### United States Environmental Protection Agency Air and Radiation (6204J) EPA # 430R10010 August 2010



# Documentation for EPA Base Case v.4.10 Using the Integrated Planning Model

### Cover: EPA Base Case v.4.10 and associated policy cases are used by the U.S. Environmental Protection Agency to project the impact of emissions policies on the electric power sector in the 48 contiguous states and the District of Columbia in the lower continental U.S. Representation of the electric power sector in Canada, Alaska, Hawaii, Puerto Rico, and U.S. Virgin Islands is also included for purposes of integrated projections. The map appearing on the cover shows the 32 model regions used to characterize the operation of the U.S. electric power system in the lower continental U.S., 11 model regions in Canada, and the 4 self-contained model regions in Alaska, Hawaii, Puerto Rico, and U.S. Virgin Islands. EPA Base Case v.4.10 was developed by EPA’s Clean Air Markets Division with technical support fr om ICF International, Inc. The IPM modeling platform is a product of ICF International, Inc and is used in support of its public and private sector clients. IPM ® 

### is a registered trademark of ICF Resources, L.L.C.

# Documentation for EPA Base Case v.4.10 Using the Integrated Planning Model

U.S. Environmental Protection Agency Clean Air Markets Division 1200 Pennsylvania Avenue, NW (6204J) Washington, D.C. 20460 (www.epa.gov/airmarkets)

August 2010

### iv 

## Acknowledgment

This document was prepared by U.S. EPA’s Clean Air Markets Division, Office of Air andRadiation. ICF Services Company, L.L.C. provided technical support under EPA Contract EP-W08-018.

### v 

## TABLE OF CONTENTS

1 INTRODUCTION....................................................................................................1-1

2 MODELING FRAMEWORK...................................................................................2-1

2.1 IPM O VERVIEW 

### ....................................................................................................................2-1
2.1.1 Purpose and Capabilities.............................................................................................2-1 2.1.2 Applications..................................................................................................................2 -2
2.2 M ODEL 

### S TRUCTURE AND 

### F ORMULATION 

### ...............................................................................2-2
2.2.1 Objective Function .......................................................................................................2-2 2.2.2 Decision Variables .......................................................................................................2-3 2.2.3 Constraints...................................................................................................................2 -4
2.3 K EY 

### M ETHODOLOGICAL 

### F EATURES OF 

### IPM...........................................................................2-4
2.3.1 Model Plants ................................................................................................................2-4 2.3.2 Model Run Years .........................................................................................................2-6 2.3.3 Cost Accounting...........................................................................................................2-6 2.3.4 Modeling Wholesale Electric Markets..........................................................................2-6 2.3.5 Load Duration Curves (LDC) .......................................................................................2-7 2.3.6 Dispatch Modeling .......................................................................................................2-9 2.3.7 Reliability Modeling......................................................................................................2-9 2.3.8 Fuel Modeling ............................................................................................................2-10 2.3.9 Transmission Modeling..............................................................................................2-10 2.3.10 Perfect Competition and Perfect Foresight................................................................2-10 2.3.11 Air Regulatory Modeling.............................................................................................2-10
2.4 H ARDWARE AND 

### P ROGRAMMING 

### F EATURES 

### ........................................................................2-11
2.4.1 Data Parameters for Model Inputs.............................................................................2-11 2.4.2 Model Outputs............................................................................................................2-12 Appendix 2-1 Load Duration Curves Used in EPA Base Case v.4.10 .....................................2-1



### 3 POWER SYSTEM OPERATIO N ASSUMPTIONS ................................................3-1

3.1 M ODEL 

### R EGIONS 

### .................................................................................................................3-1

3.2 E LECTRIC 

### L OAD 

### M ODELING 

### ..................................................................................................3-1
3.2.1 Demand Elasticity ........................................................................................................3-4 3.2.2 Net Internal Demand (Peak Demand)..........................................................................3-4 3.2.3 Regional Load Shapes.................................................................................................3-5
3.3 T RANSMISSION 

### ....................................................................................................................3-5
3.3.1 Inter-regional Transmission Capability.........................................................................3-5 3.3.2 Joint Transmission Capacity and Energy Limits ..........................................................3-9 3.3.3 Transmission Link Wheeling Charge .........................................................................3-10 3.3.4 Transmission Losses .................................................................................................3-11
3.4 I NTERNATIONAL 

### I MPORTS 

### ...................................................................................................3-11

3.5 C APACITY 

### , G ENERATION 

### , AND 

### D ISPATCH 

### ............................................................................3-11
3.5.1 Availability..................................................................................................................3 -11 3.5.2 Capacity Fa ctor..........................................................................................................3-12 3.5.3 Turndown...................................................................................................................3-13
3.6 R ESERVE 

### M ARGINS 

### ............................................................................................................3-13

3.7 P OWER 

### P LANT 

### L IFETIMES 

### ..................................................................................................3-15

3.8 H EAT 

### R ATES 

### ......................................................................................................................3-17

### vi 3.9 E XISTING 

### E NVIRONMENTAL 

### R EGULATIONS 

### ..........................................................................3-17
3.9.1 SO 2 

### Regulations ........................................................................................................3-17 3.9.2 NO x 

### Regulations ........................................................................................................3-18 3.9.3 CO 2 

### Regulations and Renewable Portfolio Standards...............................................3-20 3.9.4 State Specific Environmental Regulations.................................................................3-20 3.9.5 New Source Review (NSR) Settlements....................................................................3-20 3.9.6 Emission Assumptions for Potential (New) Units.......................................................3-20
3.10 C APACITY 

### D EPLOYMENT 

### C ONSTRAINTS 

### ..............................................................................3-21 Appendix 3-1 NO x 

### Rate Development in EPA Base Case v.4.10 .........................................3-1.1 Appendix 3-2 State Power Sector Regulations included in EPA Base Case v.4.10 .............3-2.1 Appendix 3-3 New Source Review (NSR) Settlements in EPA Base Case v.4.10 ...............3-3.1 Appendix 3-4 State Settlements in EPA Base Case v.4.10 ..................................................3-4.1 Appendix 3-5 Citizen Settlements in EPA Base Case v.4.10...............................................3-5.1 Appendix 3-6 Renewable Portfolio Standards in EPA Base Case v.4.10 .............................3-6.1 Appendix 3-7 Capacity Deployment Limits for Advanced Coal with CCS and New Nuclear in EPA Base Case v.4.10............................................................................3-7.1 Appendix 3-8 Nuclear Capacity Deployment Constraint in EPA Base Case v.4.10..............3-8.1 Appendix 3-9 Complete Availability Assumptions in EPA Base Case v.4.10........................3-9.1



### 4 GENERATING R ESOURCES................................................................................ 4-1

4.1 N ATIONAL 

### E LECTRIC 

### E NERGY 

### D ATA 

### S YSTEM 

### (NEEDS) ........................................................4-1

4.2 E XISTING 

### U NITS 

### ...................................................................................................................4-1
4.2.1 Population of Existing Units .........................................................................................4-1 4.2.2 Capacity ....................................................................................................................... 4-4 4.2.3 Plant Location ..............................................................................................................4-5 4.2.4 Online Year..................................................................................................................4- 5 4.2.5 Unit Configuration ........................................................................................................4-5 4.2.6 Model Plant Aggregation..............................................................................................4-5 4.2.7 Cost and Performance Characteristics of Existing Units .............................................4-9 4.2.8 Life Extension Costs for existing units .......................................................................4-10
4.3 P LANNED 

### -C OMMITTED 

### U NITS 

### .............................................................................................4-13
4.3.1 Population and Model Plant Aggregation...................................................................4-13 4.3.2 Capacity .....................................................................................................................4- 17 4.3.3 State and Model Region ............................................................................................4-17 4.3.4 Online and Retirement Year ......................................................................................4-18 4.3.5 Unit Configuration and Cost-and-Performance..........................................................4-18
4.4 P OTENTIAL 

### U NITS 

### ..............................................................................................................4-18
4.4.1 Methodology Used to Derive the Cost and Performance Characteristics of Conventional Potential Units .....................................................................................4-18 4.4.2 Cost and Performance for Potential Conventional Units............................................4-19 4.4.3 Short-Term Capital Cost Adder..................................................................................4-21 4.4.4 Regional Cost Adjustment .........................................................................................4-21 4.4.5 Cost and Performance for Potential Renewable Generating and Non-4- Conventional Technologies .......................................................................................4-25
4.5 N UCLEAR 

### U NITS 

### ................................................................................................................4-44
4.5.1 Existing Nuclear Units................................................................................................4-44 4.5.2 Potential Nuclear Units ..............................................................................................4-45 Appendix 4-1 Representative Wind Generation Profiles in EPA Base Case v.4.10..............4-1.1 Appendix 4-2 Representative Solar Generation Profiles in EPA Base v.4.10.......................4-2.1 Appendix 4-3 Characteristics of Existing Nuclear Units ........................................................4-3.1



### 5 EMISSION CONTROL TECHNOLOGIES .........................................................................5-1

### vii 5.1 S ULFUR 

### D IOXIDE 

### C ONTROL 

### T ECHNOLOGIES 

### ..........................................................................5-1
5.1.1 Methodology for Obtaining SO 2 

### Controls Costs...........................................................5-2
5.2 N ITROGEN 

### O XIDES 

### C ONTROL 

### T ECHNOLOGY 

### ..........................................................................5-7
5.2.1 Combustion Controls ...................................................................................................5-7 5.2.2 Post-combustion Controls............................................................................................5-8 5.2.3 Methodology for Obtaining SCR Costs for Coal Units .................................................5-9 5.2.4 Methodology for Obtaining SCR Costs for Oil/Gas Steam units................................5-12 5.2.5 Methodology for Obtaining SNCR Costs ...................................................................5-12 5.2.6 SO 2 

### and NO x 

### Controls for Units with Capacities from 25 MW to 100 MW (25 M
≤ capacity < 100 MW)................................................................................................5-13
5.3 B IOMASS 

### C O


### - FIRING 

### ..........................................................................................................5-14

5.4 M ERCURY 

### C ONTROL 

### T ECHNOLOGIES 

### ..................................................................................5-15
5.4.1 Mercury Content of Fuels...........................................................................................5-16 5.4.2 Mercury Emission Modification Factors .....................................................................5-17 5.4.3 Mercury Control Capabilities......................................................................................5-18 Appendix 5-1 Example Cost Calculation Worksheets for SO 2 

### Control Technologies in EPA Base Case v.4.10................................................................................................5-1 Appendix 5-2 Example Cost Calculation Worksheets for NO x 

### Post-Combustion Control Technologies in EPA Base Case v.4.10......................................................................5-1



### 6 CO 2 

### CAPTURE, TRANSPORT , AND STORAGE..................................................6-1

6.1 CO 2 

### C APTURE 

### .....................................................................................................................6-1

6.2 CO 2 

### S TORAGE 

### .....................................................................................................................6-2

6.3 CO 2 

### T RANSPORT 

### .................................................................................................................6-5 Appendix 6-1 CO 2 

### Storage Cost Curves in EPA Base Case 4.10 ........................................6-1.1 Appendix 6-2 CO 2 

### Transportation Matrix in EPA Base Case v.4.10 .....................................6-2.1



### 7 SET-UP PARAMETER S AND RULES ..................................................................7-1

7.1 R UN 

### Y EAR 

### M APPING 

### ............................................................................................................7-1

7.2 R ETROFIT 

### A SSIGNMENTS 

### .....................................................................................................7-1

7.3 T RADING AND 

### B ANKING 

### ........................................................................................................7-3

7.4 P OST 

### -2030 M ODELING 

### A SSUMPTIONS AND 

### C APABILITIES 

### ......................................................7-4



### 8 FINANCIAL ASSUMPTIONS.................................................................................8-1

8.1 S UMMARY OF 

### K EY 

### F INANCIAL 

### P ARAMETERS 

### ..........................................................................8-1
8.1.1 Capital Charge Rate, Book Life, and Discount Rate for Capital Expenditures ............8-1 8.1.2 ARRA Production and Investment Tax Credit (PTC and ITC) for Renewables ...........8-2 8.1.3 Discount Rate for Non-Capital Expenditures ...............................................................8-3 8.1.4 Inter-temporal Allowance Price Calculation .................................................................8-3 8.1.5 Nominal and Real Dollars ............................................................................................8-3
8.2 D EVELOPMENT OF THE 

### F INANCIAL 

### A SSUMPTIONS FOR 

### EPA B ASE 

### C ASE V 

### .4.10 .....................8-3
8.2.1 Introduction ..................................................................................................................8 -3 8.2.2 Method for Deriving Discount Rate and Capital Charge Rate in EPA Base Case v 4.10 .................................................................................................................8-4 8.2.3 Calculation of the Hybrid Capital Charge Rates ..........................................................8-7 8.2.4 Development of Merchant Financing Parameters........................................................8-8 8.2.5 Development of Utility Financing Parameters............................................................8-10 8.2.6 Development of Other Parameters ............................................................................8-10



### 9 COAL .....................................................................................................................9-1

### viii 9.1 C OAL 

### M ARKET 

### R EPRESENTATION IN 

### EPA B ASE 

### C ASE V 

### .4.10................................................9-1
9.1.1 Coal Supply Regions ...................................................................................................9-2 9.1.2 Coal Demand Regions.................................................................................................9-3 9.1.3 Coal Quality Characteristics.........................................................................................9-9 9.1.4 Emission Factors .......................................................................................................9-10 9.1.5 Coal Grade Assignments...........................................................................................9-15
9.2 C OAL 

### S UPPLY 

### C URVES 

### ......................................................................................................9-16
9.2.1 Nature of Supply Curves Developed for EPA Base Case v.4.10...............................9-16 9.2.2 Procedure Employed in Determining Mining Costs ...................................................9-17 9.2.3 Supply Curve Development .......................................................................................9-20 9.2.4 Data Sources Used to Build the Curves ....................................................................9-21 9.2.5 Procedure Used In Determining Mine Productivity ....................................................9-22 9.2.6 Procedure to Determine Total Recoverable Reserves by Region and Type .............9-22 9.2.7 New Mine Assumptions .............................................................................................9-23 9.2.8 Other Notable Procedures .........................................................................................9-23 9.2.9 Region Specific Assumptions and Outlooks ..............................................................9-23 9.2.10 Explanation of Coal Supply Curve Extensions to 2040..............................................9-25
9.3 C OAL 

### T RANSPORTATION 

### ....................................................................................................9-25
9.3.1 Coal Transportation Matrix Overview.........................................................................9-26 9.3.2 Calculation of Supply/Demand Region Distances .....................................................9-26 9.3.3 Overview of Rail Rates ..............................................................................................9-27 9.3.4 Truck rates .................................................................................................................9-3 0 9.3.5 Barge and Lake Vessel Rates ...................................................................................9-30 9.3.6 Transportation Rates fo r Imported Coal.....................................................................9-31 9.3.7 Other Transportation Costs........................................................................................9-32 9.3.8 Long-Term Escalation of Transportation Rates .........................................................9-32 9.3.9 Market Drivers Moving Forward.................................................................................9-34 9.3.10 Other Considerations .................................................................................................9-36
9.4 C OAL 

### E XPORTS 

### , I MPORTS 

### , AND 

### N ON 

### -E LECTRIC 

### S ECTORS 

### D EMAND 

### .....................................9-36 Appendix 9-1. Illustrative Example of Wood Mackenzie Costing Procedure Used in Developing EPA’s Coal Supply Curves....................................................................9-1.1 Appendix 9-2 New Mines Included in 2040 Curves...............................................................9-2.1 Appendix 9-3 Coal Transportation Matrix in EPA Base Case v.4.10 ....................................9-3.1 Appendix 9-4 Coal Supply Curves in EPA Base Case v.4.10...............................................9-4.1



### 10 NATURAL GAS ................................................................................................... 10-1

10.1 O VERVIEW OF 

### IPM’ S 

### N ATURAL 

### G AS 

### M ODULE 

### .....................................................................10-1

10.2 K EY 

### C OMPONENTS OF THE 

### N EW 

### IPM N ATURAL 

### G AS 

### M ODULE 

### .............................................10-4
10.2.1 Note on the Modeling Time Horizon and Pre- and Post-2030 Input Assumptions..............................................................................................................10-6
10.3 R ESOURCE 

### C HARACTERIZATION AND 

### E CONOMIC 

### E VALUATION 

### .............................................10-7
10.3.1 Resource and Reserves Assessment........................................................................10-8 10.3.2 Frontier Resources (Alaska and Mackenzie Delta)..................................................10-10 10.3.3 Use of the HSM resource and reserves data in EPA Base Case using IPM v.4.10 Natural Gas Module......................................................................................10-12 10.3.4 Undiscovered Resource Appreciation......................................................................10-13
10.4 E XPLORATION 

### , D EVELOPMENT 

### , AND 

### P RODUCTION 

### C OSTS AND 

### C ONSTRAINTS 

### ....................10-14
10.4.1 Exploration and Development Cost .........................................................................10-14 10.4.2 Resource Discovery and Drilling Constraints...........................................................10-17 10.4.3 Reserves-to-Production (R/P) Ratio ........................................................................10-19 10.4.4 Variable Costs, Natural Gas Liquid Share, and Crude Oil Share ............................10-19 10.4.5 Lease and Plant Gas Use........................................................................................10-20

### ix 10.5 L IQUEFIED 

### N ATURAL 

### G AS 

### (LNG) I MPORTS 

### .......................................................................10-20
10.5.1 Liquefaction Facilities and LNG Supply ...................................................................10-20 10.5.2 Regasification Facilities ...........................................................................................10-21 10.5.3 LNG Regasification Capacity Expansions ...............................................................10-22
10.6 E ND 

### U SE 

### D EMAND 

### ...........................................................................................................10-23
10.6.1 Step 1: Developing Sector Specific Econometric Models of Non-Power Sector Demand...................................................................................................................10-23 10.6.2 Step 2: Use projections based on the GMM econometric models to produce monthly gas demand curves by sector and demand node......................................10-25 10.6.3 Step 3: Develop non-electric sector natural gas demand curves that correspond to the seasons and segments in the load duration curves used in IPM ..........................................................................................................................10 -25
10.7 P IPELINE 

### N ETWORK 

### .........................................................................................................10-27
10.7.1 Network Structure ....................................................................................................10-27 10.7.2 Pipeline Transportation Costs..................................................................................10-28 10.7.3 Pipeline Capacity Expansion Logic..........................................................................10-29
10.8 G AS 

### S TORAGE 

### .................................................................................................................10-30
10.8.1 Storage Capacity and Injection/Withdrawal Constraints..........................................10-33 10.8.2 Variable Cost and Fuel Use.....................................................................................10-33 10.8.3 Storage Capacity Expansion Logic..........................................................................10-34
10.9 F UEL 

### P RICES 

### ...................................................................................................................10-35
10.9.1 Crude Oil and Natural Gas Liquids Prices ...............................................................10-35 10.9.2 Natural Gas Prices...................................................................................................10-36
10.10 O UTPUTS AND 

### P ROXY 

### N ATURAL 

### G AS 

### S UPPLY 

### C URVES 

### .....................................................10-36
10.10.1 Outputs from the New IPM Natural Gas Module......................................................10-36 10.10.2 Proxy Natural Gas Supply Curves ...........................................................................10-36 Appendix 10-1 EPA Base Case v.4.10 with AEO Gas Resource Assumptions..................10-1.1



### 11 OTHER FUELS AND FUEL EMISSION FACTOR ASSUMPTIONS ...................11-1

11.1 F UEL 

### O IL 

### ...........................................................................................................................11-1

11.2 B IOMASS 

### ...........................................................................................................................11-2

11.3 N UCLEAR 

### F UEL 

### ..................................................................................................................11-3

11.4 W ASTE 

### F UELS 

### ...................................................................................................................11-3

