.200 0.251 0.190 204.40 265.03 234.72 0.04

2041 0.261 0.209 0.264 0.200 208.49 270.33 239.41 0.04

2042 0.275 0.219 0.279 0.209 212.66 275.74 244.20 0.04

2043 0.290 0.230 0.294 0.220 216.91 281.26 249.08 0.05

Levelized Costs10 years (2014-2023) 0.068 0.061 0.066 0.055 71.17 70.03 70.60 0.014 0.017 0.013 0.015 23.36 0.012 0.014 0.012 0.013 23.13 0.0415 years (2014-2028) 0.075 0.067 0.075 0.061 86.28 96.55 91.42 0.009 0.012 0.009 0.010 16.45 0.008 0.010 0.008 0.009 17.05 0.0330 years (2014-2043) 0.099 0.084 0.099 0.078 101.33 122.95 112.14 0.005 0.006 0.005 0.006 9.06 0.004 0.005 0.004 0.005 9.39 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)

2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-R_Nominal

TTable Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: CT-R

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.056 0.047 0.045 0.039 35.40 17.2% 0.00632014 0.056 0.049 0.045 0.039 38.56 17.5% 0.0056 0.007 0.004 0.006 0.0032015 0.056 0.049 0.047 0.040 0.001 0.000 41.20 17.9% 0.0053 0.029 0.016 0.024 0.013 0.008 0.004 0.002016 0.055 0.049 0.049 0.040 0.001 0.001 37.82 18.3% 0.0049 0.031 0.017 0.027 0.014 0.028 0.015 0.022017 0.054 0.047 0.056 0.044 0.001 0.001 42.86 18.6% 0.0042 0.031 0.017 0.032 0.015 0.029 0.015 0.032018 0.058 0.051 0.062 0.048 0.001 0.001 49.29 19.0% 0.0034 0.031 0.017 0.032 0.015 0.031 0.017 0.032019 0.064 0.056 0.068 0.051 0.001 0.001 55.54 19.4% 0.0039 0.027 0.014 0.029 0.013 0.028 0.015 0.022020 0.075 0.065 0.074 0.060 0.001 0.001 104.62 19.8% 0.0046 0.025 0.013 0.024 0.012 0.025 0.013 0.022021 0.076 0.068 0.079 0.063 0.000 0.000 129.91 20.2% 0.0047 0.018 0.010 0.019 0.009 0.019 0.010 0.012022 0.081 0.072 0.080 0.067 0.000 0.000 132.51 20.6% 0.0046 0.013 0.007 0.012 0.006 0.013 0.007 0.012023 0.086 0.076 0.087 0.071 135.16 21.0% 0.0045 0.007 0.004 0.007 0.003 0.007 0.000 0.002024 0.090 0.080 0.091 0.074 137.87 21.4% 0.0056 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.095 0.084 0.098 0.078 140.62 21.8% 0.0076 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.100 0.088 0.103 0.082 143.44 22.3% 0.0078 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.106 0.092 0.110 0.086 146.30 22.7% 0.0079 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.112 0.097 0.114 0.090 149.23 23.1% 0.0081 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.118 0.102 0.120 0.095 152.21 23.6% 0.0082 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.125 0.107 0.127 0.100 155.26 24.1% 0.0084 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.132 0.112 0.134 0.105 158.36 24.6% 0.0085 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.139 0.117 0.142 0.110 161.53 25.1% 0.0087 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.147 0.123 0.150 0.116 164.76 25.6% 0.0089 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.156 0.129 0.158 0.122 168.06 26.1% 0.0090 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.165 0.136 0.167 0.128 171.42 26.6% 0.0092 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.174 0.142 0.177 0.135 174.85 27.1% 0.0094 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.184 0.150 0.186 0.142 178.34 27.7% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.194 0.157 0.197 0.149 181.91 28.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.205 0.165 0.208 0.156 185.55 28.8% 0.0099 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.217 0.173 0.220 0.164 189.26 29.4% 0.0101 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.229 0.181 0.232 0.173 193.04 29.9% 0.0103 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.242 0.190 0.245 0.182 196.91 30.5% 0.0105 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.255 0.200 0.259 0.191 200.84 31.2% 0.0107 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.059 0.052 0.057 0.046 0.001 0.001 65.89 0.004 0.020 0.011 0.019 0.010 0.017 0.009 0.0115 years (2014-2028) 0.064 0.057 0.064 0.051 0.000 0.000 79.89 0.005 0.014 0.007 0.013 0.007 0.012 0.006 0.01

30 years (2014-2043) 0.085 0.072 0.086 0.067 0.000 0.000 93.82 0.005 0.008 0.004 0.007 0.004 0.007 0.003 0.00

NOTES: General All Avoided Costs are in Nominal Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-SWe_Nominal

Table One: Avoided Cost of Electricity (Nominal $) Results : CT-SWe 

State CT Southwest Connecticut, excluding Norwalk/Sta

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%Nominal Discount Rate 3.39%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.069 0.059 0.057 0.050 0.04

2014 0.068 0.060 0.056 0.049 41.64 0.00 20.82 0.005 0.006 0.004 0.005 0.00 0.042015 0.068 0.061 0.058 0.050 44.50 0.00 22.25 0.020 0.025 0.017 0.020 0.00 0.005 0.006 0.004 0.005 0.00 0.042016 0.067 0.060 0.060 0.050 40.85 0.00 20.42 0.021 0.026 0.019 0.022 0.00 0.020 0.024 0.017 0.020 0.00 0.042017 0.065 0.057 0.067 0.053 46.29 0.00 23.15 0.021 0.026 0.022 0.024 65.79 0.020 0.024 0.020 0.022 0.00 0.042018 0.069 0.061 0.072 0.057 53.23 69.02 61.12 0.021 0.026 0.022 0.024 56.85 0.021 0.026 0.023 0.025 68.23 0.042019 0.076 0.066 0.079 0.061 59.99 77.78 68.88 0.019 0.023 0.019 0.021 46.95 0.019 0.023 0.020 0.021 58.68 0.042020 0.088 0.077 0.087 0.072 112.99 146.51 129.75 0.017 0.020 0.016 0.019 36.84 0.017 0.021 0.017 0.019 49.12 0.042021 0.089 0.080 0.092 0.075 140.31 181.93 161.12 0.012 0.016 0.013 0.014 25.36 0.013 0.016 0.013 0.015 38.04 0.042022 0.095 0.085 0.094 0.079 143.11 185.57 164.34 0.009 0.011 0.008 0.010 19.66 0.009 0.011 0.009 0.010 26.21 0.042023 0.100 0.089 0.102 0.083 145.98 189.28 167.63 0.004 0.005 0.004 0.005 13.54 0.004 0.000 0.005 0.000 20.31 0.042024 0.106 0.095 0.108 0.088 148.89 193.06 170.98 0.000 0.000 0.000 0.000 6.99 0.000 0.000 0.000 0.000 13.98 0.042025 0.114 0.102 0.117 0.095 151.87 196.92 174.40 0.000 0.000 0.000 0.000 7.27 0.04

2026 0.119 0.107 0.123 0.100 154.91 200.86 177.89 0.04

2027 0.126 0.111 0.131 0.105 158.01 204.88 181.44 0.04

2028 0.133 0.116 0.135 0.109 161.17 208.98 185.07 0.04

2029 0.140 0.122 0.143 0.115 164.39 213.16 188.77 0.03

2030 0.148 0.128 0.150 0.120 167.68 217.42 192.55 0.03

2031 0.156 0.134 0.159 0.126 171.03 221.77 196.40 0.03

2032 0.165 0.140 0.167 0.132 174.45 226.20 200.33 0.04

2033 0.174 0.147 0.176 0.139 177.94 230.73 204.34 0.04

2034 0.183 0.154 0.186 0.145 181.50 235.34 208.42 0.04

2035 0.193 0.161 0.196 0.152 185.13 240.05 212.59 0.04

2036 0.203 0.169 0.207 0.160 188.83 244.85 216.84 0.04

2037 0.215 0.177 0.218 0.168 192.61 249.75 221.18 0.04

2038 0.226 0.185 0.230 0.176 196.46 254.74 225.60 0.04

2039 0.239 0.194 0.242 0.185 200.39 259.84 230.11 0.04

2040 0.252 0.203 0.255 0.194 204.40 265.03 234.72 0.04

2041 0.266 0.213 0.269 0.203 208.49 270.33 239.41 0.04

2042 0.280 0.223 0.284 0.213 212.66 275.74 244.20 0.04

2043 0.296 0.234 0.300 0.224 216.91 281.26 249.08 0.05

Levelized Costs10 years (2014-2023) 0.070 0.062 0.068 0.056 71.17 70.03 70.60 0.014 0.017 0.013 0.015 23.36 0.012 0.014 0.012 0.013 23.13 0.0415 years (2014-2028) 0.077 0.068 0.076 0.062 86.28 96.55 91.42 0.009 0.012 0.009 0.010 16.45 0.008 0.010 0.008 0.009 17.05 0.0330 years (2014-2043) 0.100 0.086 0.101 0.080 101.33 122.95 112.14 0.005 0.006 0.005 0.006 9.06 0.004 0.005 0.004 0.005 9.39 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)

2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-SWe_Nominal

TTable Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: CT-SWe

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.057 0.048 0.046 0.040 35.40 17.2% 0.00632014 0.057 0.050 0.046 0.040 38.56 17.5% 0.0056 0.007 0.004 0.006 0.0032015 0.057 0.050 0.048 0.040 0.001 0.000 41.20 17.9% 0.0053 0.029 0.016 0.024 0.013 0.008 0.004 0.002016 0.056 0.050 0.050 0.041 0.001 0.001 37.82 18.3% 0.0049 0.031 0.017 0.027 0.014 0.028 0.015 0.022017 0.055 0.048 0.057 0.045 0.001 0.001 42.86 18.6% 0.0042 0.031 0.017 0.032 0.015 0.029 0.015 0.032018 0.060 0.052 0.063 0.048 0.001 0.001 49.29 19.0% 0.0034 0.031 0.017 0.032 0.015 0.031 0.017 0.032019 0.066 0.057 0.069 0.052 0.001 0.001 55.54 19.4% 0.0039 0.027 0.014 0.029 0.013 0.028 0.015 0.022020 0.076 0.066 0.075 0.061 0.001 0.001 104.62 19.8% 0.0046 0.025 0.013 0.024 0.012 0.025 0.013 0.022021 0.077 0.069 0.080 0.064 0.000 0.000 129.91 20.2% 0.0047 0.018 0.010 0.019 0.009 0.019 0.010 0.012022 0.083 0.074 0.081 0.068 0.000 0.000 132.51 20.6% 0.0046 0.013 0.007 0.012 0.006 0.013 0.007 0.012023 0.087 0.077 0.089 0.072 135.16 21.0% 0.0045 0.007 0.004 0.007 0.003 0.007 0.000 0.002024 0.092 0.081 0.093 0.076 137.87 21.4% 0.0056 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.097 0.086 0.100 0.080 140.62 21.8% 0.0076 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.102 0.090 0.106 0.084 143.44 22.3% 0.0078 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.108 0.094 0.112 0.088 146.30 22.7% 0.0079 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.114 0.099 0.116 0.092 149.23 23.1% 0.0081 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.121 0.104 0.123 0.097 152.21 23.6% 0.0082 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.127 0.109 0.130 0.102 155.26 24.1% 0.0084 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.135 0.114 0.137 0.107 158.36 24.6% 0.0085 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.142 0.120 0.145 0.113 161.53 25.1% 0.0087 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.150 0.126 0.153 0.118 164.76 25.6% 0.0089 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.159 0.132 0.161 0.124 168.06 26.1% 0.0090 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.168 0.138 0.170 0.131 171.42 26.6% 0.0092 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.177 0.145 0.180 0.137 174.85 27.1% 0.0094 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.187 0.153 0.190 0.144 178.34 27.7% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.198 0.160 0.201 0.152 181.91 28.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.209 0.168 0.212 0.159 185.55 28.8% 0.0099 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.221 0.176 0.224 0.168 189.26 29.4% 0.0101 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.233 0.185 0.237 0.176 193.04 29.9% 0.0103 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.247 0.194 0.250 0.185 196.91 30.5% 0.0105 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.261 0.204 0.264 0.195 200.84 31.2% 0.0107 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.060 0.053 0.058 0.047 0.001 0.001 65.89 0.004 0.020 0.011 0.019 0.010 0.017 0.009 0.0115 years (2014-2028) 0.066 0.058 0.065 0.052 0.000 0.000 79.89 0.005 0.014 0.007 0.013 0.007 0.012 0.006 0.01