11.5 F UEL 

### E MISSION 

### F ACTORS 

### ..................................................................................................11-5 Appendix 11-1 Biomass Supply Curve in EPA Base Case v.4.0 ...........................................11-1

### i 

## LIST OF TABLES

Table 1-1 Plant Types in EPA Base Case v.4.10.........................................................................1-2 Table 1-2 Emission Control Technologies in EPA Base Case v.4.10 ..........................................1-3 Table 1-3 Key Updates in the EPA Base Case v.4.10 ..................................................................1-4 Table 3-1 Mapping of NERC Regions and NEMS Regions with EPA Base Case v.4.10 Model Regions...........................................................................................................3-3 Table 3-2 Electric Load Assumptions in EPA Base Case v.4.10 .................................................3-4
Table 3-3 National Non-Coincidental Net Internal Demand .........................................................3-5 Table 3-4 Annual Transmission Capabilities of U.S. Model Regions in EPA Base Case v.4.10......................................................................................................................... 3-6 Table 3-5 Annual Joint Capacity and Energy Limits to Transmission Capabilities Between Model Regions in EPA Base Case v.4.10 .................................................................3-9 Table 3-6 International Electricity Imports in EPA Base Case v.4.10 ........................................3-11 Table 3-7 Availability Assumptions in the EPA Base Case v.4.10 .............................................3-12 Table 3-8 Seasonal Hydro Capacity Factors (%) in the EPA Base Case v.4.10 .......................3-12 Table 3-9 Planning Reserve Margins in EPA Base Case v.4.10 ...............................................3-14 Table 3-10 Lower and Upper Limits Applied to Heat Rate Data in NEEDS v.4.10 ....................3-17 Table 3-11 Emission and Removal Rate Assumptions for Potential (New) Units in EPA Base Case v.4.10 ....................................................................................................3-23
Table 4-1 Data Sources for NEEDS v.4.10 for EPA Base Case v.4.10 .......................................4-2 Table 4-2 Rules Used in Populating NEEDS v.4.10 for EPA Base Case v.4.10..........................4-3 Table 4-3 Summary Population (through 2006) of Existing Units in NEEDSv.4.10 for EPA Base Case v.4.10 ......................................................................................................4-3 Table 4-4 Hierarchy of Data Sources for Capacity in NEEDS v.4.10...........................................4-4 Table 4-5 Capacity-Parsing Algorithm for Steam Units in NEEDS v.4.10....................................4-4 Table 4-6 Data Sources for Unit Configuration in NEEDS v.4.10 for EPA Base Case v.4.10 .....4-6 Table 4-7 Aggregation Profile of Model Plants as Provided at Set Up of EPA Base Case v.4.10......................................................................................................................... 4-7 Table 4-8 VOM Assumptions (2007$) in EPA Base Case v.4.10 ................................................4-9 Table 4-9 FOM Assumptions Used in EPA Base Case v.4.10...................................................4-11 Table 4-10 Life Extension Cost Assumptions Used in EPA Base Case v.4.10..........................4-13 Table 4-11 Summary of Planned-Committed Units in NEEDS v.4.10 for EPA Base Case v.4.10.......................................................................................................................4- 14 Table 4-12 Planned-Committed Units by Model Region in NEEDS v.4.10 for EPA Base Case v.4.10..............................................................................................................4-14 Table 4-13 Performance and Unit Cost Assumptions for Potential (New) Capacity from Conventional Technologies in EPA Base Case v4.10.............................................4-20 Table 4-14 Short-Term Capital Cost Adders for New Power Plants in EPA Base Case v.4.10 (2007$)..........................................................................................................4-23 Table 4-15 Regional Cost Adjustment Fact ors for Conventional and Renewable Generating Technologies in EPA Base Case v.4.10..................................................................4-25 Table 4-16 Performance and Unit Cost Assumptions for Potential (New) Renewable and Non-Conventional Technology Capacity in EPA Base Case v4.10.........................4-27 Table 4-17 Onshore Regional Potential Wind Capacity (MW) by Wind and Cost Class in EPA Base Case v.4.10 ............................................................................................4-29 Table 4-18 Offshore Shallow Regional Pot ential Wind Capacity (MW) by Wind and Cost Class in EPA Base Case v.4.10 ..............................................................................4-32

### ii Table 4-19 Offshore Deep Regional Potentia l Wind Capacity (MW) by Wind and Cost Class in EPA Base Case v.4.10 ........................................................................................4-34 Table 4-20 Onshore Reserve Margin Contribution an Average Capacity Factor by Wind Class and Model Region..........................................................................................4-37 Table 4-21 Offshore Shallow Reserve Margin Contribution an Average Capacity Factor by Wind Class and Model Region ................................................................................4-37 Table 4-22 Offshore Deep Reserve Margin C ontribution an Average Capacity Factor by Wind Class and Model Region ................................................................................4-38 Table 4-23 Capital Cost Adjustment Factors for New Wind Plants in Base Case v.4.10...........4-39 Table 4-24 Example Calculations Of Wind Generation Potential, Reserve Margin Contribution, And Capital Cost For Onshore Wind In CA-N At Wind Class 7, Cost Class 2 ............................................................................................................4-40 Table 4-25 Solar Reserve Margin Contribution and Average Capacity Factor by Model Region .....................................................................................................................4-41 Table 4-26 Regional Assumptions on Potential Geothermal Electric Capacity..........................4-42 Table 4-27 Potential Geothermal Capacity and Co st Characteristics by Model Region ............4-42 Table 4-28 Regional Assumptions on Potential Electric Capacity from New Landfill Gas Units (MW)...............................................................................................................4-43 Table 4-29 Nuclear Upratings (MW) as Incorporated in EPA Base Case v.4.10 from AEO 2010.........................................................................................................................4- 45 Table 5-1 Summary of Emission Control Technology Retrofit Options in EPA Base Case v.4.10 ......................................................................................................................... 5-1 Table 5-2 Summary of Retrofit SO 2 

### Emission Control Performance Assumptions .......................5-2 Table 5-3 Capital Cost Modules and Their Governing Variables for SO 2 

### and NO x 

### Emission Controls .....................................................................................................................5- 3 Table 5-4 Illustrative Scrubber Costs (2007$) for Representative Sizes and Heat Rates under the Assumptions in EPA Base Case v.4.10.....................................................5-6 Table 5-5 Cost (2007$) of NO x 

### Combustion Controls for Coal Boilers (300 MW Size).................5-7
Table 5-6 Incremental Combustion NO x 

### Controls in EPA Base Case v.4.10................................5-8 Table 5-7 Summary of Retrofit NO x 

### Emission Control Performance Assumptions ......................5-8
Table 5-8 Illustrative Post Combustion NO X 

### Controls for Coal Plants Costs (2007$) for Representative Sizes and Heat Rates under the Assu Assumptions in EPA Base Case v.4.10 ....................................................................................................5-11
Table 5-9 Post-Combustion NO X 

### Controls for Oil/Gas Steam Units in EPA Base Case v.4.10 .......................................................................................................................5- 12 Table 5-10 Biomass Cofiring for Coal Plants .............................................................................5-15 Table 5-11 Mercury Clusters and Mercury C ontent of Coal by IPM Coal Types........................5-17 Table 5-12 Assumptions on Mercury Concentration in Non-Coal Fuel in EPA Base Case v.4.10 .......................................................................................................................5- 17 Table 5-13 Mercury Emission Modification Factors Used in EPA Base Case v.4.10.................5-19 Table 5-14 Definition of Acronyms for Existing Controls............................................................5-26 Table 5-15 Key to Burner Type Designations in Table 5-13 .....................................................5-26 Table 5-16 Illustrative Activated Carbon Injection Costs (2007$) for Representative Sizes under the Assumptions in EPA Base Case v.4.10...................................................5-28 Table 5-17 Assignment Scheme for Mercury Emissions Control Using Activated Carbon Injection (ACI) in EPA Base Case v.4.10.................................................................5-29 Table 6-1 Performance and Unit Cost Assumptions for Carbon Capture Retrofits on Pulverized Coal Plants...............................................................................................6-1

### iii Table 6-2 CO 2 

### Transport Cost Calculation Example – MACS to Louisiana Onshore ..................6-5 Table 7-1 Run Years and Analysis Year Mapping Used in the EPA Base Case v.4.10 ..............7-1 Table 7-2 First Stage Retrofit Assignment Scheme in EPA Base Case v.4.10............................7-2 Table 7-3 Second Stage Retrofit Assignment Scheme in EPA Base Case v.4.10 ......................7-3 Table 7-4 Trading and Banking Rules in EPA Base Case v.4.10 ................................................7-4 Table 7-5 Post-2030 Assumptions in EPA Base Case v.4.10......................................................7-5 Table 8-1 U.S. Discount Rates and Capital Charge Rates in EPA Base Case v4.10..................8-2 Table 8-2 Capital Structure Assumptions for EPA Base Case v4.10...........................................8-6
Table 8-3 Debt Rates for EPA Base Case v4.10 .........................................................................8-8 Table 8-4 Book Life, Debt Life and Depreciation Schedules for EPA Base Case v. 4.10..........8-11 Table 9-1 Coal Supply Regions in EPA Base Case.....................................................................9-2 Table 9-2 Coal Demand Regions in EPA Base Case ..................................................................9-3 Table 9-3 Coal Rank Heat Content Ranges...............................................................................9-10
Table 9-4 Coal Grade SO 2 

### Content Ranges ..............................................................................9-10 Table 9-5 Coal Quality Characterist ics by Supply Region and Coal Grade ...............................9-11
Table 9-6 SO 2 

### Emission Factors of Coal Used in EPA Base Case v.4.10.................................9-13 Table 9-7 Mercury Emission Factors of Coal Used in EPA Base Case v.4.10 ..........................9-14
Table 9-8 Ash Emission Factors of Coal Used in EPA Base Case v.4.10 .................................9-14
Table 9-9 CO 2 

### Emission Factors of Coal Used in EPA Base Case v.4.10.................................9-15 Table 9-10 Example of Coal Assignments Made in EPA Base Case ........................................9-16 Table 9-11 Basin-Level Groupings Used in Preparing v.4.0 Coal Supply Curves .....................9-17 Table 9-12 Rail Competition Definitions.....................................................................................9- 28 Table 9-13 Assumed Eastern Rail Rates (2007 mills/ton-mile)..................................................9-29 Table 9-14 Assumed Midwestern Rail Rates (2007 mills/ton-mile)............................................9-29 Table 9-15 Assumed Non-PRB Western Rail Rates (2007 mills/ton-mile) ................................9-30 Table 9-16 Assumed PRB Western Rail Rates (2007 mills/ton-mile) ........................................9-30 Table 9-17 Assumed Truck Rates (2007 Real Dollars)..............................................................9-30 Table 9-18 Assumed Barge Rates (2007 Real Dollars) .............................................................9-31 Table 9-19 Assumed Transportation Rates for Imported Coal (2007 Real Dollars)...................9-31 Table 9-20 Assumed Other Transportation Rates (2007 Real Dollars)......................................9-32 Table 9-21 EIA AEO Diesel Fuel Forecast, 2012-2030 (2007 Real Dollars) .............................9-34
Table 9-22 ABARE Forecast of Iron Ore Prices.........................................................................9-35
Table 9-23 Assumed Production Growth Rates.........................................................................9-36 Table 9-24 Coal Exports....................................................................................................... ......9-37 Table 9-25 Residential, Commercial, and Industrial Demand....................................................9-38 Table 9-26 Coal Import Limits ................................................................................................. ...9-38
Table 10-1 List of Nodes ...................................................................................................... ......10-5 Table 10-2 List of Gas Supply Regions......................................................................................10- 1 Table 10-3 List of Key Pipelines.............................................................................................. ...10-3
Table 10-4 U.S. and Canada Natural Gas Resources and Reserves......................................10-10 Table 10-5 Exploration and Development Assumptions for EPA Base Case v.4.10 ...............10-17 Table 10-6 North American LNG Regasification Facilities .......................................................10-21 Table 10-7 Summer and Winter Load Segments in EPA Base Case v.4.10 ...........................10-26 Table 10-8 List of Storage Nodes.............................................................................................1 0-31 Table 10-9 Storage Capacity and Injection/Withdrawal Rates (EOY 2010).............................10-33

### iv Table 10-10 Base Year 2011 Average Levelized Storage Capital Cost...................................10-35 Table 10-11 Proxy Natural Gas Supply Curves for EPA Base Case v.4.10.............................10-37 Table 10-12 Glossary of Natural Gas Terms Used in Documentation .....................................10-38 Table 11-1 Fuel Oil Prices by NEMS Region in EPA Base Case v.4.10....................................11-1 Table 11-2 Non-Electric Biomass Demand by NEMS Region in EPA Base Case v.4.10 ..........11-2 Table 11-3 Waste Fuels in NEEDS, v.4.10 and EPA Base Case v.4.10 ...................................11-4 Table 11-4 Fuel Emission Factor Assumptions in EPA Base Case v.4.10 ................................11-5

### v 

## LIST OF FIGURES

Figure 1-1 Modeling and Data Structures in EPA Base Case v.4.10...........................................1-4 Figure 2-1 Hypothetical Chronological Hourly Load Curve and Seasonal Load Duration Curve in EPA Base Case v.4.10................................................................................2-7 Figure 2-2 Stylized Depiction of Load Duration Curve Used in EPA Base Case v.4.10 ..............2-8 Figure 2-3 Stylized Dispatch Order in EPA Base Case v.4.10.....................................................2-9 Figure 3-1 EPA Base Case v.4.10 Model Regions ......................................................................3-2 Figure 3-2 Scheduled Retirements of Existi ng Nuclear Capacity Under 60-Year Life Assumption..............................................................................................................3-15 Figure 9-1 Map of the Coal Supply Regions in EPA Base Case v.4.10.......................................9-3 Figure 9-2 Cluster Mapping Example --- BG Coal......................................................................9-15 Figure 9-3 Cost Calculations Included When Developing Coal Supply Curves (based on a Powder River Basin Mine Supply Curve Example)..................................................9-18 Figure 9-4 Illustration of Preliminary Step in Developing a Cumulative Coal Supply Curve ......9-20 Figure 9-5 Illustration of Final Step in Developing a Cumulative Coal Supply Curve ................9-21 Figure 9-6 Example Coal Suppl y Curve in Stepped Format ......................................................9-21 Figure 9-7 Calculation of Multi-Mode Transportation Costs (Example)......................................9-26 Figure 9-8 Coal Demand Region with Multiple Coal Supply Regions ........................................9-27 Figure 9-9 Rail Cost Indices Performance (2Q2003-2Q2008) ...................................................9-33 Figure 9-10 Long-Run Marginal Cost Breakdown by Transportation Mode...............................9-34 Figure 10-1 Modeling and Data Structure in EPA Base Case v.4.10.........................................10-2 Figure 10-2 Natural Gas Module in EPA Base Case v.4.10. .....................................................10-3 Figure 10-3 Gas Transmission Network Map.............................................................................10-5 Figure 10-4 Gas Supply Regions Map .......................................................................................10-8 Figure 10-5 Gas Demand Regions Map.....................................................................................10-3 Figure 10-6 Natural Gas Storage Facility Node Map .................................................................10-7 Figure 10-7 Resource Cost Curves at the End of Year 2010...................................................10-13 Figure 10-8 Exploration & Development and Production Processes and Costs to Bring Undiscovered Resource into Reserves and Production ........................................10-14 Figure 10-9 E&D and Production Technology Improvement Factor.........................................10-15 Figure 10-10 Incremental E&D Cost (EOY 2010) by Percentage of Resource Found.............10-15 Figure 10-11 Drilling Rig Speed Constraint..............................................................................10-19 Figure 10-12 North American LNG Supply Curves ..................................................................10-21 Figure 10-13 North American LNG Regasification Facilities Map............................................10-22 Figure 10-14 Examples of Firm Demand Curves by Electric Load Segment...........................10-26 Figure 10-15 Examples of Interruptible Demand Curves by Electric Load Segment ...............10-27 Figure 10-16 New England Pipeline Corridors in 2020 ............................................................10-28 Figure 10-17 Example Pipeline Discount Curve.......................................................................10-29 Figure 10-18 Pipeline Cost Growth Factor...............................................................................10-29 Figure 10-19 Storage Cost Growth Factor ...............................................................................10-34 Figure 10-20 Example Maximum Storage Capacity Expansion...............................................10-34 Figure 10-21 Crude Oil and NGL Prices ..................................................................................10-36

### 1-1 

# 1 Introduction
This document describes the nature, structure, and capabilities of the Integrated Planning Model (IPM) and the assumptions underlying the base case (designated EPA Base Case v.4.10) that was developed by the U.S. Environmental Protection Agency (EPA) with technical support from ICF Consulting, Inc. IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector. It provides fore casts of least cost capacity expansion, electricity dispatch, and emission control strategies while meeting energy demand and environmental, transmission, dispatch, and reliability constraints. IPM can be used to evaluate the cost and emissions impacts of proposed policies to limit emissions of sulfur dioxide (SO 2 

### ), nitrogen oxides (NO x 

### ), carbon dioxide (CO 2 

### ), and mercury (Hg) from the electric power sector.

Base cases, like EPA Base Case v.4.10, serve as the starting point against which policy scenarios are compared. It is a projection of electricity sect or activity that takes into account only those Federal and state air emission laws and regulations whose provisions were either in effect or enacted and clearly delineated at the time the bas e case was finalized in August 2010. (Chapter 3 contains a detailed discussion of the environmental regulations included in EPA Base Case v.4.10.) Regulations, mandated under the Clean Air Act Amendments of 1990 (CAAA), but whose provisions either have not yet been finalized or will expire due to Court action, were not included in the base case. These include.

• Ozone and Particulate Matter (PM) Standards : EPA Base Case v.4.10 does not include the provisions of the Clean Air Interstate Rule (C AIR), a Federal regulatory measure for achieving the National Ambient Air Quality Standards (NAAQS) for ozone (8-hour standard of 0.08 ppm) and fine particles (24-hour average of 65 μ g/m3 or less and annual mean of 15 μ g/m3 for particles of diameter 2.5 micrometers or less, i.e., PM 2.5). Originally issued on March 10, 2005, CAIR was remanded back to EPA by the U.S. Court of Appeals for the District of Columbia Circuit in December 2008 and EPA was required to correct legal flaws in the regulations that had been cited in a ruling by the Court in July 2008. Until EPA’s work was completed, CAIR was temporarily reinstated. However, although CAIR’s provisions were still in effect when EPA Base Case v.4.10 was released, it is not included in the base case to allow EPA Base Case v.4.10 to be used to analyze the regulations proposed to replace CAIR.

• EPA Base Case v.4.10 includes ozone and particulate matter standards to the extent that some of the state regulations included in EPA Base Case v.4.10 contain measures to bring non-attainment areas into attainment. A summary of these state regulations can be found in Appendix 3-2 below. Apart from these state regulat ions, individual permits issued by states in response to ozone and PM standards are only captured (a) to the extent that they are reflected in the NO X 

### rates reported to EPA under Title IV and the NO X 

### Budget Program which are incorporated in the base case and (b) to the extent that SO 2 

### permit limits are used in the base case to define the choice of coal sulfur grades that are available to specific power plants.