30 years (2014-2043) 0.087 0.073 0.087 0.068 0.000 0.000 93.82 0.005 0.008 0.004 0.007 0.004 0.007 0.003 0.00

NOTES: General All Avoided Costs are in Nominal Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-SWi_Nominal

Table One: Avoided Cost of Electricity (Nominal $) Results : CT-SWi 

State CT Southwest Connecticut, including Norwalk/Sta

User-defined InputsWholesale Risk Premium (WRP) 9.00%Distribution Losses 8.00%Nominal Discount Rate 3.39%Pcnt of Capacity Bid into FCM (%Bid) 50.00%

Capacity (See note 2)

Capacity (See note 2)

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Annual Value Wint

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW-yr $/kW-yr $/kWh $/kWh $/kWh $/kWh $/kw-yr $/kWh $/kWh $/kWh $/kWh $/kW-yr $/kW

Period: a b c d e=y*1.08

f=y*(1+ac)*(

1+PTF 

Loss)*(1+W

RP)*(1+Dist 

Loss)

g=(e*%Bid)

+(f*(1+%Bi

d)) h i j k l m n o p q r 

2013 0.069 0.059 0.057 0.050 0.04

2014 0.068 0.060 0.056 0.049 41.64 0.00 20.82 0.005 0.006 0.004 0.005 0.00 0.042015 0.068 0.061 0.058 0.050 44.50 0.00 22.25 0.020 0.025 0.017 0.020 0.00 0.005 0.006 0.004 0.005 0.00 0.042016 0.067 0.060 0.060 0.050 40.85 0.00 20.42 0.021 0.026 0.019 0.022 0.00 0.020 0.024 0.017 0.020 0.00 0.042017 0.065 0.057 0.067 0.053 46.29 0.00 23.15 0.021 0.026 0.022 0.024 65.79 0.020 0.024 0.020 0.022 0.00 0.042018 0.069 0.061 0.072 0.057 53.23 69.02 61.12 0.021 0.026 0.022 0.024 56.85 0.021 0.026 0.023 0.025 68.23 0.042019 0.076 0.066 0.080 0.061 59.99 77.78 68.88 0.019 0.023 0.019 0.021 46.95 0.019 0.023 0.020 0.021 58.68 0.042020 0.088 0.077 0.087 0.072 112.99 146.51 129.75 0.017 0.020 0.016 0.019 36.84 0.017 0.021 0.017 0.019 49.12 0.042021 0.089 0.080 0.092 0.075 140.31 181.93 161.12 0.012 0.016 0.013 0.014 25.36 0.013 0.016 0.013 0.015 38.04 0.042022 0.095 0.085 0.094 0.079 143.11 185.57 164.34 0.009 0.011 0.008 0.010 19.66 0.009 0.011 0.009 0.010 26.21 0.042023 0.100 0.089 0.102 0.083 145.98 189.28 167.63 0.004 0.005 0.004 0.005 13.54 0.004 0.000 0.005 0.000 20.31 0.042024 0.106 0.095 0.108 0.088 148.89 193.06 170.98 0.000 0.000 0.000 0.000 6.99 0.000 0.000 0.000 0.000 13.98 0.042025 0.114 0.102 0.117 0.095 151.87 196.92 174.40 0.000 0.000 0.000 0.000 7.27 0.04

2026 0.119 0.107 0.124 0.100 154.91 200.86 177.89 0.04

2027 0.126 0.111 0.131 0.105 158.01 204.88 181.44 0.04

2028 0.133 0.116 0.135 0.109 161.17 208.98 185.07 0.04

2029 0.141 0.122 0.143 0.115 164.39 213.16 188.77 0.03

2030 0.148 0.128 0.150 0.120 167.68 217.42 192.55 0.03

2031 0.156 0.134 0.159 0.126 171.03 221.77 196.40 0.03

2032 0.165 0.140 0.167 0.132 174.45 226.20 200.33 0.04

2033 0.174 0.147 0.176 0.139 177.94 230.73 204.34 0.04

2034 0.183 0.154 0.186 0.145 181.50 235.34 208.42 0.04

2035 0.193 0.161 0.196 0.152 185.13 240.05 212.59 0.04

2036 0.204 0.169 0.207 0.160 188.83 244.85 216.84 0.04

2037 0.215 0.177 0.218 0.168 192.61 249.75 221.18 0.04

2038 0.226 0.185 0.230 0.176 196.46 254.74 225.60 0.04

2039 0.239 0.194 0.242 0.185 200.39 259.84 230.11 0.04

2040 0.252 0.203 0.256 0.194 204.40 265.03 234.72 0.04

2041 0.266 0.213 0.269 0.203 208.49 270.33 239.41 0.04

2042 0.280 0.223 0.284 0.213 212.66 275.74 244.20 0.04

2043 0.296 0.234 0.300 0.224 216.91 281.26 249.08 0.05

Levelized Costs10 years (2014-2023) 0.070 0.062 0.068 0.056 71.17 70.03 70.60 0.014 0.017 0.013 0.015 23.36 0.012 0.014 0.012 0.013 23.13 0.0415 years (2014-2028) 0.077 0.068 0.076 0.062 86.28 96.55 91.42 0.009 0.012 0.009 0.010 16.45 0.008 0.010 0.008 0.009 17.05 0.0330 years (2014-2043) 0.100 0.086 0.101 0.080 101.33 122.95 112.14 0.005 0.006 0.005 0.006 9.06 0.004 0.005 0.004 0.005 9.39 0.03

NOTES: General All Avoided Costs are in Year 2013 Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7 AM - 11 PM; Off-Peak Hours are all other hours

1 Avoided cost of electric energy = (wholesale energy avoided cost + REC cost to load) * risk premium, e.g. A = (V + AD) * (1+Wholesale Risk Premium)

2 Absolute value of avoided capacity costs and capacity DRIPE each year is function of quantity of kW reduction in year, PA strategy re bidding that reduction into applicable FCAs, and unit values in columns e, f, l and q. 

3 Proceeds from selling into the FCM also include the ISO-NE loss factor of 8%

Energy

 Avoided Unit Cost of Electric Energy1

Avoided Unit Cost of Electric 

Capacity2

DRIPE: 2014 vintage measures DRIPE: 2015 vintage measures

kW sold 

into FCA 

(PA to 

determine 

quantity)3

kW 

purchased 

from FCA 

(PA to 

determine 

quantity)

Weighted 

Average Avoided 

Cost Based 

on Percent 

Capacity 

Bid

Energy

Intrastate Intrastate

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Appendix B: CT-SWi_Nominal

TTable Two: Inputs to Avoided Cost Calculations Page Two of Two

Zone: CT-SWi

Winter 

Peak

Winter OffPeak Summer Peak Summer Off-Peak 2014 Installation Year 2015 Installation Year FCA Price Reserve Margin REC Costs Winter Peak Winter Off- Peak Summer Peak Summer Off-Peak Winter Peak Winter Off- Peak Summ

Pea

Units: $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW-yr % $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kWh $/kW

Period: v w x y z aa ab ac ad ae af ag ah ai aj ak 

2013 0.057 0.048 0.046 0.040 35.40 17.2% 0.00632014 0.057 0.050 0.046 0.040 38.56 17.5% 0.0056 0.007 0.004 0.006 0.0032015 0.057 0.050 0.048 0.040 0.001 0.000 41.20 17.9% 0.0053 0.029 0.016 0.024 0.013 0.008 0.004 0.002016 0.056 0.050 0.050 0.041 0.001 0.001 37.82 18.3% 0.0049 0.031 0.017 0.027 0.014 0.028 0.015 0.022017 0.055 0.048 0.057 0.045 0.001 0.001 42.86 18.6% 0.0042 0.031 0.017 0.032 0.015 0.029 0.015 0.032018 0.060 0.052 0.063 0.049 0.001 0.001 49.29 19.0% 0.0034 0.031 0.017 0.032 0.015 0.031 0.017 0.032019 0.066 0.057 0.069 0.052 0.001 0.001 55.54 19.4% 0.0039 0.027 0.014 0.029 0.013 0.028 0.015 0.022020 0.076 0.066 0.075 0.062 0.001 0.001 104.62 19.8% 0.0046 0.025 0.013 0.024 0.012 0.025 0.013 0.022021 0.077 0.069 0.080 0.064 0.000 0.000 129.91 20.2% 0.0047 0.018 0.010 0.019 0.009 0.019 0.010 0.012022 0.083 0.074 0.081 0.068 0.000 0.000 132.51 20.6% 0.0046 0.013 0.007 0.012 0.006 0.013 0.007 0.012023 0.087 0.077 0.089 0.072 135.16 21.0% 0.0045 0.007 0.004 0.007 0.003 0.007 0.000 0.002024 0.092 0.081 0.093 0.076 137.87 21.4% 0.0056 0.000 0.000 0.000 0.000 0.000 0.000 0.002025 0.097 0.086 0.100 0.080 140.62 21.8% 0.0076 0.000 0.000 0.000 0.000 0.000 0.000 0.002026 0.102 0.090 0.106 0.084 143.44 22.3% 0.0078 0.000 0.000 0.000 0.000 0.000 0.000 0.002027 0.108 0.094 0.112 0.088 146.30 22.7% 0.0079 0.000 0.000 0.000 0.000 0.000 0.000 0.002028 0.114 0.099 0.116 0.092 149.23 23.1% 0.0081 0.000 0.000 0.000 0.000 0.000 0.000 0.002029 0.121 0.104 0.123 0.097 152.21 23.6% 0.0082 0.000 0.000 0.000 0.000 0.000 0.000 0.002030 0.128 0.109 0.130 0.102 155.26 24.1% 0.0084 0.000 0.000 0.000 0.000 0.000 0.000 0.002031 0.135 0.114 0.137 0.107 158.36 24.6% 0.0085 0.000 0.000 0.000 0.000 0.000 0.000 0.002032 0.142 0.120 0.145 0.113 161.53 25.1% 0.0087 0.000 0.000 0.000 0.000 0.000 0.000 0.002033 0.150 0.126 0.153 0.118 164.76 25.6% 0.0089 0.000 0.000 0.000 0.000 0.000 0.000 0.002034 0.159 0.132 0.161 0.124 168.06 26.1% 0.0090 0.000 0.000 0.000 0.000 0.000 0.000 0.002035 0.168 0.139 0.171 0.131 171.42 26.6% 0.0092 0.000 0.000 0.000 0.000 0.000 0.000 0.002036 0.177 0.145 0.180 0.137 174.85 27.1% 0.0094 0.000 0.000 0.000 0.000 0.000 0.000 0.002037 0.187 0.153 0.190 0.144 178.34 27.7% 0.0096 0.000 0.000 0.000 0.000 0.000 0.000 0.002038 0.198 0.160 0.201 0.152 181.91 28.2% 0.0097 0.000 0.000 0.000 0.000 0.000 0.000 0.002039 0.209 0.168 0.212 0.160 185.55 28.8% 0.0099 0.000 0.000 0.000 0.000 0.000 0.000 0.002040 0.221 0.176 0.224 0.168 189.26 29.4% 0.0101 0.000 0.000 0.000 0.000 0.000 0.000 0.002041 0.233 0.185 0.237 0.176 193.04 29.9% 0.0103 0.000 0.000 0.000 0.000 0.000 0.000 0.002042 0.247 0.194 0.250 0.185 196.91 30.5% 0.0105 0.000 0.000 0.000 0.000 0.000 0.000 0.002043 0.261 0.204 0.264 0.195 200.84 31.2% 0.0107 0.000 0.000 0.000 0.000 0.000 0.000 0.00

Levelized Costs10 years (2014-2023) 0.060 0.053 0.058 0.047 0.001 0.001 65.89 0.004 0.020 0.011 0.019 0.010 0.017 0.009 0.0115 years (2014-2028) 0.066 0.058 0.065 0.052 0.000 0.000 79.89 0.005 0.014 0.007 0.013 0.007 0.012 0.006 0.01