• Regional Haze : On July 1, 1999, EPA issued Regional Haze Regulations to meet the national goal for visibility established in Section 169A of the CAAA, which calls for “prevention of any future, and the remedying of any existing, impairment of visibility in Class I areas [156 national parks and wilderness areas], which impairment results from manmade air pollution.” The regulations required states to submit revised State Implementation Plans (SIPs) that include (1) goals for improving visibility in Class I areas on the 20% worst days and allowing no degradation on the 20% best days and (2) assessments and plans for achieving Best Available Retrofit Technology (BART) emi ssion targets for sources placed in operation between 1962-1977. The revised SIPs were to be submitted between 2004-2006 for areas designated as “attainment” and “unclassified” and between 2006-2008 for “nonattainment” areas. They are represented in EPA Base Case v.4.10 to the extent that SO 2 

### permit limits derived from the SIPs are used in the base case to define the choice of coal sulfur grades that are available to specific power plants. As discussed in chapter 3, however, the base case includes the sulfur dioxide emission cap (144.7 MTons for all affected fossil fired generating units larger than 25 MW), adopted by the Western Regional Air Partnership states of Arizona,

### 1-2 New Mexico, Oregon, Utah, and Wyoming in response to Section 309 of the federal Regional Haze Rule.

In effect, EPA Base Case v.4.10 offers a projection of the electric sector assuming that the only future environmental regulations are those that were in place at the time the base case was finalized and that have a high likelihood of remaining in force. This simplifying assumption ensures that the base case is policy neutral with respect to prospective, future environmental policies. Table 1-1 lists the types of plants included in the EPA Base Case v.4.10. Table 1-2 lists the emission control technologies available for meeting emission limits in EPA Base Case v.4.10.

Table 1-1 Plant Types in EPA Base Case v.4.10
Fossil Fuel Fired Coal Steam Oil/Gas Steam Combustion Turbine Combined-Cycle Combustion Turbine Integrated Gasification Combined-Cycle (IGCC) Coal Advanced Coal with Carbon Capture Fluidized Bed Combustion
Non-Fossil Fuel Fired Nuclear Renewables and Non-Conventional Technologies Hydropower Pumped Storage Biomass IGCC Onshore Wind Offshore Shallow Wind Offshore Deep Wind Fuel Cells Solar Photovoltaics Solar Thermal Geothermal Landfill Gas
Other 1



### Note:

1 

### Includes fossil and non-fossil waste plants

### 1-3 Table 1-2 Emission Control Technologies in EPA Base Case v.4.10

Sulfur Dioxide (SO 2 

### ) Nitrogen Oxides (NO x 

### )
Limestone Forced Oxidation (LSFO) Lime Spray Dryer (LSD) Combustion controls Selective catalytic reduction (SCR) Selective non-catalytic reduction (SNCR)
Mercury (Hg) Carbon Dioxide (CO 2 

### )

Combinations of SO 2 

### , NO x 

### , and particulate control technologies Carbon Capture and Sequestration

Activated Carbon Injection

Notes: 1. Though not listed in Table 1-2, biomass co-firing, which is offered as a fuel option in EPA Base Case v.4.10, is also used as a CO 2 

### emission control option. See section 5.3 in Chapter 5 for a description of the implementation of biomass co-firing in EPA Base Case v.4.10. 2. Fuel switching between coal types and to natural gas is also a compliance option for reducing emissions in EPA Base Case v.4.10

Figure 1-1 provides a schematic of the components of the modeling and data structure used for EPA Base Case v.4.10. This report devotes a separate chapter to all the key components shown in Figure 1-1. Chapter 2 provides an overview of IPM’s modeling framework (sometimes referred to as the “IPM Engine”), highlighting the mathematical structure, notable features of the model, programming elements, and model inputs and outputs. The remaining chapters are devoted to different aspects of EPA Base Case v.4.10. Chapter 3 covers the power system operating characteristics captured in EPA Base Case v.4.10. Chapter 4 explores the characterization of electric generation resources. Emission contro l technologies (chapter 5) and carbon capture, transport and storage (chapter 6) are then presented. Chapter 7 describes certain set-up rules and parameters employed in EPA Base Case v.4.10. Chapter 8 summarizes the base case financial assumptions. The last three chapters discuss the representation and assumptions for fuels in the base case. Coal is covered in chapter 9, natural gas in chapter 10, and other fuels (i.e., fuel oil, biomass, nuclear fuel, and wast e fuels) in chapter 11 (along with fuel emission factors).

### 1-4 Figure 1-1 Modeling and Data Structures in EPA Base Case v.4.10

Table 1-3 lists key updates included in EPA Base Case v.4.10 listed in the order they appear inthis documentation report. Noted by asterisks in the second column are updates that were “nonroutine” in the sense that they constituted new modeling capabilities or notable extensions beyond the capabilities provided in previous EPA base cases or significant reviews of important assumptions. The updates that are not starred represent more routine updates. Equal in importance to the non-routine updates, the routine updates typically require substantial effort and great discipline to maintain. They are critical to the technical credibility of a detailed, bottom-up, data driven model like IPM.

Table 1-3 Key Updates in the EPA Base Case v.4.10

Description NonRoutine (*) For More Information
Modeling Framework

Use of six-segment load duration curves (2012-2030) to enable differentiation of peak and super-peak generating unit dispatch §2.3.5 - 2.3.6

Power System Operation Model region update and inclusion of Canada, Alaska, Hawaii, Puerto Rico, and U.S. Virgin Islands * §3.1

Updates based on recent data from EIA, NERC, FERC, etc. (multiple)
Capacity deployment constraints (for new advanced coal with carbon capture, carbon capture retrofits, and new nuclear) * §3.10 App. 3-07 - 3-08

### 1-5 Description NonRoutine (*) For More Information
Updated inventory of state emission regulations App.3-02
Updated inventories of NSR, state, and citizen settlements App. 3-03 - 3-05 Nuclear retirement at age 60 * §3.7 Generating ResourcesUpdates to NEEDS, the database of existing and plannedcommitted units and their emission control configurations §4.1- 4.3

Providing life extension cost option to allow existing units to continue operation over the extended 2012-2050 modeling time horizon in the new base case §4.2.8

Updated cost and performance characteristics for potential (new) conventional and nuclear generating units, based on comparative cost analyses * §4.4.1 - 4.4.2

Adding biomass gasification combined cycle and offshore (shallow and deep) wind as potential (new) renewable generating options * §4.4.5

Expanding wind resource base to include 5 wind classes (3-7) by adding new wind classes 3 and 7 using data provided by NREL * §4.4.5

Emission Control Technologies Complete update of cost and performance assumptions for SO 2 

### and NO x 

### emission controls based on engineering studies by Sargent and Lundy * §5.1 - 5.2

Inclusion of cost and performance assumptions for SO 2 

### and NO x 

### emission controls for units with capacities ranging from 25-100MW * §5.2.6

Updated cost and performance assumptions for biomass co-firing by coal units §5.3

Carbon Capture, Transport and Storage Update of the cost and performance characteristics used to represent carbon capture retrofits and new generating units with carbon capture §6.1

CO 2 

### transport and storage represented through state level transportation matrix and regional storage cost curves rather than a single cost adder * §6.2 - 6.3

Set-Up Parameters and Rules Expanded modeling time horizon out to 2050 with six model run years (2012, 2015, 2020, 2030, 2040, 2050) * §7.1 and 7.4

Five generic placeholder future generation technologies for later use in comparative scenario analyses §7.4

Financial assumptions Update of discount and capital charge rate assumptions based on a new hybrid capital cost model of utility and merchant finance structures §8.1.1 and 8.2

Capital cost adder for climate change uncertainty §8.1.1

### 1-6 Description NonRoutine (*) For More Information
Incorporation of latest legislation based tax credits for renewables and nuclear generation §8.1.1 - 8.1.2 Coal
Complete update of coal supply curves and transportation matrix * §9

Natural Gas Development of IPM natural gas module providing completely endogenous comprehensive modeling of North American natural gas system * §10

Major revision of unconventional gas resource base, particularly view of shale gas resources * §10.3

Base case variant with natural gas resource assumptions similar to AEO 2010 Appendix 10.1 Other Fuels Update of biomass supply curves and price assumptions for fuel oil, nuclear fuel and waste fuel * §11

### 2-1 

# 2 Modeling Framework
ICF International developed the Integrated Planning M odel (IPM) to support analysis of the electric sector. The EPA, in addition to other state air regulatory agencies, utilities, and public and private sector clients, has used IPM extensively for various air regulatory analyses, market studies, strategy planning, and economic impact assessments.

The first section of this chapter provides a brief overview of the model’s purpose, capabilities, and applications. The following sections are devoted to describing the IPM model’s structure and formulation, key methodological characteristics, and programming features, including its handling of model inputs and outputs. Readers may find so me overlap between sections. For example, transmission decision variables and constraints are covered in section 2.2's discussion of model structure and formulation, and transmission modeling is covered as a key methodological feature in section 2.3.9. The different perspectives of each section are designed to provide readers with information that is complementary rather than repetitive.



## 2.1 IPM Overview

IPM is a well-established model of the electr ic power sector designed to help government and industry analyze a wide range of issues related to this sector. The model represents economic activities in key components of energy markets – fuel markets, emission markets, and electricity markets. Since the model captures the linkages in electricity markets, it is well suited for developing integrated analyses of the impacts of alternative regulatory policies on the power sector. In the past, applications of IPM hav e included capacity planning, environmental policy analysis and compliance planning, wholesale pr ice forecasting, and asset valuation.

2.1.1 Purpose and Capabilities IPM is a dynamic linear programming model that generates optimal decisions under the assumption of perfect foresight. It determines the least-cost method of meeting energy and peak demand requirements over a specified period. In its solution, the model considers a number of key operating or regulatory constraints (e.g. em ission limits, transmission capabilities, renewable generation requirements, fuel market constraint s) that are placed on the power, emissions, and fuel markets. In particular, the model is well-suited to consider complex treatment of emission regulations involving trading, banking, and special provisions affecting emission allowances (like bonus allowances and progressive flow control), as well as traditional command-and-control emission policies.

IPM represents power markets through model regions that are geographical entities with distinct characteristics. While they are more numerous (for purposes of picking up local transmission behavior and bottlenecks), the model regions representing the U.S. power market in EPA Base Case v.4.10 are largely consistent with the regions and sub-regions constituting the North American Electric Reliability Council (NERC) regions and with the organizational structures of the Regional Transmission Organizations (RTOs) and Independent System Operators, which handle dispatch on most of the U.S. grid. IPM models the electric demand, generation, transmission, and distribution within each region as well as the in ter-regional transmission grid. All existing utility power generation units, including renewable resources, are modeled, as well as independent power producers and cogeneration facilities that sell electricity to the grid.

IPM provides a detailed representation of new and existing resource options, including fossil generating options (coal steam, gas-fired simple cycle combustion turbines, combined cycles, and oil/gas steam), nuclear generating options, and renewable and non-conventional (e.g., fuel cells) resources. Renewable resource options include wind, landfill gas, geothermal, solar thermal, solar photovoltaic and biomass.

IPM can incorporate a detailed representation of fuel markets and can endogenously forecast fuel prices for coal, natural gas, and biomass by balancing fuel demand and supply for electric

### 2-2 generation. The model also includes detailed fuel quality parameters to estimate emissions from electric generation.

IPM provides estimates of air emission changes , regional wholesale energy and capacity prices, incremental electric power system costs, changes in fuel use, and capacity and dispatch projections.

2.1.2 Applications IPM’s structure, formulation and set-up make it very adaptable and flexible. The necessary level of data, modeling capabilities exercised, and computational requirements can be tailored to the particular strategies and policy options being anal yzed. This adaptability has made IPM suitable for a variety of applications. These include:

Air Regulatory Assessment: Since IPM contains extensive ai r regulatory modeling features, state and federal air regulatory agencies have us ed the model extensively in support of air regulatory assessment.

Integrated Resource Planning: IPM can be used to perform least-cost planning studies that simultaneously optimize demand-side options (load management and conservation), renewable options and traditional supply-side options.

Strategic Planning: IPM can be used to assess the costs and risks associated with alternative utility and consumer resource planning strategies as characterized by the portfolio of options included in the input data base.

Options Assessment: IPM allows industry and regulatory planners to "screen" alternative resource options and option combinations based upon their relative costs and contributions to meeting customer demands.

Cost and Price Estimation: IPM produces realistic estimates of energy prices, capacity prices, fuel prices, and allowance prices. Industry and regulatory agencies have used these cost reports for due diligence, planning, litigation and economic impact assessment.



## 2.2 Model Structure and Formulation IPM employs a linear programming structure that is particularly well-suited for analysis of the electric sector to help decision makers plan syst em capacity and model the dispatch of electricity from individual units or plants. The model consists of three key structural components:

• A linear “objective function,”

• A series of “decision variables,” and

• A set of linear “constraints”.

• The sections below describe the objective f unction, key decision variables, and constraints included in IPM for EPA Base Case v.4.10.

2.2.1 Objective Function IPM’s objective function is to minimize the tota l, discounted net present value, of the costs of meeting demand, power operation constraints, and environmental regulations over the entire planning horizon. The objective function represents the summation of all the costs incurred by the electricity sector. The total resulting cost is expressed as the net present value of all the component costs. These costs, which the linear programming formulation attempts to minimize, include the cost of new plant and pollution cont rol construction, fixed and variable operating and maintenance costs, and fuel costs. Many of these cost components are captured in the objective function by multiplying the decision variables by a cost coefficient. Cost escalation factors are used in the objective function to reflect changes in cost over time. The applicable discount rates

### 2-3 are applied to derive the net present value for the entire planning horizon from the costs obtained for all years in the planning horizon.

2.2.2 Decision VariablesDecision variables represent the values which the IPM model is “solving for,” given the costminimizing objective function described in section 2.2.1 and the set of electric system constraints detailed in section 2.2.3. The decision variables values are the model’s “outputs” and represent the optimal least-cost solution for meeting the assumed constraints. Key decision variables represented in IPM are described in detail below.

Generation Dispatch Decision Variables: IPM includes decision variables representing the generation from each model power plant 1 

### . For each model plant, a separate generation decision variable is defined for each possible combination of fuel, season, model run year, and segment of the seasonal load duration curve applicable to the model plant. (See section 2.3.5 below for a discussion of load duration curves.) In the obj ective function, each plant’s generation decision variable is multiplied by the relevant heat rate and fuel price (differentiated by the appropriate step of the fuel supply curve) to obtain a fuel cos t. It is also multiplied by the applicable variable operation and maintenance (VOM) cost rate to obtain the VOM cost for the plant.

Capacity Decision Variables: IPM includes decision variables representing the capacity of each existing model plant and capacity additions associ ated with potential (new) units in each model run year. In the objective function, the dec ision variables representing existing capacity and capacity additions are multiplied by the relevant fixed operation and maintenance (FOM) cost rates to obtain the total FOM cost for a plant. The capacity addition decision variables are also multiplied by the investment cost and capital charge rates to obtain the capital cost associated with the capacity addition.

Transmission Decision Variables: IPM includes decision variables representing the electricity transmission along each transmission link between m odel regions in each run year. In the objective function, these variables are multiplied by variable transmission cost rates to obtain the total cost of transmission across each link.

Emission Allowance Decision Variables: For emission policies where allowance trading applies, IPM includes decision variables representi ng the total number of emission allowances for a given model run year that are bought and sold in that or subsequent run years. In the objective function, these year-differentiated allowance decision variables are multiplied by the market pricefor allowances prevailing in each run year. This formulation allows IPM to capture the intertemporal trading and banking of allowances.

Fuel Decision Variables: For each type of fuel and each model run year, IPM defines decision variables representing the quantity of fuel deliver ed from each fuel supply region to model plants in each demand region. Coal decision variables are further differentiated according to coal rank (bituminous, sub-bituminous, and lignite), sulfur grade (see section 1 and Table 9-4), and mercury content (see sections 5.4.1and 1 and Table 9-7). These fuel quality decision variables do not appear in the IPM objective function, but in constr aints which define the types of fuel that each model plant is eligible to use and the supply regi ons that are eligible to provide fuel to each specific model plant.

1 

### Model plants are aggregate representations of real life electric generating units. They are used by IPM to model the electric power sector. For a discussion of model plants in EPA Base Case v.4.10, see section 4.2.6.

### 2-4 2.2.3 Constraints Model constraints are implemented in IPM to accu rately reflect the characteristics of and the conditions faced by the electric sector. Among the key constraints included in EPA Base Case v.4.10 are:

Reserve Margin Constraints: Regional reserve margin constraints capture system reliability requirements by defining a minimum margin of reserve capacity (in megawatts) per year. If existing plus planned capacity is not enough to satisfy the annual regional reserve margin requirement, the model will “build” the required level of new capacity.

Demand Constraints: The model categorizes regional annual electricity demand into seasonal load segments which are used to form summer (May 1 - September 30) and winter (October 1 - April 30) load duration curves (LDC). The seas onal load segments when taken together represent all the hourly electricity load levels that must be satisfied in a region in the particular season for a particular model run year. As such, the LDC defines the minimum amount of generation required to meet the region’s electrical demand during the specific season. These requirements are incorporated in the model’s demand constraints.

Capacity Factor Constraints: These constraints specify how much electricity each plant can generate (a maximum generation level), given its capacity and seasonal availability.

Turn Down/Area Protection Constraints: The model uses these constraints to take into account the cycling capabilities of the units, i.e., w hether or not they can be shut down at night or on weekends, or whether they must operate at all times, or at least at some minimum capacity level. These constraints ensure that the model re flects the distinct operating characteristics of peaking, cycling, and base load units.

Emissions Constraints: IPM can consider an array of emissions constraints for SO 2 

### , NO X 

### , mercury, and CO 2 

### . Emission constraints can be implem ented on a plant-by-plant, regional, or system-wide basis. The constraints can be defined in terms of a total tonnage cap (e.g., tons of SO 2 

### ) or a maximum emission rate (e.g., lb/MMBtu of NO X 

### ). The scope, timing, and definition of the emission constraints depend on the required analysis.

Transmission Constraints: IPM can simultaneously model any number of regions linked by transmission lines. The constraints define either a maximum capacity on each link, or a maximum level of transmission on two or more links (joint limits) to different regions.

Fuel Supply Constraints: These constraints define the types of fuel that each model plant is eligible to use and the supply regions that are eligib le to provide fuel to each specific model plant. A separate constraint is defined for each model plant.



## 2.3 Key Methodological Features of IPM IPM is a flexible modeling tool for obtaining shor t- and long-term projections of production activity in the electric generation sector. The projections obtained using IPM are not statements of what will happen but what might happen given the assumptions and methodologies used. Chapters 3- 11 contain detailed discussions of the cost and performance assumptions specific to the EPA Base Case v.4.10. This section provides an overview of the essential methodological and structural features of IPM, that extend beyond the assumptions that are specific to EPA Base Case v.4.10.

2.3.1 Model Plants Model plants are a central structural component that IPM uses in three ways: (1) to represent aggregations of existing generating units, (2) to represent retrofit and retirement options that are available to existing units, and (3) to represent potential (new) units that the model can build.

### 2-5 Existing Units: Theoretically, there is no predefined limit on the number of units that can be included in IPM. However, to keep model size and solution time within acceptable limits, EPA utilizes model plants to represent aggregations of actual individual generating units. The aggregation algorithm groups units with similar char acteristics into model plants with a combined capacity and weighted-average characteristics that are representative of all the units comprising the model plant. Model plants are defined to maximize the accuracy of the model’s cost and emissions estimates by capturing variations in key features of those units that are critical in the base case and anticipated policy case runs. For EPA Base Case v.4.10, IPM employed an aggregation algorithm which allowed 15,023 actual existing electric generating units to be represented by 4,738 model plants. Section 4.2.6 describes the aggregation procedure used in the EPA Base Case v.4.10.

Retrofit and Retirement Options: IPM also utilizes model plants to represent the retrofit and retirement options that are available to existi ng units. EPA Base Case v.4.10 provides existing model plants with the option to retire early and with a wide range of options for retrofitting with emission control equipment. (See Chapters 5 and section 7.2 in Chapter 7 for a detailed discussion of the options that are included in the EPA Base Case v.4.10.)

The options available to each model plant are pre-defined at the model’s set-up. The retrofit and retirement options are themselves represented in IPM by model plants, which, if actuated in the course of a model run, take on all or a portion of the capacity initially assigned to a model plant which represents existing generating units. EPA Base Case v.4.10 model plants that represent potential (new) units are not given the option to take on a retrofit or retire. In setting up IPM, parent-child-grandchild relationships are pre- defined between each existing model plant (parent) and the specific retrofit and retirement model pl ants (children and grandchildren) that may replacethe parent model plant during the course of a model run. The “child” and “grandchild” modelplants are inactive in IPM unless the model finds it economical to engage one of the options provided, e.g., retrofit with particular emission controls or retire early.

Theoretically, there are no limits on the number of “child,” “grandchild,” and even “greatgrandchild” model plants (i.e., retrofit and retire ment options) that can be associated with each existing model plant. However, model size and co mputational considerations dictate that the number of successive retrofits be limited. In EPA Base Case v.4.10, a maximum of two stages of retrofit options are provided (child and grandchild, but not great-grandchild). For example, an existing model plant may be retrofit with a limestone forced oxidation (LSFO) SO 2 

### scrubber and with a selective catalytic reduction (SCR) control for NO X 

### in one model run year (stage 1) and with an activated carbon injection (ACI) for mercury control in the same or subsequent run year (stage 2). However, if it exercises this succession of retrofit options, no further retrofit or retirement options are possible beyond the second stage.

Potential (New) Units: IPM also uses model plants to r epresent new generation capacity that may be built during a model run. All the model plants representing new capacity are pre-defined at set-up, differentiated by type of technology, r egional location, and years available. When it is economically advantageous to do so, IPM “builds” one or more of these predefined model plants by raising its generation capacity from zero during the course of a model run. In determining whether it is economically advantageous to “build” new plants, IPM takes into account cost differentials between technologies, expected tec hnology cost improvements (by differentiating costs based on a plant’s vintage, i.e., build year) and regional variations in capital costs that are expected to occur over time.

Since EPA Base Case v.4.10 results are presented at the model plant level, EPA has developed a post-processor “parsing” tool designed to translate results at the model plant level into generating unit-specific results. The parsing tool produces uni t-specific emissions, fuel use, pollution control retrofit and capacity projections based on model plant results. Another post-processing activity involves deriving inputs for air quality modeling fr om IPM outputs. This entails using emission factors to derive the levels of pollutants needed in EPA’s air quality models from emissions and

### 2-6 other parameters generated by IPM. It also involv es using decision rules to assign point source locators to these emissions. (See Figure 1-1 for a graphical representation of the relationship of the post-processing tools to the overall IPM structure.)

2.3.2 Model Run Years Another important structural feature of IPM is the use of model run years to represent the full planning horizon being modeled. Mapping each year in the planning horizon into a representative model run year enables IPM to perform multiple year analyses while keeping the model size manageable. Although IPM reports results only for model run years, it takes into account the costs in all years in the planning horizon. (See section 2.3.3 below for further details.)

Often models like IPM include a final model run year t hat is not included in the analysis of results. This technique reduces the likelihood that modeling results in the last represented year will be skewed due to the modeling artifact of having to specify an end point in the planning horizon, whereas, in reality, economic decisions are likel y to persist beyond that end point. Due to the number of model run years required by EPA for analytical purposes (six in the 2012-2050 time period) and a greatly expanded suite of modeling capabilities, such an approach could not be used in EPA Base Case v.4.10. It would have increased model size too much. However boundary distortions are only a potential factor in 2050, the last modeled year. In addition, any tendency toward end-year distortions should be r educed by the longer modeling time horizon of this base case and by the relatively large number of calendar years (9) that are mapped into model run year 2050 (see Table 7-1). Nevertheless, the possibility of residual boundary effects is something to bear in mind when interpreting model results for 2050.

2.3.3 Cost Accounting As noted earlier in the chapter, IPM is a dynamic linear programming model that finds the least cost investment and electricity dispatch strategy for meeting electric demand subject to resource availability and other operating and environmental c onstraints. The cost components that IPM takes into account in deriving an optimal solution include the costs of investing in new capacity options, the cost of installing and operating pollution control technology, fuel costs, and the operation and maintenance costs associated with unit operations.

Several cost accounting assumptions are built into IPM’s objective function that ensures a technically sound and unbiased treatment of the cost of all investment options offered in the model. These features include:

All costs in IPM’s single multi-year objective function are discounted to a base year. Since the model solves for all run years simultaneously, di scounting to a common base year ensures that IPM properly captures complex inter-temporal cost relationships.

Capital costs in IPM’s objective function are r epresented as the net present value of levelized stream of annual capital outlays, not as a one-time total investment cost. The payment period used in calculating the levelized annual outla ys never extends beyond the model’s planning horizon: it is either the book life of the investment or the years remaining in the planning horizon, whichever is shorter. This treatment of capital costs ensures both realism and consistency in accounting for the full cost of each of the investment options in the model.

The cost components appearing in IPM’s objective function represent the composite cost over all years in the planning horizon rather than just the co st in the individual model run years. This permits the model to capture more accurately the escalation of the cost components over time.

2.3.4 Modeling Wholesale Electric Markets Another important methodological feature worth not ing about IPM is that it is designed to depict production activity in deregulated wholesale electric markets, not in retail markets. The model captures transmission costs and losses between IPM model regions. It is not designed to capture

### 2-7 retail distribution costs. However, the model imp licitly includes distribution losses since net energy for load, 2 

### rather than delivered sales, 3 

### is used to represent electric demand in the model. Additionally, the production costs calculated by IPM are the wholesale production costs. In reporting costs, the model does not include embedded co sts, such as carrying charges of existing units that may be part of the retail cost.

2.3.5 Load Duration Curves (LDC) IPM uses Load Duration Curves (LDCs) to provide realism to the dispatching of electric generating units. Unlike a chronological electric load curve, which is simply an hourly record of electric demand, the LDCs are created by rearranging the hourly chronological electric load data from the highest to lowest (MW) value. For modeling tractability a 6-step piecewise linear representation of the LDC is used in EPA applications of IPM.

IPM can include any number of separate LDCs for any number of user defined seasons. A season can be a single month or several months. For example, EPA Base Case v.4.10 contains two seasons: summer (May 1 – September 30) and winter (October 1– April 30). Separate summer and winter LDCs are created for each of IP M’s model regions. Figure 2-1 below presents side-by-side graphs of a hypothetical chronol ogical hourly load curve and a corresponding load duration curve for a season consisting of 3,672 hours.

Figure 2-1 Hypothetical Chronological Hourly Load Curve and Seasonal Load Duration Curve in EPA Base Case v.4.10

National electric demand growth assumptions (from AEO for EPA Base Case v.4.10) and NERC’s forecasts of peak and energy demand in each region are used to derive future seasonal load duration curves for each IPM run year in each IPM r egion from the historical data. The results of this process are individualized seasonal LDCs that capture the unique hourly electric demand profile of each region. The LDCs change over time to reflect projected future variations in electricity consumption patterns.

Within IPM, LDCs are represented by a discrete number of load segments, or generation blocks, as illustrated in Figure 2-2. EPA Base Case v.4.10 uses six load segments in its seasonal LDCs

2 

### Net energy for load is the electrical energy r equirements of an electrical system, defined as system net generation, plus energy received from others, less energy delivered to others through interchange. It includes distribution losses.

3 

### Delivered sales is the electrical energy delivered under a sales agreement. It does not include distribution losses.

### 2-8 for model run years 2012-2030 and 4 load segments in its LDCs for model run years 2040 and 2050. The reduced number of load segments in the later years was adopted out of model size considerations and a view that having a finer grained representation of dispatch was less important that far into the future. Figure 2-3 illustrates and the following text describes the 6- segment LDCs used in the base case’s earlier years. Length of time and system demand are the two parameters which define each segment of the load duration curve. The load segment represents the amount of time (along the x-axis) and the capacity that the electric dispatch mix must be producing (represented along the y-axis) to meet system load. Segment 1 in Figure 2-3 generally contains one percent of the hours in the period (i.e., "season") but represents the highest load demand value. IPM has the flexib ility to model any number of load segments; however the greater the number of segments, the greater the computational time required to reach a solution. The LDC shows all the hourly electricity load levels that must be satisfied in a region in a particular season of a particular m odel run year. Segment 1 (the “super peak” load segment with 1% of all the hours in the season) and Segment 2 (the “peak” load segment with 4% of all the hours in the season) represent all the hours when load is at the highest demand levels. Segments 2 through 6 represent hourly loads at progressively lower levels of demand. Plants are dispatched to meet this load based on economic c onsiderations and operating constraints. The most cost effective plants are assigned to meet load in all 6 segments of the load duration curve. This is discussed in greater detail in section 2.3.6 below.

Use of seasonal LDCs rather than annual LDCs allo ws IPM to capture seasonal differences in the level and patterns of customer demand for electricity. For example, in most regions air conditioner cycling only impacts customer demand patterns dur ing the summer season. The use of seasonal LDCs also allows IPM to capture seasonal vari ations in the generation resources available to respond to the customer demand depicted in an LDC. For example, power exchanges between utility systems may be seasonal in nature. Some air regulations affecting power plants are also seasonal in nature. This can impact the type of generating resources that are dispatched during a particular season. Further, because of maintenanc e scheduling for individual generating units, the capacity and utilization for these supply resources also vary between seasons.

Appendix 2-1 contains data and graphs of the 2012 summer and winter load duration curves in each of the 32 model regions in the lower continental U.S. for EPA Base Case v.4.10.

Figure 2-2 Stylized Depiction of Load Duration Curve Used in EPA Base Case v.4.10

### 2-9 2.3.6 Dispatch Modeling In IPM, the dispatching of electricity is based on the variable cost of generation. In the absence of any operating constraints, units with the lowest variable cost generate first. The marginal generating unit, i.e., the power plant that generates the last unit of electricity, sets the energy price. Physical operating constraints also infl uence the dispatch order. For example, IPM uses turndown constraints to prevent base load units from cycling, i.e., switching on and off. Turndown constraints often override the dispatch order that would result based purely on the variable cost of generation. Variable costs in combination wit h turndown constraints enable IPM to dispatch generation resources in a technically realistic fashion.

Figure 2-3 below depicts a highly stylized dispatch order based on the variable cost of generation of the resource options included in the EPA Base Case v.4.10. In Figure 2-3 a hypothetical load duration curve is subdivided according to the ty pe of generation resource that responds to the load requirements represented in the curve. Notice that the generation resources with the lowest operating cost (i.e., hydro and nuclear) respond first to the demand represented in the LDC and so are at the bottom of “dispatch stack.” They are dispatched for the maximum possible number of hours represented in the LDC because of their low operating costs. Generation resources with the highest operating cost (i.e., peaking turbines) are at the top of the “dispatch stack,” since they are dispatched last and for the minimum possible number of hours.

Figure 2-3 Stylized Dispatch Order in EPA Base Case v.4.10

2.3.7 Reliability Modeling Another methodological feature of IPM is its modeling of reliability through reserve margin requirements, which specify the amount of install ed capacity that must be in excess of peak power demand. IPM includes regional reserve margin requirements for each run year. Section 3.6 contains a discussion of the reserve margin assumptions in EPA Base Case v.4.10.

### 2-10 2.3.8 Fuel Modeling Another key methodological feature of IPM is its capability to model the full range of fuels used for electric power generation. The cost, supply, and (if applicable) quality of each fuel included in the model are defined during model set-up. Fuel price and supply are represented in EPA Base Case v.4.10 in one of three alternative ways: (1) through an embedded modeling capability that dynamically balances supply and demand to arrive at f uel prices (natural gas), (2) through a set of supply curves (coal and biomass) or (3) through an exogenous price stream (fuel oil and nuclear fuel). With the first and second approaches, t he model endogenously determines the price for that fuel by balancing the supply and dem and. IPM uses the fuel quality information (e.g., the sulfur or mercury content of different types of coal from different supply regions) to determine the emissions resulting from the combustion of that fuel.

The EPA Base Case v.4.10 includes coal, natural gas, fuel oil, nuclear fuel, biomass, and fossil and non-fossil waste as fuels for electric generation. The specific base case assumptions for these fuels are examined in chapters 9-11.

2.3.9 Transmission Modeling IPM includes a detailed representation of existing transmission capabilities between model regions along with options for building new transmission lines. The maximum transmission capabilities between regions are specified in IP M’s transmission constraints. Additions to transmission lines are represented by decision va riables defined for each eligible link and model run year. In IPM’s objective function, the decision variables representing transmission additions are multiplied by new transmission line investment cost and capital charge rates to obtain the capital cost associated with the transmission addition. Due to extensive unresolved policy issues and long-term uncertainty surrounding the building of new transmission lines in the U.S., EPA Base Case v.4.10 does not exercise IPM’s capability to model the building of new transmission lines. The specific transmission assumptions in EPA Base Case v.4.10 are described in section 3.3.

2.3.10 Perfect Competition and Perfect Foresight Two key methodological features of IPM are its assumptions of perfect competition and perfect foresight. The former means that IPM models produc tion activity in wholesale electric markets on the premise that these markets subscribe to all assumptions of perfect competition. The model does not explicitly capture any market imperfections such as market power, transaction costs, informational asymmetry or uncertainty. Howeve r, if desired, appropriately designed sensitivity analyses or redefined model parameters can be us ed to gauge the impact of market imperfections on the wholesale electric markets. Since the re tail electric market is not modeled in IPM, there are no assumptions about the extent or timing of retail deregulation.

IPM’s assumption of perfect foresight implies that economic agents know precisely the nature and timing of the constraints that will be imposed in future years. For example, under IPM there is complete foreknowledge of the levels, timing, and regulatory design of emission limits that will be imposed over the entire modeling time horizon. In making decisions, agents optimize based on this foreknowledge. However, by performing an iterative series of runs, in which new emission limits are successively added in subsequent model run years, imperfect foresight can be incorporated in IPM’s projections.

2.3.11 Air Regulatory Modeling One of the most notable features of IPM is its detailed and flexible modeling of air regulations. Treatment of air regulations is endogenous in IP M. That is, by providing a comprehensive representation of compliance options, IPM enables environmental decisions to be made within the model based on least cost considerations, rather than exogenously imposing environmental choices on model results. For example, unlike other models that enter allowance prices as an exogenous input during model set-up, IPM obtains allowance prices as an output of the endogenous optimization process of finding the least cost compliance options in response to air

### 2-11 regulations. (In linear programming terminology, they are the “shadow prices” of the respective emission constraints — a standard output produced in solving a linear programming problem.) IPM can capture a wide variety of regulatory program designs including cap-and-trade, commandand-control and renewable portfolio standards. IPM’s representation of cap-and-trade programs can include allowance banking, trading, borrowing, bonus allowance mechanisms, progressive flow controls or emission taxes. Air regulati ons can be tailored to specific geographical regions and can be restricted to specific seasons. Many of these regulatory modeling capabilities are exploited in EPA Base Case v.4.10.



## 2.4 Hardware and Programming Features IPM produces model files in standard MPS linear programming format. IPM runs on most PC-platforms. Its hardware requirements are hi ghly dependent on the size of a particular model run. For example, with almost 7.3 million decision variables and 1.2 million constraints, EPA Base Case v.4.10 is run on a 64 bit Windows Enterprise Server 2008 platform with four Intel Xeon 2.93 GHz dual core processors and 32 GB of RAM. Due to the size of the EPA base case, a commercial grade solver is required. (Benchmarking tests performed by EPA's National Environmental Scientific Computing Center using research grade solvers yielded unacceptable results.) For current EPA applications of IPM, the FICO Xpress Optimization Suite 7 linear programming solvers are used.

Two data processors -- a front-end and the post-processing tool -- support the model. The frontend creates the necessary input files used in IP M, while the post-processing tool maps IPM model-plant level outputs to individual generating units (a process called “parsing,” see section 2.3.1) and creates input files in ORL (one record line) format as needed by EPA’s air quality models.

Before it can be run, the model requires an extensive set of input parameters. These are discussed in Section 2.4.1 below. Results of m odel runs are presented in a series of detailed reports. These are described in Section 2.4.2 below.

2.4.1 Data Parameters for Model Inputs IPM requires input parameters that characterize the US electric system, economic outlook, fuel supply and air regulatory framework. Chapters 3- 11 contain detailed discussions of the values assigned to these parameters in EPA Base Case v.4.10. This section simply lists the key input parameters required by IPM:

Electric System Existing Utility Generating Resources

• Plant Capacities

• Heat Rates

• Maintenance Schedule

• Forced Outage Rate

• Minimum Generation Requirements (Turn Down Constraint)

• Fuels Used

• Fixed and Variable O&M Costs

• Emissions Limits or Emission Rates for NO x 

### , SO 2 

### , CO 2 

### , Mercury

• Existing Pollution Control Equipment and Retrofit Options

• Output Profile for Non-Dispatchable Resources

New Generating Resources

• Cost and Operating Characteristics

• Performance Characteristics

• Limitations on Availability

### 2-12 Other System Requirements

• Inter-regional Transmission Capabilities

• Reserve Margin Requirements for Reliability

• Area Protection

• System Specific Generation Requirements

• Regional Specification

Economic Outlook Electric Demand

• Firm Regional Electric Demand

• Load Curves

Financial Outlook

• Capital Charge Rate

• Discount Rate

Fuel Supply Fuel Supply Curves for Coal, Natural Gas, and Biomass Fuel Price Fuel Quality Transportation Costs for Coal, Natural Gas, and Biomass

Air Regulatory Outlook Air Regulations for NO x 

### , SO 2 

### , CO 2 

### , Mercury Other Air Regulations

2.4.2 Model Outputs IPM produces a variety of output reports. These range from extremely detailed reports, which describe the results for each model plant and run year, to summary reports, which present results for regional and national aggregates. Individual topic areas can be included or excluded at the user’s discretion. Since the entire model solu tion is stored, IPM can generate additional detailed reports from the stored solution as needed. Standard IPM reports cover the following topics:

• Generation

• Capacity mix (by plant type and presenc e or absence of emission controls)

• Capacity additions and retirements

• Capacity and energy prices

• Power production costs (capital VOM, FOM and fuel costs)