30 years (2014-2043) 0.087 0.073 0.087 0.068 0.000 0.000 93.82 0.005 0.008 0.004 0.007 0.004 0.007 0.003 0.00

NOTES: General All Avoided Costs are in Nominal Dollars

ISO NE periods: Summer is June through September, Winter is all other months. Peak hours are: Monday through Friday 7am - 11pm; Off-Peak Hours are all other hours

Capacity Energy

Avoided 

REC Costs 

to Load

DRIPE: 2014 vintage measures DRIPE: 2015 vintage m

Rest-of-Pool Rest-of-Pool

Energy

Wholesale Avoided Costs of Electricity 

Energy Electric Cross-DRIPE

Synapse Energy Economics, Inc. - AESC 2013 Appendix B 
Synapse Energy Economics. Inc. — AESC 2013 Appendix C Page C‐1  

Appendix C: Avoided Natural Gas Cost Results

Table of Exhibits

Exhibit C‐1. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Southern New

England (SNE) Assuming Some Avoidable Retail Margin (2013$/MMBtu) ..................................C‐2

Exhibit C‐2. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Southern New

England (SNE) Assuming No Avoidable Retail Margin (2013$/MMBtu).......................................C‐3

Exhibit C‐3. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Northern New

England (NNE) Assuming Some Avoidable Retail Margin (2013$/MMBtu)..................................C‐4

Exhibit C‐4. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Northern New

England (NNE) Assuming No Avoidable Retail Margin (2013$/MMBtu)......................................C‐5

Exhibit C‐5. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Vermont (VT)

Assuming Some Avoidable Retail Margin (2013$/MMBtu)..........................................................C‐6

Exhibit C‐6. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Vermont (VT)

Assuming No Avoidable Retail Margin (2013$/MMBtu) ..............................................................C‐7

   
Synapse Energy Economics. Inc. — AESC 2013 Appendix C Page C‐2  

Exhibit C‐1. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Southern New England

(SNE) Assuming Some Avoidable Retail Margin (2013$/MMBtu)

Year

Non

Heating

Hot

Water Heating All

Non

Heating Heating All

2013 5.32 5.78 6.90 6.73 5.51 6.35 6.05 6.41

2014 5.53 5.98 7.10 6.93 5.72 6.56 6.26 6.61

2015 5.54 5.98 7.09 6.93 5.72 6.56 6.26 6.61

2016 5.61 6.08 7.21 7.03 5.81 6.66 6.36 6.71

2017 5.71 6.34 7.51 7.30 5.96 6.91 6.57 6.95

2018 6.02 6.62 7.79 7.58 6.26 7.20 6.86 7.24

2019 6.46 6.95 8.08 7.90 6.66 7.53 7.22 7.58

2020 6.73 7.20 8.33 8.15 6.93 7.78 7.48 7.83

2021 6.87 7.36 8.48 8.30 7.07 7.93 7.62 7.98

2022 7.07 7.55 8.67 8.50 7.27 8.12 7.82 8.18

2023 7.20 7.67 8.80 8.62 7.40 8.25 7.95 8.30

2024 7.33 7.82 8.95 8.77 7.53 8.39 8.08 8.44

2025 7.52 7.99 9.12 8.94 7.72 8.57 8.27 8.62

2026 7.64 8.13 9.25 9.07 7.84 8.70 8.39 8.75

2027 7.76 8.25 9.37 9.19 7.96 8.82 8.51 8.87

2028 7.86 8.34 9.46 9.29 8.06 8.91 8.61 8.97

2029 8.01 8.49 9.61 9.44 8.22 9.06 8.76 9.12

2030 8.17 8.65 9.76 9.60 8.38 9.21 8.92 9.28

2031 8.33 8.81 9.92 9.76 8.54 9.37 9.08 9.44

2032 8.50 8.97 10.07 9.92 8.70 9.53 9.24 9.60

2033 8.67 9.13 10.23 10.08 8.87 9.69 9.40 9.76

2034 8.84 9.30 10.40 10.24 9.04 9.85 9.57 9.92

2035 9.01 9.47 10.56 10.41 9.22 10.02 9.74 10.09

2036 9.19 9.65 10.73 10.58 9.39 10.19 9.91 10.26

2037 9.38 9.82 10.90 10.75 9.57 10.36 10.09 10.44

2038 9.56 10.01 11.07 10.93 9.76 10.54 10.27 10.62

2039 9.75 10.19 11.25 11.11 9.95 10.72 10.45 10.80

2040 9.94 10.38 11.43 11.29 10.14 10.90 10.64 10.98

2041 10.14 10.57 11.61 11.47 10.33 11.09 10.82 11.17

2042 10.34 10.76 11.79 11.66 10.53 11.27 11.02 11.35

2043 10.55 10.96 11.98 11.85 10.73 11.46 11.21 11.55

(a) 6.25 6.75 7.88 7.70 6.46 7.32 7.02 7.38

6.67 7.17 8.30 8.12 6.88 7.74 7.44 7.80

(b) 7.80 8.28 9.38 9.22 8.01 8.83 8.54 8.90

(a) Real (constant $) riskless annual rate of return: 1.36%

(b) Values from 2029‐2043 extrapolated from Compound Annual Growth Rate (2019‐2028)

Levelized

(2014‐2024)

RESIDENTIAL COMMERCIAL & INDUSTRIAL ALL

RETAIL

END USES

Levelized

(2014‐2023)

Levelized

(2014‐2028)
Synapse Energy Economics. Inc. — AESC 2013 Appendix C Page C‐3  

Exhibit C‐2. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Southern New England

(SNE) Assuming No Avoidable Retail Margin (2013$/MMBtu)

Year

Non

Heating

Hot

Water Heating All

Non

Heating Heating All

2013 4.72 5.18 5.33 5.21 4.89 5.18 5.05 5.14

2014 4.93 5.38 5.53 5.41 5.10 5.39 5.26 5.34

2015 4.94 5.38 5.52 5.41 5.10 5.39 5.26 5.34

2016 5.01 5.48 5.64 5.51 5.19 5.49 5.36 5.44

2017 5.11 5.74 5.94 5.78 5.34 5.74 5.57 5.68

2018 5.42 6.02 6.22 6.06 5.64 6.03 5.86 5.97

2019 5.86 6.35 6.51 6.38 6.04 6.36 6.22 6.31

2020 6.13 6.60 6.76 6.63 6.31 6.61 6.48 6.56

2021 6.27 6.76 6.91 6.78 6.45 6.76 6.62 6.71

2022 6.47 6.95 7.10 6.98 6.65 6.95 6.82 6.91

2023 6.60 7.07 7.23 7.10 6.78 7.08 6.95 7.03

2024 6.73 7.22 7.38 7.25 6.91 7.22 7.08 7.17

2025 6.92 7.39 7.55 7.42 7.10 7.40 7.27 7.35

2026 7.04 7.53 7.68 7.55 7.22 7.53 7.39 7.48

2027 7.16 7.65 7.80 7.67 7.34 7.65 7.51 7.60

2028 7.26 7.74 7.89 7.77 7.44 7.74 7.61 7.70

2028 7.42 7.89 8.04 7.92 7.60 7.89 7.77 7.85

2028 7.58 8.05 8.20 8.08 7.76 8.05 7.92 8.01

2028 7.74 8.21 8.36 8.24 7.92 8.21 8.08 8.17

2028 7.91 8.38 8.52 8.41 8.09 8.37 8.25 8.34

2028 8.08 8.55 8.69 8.57 8.26 8.54 8.42 8.51

2028 8.26 8.72 8.85 8.75 8.43 8.71 8.59 8.68

2028 8.43 8.89 9.03 8.92 8.61 8.88 8.76 8.85

2028 8.62 9.07 9.20 9.10 8.79 9.06 8.94 9.03

2028 8.80 9.25 9.38 9.28 8.98 9.24 9.12 9.21

2028 8.99 9.43 9.56 9.46 9.16 9.42 9.31 9.40

2028 9.19 9.62 9.75 9.65 9.36 9.61 9.50 9.59

2028 9.39 9.82 9.94 9.84 9.55 9.80 9.69 9.78

2028 9.59 10.01 10.13 10.04 9.76 9.99 9.89 9.97

2028 9.80 10.21 10.33 10.24 9.96 10.19 10.09 10.18

2028 10.01 10.42 10.53 10.44 10.17 10.39 10.30 10.38

(a) 5.65 6.15 6.31 6.18 5.84 6.16 6.02 6.11

6.08 6.57 6.73 6.60 6.26 6.58 6.44 6.53

(b) 7.21 7.69 7.84 7.72 7.39 7.69 7.56 7.65

(a) Real (constant $) riskless annual rate of return: 1.36%

(b) Values from 2029‐2043 extrapolated from Compound Annual Growth Rate (2019‐2028)

RESIDENTIAL COMMERCIAL & INDUSTRIAL ALL

RETAIL

END USES

Levelized

(2014‐2023)

Levelized

(2014‐2028)

Levelized

(2014‐2024)
Synapse Energy Economics. Inc. — AESC 2013 Appendix C Page C‐4  

Exhibit C‐3. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Northern New England

(NNE) Assuming Some Avoidable Retail Margin (2013$/MMBtu)

Year

Non

Heating

Hot

Water Heating All

Non

Heating Heating All

2013 5.21 5.81 6.83 6.63 5.38 6.03 5.78 5.92

2014 5.07 5.79 6.84 6.61 5.29 6.24 5.99 6.12

2015 5.14 6.49 7.75 7.35 5.59 6.24 5.99 6.12

2016 5.50 6.12 7.15 6.94 5.68 6.34 6.09 6.22

2017 5.94 6.38 7.34 7.19 6.05 6.59 6.30 6.46

2018 6.07 6.81 7.87 7.64 6.30 6.88 6.59 6.75

2019 6.34 8.19 9.61 9.08 6.98 7.21 6.95 7.09

2020 6.60 8.44 9.86 9.33 7.23 7.46 7.21 7.34

2021 6.73 8.61 10.04 9.50 7.38 7.61 7.35 7.49

2022 6.94 8.83 10.26 9.72 7.59 7.80 7.55 7.69

2023 7.06 8.96 10.39 9.85 7.71 7.93 7.68 7.81

2024 7.19 9.12 10.57 10.02 7.86 8.07 7.81 7.95

2025 7.37 9.31 10.75 10.20 8.04 8.25 8.00 8.13

2026 7.49 9.44 10.90 10.34 8.16 8.38 8.12 8.26

2027 7.62 9.58 11.03 10.48 8.30 8.50 8.24 8.38

2028 7.71 9.67 11.13 10.57 8.39 8.59 8.34 8.48

2029 7.86 9.83 11.29 10.73 8.54 8.75 8.50 8.63

2030 8.01 9.99 11.46 10.90 8.70 8.90 8.65 8.79

2031 8.17 10.16 11.63 11.06 8.86 9.06 8.81 8.95

2032 8.33 10.33 11.80 11.23 9.03 9.22 8.97 9.11

2033 8.50 10.50 11.98 11.40 9.20 9.38 9.14 9.27

2034 8.67 10.68 12.15 11.58 9.37 9.55 9.31 9.44

2035 8.84 10.86 12.33 11.76 9.54 9.71 9.48 9.61

2036 9.01 11.04 12.52 11.94 9.72 9.89 9.65 9.79

2037 9.19 11.23 12.70 12.12 9.90 10.06 9.83 9.96

2038 9.37 11.41 12.89 12.30 10.08 10.24 10.01 10.14

2039 9.56 11.60 13.08 12.49 10.27 10.42 10.19 10.33

2040 9.75 11.80 13.27 12.68 10.46 10.60 10.38 10.51

2041 9.94 12.00 13.47 12.88 10.65 10.79 10.57 10.70

2042 10.14 12.20 13.67 13.08 10.85 10.98 10.77 10.90

2043 10.34 12.40 13.87 13.28 11.05 11.17 10.97 11.09

(a) 6.11 7.41 8.66 8.28 6.54 7.01 6.75 6.89

6.53 8.04 9.35 8.91 7.04 7.43 7.17 7.31

(b) 7.64 9.38 10.76 10.26 8.24 8.53 8.28 8.41

(a) Real (constant $) riskless annual rate of return: 1.36%

(b) Values from 2029‐2043 extrapolated from Compound Annual Growth Rate (2019‐2028)