• Fuel consumption

• Fuel supply and demand

• Fuel prices for coal, natural gas, and biomass

• Emissions (NO X 

### , SO 2 

### , CO 2 

### , and Hg)

• Emission allowance pricesAppendix 2-1.1Appendix 2-1 Load Duration Curves Used in EPA Base Case v.4.10This is a small exerpt of the data and graphs in Appendix 2-1. The complete data set in spreadsheet format and complete set ofgraphs can bedownloaded via the link found atwww.epa.gov/airmarkets/progsregs/epa-ipm/BaseCasev410.htmMonth11111111111111111111111111Hours1234567891011121314151617181920212223242526DayHours12345678910111213141516171819202122232412AZNM11,18210,86610,65910,62810,72410,96911,25011,30611,55311,70711,53311,29410,97710,64310,36710,30410,51511,67612,47512,51112,41412,07611,54710,89810,46710,332CA-N11,12711,19611,47711,97612,08212,26212,64512,87712,81212,61512,45712,27412,41813,41015,25615,21314,88514,38613,56612,50111,79311,24210,89910,82910,94311,443CA-S15,32314,63614,23414,05614,12914,34714,27514,44814,94015,42715,69015,78515,71315,61915,59815,59916,95119,00919,11118,80318,26117,32216,04014,94114,26013,937COMD10,1199,7639,4449,2649,1409,1379,2209,4199,4149,4899,6989,90810,01610,03210,0489,9879,96510,55211,44811,49511,42811,29511,03710,54510,0259,654DSNY2,8612,7082,5972,5262,4972,5192,5752,6382,7582,9363,1073,2293,2913,2863,2563,2823,4543,7113,7103,6113,4893,2832,9992,7182,5202,421ENTG14,74714,69514,58814,49114,47814,55414,92415,41115,66916,07216,33716,39216,10815,65415,18314,89814,95615,49516,58117,18417,32017,20516,94216,30915,53915,586ERCT29,98729,79629,71029,85730,39531,48732,63733,01933,02433,20832,99632,35131,18830,01128,90628,27728,40930,69934,03034,69735,01634,56833,28331,78330,78930,379FRCC19,75718,68817,47116,76616,41216,47616,78017,05418,78721,12823,28824,51425,26225,43125,22925,05124,84525,63626,46525,71624,56923,23621,47419,41917,65816,542GWAY9,0168,9188,8528,7828,8578,9959,2009,2989,4759,78110,00010,0199,8749,7189,5469,5099,86310,77510,89510,86010,77810,55810,1409,7259,5169,436LILC2,1892,0801,9911,9371,9101,9091,9271,9491,9952,0852,1922,2762,3252,3422,3482,3692,4852,6152,6072,5682,5172,4162,2562,0851,9591,892MACE14,99714,40813,92813,66313,57513,62913,81814,00314,20414,78115,36315,77116,00216,04416,06416,13316,70217,56717,54017,32417,01316,38915,52014,53813,91913,590MACS6,4746,2676,0565,9085,8855,9616,0706,1146,1946,4586,7426,9637,0517,0226,9346,9387,0387,5197,5437,5007,3557,1496,7936,4566,1986,060MACW6,4186,0005,6065,4635,4415,5525,7195,9056,2066,7137,2087,4957,5167,4187,2467,2547,5918,2678,2868,1858,0367,6446,9766,3005,9735,745MECS9,3329,0638,8678,7328,6838,7588,8829,0329,0789,2729,5829,8299,9389,9469,9129,95510,14910,69310,91010,84610,76510,57510,2319,8339,5539,342MRO20,43719,61619,01218,59918,59318,90119,60320,37020,73721,38621,79821,93621,73321,28820,76120,82921,99025,09426,50526,41625,72424,61223,10221,44220,07419,395NENG12,38011,71611,27211,03511,02011,18111,50211,86712,43813,28314,09714,62514,76914,68514,54514,67015,59716,26816,04315,59514,98514,03912,81611,73811,05310,664NWPE6,2426,0866,0185,9906,0276,1496,2966,4316,6056,7596,8186,8156,7626,6736,6216,6527,0107,3907,3707,2737,1456,8456,4236,0665,9435,852NYC4,8384,6454,4494,2954,2374,2384,3154,3764,4774,6714,8895,0585,1755,2465,3045,3545,5075,6875,6645,5935,5005,3585,1104,7724,4634,269PNW19,72919,28919,06819,03519,34219,82720,40320,92521,53122,17422,69722,84322,76822,50522,24822,33423,17823,76823,54523,06422,35421,36020,10418,90618,18517,856RFCO17,02916,73516,33516,16616,16716,39716,78517,21417,48017,85218,62519,26719,64219,67319,68219,75520,14121,03621,35821,16220,91520,37019,68718,92018,24617,890RFCP21,42520,58919,79919,48319,51219,93220,60721,42022,25723,43524,54025,25626,27626,25526,25326,42127,01828,75329,20729,06628,83827,91326,51524,91223,86823,330RMPA5,9955,9045,9146,0686,2056,2816,4846,7186,7836,7366,6136,6416,5686,5997,1378,1858,3848,2478,0697,6936,9666,3555,9985,7735,6815,732SNV2,6662,6372,6092,6012,6022,6212,5952,5152,4862,4882,4942,4912,4772,4462,4322,4392,5762,7862,8052,7882,7622,7122,6482,5592,5002,465Appendix 2-1.2ERCT 2012 Summer0100002000030000400005000060000700001 301 601 901 1201 1501 1801 2101 2401 2701 3001 3301 3601Hour

MWAppendix 2-1.3ERCT 2012 Winter01000020000300004000050000600001 301 601 901 1201 1501 1801 2101 2401 2701 3001 3301 3601 3901 4201 4501 4801Hour

MW

### 3-1 

# 3 Power System Operation Assumptions
This section describes the assumptions pertaining to the North American electric power system as represented in EPA Base Case v.4.10.



## 3.1 Model Regions EPA Base Case v.4.10 models the US power sector in the contiguous 48 states and the District of Columbia and the Canadian power sector in the 10 provinces (with Newfoundland and Labrador represented as two regions on the electricity net work even though politically they constitute a single province 4 

### ) as an integrated network. Alaska, Hawaii, Puerto Rico, and US Virgin Islands are represented in Base Case v.4.10 as separate entities with their own self contained electricity grids.

There are 32 IPM model regions covering the US 48 states and District of Columbia. The IPM model regions are approximately consistent with the configuration of the 8 NERC regions, being disaggregations of North American Reliability Council (NERC) control areas. An attempt has been made to have the US IPM model regions reflec t the administrative structure of regional transmission organizations (RTOs) and independent system operators (ISOs). Further disaggregation into 32 model regions allows a more accurate characterization of the operation of the US power markets by providing the ability to represent transmission bottlenecks within the 8 NERC regions and across RTOs and ISOs.

Disaggregations that were made in the most recent previous IPM base case were retained in Base Case 2010. Notable disaggregations include

• NERC region RFC (Reliability First Corporation) includes three portions of former NERC regions — the non-Kentucky part of ECAR, MAAC, and a portion of MAIN. The remaining portion of MAIN has been renamed COMD. ECAR has been disaggregated into RFCO, MECS, and RFCP and MAAC has been dis aggregated into MACE, MACS, and MACW.

• NERC subregion WECC-AZ-NM-SNV has been disaggregated into AZNM and SNV

• NERC subregion WECC-California ISO has been disaggregated into CA-N and CA-S

• NERC Region SERC has been disaggregated into 7 IPM regions (ENTG, SOU, VACA, VAPW, TVA, TVAK (formerly ECAK), and GWAY (formerly a portion of MANO).

Several region boundaries were adjusted to reflect recent organizational changes. There were also several name changes: MANO to GWAY, ECAM to RFCO, ECAP to RFCP, and ECAK to TVAK.

The 11 Canadian model regions are defined stri ctly along provincial political boundaries.

Figure 3-1 contains a map showing all the EPA Base Case 2010 model regions. Table 3-1 defines the abbreviated region names appearing on the map and gives an approximate crosswalk between the IPM model regions, the NERC regions , and regions used in the Energy Information Administration’s (EIA’s) National Energy Model System (NEMS) which is the basis for EIA’s Annual Energy Outlook (AEO) reports.



## 3.2 Electric Load Modeling Net energy for load and net internal demand are inputs to IPM that together are used to represent the grid-demand for electricity. Net energy for load is the projected annual electric grid-demand, prior to accounting for intra-regional transmission and distribution losses. Net internal demand

4 

### This results in a total of 11 Candian model regions being represented in EPA Base Case v.4.10

### 3-2 (peak demand) is the maximum hourly demand within a given year after removing interruptible demand. Table 3-2 shows the electric demand assumptions (expressed as net energy for load) used in EPA Base Case v.4.10. It is based on the net energy for load in AEO 2010 5



### .
Figure 3-1 EPA Base Case v.4.10 Model Regions

For purposes of documentation, Table 3-2 present s the national net energy for load. However, EPA Base Case v.4.10 models regional breakdowns of net energy for load. The regional net energy for load is derived from the national net energy for load based on the regional demand distribution in NERC electric demand forecasts. Model regions that represent subregions of a NERC region are apportioned their net energy for load based on the regional load shapes, which are developed by aggregating load for contro l areas within each model region.

5 

### The electricity demand in EPA Base Case v.4.10 for the U.S. lower 48 states and the District of Columbia is obtained by summing the "Total Net Energy for Load" for the NEMS Electric Market Module regions as reported in the "Electric Po wer Projections for Electricity Market Module Regions -- Electricity and Renew able Fuel Tables 72-84" at http://www.eia.doe.gov/oiaf/aeo/aeoref_tab.html

.

### 3-3 Table 3-1 Mapping of NERC Regions and NEMS Regions with EPA Base Case v.4.10 Model Regions NERC Region NEMS Region Model Region Model Region Description

TRE ERCOT ERCT Texas Regional Entity
FRCC FL FRCC Florida Reliability Coordinating Council
MAPP MRO Midwest Regional Planning Organization MRO MAIN WUMS Wisconsin-Upper Michigan
NE NENG New England Power Pool
DSNY Downstate New York
LILC Long Island Company
NYC New York City NPCC NY

UPNY Upstate New York
RFCO Reliability First Corporation - MISO
MECS Michigan Electric Coordination System ECAR
RFCP Reliability First Corporation - PJM
MACE Legacy Mid-Atlantic Area Council - East
MACS Legacy Mid-Atlantic Area Council - South MAAC
MACW Legacy Mid-Atlantic Area Council - West RFC

MAIN COMD Commonwealth Edison
MAIN GWAY Gateway
ECAR TVAK Tennessee Valley Authority - MISO-KY
SOU Southern Company
TVA Tennessee Valley Authority
ENTG Entergy
VACA Virginia-Carolinas SERC
STV

VAPW Dominion Virginia Power
SPPN Southwest Power Pool - North SPP SPP SPPS Southwest Power Pool - South

AZNM Western Electricity Coordinating Council - Arizona, New MexicoWECCAZ-NMSNV RA SNV Western Electricity Coordinating Council - Southern Nevada
CA-N Western Electricity Coordinating Council - California North WECC- California ISO CNV CA-S Western Electricity Coordinating Council - California South

PNW Western Electricity Coordinating Council - Pacific NorthwestWECCNWPP NWP NWPE Western Electricity Coordinating Council - Northwest Power Pool EastWECCRMPA RA RMPA Western Electricity Coordinating Council - Rocky Mountain Power Area CNAB Alberta
CNBC British Columbia
CNMB Manitoba
CNNB New Brunswick
CNNF Newfoundland
CNNL Labrador
CNNS Nova Scotia Canada

CNON Ontario

### 3-4 NERC Region NEMS Region Model Region Model Region Description

CNPE Prince Edward Island CNPQ Quebec
CNSK Saskatchewan
ALSK Alaska
HAWI Hawaii
VIUS U.S. Virgin Islands Other

PRCW Puerto Rico

Table 3-2 Electric Load Assumptions in EPA Base Case v.4.10

Year Net Energy for Load (Billions of kWh)
2012 4,043 2015 4,086 2020 4,302 2030 4,703 2040 5,113 2050 5,568
Note: This data is an aggregation of the model-region-specific net energy loads used in the EPA Base Case v.4.10.

3.2.1 Demand Elasticity EPA Base Case v.4.10 has the capability to model the impact of the price of power on electricity demand. However, this capability is typically only exercised for sensitivity analyses where different price elasticities of demand are specif ied for purposes of comparative analysis. The default base case assumption is that the electric ity demand shown in Table 3-2 is not affected by price and must be met, i.e., the price elasticity of demand is zero 6



### .
3.2.2 Net Internal Demand (Peak Demand) EPA Base Case v.4.10 has separate regional winter and summer peak demand values, as derived from each region’s seasonal load duration curve (found in Appendix 2-1). Peak projections were estimated based on AEO 2010 load factors and the estimated energy demand projections shown in Table 3-2. Table 3-3 (“National Non-Co incidental Net Internal Demand”) illustrates the national sum of each region’s winter and summer peak demand. Because each region’s seasonalpeak demand need not occur at the same time, the national peak demand is defined as noncoincidental.

6 

### Occasionally, e.g., when performing modeling of climate policies, the demand assumptions shown in Table 3-2 will be replaced with projec tions of demand from economy-wide computable general equilibrium (CGE) models which themselv es take into account demand elasticity. However, even in such cases the IPM demand elasticity capabilities will not be utilized and the resulting IPM runs will be considered “policy” rather than “base case” runs.

### 3-5 Table 3-3 National Non-Coincidental Net Internal Demand Peak Demand (GW) Year Winter Summer 2012 646 758 2015 655 771 2020 693 816 2030 768 908 2040 843 1,001 2050 929 1,105 Note: This data is an aggregation of the model-region-specific peak demand loads used in the EPA Base Case v.4.10.

3.2.3 Regional Load Shapes EPA uses year 2007 as the meteorological year in its air-quality modeling. In order for EPA Base Case v.4.10 to be consistent, the year 2007 was selected as the “normal weather year” 7 

### for all IPM regions. The proximity of the 2007 cumulative annual heating degree days (HDDs) and cooling degree days (CDDs) to the long-term average cumulative annual HHDs and CDDs over the period 1971 to 2000 was estimated and found to be reasonable close. The 2007 chronological hourly load data were assembled by aggregating individual utility load curves taken from Federal Energy Regulatory Commission Form 714 data.



## 3.3 Transmission The United States and Canada can be broken down into several power markets that are interconnected by a transmission grid. As discussed earlier, EPA Base Case 4.10 characterizes the U.S. lower 48 states, the District of Columbia, and Canada into 43 different power market regions by means of 32 model regions in the U.S. and 11 in Canada. EPA Base Case 4.10 includes explicit assumptions regarding the transmission grid connecting these modeled power markets. This section details the assumptions about the transfer capabilities, wheeling costs and inter-regional transmission used in EPA Base Case 4.10.

3.3.1 Inter-regional Transmission Capability
Table 3-4 8 

### shows the firm and non-firm Total Transfer Capabilities (TTCs) between model regions. TTC is a metric that represents the capability of the power system to import or export power reliably from one region to another. The purpose of TTC analysis is to identify the sub-markets created by key commercially significant constr aints. Firm TTCs, also called Capacity TTCs, specify the maximum power that can be transferred reliably, even after the contingency loss of a single transmission system element such as a transmission line or a transformer (N-1). Firm TTCs provide a high level of reliability and are therefore used for capacity transfers. Non-firm TTCs, also called Energy TTCs, represent the maximum power that can be transferred reliably when all facilities are under normal operation (N-0). They specify the sum of the maximum firm transfer capability between sub-regions plus incremental curtailable non-firm transfer capability. Non-firm TTCs are used for energy transfers since they provide a lower level of reliability than

7 

### The term “normal weather year” refers to a repres entative year whose weather is closest to the long-term (e.g., 35 year) average weather. The selection of a “normal weather year” can be made, for example, by comparing the cumulative annual heating degree days (HDDs) and cooling degree days (CDDs) in a candidate year to the l ong-term average. For any individual day, heating degree days indicate how far the average temperature fell below 65 degrees F; cooling degree days indicate how far the temperature averaged above 65 degrees F. Cumulative annual heating and cooling degree days are the sum of all the HDDs and CDDs, respectively, in a given year.

8 

### In the column headers in Table 3-4 the term “Energy (MW)” is equivalent to non-firm TTCs and the term “Capacity (MW)” is equivalent to firm TTCs.

### 3-6 Firm TTCs, and transactions using Non-firm TTCs can be curtailed under emergency or contingency conditions.

Table 3-4 Annual Transmission Capabilities of U.S. Model Regions in EPA Base Case v.4.10

From To Energy (MW) Capacity (MW) Wheeling Charge (mills/kWh)
CA-S 3,627 2,428 2.9
NWPE 300 300 -- RMPA 690 690 -- SNV 4,634 4,634 -- AZNM

SPPS 400 400 2.9 CA-S 3,700 3,700 -- NWPE 150 100 2.9 CA-N
PNW 3,675 3,675 2.9
AZNM 3,627 2,428 2.9 CA-N 3,000 2,400 -- NWPE 1,400 1,400 2.9
PNW 3,100 3,100 2.9 CA-S

SNV 4,688 4,688 2.9 GWAY 2,050 2,050 2.9 MRO 825 825 2.9 RFCO 1,620 1,110 2.9 RFCP 4,500 788 -- COMD

WUMS 825 825 2.9 LILC 1,290 1,290 -- MACE 2,000 2,000 2.9 NENG 1,120 1,120 2.9 NYC 3,700 3,700 -- DSNY

UPNY 3,400 3,400 -- GWAY 910 140 2.9 MRO 150 150 2.9 SOU 2,250 2,250 2.9 SPPN 1,120 140 2.9 SPPS 4,494 735 2.9 ENTG

TVA 1,681 1,681 2.9 ENTG 1,001 1,001 2.9 ERCT SPPS 979 979 2.9 FRCC SOU 2,000 2,000 2.9 COMD 1,100 1,100 2.9 ENTG 2,804 2,100 2.9 MRO 405 405 -- RFCO 6,299 1,848 -- SPPN 285 285 2.9
TVA 1,812 1,812 2.9 GWAY

TVAK 200 200 2.9 DSNY 530 530 -- MACE 650 590 2.9 LILC

NENG 616 616 2.9

### 3-7 From To Energy (MW) Capacity (MW) Wheeling Charge (mills/kWh)
NYC 420 420 -- DSNY 500 500 2.9 LILC 650 521 2.9 MACW 2,000 2,000 -- MACE

NYC 1,200 600 2.9 MACW 3,500 3,000 -- RFCP 2,500 750 -- MACS
VAPW 2,600 2,600 -- MACE 6,200 5,800 -- MACS 5,000 1,350 -- RFCO 2,208 504 2.9 RFCP 3,300 2,044 -- MACW

UPNY 1,085 1,085 2.9 CNON 1,968 1,968 2.9 RFCO 2,776 1,904 -- MECS RFCP 3,900 683 2.9 COMD 610 610 2.9 CNON 100 100 2.9 CNSK 165 165 2.9 ENTG 2,000 2,000 2.9 GWAY 320 320 -- NWPE 200 200 2.9
RMPA 310 310 2.9 SPPN 1,494 1,494 2.9 MRO

WUMS 800 800 -- CNNB 1,000 1,000 2.9 CNPQ 803 803 2.9 DSNY 980 980 2.9 NENG

LILC 616 473 2.9
AZNM 265 265 -- CA-N 160 120 2.9 CA-S 1,920 1,920 2.9 MRO 150 150 2.9
PNW 2,002 2,002 -- RMPA 749 749 -- NWPE

SNV 300 250 -- DSNY 1,999 1,999 -- NYC LILC 175 175 -- CA-N 4,000 4,000 2.9 CA-S 3,100 3,100 2.9 CNBC 2,000 1,000 2.9 PNW

NWPE 1,505 1,505 -- COMD 2,760 1,360 2.9 GWAY 7,078 3,504 -- MACW 3,100 2,274 2.9 RFCO

MECS 4,603 825 --

### 3-8 From To Energy (MW) Capacity (MW) Wheeling Charge (mills/kWh)
RFCP 12,908 7,951 2.9
TVAK 815 270 2.9 COMD 3,100 3,100 -- MACS 2,500 350 -- MACW 3,900 1,075 -- MECS 3,700 1,762 2.9 RFCO 15,041 8,525 2.9
TVA 1,000 1,000 2.9
TVAK 1,000 537 2.9 VACA 3,002 2,042 2.9 RFCP

VAPW 3,080 953 -- AZNM 690 690 -- MRO 310 310 2.9 RMPA NWPE 735 735 -- AZNM 4,785 4,785 -- CA-S 4,688 4,688 2.9 SNV
NWPE 300 300 -- ENTG 2,950 2,950 2.9 FRCC 3,600 3,600 2.9 TVA 3,742 3,742 2.9 SOU

VACA 1,358 1,358 2.9 ENTG 3,745 1,260 2.9 GWAY 1,200 1,200 2.9 MRO 600 600 2.9 SPPN

SPPS 700 700 -- AZNM 400 400 2.9 ENTG 9,030 2,310 2.9 ERCT 650 650 2.9 SPPS

SPPN 1,200 1,200 -- ENTG 2,919 2,919 2.9 GWAY 1,550 1,550 2.9 RFCP 1,500 263 2.9 SOU 2,258 2,258 2.9
TVAK 2,000 1,073 -- TVA

VACA 664 664 2.9 GWAY 200 200 2.9 RFCO 3,365 1,225 2.9 RFCP 1,000 175 2.9 TVAK

TVA 1,500 632 -- CNON 2,000 1,325 2.9 CNPQ 1,000 1,000 2.9 DSNY 4,550 4,550 -- MACW 735 735 2.9 UPNY

NENG 150 150 2.9 RFCP 4,117 438 2.9 SOU 3,242 3,242 2.9 VACA

TVA 3,586 3,586 2.9

### 3-9 From To Energy (MW) Capacity (MW) Wheeling Charge (mills/kWh)
VAPW 1,942 1,942 2.9 MACS 2,100 2,100 -- RFCP 5,460 1,952 -- VAPW VACA 1,849 1,849 2.9 COMD 1,125 1,125 2.9 WUMS MRO 270 270 --

The amount of energy and capacity transferred on a given transmission link is modeled on a seasonal (summer and winter) basis for all run years in the EPA Base Case 4.10. All of the modeled transmission links have the same Total Transfer Capabilities for both the winter and summer seasons, which means that the maximum firm and non-firm TTCs for each link is the same for both winter and summer. Wherever ava ilable, the maximum values for firm and non-firm TTCs were obtained from public sources. Where public sources were not available, the maximum values for firm and non-firm TTCs are based on ICF’s expert view.

It should be noted that each transmission link bet ween model regions shown in Table 3-4 represents a one-directional flow of power on that link. This implies that the maximum amount of flow of power possible from region A to region B may be more or less than the maximum amount of flow of power possible from region B to region A.

3.3.2 Joint Transmission Capacity and Energy Limits Table 3-5 shows the annual joint limits to the transmission capabilities between model regions, which are identical for the firm (capacity) and non-firm (energy) transfers. The joint limits were developed from the 2004 NERC Summer Assessment and 2004 NERC Winter Assessment. A joint limit represents the maximum simultaneous firm or non-firm power transfer capability of a group of interfaces. It restricts the amount of firm or non-firm transfers between one model region (or group of model regions) and a different group of model regions). For example, the New England model region is connected to multiple model regions contained in the state of New York, with each link between New England and a New York model region described by its own TTCs. However, there is a maximum limit on the total amount of transfers that the New England region may transfer to the whole of New York, which is represented by the annual joint capacity limit between the New England model region and the relevant New York model regions.

Table 3-5 Annual Joint Capacity and Energy Limits to Transmission Capabilities Between Model Regions in EPA Base Case v.4.10 Region Connections Transmission Path Joint Constraint Limit RFCO to MACW RFCP to MACS ECAR to MAAC RFCP to MACW 1,385

RFCO to COMD RFCP to COMD RFCO to GWAY ECAR to MAIN

TVAK to GWAY 2,593

TVAK to TVA ECAR to TVA RFCP to TVA 3,561

RFCP to VACA ECAR to VACAR RFCP to VAPW 2,022

ENTG to SPPN ENTG to SPP ENTG to SPPS 338

### 3-10 Region Connections Transmission Path Joint Constraint Limit LILC to DSNY LILC to NYC & DSNY LILC to NYC 530

MACS to RFCP MACW to RFCO MAAC to ECAR MACW to RFCP 4,715

MACE to DSNY MACE to LILC MACE to NYC MAAC to NPCC

MACW to UPNY 1,708

COMD to RFCO COMD to RFCP GWAY to TVAK MAIN to ECAR

GWAY to RFCO 3,649

COMD to MRO GWAY to MRO MAIN to MAPP WUMS to MRO 962

MRO to COMD MRO to GWAY MAPP to MAIN MRO to WUMS 1,238

MRO to NWPE MAPP to WECC MRO to RMPA 710

NENG to DSNY NENG to UPNY NENG to NY NENG to LILC 1,550

DSNY to MACE LILC to MACE NYC to MACE NPCC to MAAC

UPNY to MACW 2,353

DSNY to NENG LILC to NENG NY to NENG UPNY to NENG 1,750

DSNY to LILC NYC & DSNY to LILC NYC to LILC 1,465

SPPN to ENTG SPP to ENTG SPPS to ENTG 1,362

TVA to TVAK TVA to ECAR TVA to RFCP 1,226

VACA to RFCP VACAR to ECAR VAPW to RFCP 4,278

NWPE to MRO WECC to MAPP RMPA to MRO 660

Note: Source: 2004 NERC Summer Assessment, 2004 NERC Winter Assessment

3.3.3 Transmission Link Wheeling Charge Transmission wheeling charge is the cost of trans ferring electric power from one region to another using the transmission link. The EPA Base Case 4.10 assumes a wheeling charge of 2.9 mills per kWh for electricity transmission between IPM model regions that fall within different market

### 3-11 regions, such as transmission between Northern California and the Pacific Northwest. However, the wheeling charge is not applied to transmissi on between model regions that are within the same market region, such as transmission bet ween Northern California (model region CA-N) and Southern California (model region CA-S). The wheeling charge applied between IPM model regions can be found in Table 3-4.

3.3.4 Transmission Losses The EPA Base Case 4.10 assumes a two percent inter-regional transmission loss of energy transferred, in line with EIA’s Annual Energy Outlook (AEO) 2010.



## 3.4 International Imports The U.S. electric power system is connected with the transmission grids in Canada and Mexico and the three countries actively trade in electricity. The Canadian power market is endogenously modeled in EPA Base Case v.4.10 but Mexico is not. International electric trading between the U.S. and Mexico is represented by an assumption of net imports based on information from AEO 2010. Table 3-6 summarizes the assumptions on net imports into the US from Mexico.

Table 3-6 International Electricity Imports in EPA Base Case v.4.10 2012 2015 2020 2030 2040 2050 Net Imports from Mexico (billions kWh) 1.57 1.57 1.11 0.89 0.89 0.89

Notes: Imports & exports transactions from Canada are endogenously modeled in IPM. Source: AEO 2010


## 3.5 Capacity, Generation, and Dispatch While the capacity of existing units is an exogenous input into IPM, the dispatch of those units is an endogenous decision that the model makes. The capacity of existing generating units included in EPA Base Case v.4.10 can be found in the National Electrical Energy Data System (NEEDSv.4.10), a database which provides IPM with information on all currently operating and plannedcommitted electric generating units. NEEDS v.4.10 is discussed in full in Chapter 4.

A unit’s generation over a period of time is defined by its dispatch pattern over that duration of time. IPM determines the optimal economic di spatch profile given the operating and physical constraints imposed on the unit. In EPA Base Case v.4.10 unit specific operational and physical constraints are generally represented through availability and turndown constraints. However, for some unit types, capacity factors are used to capture the resource or other physical constraints on generation. The two cases are discussed in more detail in the following sections.

3.5.1 Availability Power plant “availability” is the percentage of time that a generating unit is available to provide electricity to the grid. Availability takes into account both scheduled maintenance and forced outages; it is formally defined as the ratio of a unit’s available hours adjusted for derating of capacity (due to partial outages) to the total number of hours in a year when the unit was in an active state. For most types of units in IPM, availability parameters are used to specify an upper bound on generation to meet demand. Table 3-7 summarizes the availability assumptions used in EPA Base Case v.4.10. They are based on data from North American Electric Reliability Council’s Generating Availability Data System (NERC GADS) 2001 to 2005 and AEO 2010. Appendix 3-9 shows the availability assumptions for all generating units in EPA Base Case v.4.10.

### 3-12 Table 3-7 Availability Assumptions in the EPA Base Case v.4.10 Unit Type Annual Availability (%) Biomass 83 Coal Steam 32 - 95 Combined Cycle 85 Combustion Turbine 89 - 91 Gas/Oil Steam 78 - 92

Geothermal 87 IGCC 85 Pumped Storage 90 Solar 90
Wind 95
Notes: Values shown are a range of all of the values modeled within the EPA Base Case v.4.10. Availabilities of coal steam units are based on historical capacity factors.

In the EPA Base Case v.4.10, separate seasonal (summer and winter) availabilities are defined. For the fossil and nuclear unit types shown in Table 3-7, summer and winter availabilities differ only in that no planned maintenance is assumed to be conducted during the on-peak summer (June, July and August) months. Characterizing the availability of hydro, solar and wind technologies is more complicated due to the seasonal and locational variations of the resources. The procedures used to represent seasonal variati ons in hydro are presented in section 3.5.2 and of wind and solar in section 4.4.5.

3.5.2 Capacity Factor Generation from certain types of units is constrained by resource limitations. These technologies include hydro, wind and solar. For such technologies, IPM uses capacity factors or generation profiles, not availabilities, to define the upper bound on the generation obtainable from the unit. The capacity factor is the percentage of the ma ximum possible power generated by the unit. For example, a photovoltaic solar unit would have a capacity factor of 27% if the usable sunlight were only available that percent of the time. For su ch units, explicit capacity factors or generation profiles mimic the resource availability. The seasonal capacity factor assumptions for hydro facilities contained in Table 3-8 were derived from EIA Form 906 from 2002 through 2006 data. A discussion of capacity factors and generation profile s for wind and solar technologies is contained in section 4.4.5 and Appendices 4-1 and 4-2.

Table 3-8 Seasonal Hydro Capacity Factors (%) in the EPA Base Case v.4.10 Model Region Winter Capacity Factor Summer Capacity Factor Annual Capacity Factor AZNM 27.4% 32.2% 29.4% CA-N 36.7% 50.1% 42.3% CA-S 38.7% 50.4% 43.6% COMD 40.6% 45.5% 42.6% DSNY 57.8% 50.2% 54.6% ENTG 35.4% 32.5% 34.2% ERCT 13.5% 19.6% 16.1% FRCC 48.4% 47.4% 48.0% GWAY 19.2% 22.5% 20.6% MACE 30.9% 29.2% 30.2% MACS 14.8% 18.7% 16.4% MACW 47.5% 33.7% 42.3% MECS 54.1% 56.9% 55.3%

### 3-13 Model Region Winter Capacity Factor Summer Capacity Factor Annual Capacity Factor MRO 31.8% 43.7% 36.8% NENG 44.9% 41.1% 43.3% NWPE 28.7% 47.6% 36.6% PNW 40.6% 44.0% 42.0% RFCO 66.0% 89.2% 75.6% RFCP 32.7% 30.9% 31.9% RMPA 18.0% 31.5% 23.7% SNV 18.0% 23.3% 20.2% SOU 25.3% 22.1% 24.0% SPPN 16.5% 17.8% 17.0% SPPS 21.2% 27.2% 23.7% TVA 43.2% 37.1% 40.7% TVAK 32.4% 38.6% 35.0% UPNY 66.8% 63.1% 65.2% VACA 23.7% 22.8% 23.3% VAPW 22.8% 19.0% 21.2% WUMS 52.6% 57.3% 54.6%
Note: Annual capacity factor is provided for information purposes only. It is not directly used in modeling.

Capacity factors are also used to define t he upper bound on generation obtainable from nuclear units. This rests on the assumption that nuclear units will dispatch to their availability, and, consequently, capacity factors and availabilities are equivalent. The capacity factors (and, consequently, the availabilities) of existing nuclear units in EPA Base Case v.4.10 vary from region to region and over time. Further discussion of the nuclear capacity factor assumptions in EPA Base Case v.4.10 is contained in Section 4.5.

3.5.3 Turndown Turndown assumptions in EPA Base Case v.4.10 are used to prevent coal and oil/gas steam units from operating strictly as peaking units, which would be inconsistent with their operating capabilities. Specifically, the turndown constraints in EPA Base Case v.4.10 require coal steam units to dispatch no less than 50% of the unit c apacity in the five base- and mid-load segments of the load duration curve in order to dispatch 100% of the unit in the peak load segment of the LDC. Oil/gas steam units are required to dispatch no le ss than 25% of the unit capacity in the five baseand mid-load segments of the LDC in order to dis patch 100% of the unit capacity in the peak load segment of the LDC. These turndown constraints were developed by ICF International through detailed assessments of the historical experience and operating characteristics of the existing fleet of coal steam and oil/gas steam units’ capacities.



## 3.6 Reserve Margins A reserve margin is a measure of the system’s generating capability above the amount required to meet the net internal demand (peak load) require ment. It is defined as the difference between total dependable capacity and annual system peak load divided by annual system peak load. It is expressed in percent. In practice, each NERC region has a reserve margin requirement, or comparable reliability standard, which is designed to encourage electric suppliers in the region to build beyond their peak requirements to ensure the reliability of the electric generation system within the region.

In IPM reserve margins are used to depict the reliability standards that are in effect in each NERC region. Individual reserve margins for each NERC r egion are derived either directly or indirectly

### 3-14 from NERC’s electric reliability reports. They are based on reliability standards such as loss of load expectation (LOLE), which is defined as the ex pected number of days in a specified period in which the daily peak load will exceed the available capacity. EPA Base Case v.4.10 reserve margin assumptions are shown in Table 3-9.

Table 3-9 Planning Reserve Margins in EPA Base Case v.4.10 Model Region Reserve Margin AZNM 15.7% CA-N 16.7% CA-S 16.7% CNAB 12.8% CNBC 12.8% CNMB 15.0% CNNB 20.0% CNNF 20.0% CNNL 20.0% CNNS 20.0% CNON 18.3% CNPE 20.0% CNPQ 10.0% CNSK 15.0% COMD 15.0% DSNY 16.5% ENTG 15.0% ERCT 12.5% FRCC 15.0% GWAY 15.0%

LILC 16.5% MACE 15.0% MACS 15.0% MACW 15.0% MECS 15.0% MRO 15.0% NENG 16.0% NWPE 10.8%

NYC 16.5% PNW 10.8% RFCO 15.0% RFCP 15.0% RMPA 14.3% SNV 15.7% SOU 15.0% SPPN 13.6% SPPS 13.6% TVA 12.0% TVAK 15.0% UPNY 16.5% VACA 15.0% VAPW 15.0% WUMS 16.0%

### 3-15 

## 3.7 Power Plant Lifetimes EPA Base Case v.4.10 does not include any pre-specified assumptions about power plant lifetimes, except for nuclear units. All conventional fossil units (i.e., coal, oil/gas steam, combustion turbines, and combined cycle) and nuclear units can be retired during a model run for economic reasons. Other types of units are not provided an economic retirement option.

Nuclear Retirement at Age 60: Existing nuclear units are forced to retire in EPA Base Case v.4.10 at the completion of age 60. Today’s nuclear fleet totals more than 100 GW. A 60-year lifetime reduces the current fleet to under 5 GW in 2050. This is illustrated in Figure 3-2. For a complete listing of the existing nuclear units represented in EPA Base Case v.4.10, including their online year and other characteristics, see Appendix 4-3.

Figure 3-2 Scheduled Retirements of Existing Nuclear Capacity Under 60-Year Life Assumption

Impact of 60-Year Lifetime on Existing Nuclear Fleet

0 1 2 3 4 5 6 7 8 9 10

2010 2015 2020 2025 2030 2035 2040 2045 2050

YearCapacity (GW) Retired in Year

0 20 40 60 80 100 120Total Capacity (GW) Remaining

Retired in Year Remaining Capacity



### The 60-year lifetime assumption is based on several factors. At the time that this base case was prepared there were many instances of the U.S. Nuclear Regulatory Commission (NRC) granting license extensions of 20 years beyond the initial 40 year operating licenses authorized by the NRC for commercial nuclear power plants under the Atomic Energy Act of 1954. At the time of the release of EPA Base Case v.4.10, the NRC had granted license renewals to 50 operating reactors allowing them to operate for 60 years with fifteen additional applications under review and the owners of 21 other units announcing their intention to file for 20-year license extensions. All of these applications would allow the units to operate to age 60.

### 3-16 At the same time, there were no units in the U.S. nuclear fleet licensed to operate past age 60 9



### . In keeping with the practice of the EPA base case representing legal provisions that are on the books or immediately pending, a conservative approach was adopted of reflecting the current maximum licensing period of 60 years for the nuclear units in EPA Base Case v.4.10.

Another factor in the decision to implement the 60-year nuclear life assumption is the degree of uncertainty surrounding nuclear life extensions pas t age 60. As noted in EIA’s review of the 60 year nuclear life question, uncertainties include:

• The absence, to date, of publicly available plans and cost estimates for potential major capital expenditures involved with extensions to age 80 such as the replacement of reactor vessels, containment structures, or buried piping and cables.

• Possible future additional regulatory requirement s which could result in expensive upgrades at nuclear power plants and figure into life extension decisions. Among those mentioned in EIA’s review was a rule that was recently the subject of the Supreme Court case Entergy Corp v. Riverkeeper10, which focused on whether or not the EPA could conduct cost-benefit analyses to determine whether a plant needed to replace open-cycle cooling water systems with closed-cycle systems.

The assumption of nuclear retirements at age 60 in EPA Base Case v.4.10 contrasts to a certain degree with the assumption made in AEO 2010. Due to AEO 2010’s shorter time horizon compared to the EPA base case (i.e., 2035 compared to 2050), EIA did not have to explicitly adopt an 80 year nuclear life assumption (as would have been necessary in EPA Base Case v.4.10), only that “the operating lives of existi ng nuclear power plants would be extended at least through 2035. 11 

### ” The basis for the decision appears to be that “The nuclear industry has expressed strong interest in continuing the operati on of existing nuclear facilities, and no particular technical issues have been identified t hat would impede their continued operation. 12 

### ”

Although the adopted assumptions differ in EPA Base Case v.4.10 and AEO 2010, there is agreement on the importance of performing side cases using the alternative assumptions. In the case of EPA Base Case v.4.10 this will mean performing sensitivity analysis runs with an 80 nuclear lifetime assumption.

9 

### The Energy Information Administration has an e xcellent review and summary of the issues involved in the 60 year nuclear life question. Although EPA’s base case does not adopt the same assumption as AEO 2010, the text in this section relied heavily on the EIA review. With respect to the status of applications for renewals beyond age 60, the EIA review notes the following: “In December 2009, the Oyster Creek Generating St ation in Lacey Township, New Jersey, became the first nuclear power plant in the United States to begin its 40th year of operation. With Oyster Creek and other nuclear plants of similar vintage just beginning to enter their first period of license renewal, it probably will be at least 5 to 10 years before there is any clear indication as to whether plant operators will be likely to seek further extensions of their plants’ operating lives.” The EIA review also observes “. . . the NRC and the nucl ear power industry are preparing applications for license renewals that would allow continued operat ion beyond 60 years, the first of which is scheduled to be submitted by 2013. In February 2008, DOE and the NRC hosted a joint workshop titled “Life Beyond 60,” with a broad group of nuclear industry stakeholders meeting to discuss this issue. The workshop’s summary report outlined many of the technical research needs that participants agreed were important to extending the life of the existing fleet of U.S. nuclear plants.” Energy Information Administration (EIA), U.S. D epartment of Energy, “U.S. nuclear power plants: Continued life or replacement after 60?” Annual Energy Outlook 2010 with Projections to 2035 (DOE/EIA-0383(2010)), May 11, 2010, www.eia.doe.gov/oiaf/aeo/nuclear_power.html

. 10 

### Supreme Court of the United States, “Entergy Corp. v. Riverkeeper, Inc., et al.,” No. 07-588 (October Term, 2008 www.supremecourtus.gov/opinions/ 08pdf/07-588.pdf.

11 

### EIA, op.cit.

12 

### EIA, ibid.

### 3-17 

## 3.8 Heat Rates Heat rates, expressed in BTUs per KWh. are a me tric of the efficiency of a generating unit. As in previous versions of NEEDS, it is assumed in NEEDS v.4.10 that heat rates of existing units will remain constant over time. This assumption refl ects two offsetting factors: (1) plant efficiencies tend to degrade over time and (2) increased maintenance and component replacement work to maintain or improve plant efficiency.

The heat rates in EPA Base Case v.4.10 are based on values from AEO 2008. These values were screened and adjusted using a procedure developed by EPA to ensure that the heat rates used in EPA Base Case v.4.10 are within the engineering capabilities of the generating unit types. Based on engineering analysis, the upper and lower heat rate limits shown in Table 3-10 were applied to coal steam, oil/gas steam, combined cycle, com bustion turbine, and internal combustion engines. If the reported heat rate for such a unit was below the applicable lower limit or above the upper limit, the limit was substituted for the reported value.