RESIDENTIAL COMMERCIAL & INDUSTRIAL ALL

RETAIL

END USES

Levelized

(2014‐2023)

Levelized

(2014‐2028)

Levelized

(2014‐2024)
Synapse Energy Economics. Inc. — AESC 2013 Appendix C Page C‐5  

Exhibit C‐4. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Northern New England

(NNE) Assuming No Avoidable Retail Margin (2013$/MMBtu)

Year

Non

Heating

Hot

Water Heating All

Non

Heating Heating All

2013 4.70 5.30 5.50 5.34 4.92 5.31 5.14 5.25

2014 4.56 5.28 5.51 5.32 4.83 5.28 5.08 5.21

2015 4.63 5.98 6.42 6.06 5.13 5.99 5.61 5.85

2016 4.99 5.61 5.82 5.65 5.22 5.62 5.45 5.56

2017 5.43 5.87 6.01 5.90 5.59 5.87 5.75 5.83

2018 5.56 6.30 6.54 6.35 5.84 6.31 6.10 6.23

2019 5.83 7.68 8.28 7.79 6.52 7.70 7.18 7.51

2020 6.09 7.93 8.53 8.04 6.77 7.95 7.43 7.76

2021 6.22 8.10 8.71 8.21 6.92 8.12 7.60 7.93

2022 6.43 8.32 8.93 8.43 7.13 8.33 7.81 8.14

2023 6.55 8.45 9.06 8.56 7.25 8.46 7.93 8.27

2024 6.68 8.61 9.24 8.73 7.40 8.63 8.09 8.43

2025 6.86 8.80 9.42 8.91 7.58 8.81 8.27 8.61

2026 6.98 8.93 9.57 9.05 7.70 8.95 8.40 8.75

2027 7.11 9.07 9.70 9.19 7.84 9.08 8.54 8.89

2028 7.20 9.16 9.80 9.28 7.93 9.18 8.63 8.98

2028 7.35 9.32 9.97 9.44 8.09 9.34 8.79 9.14

2028 7.51 9.49 10.14 9.61 8.25 9.51 8.95 9.31

2028 7.67 9.66 10.31 9.78 8.41 9.68 9.12 9.47

2028 7.83 9.83 10.48 9.95 8.58 9.85 9.29 9.65

2028 8.00 10.00 10.66 10.13 8.75 10.02 9.46 9.82

2028 8.17 10.18 10.84 10.31 8.92 10.20 9.64 10.00

2028 8.35 10.36 11.03 10.49 9.09 10.38 9.82 10.18

2028 8.52 10.55 11.21 10.67 9.27 10.57 10.00 10.36

2028 8.71 10.73 11.41 10.86 9.46 10.75 10.18 10.55

2028 8.89 10.93 11.60 11.05 9.64 10.94 10.37 10.74

2028 9.08 11.12 11.80 11.25 9.84 11.14 10.57 10.93

2028 9.28 11.32 12.00 11.45 10.03 11.34 10.76 11.13

2028 9.47 11.52 12.20 11.65 10.23 11.54 10.96 11.33

2028 9.68 11.72 12.41 11.86 10.43 11.74 11.16 11.53

2028 9.88 11.93 12.62 12.07 10.64 11.95 11.37 11.74

(a) 5.60 6.91 7.33 6.99 6.09 6.92 6.55 6.79

6.03 7.53 8.02 7.62 6.58 7.54 7.12 7.39

(b) 7.14 8.88 9.45 8.99 7.79 8.89 8.41 8.72

(a) Real (constant $) riskless annual rate of return: 1.36%

(b) Values from 2029‐2043 extrapolated from Compound Annual Growth Rate (2019‐2028)

RESIDENTIAL COMMERCIAL & INDUSTRIAL ALL

RETAIL

END USES

Levelized

(2014‐2023)

Levelized

(2014‐2028)

Levelized

(2014‐2024)
Synapse Energy Economics. Inc. — AESC 2013 Appendix C Page C‐6  

Exhibit C‐5. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Vermont (VT) Assuming

Some Avoidable Retail Margin (2013$/MMBtu)

Year

Non

Heating

Hot

Water Heating All

Non

Heating Heating All

2013 5.59 6.10 7.28 7.10 5.31 5.76 5.58 6.19

2014 5.68 6.19 7.36 7.19 5.40 5.84 5.66 6.27

2015 5.75 6.34 7.55 7.35 5.50 6.00 5.79 6.42

2016 5.81 6.41 7.62 7.42 5.56 6.07 5.86 6.49

2017 6.11 6.71 7.92 7.72 5.86 6.37 6.16 6.79

2018 5.85 6.39 7.58 7.40 5.58 6.05 5.86 6.48

2019 6.65 7.27 8.48 8.28 6.41 6.92 6.71 7.34

2020 6.93 7.53 8.73 8.53 6.68 7.18 6.97 7.60

2021 7.11 7.73 8.95 8.74 6.87 7.39 7.18 7.81

2022 7.37 7.98 9.19 8.99 7.13 7.63 7.42 8.05

2023 7.54 8.14 9.34 9.14 7.29 7.79 7.58 8.21

2024 7.68 8.30 9.51 9.31 7.44 7.95 7.74 8.37

2025 7.86 8.48 9.68 9.48 7.62 8.13 7.92 8.55

2026 8.03 8.64 9.85 9.65 7.79 8.30 8.09 8.72

2027 8.18 8.79 10.00 9.80 7.94 8.44 8.23 8.86

2028 8.28 8.89 10.09 9.89 8.03 8.54 8.33 8.96

2029 8.47 9.07 10.27 10.06 8.21 8.72 8.52 9.14

2030 8.65 9.26 10.45 10.25 8.40 8.91 8.70 9.33

2031 8.85 9.45 10.63 10.43 8.59 9.10 8.89 9.52

2032 9.04 9.64 10.82 10.62 8.78 9.29 9.09 9.71

2033 9.24 9.83 11.01 10.81 8.98 9.49 9.28 9.90

2034 9.45 10.03 11.20 11.00 9.19 9.69 9.49 10.10

2035 9.66 10.24 11.40 11.20 9.40 9.90 9.69 10.31

2036 9.87 10.44 11.60 11.40 9.61 10.11 9.91 10.51

2037 10.09 10.66 11.80 11.60 9.83 10.32 10.12 10.73

2038 10.31 10.87 12.01 11.81 10.06 10.54 10.34 10.94

2039 10.54 11.09 12.22 12.02 10.28 10.76 10.57 11.16

2040 10.77 11.32 12.43 12.24 10.52 10.99 10.80 11.39

2041 11.01 11.55 12.65 12.46 10.76 11.23 11.04 11.62

2042 11.26 11.78 12.87 12.68 11.00 11.47 11.28 11.85

2043 11.51 12.02 13.10 12.91 11.26 11.71 11.52 12.09

(a) 6.46 7.05 8.25 8.05 6.20 6.70 6.50 7.12

6.94 7.53 8.74 8.54 6.68 7.19 6.98 7.61

(b) 8.25 8.84 10.02 9.81 8.00 8.49 8.29 8.91

(a) Real (constant $) riskless annual rate of return: 1.36%

(b) Values from 2029‐2043 extrapolated from Compound Annual Growth Rate (2019‐2028)

RESIDENTIAL COMMERCIAL & INDUSTRIAL ALL

RETAIL

END USES

Levelized

(2014‐2023)

Levelized

(2014‐2028)

Levelized

(2014‐2024)
Synapse Energy Economics. Inc. — AESC 2013 Appendix C Page C‐7  

Exhibit C‐6. Avoided Cost of Natural Gas Delivered to Retail Customers by End Use for Vermont (VT) Assuming

No Avoidable Retail Margin (2013$/MMBtu)

Year

Non

Heating

Hot

Water Heating All

Non

Heating Heating All

2013 4.97 5.48 5.65 5.51 5.16 5.49 5.35 5.44

2014 5.06 5.57 5.73 5.60 5.25 5.57 5.43 5.52

2015 5.13 5.72 5.92 5.76 5.35 5.73 5.56 5.67

2016 5.19 5.79 5.99 5.83 5.41 5.80 5.63 5.74

2017 5.49 6.09 6.29 6.13 5.71 6.10 5.93 6.04

2018 5.23 5.77 5.95 5.81 5.43 5.78 5.63 5.73

2019 6.03 6.65 6.85 6.69 6.26 6.65 6.48 6.59

2020 6.31 6.91 7.10 6.94 6.53 6.91 6.74 6.85

2021 6.49 7.11 7.32 7.15 6.72 7.12 6.95 7.06

2022 6.75 7.36 7.56 7.40 6.98 7.36 7.19 7.30

2023 6.92 7.52 7.71 7.55 7.14 7.52 7.35 7.46

2024 7.06 7.68 7.88 7.72 7.29 7.68 7.51 7.62

2025 7.24 7.86 8.05 7.89 7.47 7.86 7.69 7.80

2026 7.41 8.02 8.22 8.06 7.64 8.03 7.86 7.97

2027 7.56 8.17 8.37 8.21 7.79 8.17 8.00 8.11

2028 7.66 8.27 8.46 8.30 7.88 8.27 8.10 8.21

2028 7.85 8.45 8.64 8.48 8.06 8.45 8.28 8.39

2028 8.04 8.64 8.82 8.67 8.25 8.64 8.47 8.58

2028 8.23 8.83 9.01 8.85 8.44 8.83 8.66 8.77

2028 8.43 9.02 9.21 9.05 8.64 9.02 8.86 8.96

2028 8.63 9.22 9.40 9.24 8.84 9.22 9.06 9.16

2028 8.84 9.43 9.60 9.45 9.05 9.43 9.26 9.37

2028 9.06 9.63 9.81 9.65 9.26 9.63 9.47 9.58

2028 9.28 9.85 10.02 9.86 9.47 9.85 9.68 9.79

2028 9.50 10.06 10.23 10.08 9.69 10.06 9.90 10.01

2028 9.73 10.28 10.45 10.30 9.92 10.28 10.13 10.23

2028 9.97 10.51 10.67 10.52 10.15 10.51 10.35 10.46

2028 10.21 10.74 10.90 10.75 10.39 10.74 10.59 10.69

2028 10.45 10.98 11.13 10.99 10.63 10.98 10.83 10.93

2028 10.71 11.22 11.37 11.23 10.88 11.22 11.07 11.17

2028 10.97 11.47 11.61 11.47 11.13 11.47 11.32 11.42

(a) 5.83 6.42 6.62 6.46 6.05 6.43 6.26 6.37

6.32 6.91 7.11 6.95 6.54 6.92 6.75 6.86

(b) 7.64 8.23 8.41 8.25 7.85 8.23 8.06 8.17

(a) Real (constant $) riskless annual rate of return: 1.36%

(b) Values from 2029‐2043 extrapolated from Compound Annual Growth Rate (2019‐2028)

RESIDENTIAL COMMERCIAL & INDUSTRIAL ALL

RETAIL

END USES

Levelized

(2014‐2023)

Levelized

(2014‐2028)

Levelized

(2014‐2024)
Synapse Energy Economics. Inc. — AESC 2013 Appendix D Page D‐1  

Appendix D: Avoided Costs of Other Fuels

Table of Exhibits

Exhibit D‐1. AESC 2013 Forecast Weighted Average Avoided Cost of Petroleum Fuels by Sector, and

Other Fuels................................................................................................................................... D‐2

Exhibit D‐2. Crude Oil and Fuel Prices by Sector in New England ‐ AESC 2013 Forecast (2013$)D‐3

Exhibit D‐3. Forecast mix of petroleum related fuels by grade by sector, as a percentage of 2013 forecast

..................................................................................................................................................... D‐4

Exhibit D‐4. Pollutant Emission Values by Year in 2013$/MMBtu............................................... D‐5
Synapse Energy Economics. Inc. — AESC 2013 Appendix D 

Exhibit D‐1. AESC 2013 Forecast Weighted Average Avoided Cost of Petroleum Fuels by Sector, and Other Fuels