Table 3-10 Lower and Upper Limits Applied to Heat Rate Data in NEEDS v.4.10 Heat Rate (Btu/kWh) Plant Type Lower Limit Upper Limit Coal Steam 8,300 14,500 Oil/Gas Steam 8,300 14,500 Combined Cycle - Natural Gas 5,500 15,000 Combined Cycle - Oil 6,000 15,000 Combustion Turbine - Natural Gas - 80 MW and above 8,700 18,700 Combustion Turbine - Natural Gas < 80 MW 8,700 36,800 Combustion Turbine - Oil and Oil/Gas - 80 MW and above 6,000 25,000 Combustion Turbine - Oil and Oil/Gas < 80 MW 6,000 36,800 IC Engine - Natural Gas 8,700 18,000 IC Engine - Oil and Oil/Gas - 5 MW and above 8,700 20,500 IC Engine - Oil and Oil/Gas < 5 MW 8,700 42,000



## 3.9 Existing Environmental Regulations This section describes the existing federal, regional, and state SO 2 

### , NO x 

### , mercury, and CO 2 

### emissions regulations that are represented in the EPA Base Case v.4.10. The first three subsections discuss national and regional regulations. The next two subsections describe state level environmental regulations and a variety of legal settlements. The last subsection presents emission assumptions for potential units.

Note on Clean Air Interstate Rule (CAIR): In December 2008 the U.S. Court of Appeals for the District of Columbia Circuit remanded CAIR to EPA to correct legal flaws in the proposed regulations as cited in the Court’s July 2008 ruling. Until EPA’s work was completed, CAIR, which includes a cap-and-trade system for SO 2 

### and NO x 

### emissions, was temporarily reinstated. However, although CAIR’s provisions were still in effect when EPA Base Case v.4.10 was released, it is not included in the base case to allow EPA Base Case v.4.10 to be used to analyze the regulations proposed to replace CAIR.

3.9.1 SO 2 

### Regulations Unit-level Regulatory SO 2 

### Emission Rates and Coal Assignments: Before discussing the national and regional regulations affecting SO 2 

### , it is important to note that unit-level SO 2 

### regulations arising out of State Implementation Plan (SIP) requirements, which are not only state specific but also county specific, are captured at model set-up in the coal choices given to coal fired existing units in EPA Base Case v.4.10. The SIP requirements define “regulatory SO 2

### 3-18 emission rates.” Since SO 2 

### emissions are dependent on the sulfur content of the fuel used, the regulatory SO 2 

### emission rates are used in IPM to define fuel capabilities.

For instance, a unit with a regulatory SO 2 

### emission rate of 3.0 lbs/MMBtu would be provided only with those combinations of fuel choices and SO 2 

### emission control options that would allow the unit to achieve an out-of-stack rate of 3.0 lbs/MMBtu or less. If the unit finds it economical, it may elect to burn a fuel that would achieve a lower SO 2 

### rate than its specified regulatory emission limit. In EPA Base Case v.4.10 there are 6 different sulfur grades of bituminous coal, 3 different grades of sub-bituminous coal, 3 different grades of lignite , and 1 sulfur grade of residual fuel oil. There are 2 different SO 2 

### scrubber options for coal units. Further discussion of fuel types and sulfur content is contained in Chapter 9. Further discussion of SO 2 

### control technologies is contained in Chapter 5.

National and Regional SO 2 

### Regulations: The national program affecting SO 2 

### emissions in EPA Base Case v.4.10 is the SO 2 

### allowance trading program established under Title IV of the Clean Air Act Amendments (CAAA) of 1990, which set a goal of reducing annual SO 2 

### emissions by 10 million tons below 1980 levels. The program, which became fully operational in year 2000, affects all SO 2 

### emitting electric generating units greater than 25 MWs. The program provides trading and banking of allowances over time acro ss all affected electric generation sources.

The annual SO 2 

### caps over the modeling time horizon in EPA Base Case v.4.10 reflect the provisions in Title IV. Since EPA Base Case v.4.10 uses year 2012 as the first analysis year, a projection of allowance banking behavior thr ough the end of 2011 and specification of the available 2012 allowances are needed to initialize the modeling. EPA developed the projection of the banked allowances (11 million) going into 2012. Calculating the available 2012 allowances involved deducting allowance surrenders due to NSR settlements and state regulations from the 2012 SO 2 

### cap of 8.95 million tons. The surrenders totaled 270.6 thousand tons in allowances, leaving 8.679 million of 2012 allowances remaini ng. Table 7-4 shows the initial bank and 2012 allowance specification along with the SO 2 

### caps for the entire modeling time horizon. Specifics of the allowance surrender requirements under state regulations and NSR settlements can be found in Appendices 3-2 and 3-3.

EPA Base Case v.4.10 also includes a representation of the Western Regional Air Partnership (WRAP) Program, a regional initiative involv ing Arizona, New Mexico, Oregon, Utah, Wyoming directed toward addressing visibility issues in the Grand Canyon and affecting SO 2 

### emissions starting in 2018. The WRAP specifications for SO 2 

### are presented in Table 7-4.

3.9.2 NO x 

### Regulations
Much like SO 2 

### regulations, existing NO x 

### regulations are represented in EPA Base Case v.4.10 through a combination of system level NO x 

### programs and generation unit-level NO x 

### limits.

The system level NO x 

### regulation represented in EPA Base Case v.4.10 is the NO x 

### SIP Call trading program. This trading program affects all fossil units in 20 northeastern states 13 

### and the District of Columbia. The program is only in effect during the ozone season (May - September). The program includes state-specific NO x 

### budgets. However, since the program allows for trading among units in different states, the total annual NO X 

### SIP Call budget of 527,580 tons is used in EPA Base Case v.4.10, rather than the state-specific budgets. The specifications for the SIP Call are presented in Table 7-4.

13 

### The states included in the SIP Call program are Alabama, Connecticut, Delaware, Illinois, Indiana, Kentucky, Maryland, Massachusetts, Mich igan, Missouri, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Virginia, and West Virginia.

### 3-19 The representation of unit-level NO x 

### limits includes Title IV unit specific rate limits and Clean Air Act Reasonable Available Control Technology (RACT) requirements for controlling NO x 

### emissions from electric generating units in ozone non-attainment areas or in the Ozone Transport Region 14 

### (OTR). Both of these limits are captured in the specific NO x 

### emission rates assigned to each unit represented in the base case. Unlike SO 2 

### emission rates, NO x 

### emission rates are assumed not to vary with fuel, but are dependent on the combustion properties of the generating unit. Under the EPA Base Case v.4.10 the NO x 

### emission rate of a unit can only change if the unit is retrofitted with NO x 

### pollution control equipment.

NO x 

### Rates in NEEDS, v.4.10 Database: The NO x 

### rates in the current base case were derived, wherever possible, directly from actual monitored NO x 

### emission rate data reported to EPA under the Acid Rain and NO x 

### Budget Program in 2007. The emission rates themselves reflect the impact of the applicable NO x 

### regulations. For coal-fired units, NO X 

### rates were used in combination with detailed engineering assessments of NO X 

### combustion control performance to prepare a set of four possible starting NO x 

### rates to assign to a unit depending on the specific policy affecting that unit in a model run.

The reason for having four NO x 

### rates in NEEDS is to allow all possible modeling scenarios involving NO x 

### controls to be set up. The four NO x 

### rates are designated as Mode 1–4, and are designed to include all the NO x 

### rates possible for a unit with its current configuration of NO x 

### combustion and post-combustion controls. The four NO x 

### rates are:

• Mode 1: Applies to units not covered by a NO X 

### control policy. Specifically, this is the NO X 

### rate with post-combustion controls shut off. For units without post-combustion controls, it’s their uncontrolled NO X 

### rate.

• Mode 2: A unit, which has post-combustion controls, runs them, but a unit without postcombustion controls operates as usual.

• Mode 3: Applies to the off-season NO X 

### rate for units affected by a seasonal NO X 

### policy. For units with post-combustion controls, this is the NO X 

### rate with post-combustion controls shut off. For units without post-combustion controls, it’s the NO X 

### rate with state-of-the-art combustion controls operating. (Exception: In the SIP Call region current combustion controls are assumed to be retained.)

• Mode 4: NO X 

### rate applicable under a NO X 

### policy. For SCR units, it’s the NO X 

### rate with the SCR operating. For SNCR units, it’s the NO X 

### rate with SNCR operating plus state-of-the-artcombustion controls operating if required to attain rate limits. For units without postcombustion controls, it’s the NO X 

### rate with state-of-the-art combustion controls operating. (Exception: In the SIP Call region current combustion controls are assumed to be retained.)

The program that sets up a new model run uses a series of algorithms (decision rules) to determine which of the four NO X 

### rates is selected:

• A unit covered under an annual NO X 

### emission limit is assigned the Mode 4 NO X 

### rate (winter and summer seasons).

• A unit covered by a summer season NO X 

### emission limit, but not an annual NO X 

### limit, is assigned the Mode 4 NO X 

### rate in the summer season but the Mode 3 NO X 

### rate in the winter

season.

• A unit covered by a mercury emission limit and not by a NO X 

### emission limit is assigned the Mode 2 NO X 

### rate in both winter and summer seasons. (Note: In the case of mercury limits, Mode 2 applies since it implies operation of an SCR or SNCR. This equipment, in combination with SO 2 

### and particulate controls, offers as a co-benefit the reduction and capture

14 

### The OTR consists of the following states: Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, New Jersey, P ennsylvania, Delaware, Maryland, District of Columbia, and northern Virginia.

### 3-20 of mercury. See Chapter 5 in the v.4.10 doc umentation for a discussion of the calculation mercury emission modification factors (EMF).)

• A unit not covered by either an annual or a summer NO X 

### limit nor mercury control requirements is assigned the Mode 1 NO X 

### rate in both winter and summer seasons. The Mode 1-4 NO x 

### rates for each generating unit are included in the NEEDS, v.4.10 database, described in Chapter 4. Appendix 3-1 and accom panying Tables 3-1.1, 3-1.2, and 3-1.3 give further information on the procedures employed to derive the four NO X 

### rate modes and give specific examples of generating units that fit each of the Mode 1-4 specifications.

Additional NO X 

### rate assumptions include default NO X 

### rates of 0.25 lbs/MMBtu for existing biomass units and 0.09 lbs/MMBtu for existing landfill gas units.

3.9.3 CO 2 

### Regulations and Renewable Portfolio Standards The Regional Greenhouse Gas Initiative (RGGI) is a year-round CO 2 

### cap and trade program affecting fossil fired electric power plants 25 MW or larger in Connecticut, Delaware, Maine, New Hampshire, New Jersey, New York, Vermont, R hode Island, Massachusetts, and Maryland. Table 7-4 shows the specifications for RGGI that are implemented in EPA Base Case v.4.10.

Renewable Portfolio Standards (RPS) generally refer to various state-level policies that require the addition of renewable generation to meet a spec ified share of state-wide generation In EPA Base Case v.4.10 the state RPS requirements are represented at a regional level utilizing the aggregate regional representation of RPS requirements that is implemented in AEO 2010 15 

### as shown in Appendix 3-6. This appendix shows t he RPS requirements that apply to the NEMS (National Energy Modeling System) regions used in AEO. The RPS requirement for a particular NEMS region applies to all IPM regions that are predominantly contained in that NEMS region.

3.9.4 State Specific Environmental Regulations EPA Base Case v.4.10 represents laws and regulations in 25 states affecting emissions from the electricity sector. The laws and regulations had to either be on the books or expected to come into force. Appendix 3-2 summarizes the provisions of state laws and regulations that are represented in EPA Base Case 4.10.

3.9.5 New Source Review (NSR) Settlements The New Source Review, (NSR) settlements refe r to legal agreements with companies resultingfrom the permitting process under the CAAA which requires industry to undergo an EPA preconstruction review of proposed environmental contro ls either on new facilities or as modifications to existing facilities where there would result a “significant increase” in a regulated pollutant. EPA Base Case v.4.10 includes NSR settlements with 20 electric power companies. A summary of the units affected and how the settlements were modeled can be found in Appendix 3-3.

Seven state settlements and five citizen settlements are also represented in EPA Base Case v.4.10. These are summarized in Appendices 3-4 and 3-5 respectively.

3.9.6 Emission Assumptions for Potential (New) Units Emissions from existing and planned/committed units vary from installation to installation based on the performance of the generating unit and the emi ssions regulations that are in place. In contrast, there are no location-specific variations in the emission and removal rate capabilities of potential new units. In IPM, potential new uni ts are modeled as additional capacity and generation that may come on line in each model region. Across all model regions the emission and removal

15 

### Energy Information Administration, U.S. Department of Energy, Assumptions to Annual Energy Outlook 2010: Renewable Fuels Module (DOE/EIA-0554(2010)), April 9, 2010, Table 13.4 “Aggregate Regional RPS Requirements, www.eia.doe.gov/oiaf/aeo/assumption/renewable.html and www.eia.doe.gov/oiaf/aeo/assumption/pdf/renewable_tbls.pdf

### 3-21 rate capabilities of potential new units are the sa me. It should be noted that, new coal units cannot be built in the CA-N, CA-S, NYC, LILC, or NENG model regions due to particularly stringent state emission limits placed on fossil fired units. The specific assumptions regarding the emission and removal rates of potential new units in EPA Base Case v.4.10 are presented in Table 3-11. (Note: Nuclear, wind, solar, and fuel cell technologies are not included in Table 3-11 because they do not emit any of the listed pollutants.) For additional details on the modeling of potential new units see Chapter 4.



## 3.10 Capacity Deployment Constraints Due to its extended time horizon and the policies that EPA Base Case v.4.10 is expected to be used to analyze, capacity deployment constraint s for the more capital intensive generation technologies and retrofits (new nuclear, advanced coal with carbon capture, and carbon capture retrofits) were incorporated into the base case. The deployment constraints are intended to capture factors that are likely to place an upper bound on the amount of these technologies that can be built in any given model run year over the modeling time horizon. Such limiting factors include:

• production capacity limitations (including t he number of engineering and construction (E/C) firms capable of executing large power projects in the U.S., the number of large projects each such firm can handle, and the number of multi-billion dollar projects a firm can take on in parallel),

• general limitations in the domestic infrastructure for heavy manufacturing,

• financial limitations (number of projects that can obtain financing simultaneously at an acceptable level of risk),

• workforce limitations (limitations in t he skilled engineering and construction labor force, replacement challenges caused by an agi ng workforce, on the one hand, and inadequate training infrastructure for new entrants, on the other).

The capacity deployment constraints are based on assessments by EPA power sector engineering staff of historical trends and projections of capability going forward. Conceptually, the procedure used to develop these constraints consisted of the following steps:

1. Start by estimating the maximum number of E/C firms that will be available over the time horizon. 2. Estimate the maximum number of a particu lar type of generating unit (e.g., 600 MW advanced coal plant with carbon capture) that a single E/C firm can complete in the first 5-year period (2015-2020). 3. Multiply the number of E/C firms estimated in Step 1 by the number of units per firm found in Step 2 to obtain the maximum number of these generating units that can be completed in the first period. 4. Determine if there will be competition from other competing technologies for the same productive capacity and labor force used for the technology analyzed in steps 2 and 3. If not, go to Step 7. If so, go to Step 5. 5. Establish an equivalency table showing how much capacity could be built if the effort required to build 1 MW of the type of technology analyzed in steps 2 and 3 were instead used to build another type of generating technology (e.g., 1600 MW nuclear plant). 6. Based on these calculations build a production possibility frontier showing the maximum mix of the two generating technologies that can be added in the first 5-year period. 7. Over the subsequent five year periods assume that the E/C firms have increased capabilities relative to the previous five year period. Represent the increased capability by a capability multiplier. For example, it might be assumed that each succeeding 5-year period the E/C firms can design and build 1.4 as much as in the immediately preceding 5-year period. Multiply the capacity deployment limit(s) from the preceding period by the capability multiplier to derive the capacity deployment limit for the subsequent period.

### 3-22 8. If necessary, prevent sudden spikes in capacit y in later periods when there has been little or no build up in preceding periods by tying the am ount of capacity that can be built in a given period to the amount of capacity built in preceding periods.

Appendix 3-07 shows the joint capacity deploym ent constraint on advanced coal with carbon capture and storage (CCS) and new nuclear. Appendix 3-08 shows the capacity deployment constraint on new nuclear in itself. The bar graph in Appendix 3-08 illustrates how building capacity in earlier years increases the maximum capacity that can be built over the entire modeling time horizon.3-23Table 3-11 Emission and Removal Rate Assumptions for Potential (New) Units in EPA Base Case v.4.10GasControls,Removal,andEmissionsRatesSupercriticalPulverizedCoal - WetScrubberSupercriticalPulverizedCoal - DryScrubberIntegratedGasificationCombinedCycleAdvancedCoal withCarbonCaptureAdvancedCombinedCycleAdvancedCombustion TurbineBiomassConventionalDirect-FiredBoilerBiomassGasificationCombinedCycleGeothermalLandfillGasSO2Removal /EmissionsRate98% with afloor of 0.06lbs/MMBtu93% with afloor of 0.065lbs/MMBtu99% 99% None None0.08lbs/MMBtu0.08lbs/MMBtuNone NoneNOxEmissionRate0.06lbs/MMBtu0.06lbs/MMBtu0.013lbs/MMBtu0.013lbs/MMBtu0.011lbs/MMBtu0.011lbs/MMBtu0.36lbs/MMBtu0.102lbs/MMBtuNone0.09lbs/MMBtuHgRemoval /EmissionsRate90% 90% 90% 90%NaturalGas:0.000138lbs/MMBtuOil:0.483lbs/MMBtuNaturalGas:.000138lbs/MMBtuOil:0.483lbs/MMBtu0.57lbs/MMBtu0.57lbs/MMBtu3.70 NoneCO2Removal /EmissionsRate205.2 - 217.3lbs/MMBtu205.2 - 217.3lbs/MMBtu205.2 - 217.3lbs/MMBtu90%NaturalGas:117.08lbs/MMBtuOil:161.39lbs/MMBtuNaturalGas:117.08lbs/MMBtuOil:161.39lbs/MMBtuNone None None None

### Appendix 3-1.1 

## Appendix 3-1 NO x



## Rate Development in EPA Base Case v.4.10

The following questions (Q) and answers (A) are intended to provide further background on the four NO X 

### rates found in the NEEDS, v.4.10 database.

Q1: Why are four NO X 

### rates included in NEEDS?

A1: The four NO X 

### rates in NEEDS represent a menu of all the NO X 

### rates applicable to a specific generating unit with only its current configuration of NO x 

### combustion and post-combustion controls under all the conceivable policies involving NO x 

### controls that might be modeled in the future. By defining this menu up front for every generating unit, the program that sets up an IPM run can follow a set of decision rules to select the rate(s) appropriate for the unit in the particular policy being modeled consistent with the unit’s existing set of combustion and post-combustion NO x 

### controls.

Q2: What operational states do the four NO x 

### rates represent?

A2: Before answering this question, let’s name the four NO x 

### rates that are in NEEDS:

Mode 1= Uncontrolled Base Rate Mode 2= Controlled Base Rate Mode 3= Uncontrolled Policy Rate Mode 4 = Controlled Policy Rate

The operational states associated with each of the four NO x 

### rates are shown in the second and third columns in the table below.

Q3: What NO x 

### policies in a model run result in the assignment of each of the NO x 

### rates?

A3: The policies causing each rate to be assigned are shown in the last column in the table below.