Residentia Industrial

Year

Distillate 

Fuel Oil

Distillate 

Fuel Oil

Residual 

Fuel Oil

Weighted 

Average

Distillate 

Fuel Oil

Residual 

Fuel Oil

Weighted 

Average

Cord 

Wood Pellets Kerosene Propane Kerosene

$/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu

2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$

2013 27.02 $26.70 $19.81 $25.56 $27.91 $20.63 $26.18 $9.79 $16.32 $29.68 $27.94 $27.982014 24.90 $23.66 $13.93 $21.87 $25.29 $18.28 $24.67 $9.02 $15.04 $27.36 $25.81 $25.362015 24.66 $23.50 $13.80 $21.78 $22.79 $13.81 $22.17 $8.94 $14.90 $27.10 $24.83 $22.852016 25.76 $24.58 $14.50 $22.78 $23.57 $14.20 $23.20 $9.34 $15.56 $28.30 $25.67 $23.632017 26.45 $25.30 $14.81 $23.41 $24.19 $14.63 $23.85 $9.59 $15.98 $29.06 $26.75 $24.252018 26.81 $25.71 $15.29 $23.82 $24.72 $14.81 $24.44 $9.72 $16.19 $29.46 $27.29 $24.782019 27.24 $26.18 $15.52 $24.22 $25.16 $15.29 $25.03 $9.87 $16.45 $29.92 $27.80 $25.222020 27.62 $26.54 $15.93 $24.57 $25.66 $15.52 $25.44 $10.01 $16.68 $30.35 $28.28 $25.732021 28.05 $27.03 $16.28 $25.03 $25.99 $15.93 $25.69 $10.16 $16.94 $30.81 $28.62 $26.062022 28.47 $27.56 $16.65 $25.51 $26.54 $16.28 $26.17 $10.32 $17.19 $31.27 $29.00 $26.612023 28.91 $27.98 $17.02 $25.90 $27.15 $16.65 $26.70 $10.48 $17.46 $31.76 $29.33 $27.232024 29.37 $28.41 $17.43 $26.31 $27.54 $17.02 $27.15 $10.64 $17.74 $32.26 $29.62 $27.622025 29.86 $28.88 $17.76 $26.74 $27.96 $17.43 $27.67 $10.82 $18.03 $32.80 $29.90 $28.032026 30.30 $29.29 $18.20 $27.15 $28.41 $17.76 $28.20 $10.98 $18.30 $33.29 $30.18 $28.492027 30.75 $29.71 $18.65 $27.56 $28.81 $18.20 $28.59 $11.14 $18.57 $33.78 $30.41 $28.892028 31.22 $30.14 $19.11 $27.99 $29.20 $18.65 $28.95 $11.31 $18.85 $34.29 $30.64 $29.282029 31.69 $30.63 $19.56 $28.44 $29.69 $19.06 $29.42 $11.48 $19.14 $34.82 $30.97 $29.772030 32.18 $31.13 $20.02 $28.91 $30.18 $19.49 $29.90 $11.66 $19.43 $35.35 $31.31 $30.262031 32.67 $31.63 $20.49 $29.37 $30.69 $19.92 $30.39 $11.84 $19.73 $35.89 $31.65 $30.772032 33.17 $32.14 $20.97 $29.85 $31.20 $20.36 $30.89 $12.02 $20.03 $36.44 $31.99 $31.282033 33.67 $32.67 $21.46 $30.33 $31.72 $20.82 $31.39 $12.20 $20.34 $36.99 $32.34 $31.802034 34.19 $33.19 $21.96 $30.83 $32.25 $21.28 $31.90 $12.39 $20.65 $37.56 $32.69 $32.342035 34.71 $33.73 $22.48 $31.33 $32.79 $21.76 $32.42 $12.58 $20.96 $38.13 $33.05 $32.882036 35.24 $34.28 $23.00 $31.83 $33.34 $22.24 $32.95 $12.77 $21.28 $38.71 $33.41 $33.422037 35.78 $34.83 $23.54 $32.35 $33.89 $22.74 $33.49 $12.96 $21.61 $39.31 $33.77 $33.982038 36.32 $35.40 $24.09 $32.87 $34.46 $23.24 $34.04 $13.16 $21.94 $39.91 $34.14 $34.552039 36.88 $35.97 $24.66 $33.41 $35.03 $23.76 $34.59 $13.36 $22.27 $40.52 $34.51 $35.132040 37.44 $36.56 $25.23 $33.95 $35.62 $24.29 $35.16 $13.57 $22.61 $41.13 $34.88 $35.712041 38.01 $37.15 $25.82 $34.50 $36.21 $24.83 $35.73 $13.78 $22.96 $41.76 $35.26 $36.312042 38.59 $37.75 $26.43 $35.06 $36.82 $25.39 $36.31 $13.99 $23.31 $42.40 $35.64 $36.922043 39.18 $38.36 $27.05 $35.63 $37.43 $25.95 $36.91 $14.20 $23.66 $43.05 $36.03 $37.53

Levelized Costs2014-2023 18.49 $17.74 $10.56 $16.42 $17.27 $10.70 $17.01 $6.70 $11.17 $20.31 $18.80 $17.312014-2028 21.62 $20.80 $12.57 $19.26 $20.22 $12.58 $19.95 $7.84 $13.06 $23.75 $21.83 $20.272014-2043 31.18 $30.16 $19.27 $27.97 $29.33 $18.93 $28.97 $11.30 $18.83 $34.25 $30.50 $29.40Notes

Calculation based on fuel oil forecast percentages by sector (Appendix D-3) multiplied by fuel oil forecast price by sector (Appendix D-2)2029-2041 costs extrapolated based on 2019-2028 compound annual growth rate

Commercial Industrial Residential

Fuel Oils Other Fuels
Synapse Energy Economics. Inc. — AESC 2013 Appendix D 

Exhibit D‐2. Crude Oil and Fuel Prices by Sector in New England ‐ AESC 2013 Forecast (2013$)

Year

AEO 2013 

Forecast 

Imported 

Low Sulfur 

Crude

WTI NYMEX 

Futures 

Swaps as of 

March 15 

2013 (e)

AESC 2013 

Forecast 

Imported 

Low-Sulfur 

Crude

AESC 2013 

Forecast 

Imported 

Low-Sulfur 

Crude

Distillate Fuel 

Oil

Residual Fuel 

Oil Steam Coal

 Distillate 

Fuel Oil Kerosene

Cord 

Wood

 Distillate 

Fuel Oil

 Residual 

Fuel Kerose

$/bbl $/bbl $/bbl $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMBtu $/MMB

2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 2013$ 

2013 100.01 93.46 16.11 23.59 12.74 3.98 27.02 29.68 9.79 26.70 19.81 22014 100.21 88.65 88.65 15.29 20.89 10.77 3.75 24.90 27.36 9.02 23.66 13.93 22015 99.08 84.36 87.75 15.13 20.69 10.66 3.99 24.66 27.10 8.94 23.50 13.80 22016 100.21 81.13 93.50 16.12 21.68 11.27 3.77 25.76 28.30 9.34 24.58 14.50 22017 102.36 78.67 99.26 17.11 22.33 11.56 3.80 26.45 29.06 9.59 25.30 14.81 22018 104.55 76.63 101.96 17.58 22.69 12.03 3.76 26.81 29.46 9.72 25.71 15.29 22019 106.78 74.92 104.61 18.04 23.11 12.26 3.70 27.24 29.92 9.87 26.18 15.52 22020 109.07 73.29 107.00 18.45 23.50 12.67 3.71 27.62 30.35 10.01 26.54 15.93 32021 111.40 71.85 109.34 18.85 23.92 13.02 3.71 28.05 30.81 10.16 27.03 16.28 32022 113.79 111.72 19.26 24.34 13.39 3.75 28.47 31.27 10.32 27.56 16.65 32023 116.22 114.16 19.68 24.79 13.77 3.78 28.91 31.76 10.48 27.98 17.02 32024 118.71 116.64 20.11 25.24 14.18 3.81 29.37 32.26 10.64 28.41 17.43 32025 121.25 119.18 20.55 25.73 14.50 3.83 29.86 32.80 10.82 28.88 17.76 32026 123.84 121.78 21.00 26.18 14.95 3.87 30.30 33.29 10.98 29.29 18.20 32027 126.50 124.43 21.45 26.63 15.39 3.90 30.75 33.78 11.14 29.71 18.65 32028 129.20 127.14 21.92 27.09 15.86 3.94 31.22 34.29 11.31 30.14 19.11 3

Levelized Costs2014-2023 17.49 22.74 12.10 3.77 26.84 29.48 9.72 25.75 15.33 2

2014-2028 19.51 23.81 12.99 3.80 27.91 30.66 10.11 26.84 16.23 32014-2043 21.93 27.11 16.11 3.96 31.18 34.25 11.30 30.16 19.27 1Notes

Crude Oil forecasts based on EIA historical and projected values from AEO 2013 Table A12; West Texas Intermediate Electric Generation Forecast based on AEO 2013-ER Table S11; Sector fuel price forecast based on low-sulfur fuel price 

Crude Oil Prices

Fuel Prices for Electric Generation in New 

England Residential Commercial
Synapse Energy Economics. Inc. — AESC 2013 Appendix D Page D‐4  

Exhibit D‐3. Forecast mix of petroleum related fuels by grade by sector, as a percentage of 2013 forecast

Residential Commercial Industrial 

Year Distillate 

Fuel Oil 

$/MMBtu 

Percent 

Distillate 

Fuel Oil 

$/MMBtu 

Percent 

Residual 

Fuel 

$/MMBtu 

Percent 

Distillate 

Fuel Oil 

$/MMBtu 

Percent 

Residual 

Fuel Oil 

$/MMBtu 

 Percent 

2013 100% 84% 16% 66% 38% 

2014 100% 82% 18% 69% 40% 

2015 100% 82% 18% 72% 42% 

2016 100% 82% 18% 73% 42% 

2017 100% 82% 18% 73% 43% 

2018 100% 82% 18% 73% 44% 

2019 100% 82% 18% 73% 44% 

2020 100% 81% 19% 73% 44% 

2021 100% 81% 19% 72% 44% 

2022 100% 81% 19% 72% 44% 

2023 100% 81% 19% 71% 44% 

2024 100% 81% 19% 71% 44% 

2025 100% 81% 19% 72% 44% 

2026 100% 81% 19% 72% 43% 

2027 100% 81% 19% 72% 43% 

2028 100% 80% 20% 72% 43% 

Notes: Calculations based on AEO 2013 Supplemental Table One for New England Fuel 

and Sector Consumption

http://www.eia.gov/forecasts/aeo/er/supplement/suptab_1.xlsx
Synapse Energy Economics. Inc. — AESC 2013 Appendix D 

Exhibit D‐4. Pollutant Emission Values by Year in 2013$/MMBtu

SO2 

(2013$/MMBtu)

NOx 

(2013$/MMBtu)

 CO2 

(2013$/MMBtu)

 CO2 at 

$100/ton 

(2013$/MMBtu)

SO2 

(2013$/MMBtu)

NOx 

(2013$/MMBtu)

 CO2 

(2013$/MMBtu)

 CO2 at 

$100/ton 

(2013$/MMBtu)

SO2 

(2013$/MMBtu)

(2013

2013 $0.0000 $0.0018 $0.2422 $8.6500 $0.0000 $0.0023 $0.2296 $8.2000 $0.0000 2014 $0.0000 $0.0018 $0.3724 $8.6500 $0.0000 $0.0024 $0.3530 $8.2000 $0.0000 2015 $0.0000 $0.0018 $0.4748 $8.6500 $0.0000 $0.0024 $0.4501 $8.2000 $0.0000 2016 $0.0000 $0.0019 $0.5811 $8.6500 $0.0000 $0.0025 $0.5509 $8.2000 $0.0000 2017 $0.0000 $0.0019 $0.6915 $8.6500 $0.0000 $0.0025 $0.6556 $8.2000 $0.0000 2018 $0.0000 $0.0019 $0.8061 $8.6500 $0.0000 $0.0026 $0.7642 $8.2000 $0.0000 2019 $0.0000 $0.0020 $0.9250 $8.6500 $0.0000 $0.0026 $0.8769 $8.2000 $0.0000 2020 $0.0000 $0.0020 $2.0170 $8.6500 $0.0000 $0.0027 $1.9121 $8.2000 $0.0000 2021 $0.0000 $0.0021 $2.2888 $8.6500 $0.0000 $0.0028 $2.1698 $8.2000 $0.0000 2022 $0.0000 $0.0021 $2.5707 $8.6500 $0.0000 $0.0028 $2.4370 $8.2000 $0.0000 2023 $0.0000 $0.0021 $2.8629 $8.6500 $0.0000 $0.0029 $2.7140 $8.2000 $0.0000 2024 $0.0000 $0.0022 $3.1658 $8.6500 $0.0000 $0.0029 $3.0011 $8.2000 $0.0000 2025 $0.0000 $0.0022 $3.4796 $8.6500 $0.0000 $0.0030 $3.2986 $8.2000 $0.0000 2026 $0.0000 $0.0023 $3.8048 $8.6500 $0.0000 $0.0030 $3.6068 $8.2000 $0.0000 2027 $0.0000 $0.0023 $4.1415 $8.6500 $0.0000 $0.0031 $3.9261 $8.2000 $0.0000 2028 $0.0000 $0.0024 $4.4902 $8.6500 $0.0000 $0.0032 $4.2566 $8.2000 $0.0000 

Levelized (2013$/MMBtu)5 year (2014-18) $0.0000 $0.0019 $0.5823 $8.6500 $0.0000 $0.0025 $0.5520 $8.2000 $0.0000 10 year (2014-23) $0.0000 $0.0020 $1.3256 $8.6500 $0.0000 $0.0026 $1.2567 $8.2000 $0.0000 15 year (2014-28) $0.0000 $0.0021 $2.0977 $8.6500 $0.0000 $0.0027 $1.9886 $8.2000 $0.0000 NotesBased on pollution emission rates for Number 2 fuel oilPollutant values based on emission allowance prices detailed in Exhibit 4-1. 

Pollutant Emission Values by Year in 2013$/MMBtu

Residential Commercial 
Synapse Energy Economics. Inc. — AESC 2013 Appendix E Page E‐1  

Appendix E: Common Financial Parameters for  

AESC 2013

Table of Exhibits

Exhibit E‐1. Summary of Common Financial Parameters AESC 2011 versus AESC 2013...........................E‐1

Exhibit E‐2. Comparison of Real Discount Rate Estimates.........................................................................E‐3

Exhibit E‐3. GDP Price Index and Inflation Rate.........................................................................................E‐4

1.1 Introduction

AESC 2013 requires converting nominal dollars to constant 2013 dollars (2013$) as well as using a real

discount rate for calculating illustrative levelized avoided costs. The workbooks in Appendix B allow

users to specify their own discount rate.  

AESC 2013 uses a long‐term inflation rate and a real discount rate. Those values are summarized below:  

Exhibit E‐1. Summary of Common Financial Parameters AESC 2011 versus AESC 2013

AESC 2011 AESC 2013

Inflation Rate 2.00% 2.00%

Real Discount Rate 2.46% 1.36%

1.2 Inflation Rate

AESC 2013 uses a forecast of long‐term inflation rate of 2.00 percent. The 2.00 percent inflation is

consistent with the 20‐year annual average inflation rate from 1992 to 2012, of 2.07 percent, derived

from the Gross Domestic Product (GDP) chain‐type price index. In light of the current economic

conditions, the Project Team also examined projections of long‐term inflation made by the

Congressional Budget Office (CBO) in January 2013. The CBO projections of long‐term inflation are 2.0

percent.1

  

                                                            

1 CBO, The Budget and Economic Outlook: Fiscal Years 2013 to 2023, summary page. Available at

http://www.cbo.gov/publication/43907. Accessed on March 17, 2013.
Synapse Energy Economics. Inc. — AESC 2013 Appendix E    Page E‐2  

1.3 Real Discount Rate

AESC 2013 requires the calculation of illustrative levelized avoided costs expressed in 2013$ for intervals

of: 1) 10 years (2014‐2023); 2) 15 years (2014‐2028); and 3) 30 years (2014‐2043) using an identified real

discount rate.2

  

The derived real discount rate for AESC 2013 is based upon the average of March 2013 nominal rates of

return for 30‐year Treasury Bonds and the forecast long‐term inflation rate (2.00 percent) according to

this formula3:

Real discount rate = ((1+nominal long‐term rate)/ (1+inflation rate)‐1)  

This formula results in a real discount rate of 1.36 percent that can be used for calculations of levelized

costs through periods as long as 30 years. The AESC 2013 real discount rate is much lower than the rate

of 2.46 percent used in AESC 2011. For comparison purposes, we examined projections made by the

CBO of nominal rates of return for ten‐year Treasury notes for the 2019‐2023 period.4 The CBO

projections of nominal rates of return, which are in the order of 5.2 percent, result in real discount rates

of 3.14 percent based on forecast inflation of 2.0 percent. However, because we are calculating levelized

costs through periods as long as 30 years, we use a real discount rate of 1.36 percent. Exhibit E‐2

presents a summary of the values we compared.

1.4 Conversion to Constant 2013$

AESC 2013 requires all forecasts to be expressed in real 2013$. Therefore, the Project Team developed a

set of inflators to convert nominal dollars from prior years (pre‐2013) into 2013$ and a set of deflators

to convert nominal dollars from future years (post‐2013) into 2013$. The inflator and deflator values are

presented at the end of this appendix. The inflators are calculated from the Gross Domestic Product

(GDP) chain‐type price index published by the U.S. Department of Commerce’s Bureau of Economic

Analysis (BEA).5 Deflators for future values use the long‐term inflation rate of 2.00 percent.

1.5 Escalation Assumptions for Various Avoided Cost Components

The Project Team developed escalation assumptions used to extrapolate the forecasts from 2029

through 2043. For example, for the period from 2029 to 2043 for the annual wholesale energy prices,

AESC 2013 uses an escalation assumption based on the (2024‐2028) compound annual growth rate for

                                                            

2 The Excel workbooks allow members of the Study Group to input any discount rate to calculate levelized avoided costs.

3 This approach was used in AESC 2005, 2007, 2009, and 2011.

4 Summary Table 2, CBO (2013).  

5 BEA, Table 1.1.9 Implicit Price Deflators for Gross Domestic Product, downloaded February 28, 2013.
Synapse Energy Economics. Inc. — AESC 2013 Appendix E    Page E‐3  

each costing period based on the Market Analytics results. For other value streams of avoided cost

components, we note the escalation assumptions.

Exhibit E‐2. Comparison of Real Discount Rate Estimates

Comparative Estimates of Financial Parameters

                 

Parameter /

Source AESC 2009 AESC 2011   AESC 2013

Congressional Budget Office

(1, 2)

Jan‐2011 Jan‐2013

                 

Long Term

Nominal Rate 3.78% 4.51% 3.39% 5.40% 5.20%

Source 30 year T‐Bills as of

March 2009

30 year T‐Bills as

of February 2011

30 year T‐Bills

over last six

years.

Forecast ‐ 10

yr Treasury

notes 2017‐

2021

Forecast ‐ 10

yr Treasury

notes 2019‐

2023

                 

Inflation Rate

(GDP Deflator) 2.00% 2.00% 2.00% 2.00% 2.00%

Source

Less than 20 year

historic average

inflation of 2.44%,

but lowered in

response to

economic forecasts.

Consistent with 20

year historic

average inflation

of 2.16%, but

slightly lower to

reflect economic

forecasts.  

Consistent with

20 year historic

average

inflation of

2.07%, but

slightly lower to

reflect

economic

forecasts.  

Consistent

with GDP price

index 2015 ‐ 

2021 forecast.

Core PCE

Price Index

2015‐2023

                 

Long Term Real

Rate 2.22% 2.46% 1.36% 3.33% 3.14%

Source Calculated from long term nominal rate and the inflation rate.

Note:   In AESC 2009, the Study Group selected a long term real rate that was the same as

for the previous study.

CBO Sources:

1 The Budget and Economic Outlook: Fiscal Years 2011 to 2021, Congressional Budget Office,

January 2011, Summary introduction and Table 2.

2 The Budget and Economic Outlook: Fiscal Years 2013 to 2023, Congressional Budget Office,

February 2013, Summary introduction and Table 2.
Synapse Energy Economics. Inc. — AESC 2013 Appendix E    Page E‐4  

Exhibit E‐3. GDP Price Index and Inflation Rate

Year

GDP Chain‐Type

Price Index

Annual

Inflation

Conversion from

nominal $ to 2013$

1990 72.26 1.621

1991 74.82 3.55% 1.565

1992 76.60 2.37% 1.529

1993 78.29 2.21% 1.496

1994 79.94 2.11% 1.465

1995 81.61 2.08% 1.435

1996 83.16 1.90% 1.408

1997 84.63 1.77% 1.384

1998 85.58 1.13% 1.368

1999 86.84 1.47% 1.349

2000 88.72 2.17% 1.320

2001 90.73 2.26% 1.291

2002 92.20 1.62% 1.270

2003 94.14 2.10% 1.244

2004 96.79 2.82% 1.210

2005 100.00 3.32% 1.171

2006 103.23 3.23% 1.134

2007 106.23 2.90% 1.102

2008 108.58 2.22% 1.079

2009 109.53 0.87% 1.069

2010 110.99 1.34% 1.055

2011 113.36 2.13% 1.033

2012 115.38 1.78% 1.015

2013 117.11 1.50% 1.000

2014 119.45 2.00% 0.980

2015 121.84 2.00% 0.961

2016 124.28 2.00% 0.942

2017 126.77 2.00% 0.924

2018 129.30 2.00% 0.906

2019 131.89 2.00% 0.888

2020 134.52 2.00% 0.871

2021 137.22 2.00% 0.853

2022 139.96 2.00% 0.837

2023 142.76 2.00% 0.820

2024 145.61 2.00% 0.804

2025 148.53 2.00% 0.788

2026 151.50 2.00% 0.773

2027 154.53 2.00% 0.758

2028 157.62 2.00% 0.743

2029 160.77 2.00% 0.728

2030 163.98 2.00% 0.714
Synapse Energy Economics. Inc. — AESC 2013 Appendix F Page F‐1  

Appendix F: Selected Input Assumptions

Table of Exhibits

Exhibit F‐1. AESC 2013 Renewable Portfolio Standard Targets, REC Price Forecast, and Avoided RPS Costs

by State in $/MWh of Load (2013$) .......................................................................................................... F‐2

Exhibit F‐2. Alternative Compliance Payment (ACP) Rates, by RPS Class, by Year (2013$).......................F‐5
Synapse Energy Economics. Inc. — AESC 2013 Appendix F 

Exhibit F‐1. AESC 2013 Renewable Portfolio Standard Targets, REC Price Forecast, and Avoided RPS Costs by State in $/MWh of Load (2013

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2

Class 1 10.0% 11.0% 12.5% 14.0% 15.5% 17.0% 18.5% 20.0% 20.0% 20.0% 20.0% 20.0% 20.0% 2Class 2 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3Class 3 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4Class 1 $ 54.00 $ 42.16 $ 34.80 $ 27.71 $ 20.89 $ 14.33 $ 15.46 $ 16.74 $ 16.65 $ 16.18 $ 15.31 $ 19.05 $ 26.19 $ 

Class 2 $ 0.55 $ 0.54 $ 0.53 $ 0.52 $ 0.51 $ 0.50 $ 0.49 $ 0.48 $ 0.47 $ 0.46 $ 0.45 $ 0.44 $ 0.43 $ 

Class 3 $ 10.00 $ 9.80 $ 9.61 $ 9.42 $ 9.24 $ 9.06 $ 8.88 $ 8.71 $ 8.53 $ 8.37 $ 8.20 $ 8.04 $ 7.88 $ 

Loss Adjustment 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 

Class 1 $5.83 $5.01 $4.70 $4.19 $3.50 $2.63 $3.09 $3.62 $3.60 $3.50 $3.31 $4.11 $5.66 $Class 2 $0.02 $0.02 $0.02 $0.02 $0.02 $0.02 $0.02 $0.02 $0.02 $0.01 $0.01 $0.01 $0.01 $Class 3 $0.43 $0.42 $0.42 $0.41 $0.40 $0.39 $0.38 $0.38 $0.37 $0.36 $0.35 $0.35 $0.34 $Class 1 6.0% 7.0% 8.0% 9.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 1

Class 2 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 3

Class 1 $ 20.00 $ 20.59 $ 17.39 $ 14.31 $ 11.35 $ 8.49 $ 9.88 $ 11.51 $ 13.44 $ 15.70 $ 18.36 $ 21.49 $ 24.90 $ 

Class 2 $ 0.15 $ 0.15 $ 0.14 $ 0.14 $ 0.14 $ 0.14 $ 0.13 $ 0.13 $ 0.13 $ 0.13 $ 0.12 $ 0.12 $ 0.12 $ 

Loss Adjustment 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 

Class 1 $1.30 $1.56 $1.50 $1.39 $1.23 $0.92 $1.07 $1.24 $1.45 $1.70 $1.98 $2.32 $2.69 $Class 1 8.00% 9.00% 10.00% 11.00% 12.00% 13.00% 14.00% 15.00% 16.00% 17.00% 18.00% 19.00% 20.00% 21Class 2 3.60% 3.60% 3.60% 3.60% 3.60% 3.60% 3.60% 3.60% 3.60% 3.60% 3.60% 3.60% 3.60% 3Class 2‐WTE 3.50% 3.50% 3.50% 3.50% 3.50% 3.50% 3.50% 3.50% 3.50% 3.50% 3.50% 3.50% 3.50% 3APS 3.00% 3.50% 3.75% 4.00% 4.25% 4.50% 4.75% 5.00% 5.25% 5.50% 5.75% 6.00% 6.25% 6Class 1 $ 63.00 $ 59.80 $ 48.16 $ 36.96 $ 26.17 $ 15.80 $ 17.02 $ 18.40 $ 18.39 $ 18.02 $ 17.24 $ 21.06 $ 28.28 $ 

Class 2 $ 25.00 $ 19.09 $ 13.40 $ 13.40 $ 13.40 $ 13.40 $ 13.40 $ 13.40 $ 13.40 $ 13.40 $ 13.40 $ 13.40 $ 13.40 $ 

Class 2‐WTE $ 6.50 $ 6.37 $ 6.25 $ 6.13 $ 6.00 $ 5.89 $ 5.77 $ 5.66 $ 5.55 $ 5.44 $ 5.33 $ 5.23 $ 5.13 $ 

APS $ 19.50 $ 19.29 $ 19.29 $ 19.29 $ 19.29 $ 19.29 $ 19.29 $ 19.29 $ 19.29 $ 19.29 $ 19.29 $ 19.29 $ 19.29 $ 

Loss Adjustment 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 

Class 1 $5.44 $5.81 $5.20 $4.39 $3.39 $2.22 $2.57 $2.98 $3.18 $3.31 $3.35 $4.32 $6.11 $

Class 2 $0.97 $0.74 $0.52 $0.52 $0.52 $0.52 $0.52 $0.52 $0.52 $0.52 $0.52 $0.52 $0.52 $

Class 2‐WTE $0.25 $0.24 $0.24 $0.23 $0.23 $0.22 $0.22 $0.21 $0.21 $0.21 $0.20 $0.20 $0.19 $

APS $0.63 $0.73 $0.78 $0.83 $0.89 $0.94 $0.99 $1.04 $1.09 $1.15 $1.20 $1.25 $1.30 $

CONNECTICUT

RPS Targets (%)

REC Prices

($/MWh)

Avoided RPS

Cost: $/MWh of

Load

MAINE

RPS Targets (%)

REC Prices

($/MWh)

Avoided RPS

MASSACHUSETTS

RPS Targets (%)

REC Prices

($/MWh)

Avoided RPS

Cost: $/MWh of

Load
Synapse Energy Economics. Inc. — AESC 2013 Appendix F 

Exhibit F‐1 (continued from prior page). AESC 2013 Renewable Portfolio Standard Targets, REC Price Forecast, and Avoided RPS Costs by S

(2013$)

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2

Class 1 3.8% 4.6% 5.4% 6.1% 6.8% 7.5% 8.2% 8.9% 9.6% 10.3% 11.0% 11.7% 12.4% 1Class 1 ‐ Thermal 0.0% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% 1.8% 2.0% 2.2% 2.4% 2.6% 2

Class 2 0.20% 0.30% 0.30% 0.30% 0.30% 0.30% 0.30% 0.30% 0.30% 0.30% 0.30% 0.30% 0.30% 0Class 3 1.5% 7.0% 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% 8

Class 4 1.3% 1.4% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1Class 1 $ 54.00 $ 52.94 $ 42.91 $ 33.24 $ 23.94 $ 15.00 $ 16.23 $ 17.62 $ 18.08 $ 18.18 $ 17.86 $ 21.24 $ 27.56 $ 

Class 1 ‐ Thermal $ ‐ $ 22.06 $ 21.84 $ 21.63 $ 21.42 $ 21.21 $ 21.00 $ 20.79 $ 20.59 $ 20.39 $ 20.19 $ 19.99 $ 19.79 $ 

Class 2 $ 55.00 $ 49.01 $ 48.53 $ 38.81 $ 27.48 $ 16.59 $ 17.87 $ 19.32 $ 19.31 $ 18.92 $ 18.10 $ 22.11 $ 29.69 $ 

Class 3 $ 28.00 $ 28.35 $ 28.35 $ 27.71 $ 20.89 $ 14.33 $ 15.46 $ 16.74 $ 16.65 $ 16.18 $ 15.31 $ 19.05 $ 26.19 $ 

Class 4 $ 26.00 $ 23.85 $ 23.85 $ 23.85 $ 20.89 $ 14.33 $ 15.46 $ 16.74 $ 16.65 $ 16.18 $ 15.31 $ 19.05 $ 23.85 $ 

Loss Adjustment 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 

Class 1 $2.22 $2.63 $2.50 $2.19 $1.76 $1.21 $1.44 $1.69 $1.87 $2.02 $2.12 $2.68 $3.69 $

Class 1 ‐ Thermal $0.00 $0.10 $0.14 $0.19 $0.23 $0.27 $0.32 $0.36 $0.40 $0.44 $0.48 $0.52 $0.56 $

Class 2 $0.12 $0.16 $0.16 $0.13 $0.09 $0.05 $0.06 $0.06 $0.06 $0.06 $0.06 $0.07 $0.10 $

Class 3 $0.45 $2.14 $2.45 $2.39 $1.81 $1.24 $1.34 $1.45 $1.44 $1.40 $1.32 $1.65 $2.26 $

Class 4 $0.37 $0.36 $0.39 $0.39 $0.34 $0.23 $0.25 $0.27 $0.27 $0.26 $0.25 $0.31 $0.39 $

$5.64 $5.28 $4.22 $3.01 $3.40 $3.83 $4.05 $4.18 $4.23 $5.23 $6.99 

New 5.5% 6.5% 8.0% 9.5% 11.0% 12.5% 14.0% 14.0% 14.0% 14.0% 14.0% 14.0% 14.0% 1Existing 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 

New $ 63.00 $ 57.84 $ 46.72 $ 36.02 $ 25.71 $ 15.80 $ 17.02 $ 18.40 $ 18.38 $ 17.99 $ 20.84 $ 24.61 $ 28.28 $ 

Existing $ 0.60 $ 0.59 $ 0.58 $ 0.57 $ 0.55 $ 0.54 $ 0.53 $ 0.52 $ 0.51 $ 0.50 $ 0.49 $ 0.48 $ 0.47 $ 

Loss Adjustment 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 

New $3.74 $4.06 $4.04 $3.70 $3.05 $2.13 $2.57 $2.78 $2.78 $2.72 $3.15 $3.72 $4.28 $Existing $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $

RPS Targets (%) New 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 

REC Prices

($/MWh) New $          ‐ ‐ $          ‐ $         ‐ $         ‐ $         ‐ $         ‐ $         ‐ $         ‐ $         ‐ $         ‐ $         ‐ $         ‐ $         

$  

Loss Adjustment 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 

Avoided RPS New $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $

VERMONT NEW HAMPSHIRE

RPS Targets (%)

REC Prices

($/MWh)

Avoided RPS

Cost: $/MWh of

Load

RHODE ISLAND

RPS Targets (%)

REC Prices

($/MWh)

Avoided RPS

Cost: $/MWh of
Synapse Energy Economics. Inc. — AESC 2013 Appendix F 

Exhibit F‐1 (continued from prior page). AESC 2013 Renewable Portfolio Standard Targets, REC Price Forecast, and Avoided RPS Costs by S

(2013$)

Assumptions:

1 RPS Targets for CT, ME, MA, NH & RI are based on state‐specific legislation and regulation in effect as of May 15, 201

2

Vermont currently has a non‐binding goal of 20% by 2017. VT's minimum obligation is to meet incremental load gro

qualifying resources. A minimum goal of generating 5% of VT's 2005 sales is also included in the current law. No REC 

AESC 2013 study assumes that VT does not adopt a new, binding RPS.

3 For Class I requirements, 2013 REC prices are based on historic average broker quotes from January to March 2013. 

2013 REC prices are based on average 2012 prices and average 2013 broker prices from Jan to Mar 2013 and/or bid‐

4 Prices for MA Class I, CT Class I, NH Class I, ME Class I, and RI "New" are based on broker quotes in 2013 and the new 

beginning in 2018. Prices are interpolated between the two for 2014 through 2017.

5 CT Class II, MA Class II‐WTE, ME Class II, and RI "Existing" REC markets are in surplus. Therefore, REC prices in these 

remain relatively constant.

6

The MA Class II market has overlapping eligibility with CT Class I. In addition, while there is theoretically ample supp

generators than expected have undertaken the steps necessary to comply with the eligibility criteria and become ce

Class II market is currently in shortage. As a result, the legislature has directed the DOER to take measures necessary 

balance. Beginning in 2015, therefore, MA Class II REC prices are assumed to be the lesser of CT Class I and 50% of th

Compliance Payment (ACP) rate.

7 REC prices for MA APS and NH‐I Thermal Carve‐Out are forecasted at 90% of the Alternative Compliance Payment (A8 The CT Class III market has an administratively set REC price floor of $10. Based on the performance of this market t

prices are expected to remain at $10 per MWh throughout the study period.

9 Existing solar facilities across New England are eligible for NH Class II. As such, this market is expected to remain in b

above the MA Class I REC price for the remainder of the study period.

10

The NH Class III and NH Class IV markets have overlapping eligibility with CT Class I. In the long run, therefore, NH‐III 

assumed to be the lesser of CT Class I and 90% of their respective Alternative Compliance Payment (ACP) rates. The 

uncertainty. The average NH‐III REC price for Jan‐Mar 2013 was $28/MWh (based on broker quotes). REC trading ce

legislature reduced the 2013 NH‐III target by approximately 75%. Nonetheless, in a year in which CT‐I is expected to 

available RECs, the NH‐III REC price is expected to remain ~$28.
Synapse Energy Economics. Inc. — AESC 2013 Appendix F 

Exhibit F‐2. Alternative Compliance Payment (ACP) Rates, by RPS Class, by Year (2013$)

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 20

Connecticut Class 1 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55

Class 2 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55

Class 3 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55.00 $55

Maine Class 1 $65.27 $66.58 $67.91 $69.27 $70.65 $72.06 $73.50 $74.97 $76.47 $78.00 $79.56 $81.16 $82.78 $84

Class 2 $65.27 $66.58 $67.91 $69.27 $70.65 $72.06 $73.50 $74.97 $76.47 $78.00 $79.56 $81.16 $82.78 $84

Massachusetts Class 1 $65.27 $66.58 $67.91 $69.27 $70.65 $72.06 $73.50 $74.97 $76.47 $78.00 $79.56 $81.16 $82.78 $84

Class 2 $26.79 $27.33 $27.87 $28.43 $29.00 $29.58 $30.17 $30.77 $31.39 $32.02 $32.66 $33.31 $33.98 $34Class 2‐WTE $10.72 $10.93 $11.15 $11.38 $11.60 $11.84 $12.07 $12.31 $12.56 $12.81 $13.07 $13.33 $13.60 $13APS $21.43 $21.86 $22.30 $22.74 $23.20 $23.66 $24.13 $24.62 $25.11 $25.61 $26.12 $26.65 $27.18 $27

New Hampshire Class 1 $55.00 $55.55 $56.11 $56.67 $57.23 $57.81 $58.38 $58.97 $59.56 $60.15 $60.75 $61.36 $61.98 $62

Class 1 ‐ Thermal $25.00 $25.25 $25.50 $25.76 $26.02 $26.28 $26.54 $26.80 $27.07 $27.34 $27.62 $27.89 $28Class 2 $55.00 $55.55 $56.11 $56.67 $57.23 $57.81 $58.38 $58.97 $59.56 $60.15 $60.75 $61.36 $61.98 $62Class 3 $31.50 $32.13 $32.77 $33.43 $34.10 $34.78 $35.47 $36.18 $36.91 $37.65 $38.40 $39.17 $39.95 $40Class 4 $26.50 $27.03 $27.57 $28.12 $28.68 $29.26 $29.84 $30.44 $31.05 $31.67 $32.30 $32.95 $33.61 $34

Rhode Island New $65.27 $66.58 $67.91 $69.27 $70.65 $72.06 $73.50 $74.97 $76.47 $78.00 $79.56 $81.16 $82.78 $84

Existing $65.27 $66.58 $67.91 $69.27 $70.65 $72.06 $73.50 $74.97 $76.47 $78.00 $79.56 $81.16 $82.78 $84

Vermont Not ApplicableAssumptions:

Inflation Rate 2% Source: AESC 2013 "Common Financial Parameters"
Synapse Energy Economics. Inc. — AESC 2013 Appendix G Page G‐1  

Appendix G: Survey of Transmission and Distribution

Capacity Values

Table of Exhibits

Exhibit G‐1. Summary of Electric Utilities’ T&D Cost Survey .................................................................... G‐1

1.1 Introduction

We surveyed the sponsoring electric utilities to determine: 1) the avoided Transmission and Distribution

cost estimates, and 2) the methodology on which these estimates were based. The following exhibit

summarizes the results of the survey:

Exhibit G‐1. Summary of Electric Utilities’ T&D Cost Survey

    Transmission Distribution

Company Year $ $kW‐year   $kW‐year   Methodology

CL&P 2013 $1.30 $30.94 ICF Tool

WMECO 2011 $22.27 $76.08 ICF Tool

NSTAR 2011 $21.00 $68.79 ICF Tool

National Grid MA 2013 $88.64 $111.37 ICF Tool

National Grid RI 2013 $20.62 $20.62 ICF Tool

PSNH 2013 $16.70 $53.35 ICF Tool

United Illuminating 2013 $2.64 $47.82 B&V Report

Unitil MA 2013 NA $171.15 ICF Tool

Unitil NH 2013 $73.03 $29.26 ICF Tool

Vermont

(statewide) 2012 $48 $102 Historical

Burlington Electric

Department 2012 $48 — Historical

Notes

NA= Not applicable

ICF Tool = ICF workbook developed in 2005.

B&V Report = United Illuminating Avoided Transmission & Distribution Cost

Study Report, Black & Veatch, September 2009.
Synapse Energy Economics. Inc. — AESC 2013 Appendix G Page G‐2  

Connecticut Light and Power and Unitil did not respond to our inquiry.  

Descriptions of the methodology used by respondents are detailed below.

1.2 ICF Tool

A complete description of the ICF model used by National Grid and other electric utilities was detailed in

the AESC 2005 report. In summary, the ICF Tool is a workbook developed by ICF as part of the 2005 AESC

Study.  Inputs for the workbook are: 1) historical and budgeted future capital costs,2)  historical and

future load, and 3) various accounting parameters from FERC Form 1 data.   

Analysis period cost data is divided by analysis period load data to derive an average capital cost/kW.  

This is multiplied by a factor representing the percentage of capital costs that is avoidable by energy

efficiency (another input variable). This avoidable $/kW is further modified by a carrying charge

determined from the accounting inputs, to develop an annualized avoided capacity value in $/kW.  

Based on review of some of the carrying charge calculations in the AESC 2009 study, National Grid has

updated this part of the workbook to create the updated ICF Tool. Other utilities have updated the

workbook at other intervals.  

National Grid indicated that its practice is to use five years of historical and forecast data for both

transmission and distribution data in developing the avoided transmission and distribution capacity

values.   

1.3 Black and Veatch Report

United Illuminating’s methodology is detailed in a Black and Veatch Report. Black and Veatch’s

methodology follows briefly:

 Identification of historical and future T&D capacity additions which could have been

fully or partially avoided with additional energy efficiency programs.

 Collection of historical costs plus AFUDC associated with projects identified in the first

step. Calculated project costs are then divided by each project’s incremental MW load

carrying capacity to derive a marginal capital cost for capacity per MW.     

 Calculation of marginal O&M expenses.  

 Converting marginal capital costs to annual costs adjusting for revenue requirements

based on accounting inputs.

 Calculation of energy efficiency savings based on historical and projected load growth.

 Calculations of annual avoided cost based on annual costs and identified energy

efficiency savings.  
Synapse Energy Economics. Inc. — AESC 2013 Appendix G Page G‐3  

1.4 Vermont

In 2012, the Vermont distribution utilities, efficiency utility, and Department of Public Service jointly

reviewed and updated avoided T&D costs and filed those estimates with the Vermont Public Service

Board (Docket EEU‐2011‐02 – EEU Avoided Costs – T&D Component Working Group Recommendation,

August 31, 2012).  

The statewide estimates are based on load‐related investments in the last decade for which Vermont

experienced significant load growth, ending in 1996. The statewide avoided costs are reduced to reflect

the reduction in line losses that would be associated with increasing T&D capacity. The annual avoided

T&D costs start at $159/kW‐year in 2013 and decline gradually, resulting in a real‐levelized value of

$150/kW‐year over a 29‐year period.

The Burlington Electric Department expects that no load‐related distribution investments would be

required over the next 20 years, even without energy‐efficiency programs, and therefore uses the

statewide avoided transmission cost but not the avoided distribution cost.
Synapse Energy Economics. Inc. — AESC 2013 Appendix H Page H‐1  

Appendix H: Pooled Transmission Losses Methodology  

Table of Exhibits

Exhibit H‐1. PTF Losses vs. Non‐PTF Demand for the Top 100 Summer Hours, 2010 .............................. H‐1

Exhibit H‐2. PTF Losses vs. Non‐PTF Demand for the Top 100 Summer Hours, 2011 .............................. H‐2

Exhibit H‐3. PTF Losses vs. Non‐PTF Demand for the Top 100 Summer Hours, 2012 .............................. H‐2

Exhibit H‐4. PTF Losses vs. Non‐PTF Demand for the Top Days with Peak Summer Hours, 2010............ H‐3

Exhibit H‐5. PTF Losses vs. Non‐PTF Demand for the Top Days with Peak Summer Hours, 2011............ H‐4

Exhibit H‐6. PTF Losses vs. Non‐PTF Demand for the Top Days with Peak Summer Hours, 2012............ H‐5

This appendix provides a more detailed description of the methodology used to determine the AESC

2013 PTF loss value.

AESC 2013 revisited AESC 2009 computation to determine whether changes in system configuration

(e.g., added transmission, new generation in Connecticut) and changing dispatch due to fuel prices and

environmental costs had changed marginal losses at peak. Applying the methodology from AESC 2009 to

the top 100 hours in 2010 produced an estimate of 2.0 percent marginal losses, very similar to the

estimates from 2007 and 2008, as shown in Exhibit H‐1.

Exhibit H‐1. PTF Losses vs. Non‐PTF Demand for the Top 100 Summer Hours, 2010

The data trends for 2011 and 2012 were less conclusive, as shown in Exhibit H‐2 and Exhibit H‐3.

y = 0.0202x ‐ 173.28

R2 = 0.3555

250

300

350

400

450

23000 24000 25000 26000 27000

PTF Losses

Non‐PTF Demand
Synapse Energy Economics. Inc. — AESC 2013 Appendix H    Page H‐2  

Exhibit H‐2. PTF Losses vs. Non‐PTF Demand for the Top 100 Summer Hours, 2011

Exhibit H‐3. PTF Losses vs. Non‐PTF Demand for the Top 100 Summer Hours, 2012

In principle, marginal losses might have declined, if the units dispatched at peak load were closer to load

than previously, but the 20‐fold decrease observed from 2010 to 2012 appeared implausible and the

negative slope in 2011 even more so. These 2011 and 2012 results suggest that something else was

changing within the high‐load hours (such as availability of imports, demand for exports, outages of

major units, and the changing geographical mix of load).

A more detailed examination of the relationship between the system losses against real‐time demand

for the six days with the greatest number of hours in the 100 hours of highest load was more

informative. These six days include the peak hours for each year and 59 to 63 of the top 100 hours in

each year.

For each of the three years, hourly losses in each of the top six days show a clear upward trend as a

function of load. The specifics of each day’s losses and demand differed, but they all trend similarly, as

shown in Exhibit H‐4, Exhibit H‐5, and Exhibit H‐6. The table under each graph shows the slope and R2

y = ‐0.0012x + 394.57

R2 = 0.0037

250

300

350

400

450

21500 23500 25500 27500

PTF Losses

Non‐PTF Demand

y = 0.001x + 293.53

R2 = 0.0016

250

270

290

310

330

350

370

390

22000 23000 24000 25000 26000

PTF Losses

Non‐PTF Demand
Synapse Energy Economics. Inc. — AESC 2013 Appendix H    Page H‐3  

for each day, as well as the average of those slopes, weighted by the number of peak hours represented

in each day.   

Exhibit H‐4. PTF Losses vs. Non‐PTF Demand for the Top Days with Peak Summer Hours, 2010

Top Days Hours in Top 100 Slope R2

7/6/2010 12 0.0258 0.7761

7/7/2010 11 0.0068 0.2217

7/8/2010 7 0.0028 0.0235

8/31/2010 9 0.0299 0.8938

9/1/2010 10 0.0110 0.4917

9/2/2010 10 0.0223 0.507

Average slope, weighted by days 0.0171

250

270

290

310

330

350

370

390

410

23000 24000 25000 26000 27000

PTF Losses

Non‐PTF Demand

7/6/2010

7/7/2010

7/8/2010

8/31/2010

9/1/2010

9/2/2010

Linear (7/6/2010)

Linear (7/7/2010)

Linear (7/8/2010)

Linear (8/31/2010)

Linear (9/1/2010)
Synapse Energy Economics. Inc. — AESC 2013 Appendix H    Page H‐4  

Exhibit H‐5. PTF Losses vs. Non‐PTF Demand for the Top Days with Peak Summer Hours, 2011

Top Days Hours in Top 100 Slope R2

7/11/2011a 9 0.0183 0.8797

7/12/2011 12 0.0148 0.505

7/19/2011 7 0.0152 0.1982

7/20/2011 8 0.0180 0.4484

7/21/2011 12 0.0182 0.532

7/22/2011 14 0.0131 0.6919

Average slope, weighted by days 0.0160 a

One high‐loss outlier was excluded.

290

310

330

350

370

390

410

21000 23000 25000 27000

PTF Losses

Non‐PTF Demand

7/11/2011

7/12/2011

7/19/2011

7/20/2011

7/21/2011

7/22/2011

Linear (7/11/2011)

Linear (7/12/2011)

Linear (7/19/2011)
Synapse Energy Economics. Inc. — AESC 2013 Appendix H    Page H‐5  

Exhibit H‐6. PTF Losses vs. Non‐PTF Demand for the Top Days with Peak Summer Hours, 2012

Top Days Hours in Top 100 Slope R2

6/20/2012 10 0.0181 0.8397

6/21/2012 12 0.0126 0.4956

7/16/2012 10 0.0154 0.354

7/17/2012 12 0.0030 0.1229

7/18/2012 9 0.0114 0.6263

8/3/2012 10 0.0079 0.4467

Average slope, weighted by days 0.0112

250

270

290

310

330

350

370

22000 23000 24000 25000 26000

PTF Losses

Non‐PTF Demand

6/20/2012

6/21/2012

7/16/2012

7/17/2012

7/18/2012

8/3/2012

Linear (6/20/2012)

Linear (6/21/2012)

Linear (7/16/2012)