Interpreting the Mode 1 – 4 NO x 

### Rates in NEEDS

Name Operational State of NO x 

### Controls NO x 

### Policies Causing This Rate To be Assigned Units with post combustion NO x 

### controls: Do they operate the controls? No Mode 1 = Uncontrolled

Base Rate Units without postcombustion controls: Dothey upgrade to state-offthe-art combustion controls? No If the unit is not covered by any NO x 

### limit in the run, pre-assign this as its NO x 

### rate

Units with post combustion NO x 

### controls: Do they operate the controls? Yes

Mode 2 = Controlled Base Rate Units without postcombustion controls: Dothey upgrade to state-offthe-art combustion controls? No If the unit is covered by a mercury policy, pre-assign this as its NO x 

### rate

Explanation: Post-combustion NO x 

### controls figure in mercury reduction but NO x 

### combustion controls do not, so the operational state (in column 2) fits the requirements of the policy Units with post combustion NO x 

### controls: Do they operate the controls? No Mode 3 = Uncontrolled Policy Rate Units without postcombustion controls: Dothey upgrade to state-offYes If the unit is covered by a summer NO x 

### limit pre-assign this as its winter NO x 

### rate.

### Appendix 3-1.2 Name Operational State of NO x 

### Controls NO x 

### Policies Causing This Rate To be Assigned the-art combustion controls? Units with post combustion NO x 

### controls: Do they operate the controls? Yes Mode 4 = Controlled Policy Rate Units without postcombustion controls: Dothey upgrade to state-offthe-art combustion controls? Yes If the unit is covered by a summer NO x 

### limit pre-assign this as its summer NO x 

### rate.

If the unit is covered by an annual NO x 

### limit, pre-assign this as its winter and summer NO x 

### rates.

Q4: How are the values of the Mode 1-4 NO x 

### rates derived?

A4: We start with the emission data reported to EPA for a specific year under Title IV of the Clean Air Act Amendments of 1990 (Acid Rain Program) and NO x 

### Budget Program. Using this data, NO x 

### rates are derived for the summer and winter seasons.

Calculations can get complex, so we’ll illustrate it here for coal units only and with the assumption that the data were absolutely complete and cons istent with what engineering theory tells us its values should be. Otherwise, we apply additional screens. Explaining them is beyond the scope of this illustration. Basically, here’s how the values would be derived:

Mode 1 For all coal units Mode 1 = Winter NO x 

### rate

Mode 2 For coal units without NO x 

### post-combustion controls Mode 2 = Mode 1 rate

For coal units with NO x 

### post-combustion controls

, Min{max[Mode 1 NO x 

### rate * (1-removal efficiency), floor rate], ETS Summer NO x 

### rate}

Where For an SCR

, Removal efficiency = 90% Floor rate = 0.06 lb/MMBtu; For an SNCR

, Removal efficiency = 35% No floor rate is applicable

Mode 3 Step 1: Pre-screen units that already have state of art (SOA) combustion controls from units that have non-SOA combustion controls from units that have no combustion controls

For coal units without post-combustion NO x 

### controls

For units listed as not having combustion controls Make sure their NO x 

### rates do not indicate that they really do have SOA control If Mode 1 > Cut-off (in Table 3-1.2), then Mode 1 = Base rate. Go to Step 3 If Mode 1 ≤ Cut-off (in Table 3-1.2), then the unit has SOA control and Go to Step 5 using the Mode 1 rate as the provisional SOA NO x 

### rate.

For coal listed with combustion controls If Mode 1 > Cut-off (in Table 3-1.2), then unit has non-SOA combustion controls. Go to Step 2

### Appendix 3-1.3 If Mode 1 ≤ Cut-off (in Table 3-1.2), then the unit has SOA control and Go to Step 5 using the Mode 1 rate as the provisional SOA NO x 

### rate.

For coal units with post-combustion NO x 

### controls

For coal units with SCR Mode 1 = Mode 3

For coal units with SNCR If Mode 1 ≤ Cut-off (in Table 3-1.2), then the unit has SOA control and Mode 1 = Mode 3 If Mode 1 > Cut-off (in Table 3-1.2), then unit has non-SOA combustion controls. Go to Step 2

Step 2: For units with non-SOA combustion controls, determine their Base NO x 

### rate, i.e., the unit’s uncontrolled emission rate without combustion cont rols, using the appropriate equation (not in boldface italics) in Table 3-1.3 to back calculate their Base NO x 

### rate. Use the default Base NO x 

### rate values if back calculations can’t be performed. Once the Base NO x 

### rate is obtained, go to Step 3.

Step 3: Use the appropriate equations (in boldface italics) in Table 3-1.3 to calculate the NO x 

### rate with SOA combustion controls.

Step 4: Compare the value calculated in Step 3 to the applicable NO x 

### floor rate in Table 3-1:2.

For units with post-combustion controls If the value from Step 3 is ≥ floor, use the Step 3 value as Mode 3 NO x 

### rate. Otherwise, use the floor as the Mode 3 NO x 

### rate.

For units without post-combustion controls If the value from Step 3 is ≥ floor, use the Step 3 value as the provisional SOA NO x 

### rate. Otherwise, use the floor as their provisional SOA NO x 

### rate. Go to Step 5.

Step 5: For units without post combustion controls compare the provisional SOA NO x 

### rate obtained in previous steps to their Summer NO x 

### rate. If Summer NO x 

### rate < provisional SOA NO x 

### rate, then Mode 3 = summer NO x 

### rate. If Summer NO x 

### rate ≥ provisional SOA NO x 

### rate, then Mode 3 = provisional SOA NO x 

### rate.

Mode 4

For units without post-combustion controls Mode 4 = Mode 3

For units with SCR post-combustion controls Mode 4 = Mode 2

For units with SNCR post-combustion controls Mode 4 = minimum {(1-.35) * Mode 3, Summer NO x 

### rate}

Note: The (1-.35) term in the equation above represents the 35% NO x 

### removal efficiency of SNCR.

Q5: Is there anything else that might be useful to understand about the Mode 1 – 4 NO x 

### rates.

### Appendix 3-1.4 A5: There are several things to note about the Modes 1-4 designations. “Controlled” refers to the rates provided by post combustion NO x 

### controls, i.e., selective catalytic reduction (SCR) or selective non-catalytic reduction (SNCR), if they are present at the unit. For generating units that do not have post-combustion controls, the controlled rate will be the same as the uncontrolled rate. For generating units that do have post-com bustion controls, the controlled and uncontrolled rates will differ. Base and Policy NO x 

### rates will be same if the unit has state-of-the-art NO x 

### combustion controls or is in the SIP Call region where current combustion controls are assumed to be retained. Base and policy rates will differ if a unit does not currently have state-of-the-art combustion controls that would be installed in response to a NO x 

### policy. Examples of each of these instances are shown in Table 3-1.1.

Other things worth noting are: (a) In general, winter NO x 

### rates reported in EPA’s Emission Tracking System were used as proxies for the uncontrolled base NO x 

### rates. (b) If a unit does not report having combustion contro ls, but has an emission rate below a specific cut-off rate (shown in Table 3-1.2), it is considered to have combustion controls. (c) For units with combustion controls that were not state-of-the-art, emission rates without those combustion controls were back-calculated and then policy rates were derived assuming the reductions provided by state-of-the art combustion controls. (d) The NO x 

### rates achievable by state-of-the-art co mbustion controls vary by coal rank (bituminous and sub-bituminous) and boiler type. The equations used to derive these rates are shown in Table 3-1.3.

Q6: What are examples of the Mode 1-4 NO x 

### for some actual operating generating units?

A6: Table 3-1.1 gives the Mode 1-4 NO x 

### rates for real generating units. They are meant to illustrate a range of situations that can arise.Appendix 3-1.5Table 3-1.1 Examples of Base and Policy NOxRates Occurring in EPA Base Case v.4.10PlantNameUnique IDPostCombustionControlUncontrolledNOxBaseRateControlledNOxBaseRateUncontrolledNOxPolicyRateControlledNOxPolicyRateExplanationSituation 1: For generating units thatdo not have post-combustion controls, the controlled and uncontrolled rates will be thesame.FourCorners2442_B_1None0.8090.8090.5240.524Situation 4 also applies, i.e., unit had LNB andnow added OFA so see drop in policy rates.Situation 2: For generating units that do have post-combustion controls, the controlled anduncontrolled rates will differ.Big Sandy1353_B_BSU2SCR0.6380.0640.6380.064(1) Has SCR so see difference betweenuncontrolled and controlled rates(2) Situation 3b also applies.Situation 3a: Base and Policy NOxrates will be same if the unit has state-of-the-art NOxcombustion controls or . . .GreeneCounty10_B_2None0.3160.3160.3160.316Situation 1 also applies.Roxboro2712_B_1SCR0.9000.0840.9000.084Situation 2 also applies.Situation 3b: . . . is in the SIP Call region where current combustion controls are assumed to be retained.Thomas Hill2168_B_MB3SCR0.2230.0600.2230.060Situation 2 also applies.Waukegan883_B_17None0.7100.7100.7100.710(1) Has NOxcombustion control and is in SIP sodoesn't get added combustion control. High NOxrate because it is a cyclone unit(2) Situation 1 also applies.Situation 4: Base and policy rates will differ if a unit does notcurrently have state-of-the-art combustion controls and would install such controls inresponse to a NOxpolicy.ClayBoswell1893_B_4SNCR0.2310.1500.1520.099(1) Drop in uncontrolled policy NOxratecompared to uncontrolled base rate is due toaddition of combustion controls. (Note 0.32 isfloor.)(2) Unit has SNCR so Situation #2a also appliesand you see a 35% drop between uncontrolledand controlled NOxrates.

### Appendix 3-1.6 Table 3-1.2 Cutoff and Floor NO x 

### Rates (lb/MMBtu) in EPA Base Case v.4.10 Cutoff Rate (lbs/MMBtu) Floor Rate (lbs/MMBtu) Boiler Type Bituminous Subbituminous Lignite Bituminous Subbituminous Lignite Wall-Fired Dry-Bottom 0.43 0.33 0.29 0.32 0.18 0.18
TangentiallyFired 0.34 0.24 0.22 0.24 0.12 0.17

Cell-Burners 0.43 0.43 0.43 0.32 0.32 0.32 Cyclones 0.62 0.67 0.67 0.47 0.49 0.49VerticallyFired 0.57 0.44 0.44 0.49 0.25 0.25



### Table 3-1.3 NO x 

### Removal Efficiencies for Different Combustion Control Configurations in EPA Base Case v.4.10 (State of the art configurations are shown in bold italic.)
Boiler Type Coal Type Combustion Control Technology Fraction of Removal Default Removal

LNB 0.163 + 0.272* Base NO x 

### 0.568 Dry Bottom Wall-Fired Bituminous LNB + OFA 0.313 + 0.272* Base NO x 

### 0.718

LNB 0.135 + 0.541* Base NO x 

### 0.574 Dry Bottom Wall-Fired Subbituminous /Lignite LNB + OFA 0.285 + 0.541* Base NO x 

### 0.724

LNC1 0.162 + 0.336* Base NO x 

### 0.42

LNC2 0.212 + 0.336* Base NO x 

### 0.47TangentiallyFired Bituminous

LNC3 0.362 + 0.336* Base NO x 

### 0.62

LNC1 0.20 + 0.717* Base NO x 

### 0.563

LNC2 0.25 + 0.717* Base NO x 

### 0.613TangentiallyFired Subbituminous /Lignite

LNC3 0.35 + 0.717* Base NO x 

### 0.713

Notes:

LNB = Low NO x 

### Burner OFA = Overfire Air LNC = Low NO x 

### Control

### Appendix 3-2.1 

## Appendix 3-2 State Power Sector Regulations included in EPA Base Case v.4.10

State/ Region Bill Emission Type Emission Specifications Implementation Status

Alabama Alabama Administrative Code Chapter 335-3-8 NO x 0.02 lbs/MMBtu annual PPMDV for combined cycle EGUs which commenced operation after April 1, 2003 2003

Arizona Title 18, Chapter 2, Article 7 Hg 90% removal of Hg content of fuel or 0.0087 lb/GWH-hr annual reduction for all non-cogen coal units > 25 MW 2017

NO x 9.68 MTons annual cap for list of entities in Appendix A of "Annual RECLAIM Audit Market Report for the Compliance Year 2005" (304 entities) California CA Reclaim Market

SO 2 4.292 MTons annual cap for list of entities in Appendix A of "Annual RECLAIM Audit Market Report for the Compliance Year 2005" (304 entities) 1994

Colorado 40 C.F.R. Part 60 Hg 2012 & 2013: 80% reduction of Hg content of fuel or 0.0174 lb/GW-hr annual reduction for Pawnee Station 1 and Rawhide Station 101 2014 through 2016: 80% reduction of Hg content of fuel or 0.0174 lb/GW-hr annual reduction for all coal units > 25 MW 2017 onwards: 90% reduction of Hg content of fuel or 0.0087 lb/GW-hr annual reduction for all coal units > 25 MW 2012

Executive Order 19 and Regulations of

Connecticut State Agencies

(RCSA) 22a174-22 NO x 0.15 lbs/MMBtu annual rate limit for all fossil units > 15 MW

Executive Order 19, RCSA 22a-198 & Connecticut General Statues (CGS) 22a-198 SO 2 0.33 lbs/MMBtu annual rate limit for all fossil units > 15 MW 2003

Connecticut

Public Act No. 03-72 & RCSA 22a-198 Hg 90% removal of Hg content of fuel or 0.0087 lb/GW-hr annual reduction for all coal-fired units 2008

Regulation 1148: Control of Stationary Combustion Turbine EGU

Emissions NO x 0.19 lbs/MMBtu ozone season PPMDV for stationary, liquid fuel fired CT EGUs >1 MW 0.39 lbs/MMBtu ozone season PPMDV for stationary, gas fuel fired CT EGUs >1 MW 2009

NO x 0.125 lbs/MMBtu rate limit of NO x annually for all coal and residual-oil fired units > 25 MW Delaware

Regulation No. 1146: Electric

Generating Unit (EGU) Multi-Pollutant

Regulation

SO 2 0.26 lbs/MMBtu annual rate limit for coal and residual-oil fired units > 25 MW 2009

### Appendix 3-2.2 State/ Region Bill Emission Type Emission Specifications Implementation Status

Hg 2012: 80% removal of Hg content of fuel or 0.0174 lb/GW-hr annual reduction for all coal units > 25 MW 2013 onwards: 90% removal of Hg content of fuel or 0.0087 lb/GW-hr annual reduction for all coal units > 25 MW

Georgia Multipollutant Control for Electric Utility Steam Generating Units SCR, FGD, and Sorbent Injection Baghouse controls to be installed The following plants must install controls: Bowen, Branch, Hammond, McDonough, Scherer, Wansley, and Yates Implementation from 2008 through 2015, depending on plant and control type

Title 35, Section 217.706 NO x 0.25 lbs/MMBtu summer season rate limit for all fossil units > 25 MW 2004

NO x 0.11 lbs/MMBtu annual rate limit and ozone season rate limit for all Dynergy and Ameren coal steam units > 25 MW 2012

SO 2 2013 & 2014: 0.33 lbs/MMBtu annual rate limit for all Dynergy and Ameren coal steam units > 25 MW 2015 onwards: 0.25 lbs/MMBtu annual rate limit for all Dynergy and Ameren coal steam units > 25 MW 2013 Title 35, Part 225, Subpart B: Control of Hg Emissions from Coal Fired Electric Generation Units Hg 90% removal of Hg content of fuel or 0.08 lbs/GW-hr annual reduction for all Ameren and Dynergy coal units > 25 MW 2015

NO x 0.11 lbs/MMBtu ozone season and annual rate limit for all specified Midwest Gen coal steam units 2012

SO 2 0.44 lbs/MMBtu annual rate limit in 2013, decreasing annually to 0.11 lbs/MMBtu in 2019 for all specified Midwes t Gen coal steam units 2013 Illinois

Title 35 Part 225; Subpart F:

Combined Pollutant Standards Hg 90% removal of Hg content of fuel or 0.08 lbs/GWh annual reduction for all specified Midwest Gen coal steam units 2015

Louisiana Title 33 Part II - Chapter 22, Control of Nitrogen Oxides NO x 1.2 lbs/MMBtu ozone season PPMDV for all single point sources that emit or have the potential to emit 5 tons or more of SO 2 into the atmosphere 2005

Chapter 145 NO x Control

Program NO x 0.22 lbs/MMBtu annual rate limit for all fossil fuel units > 25 MW built before 1995 with a heat input capacity < 750 MMBtu/hr

0.15 lbs/MMBtu annual rate limit for all fossil fuel units > 25 MW built before 1995 with a heat input capacity > 750 MMBtu/hr

0.20 lbs/MMBtu annual rate limit for all fossil fuel fired indirect heat exchangers, primary boilers, and resource recovery units with heat input capacity > 250 MMBtu/hr 2005

Maine

Statue 585-B Title 38, Chapter 4: Protection and Improvement of Air Hg 25 lbs annual cap for any facility including EGUs 2010

### Appendix 3-2.3 State/ Region Bill Emission Type Emission Specifications Implementation Status

NO x 7.3 MTons summer cap and 16.7 MTons annual cap for 15 specific existing coal steam units

SO 2 2009 through 2012: 48.6 MTons annual cap for 15 specific existing coal steam units 2013 onwards: 37.2 MTons annual cap for 15 specific existing coal steam units Maryland Maryland Healthy Air Act

Hg 2010 through 2012: 80% removal of Hg content of fuel for 15 specific existing coal steam units 2013 onwards: 90% removal of Hg content of fuel for 15 specific ex isting coal steam units 2009

NO x 1.5 lbs/MWh annual GPS for Bayton Point, Mystic Generating Station, Somerset Station, Mount Tom, Canal, and Salem Harbor

SO 2 3.0 lbs/MWh annual GPS for Bayton Point, Mystic Generating Station, Somerset Station, Mount Tom, Canal, and Salem Harbor

Massachusetts 310 CMR 7.29

Hg 2012: 85% removal of Hg content of fuel or 0.00000625 lbs/MWh annual GPS for Brayton Point, Mystic Generating Station, Somerset Station, Mount Tom, Canal, and Salem Harbor 2013 onwards: 95% removal of Hg content of fuel or 0.00000250 lbs/MWh annual GPS for Brayton Point, Mystic Generating Station, Somerset Station, Mount Tom, Canal, and Salem Harbor 2006

Michigan Part 15. Emission Limitations and Prohibitions -

Mercury Hg 90% removal of Hg content of fuel annually for all coal units > 25 MW 2015

Minnesota Minnesota Hg Emission Reduction Act Hg 90% removal of Hg content of fuel annually for all coal units > 250 MW 2008

Missouri 10 CSR 10- 6.350 NO x 0.25 lbs/MMBtu annual rate limit for all fossil fuel units > 25 MW in the following counties: Bollinger, Butler, Cape Girardeau, Carter, Clark, Crawford, Dent, Dunklin, Gasconade, Iron, Lewis, Lincoln, Madison, Marion, Mississippi, Montgomery, New Madrid, Oregon, Pemiscot, Perry, Phelps, Pike, Ralls, Reynolds, Ripley, St. Charles, St. Francois, Ste. Genevieve, Scott, Shannon, Stoddard, Warren, Washington and Wayne 0.18 lbs/MMBtu annual rate limit for all fossil fuel units > 25 MW the following counties: City of St. Louis, Franklin, Jefferson, and St. Louis 0.35 lbs/MMBtu annual rate limit for all fossil fuel units > 25 MW in the following counties: Buchanan, Jackson, Jasper, Randolph, and any other county not listed 2004

Montana Montana Mercury Rule

Adopted 10/16/06 Hg 0.90 lbs/TBtu annual rate limit for all non-lignite coal units 1.50 lbs/TBtu annual rate limit for all lignite coal units 2010

### Appendix 3-2.4 State/ Region Bill Emission Type Emission Specifications Implementation Status

RSA 125-O: 11-18 Hg 80% reduction of aggregated Hg content of the coal burned at the facilities for Merrimack Units 1 & 2 and Schiller Units 4, 5, & 6 2012

NO x 2.90 MTons summer cap for all fossil steam units > 250 MMBtu/hr operated at any time in 1990 and all new units > 15 MW 3.64 MTons annual cap for Merrimack 1 & 2, Newington 1, and Schiller 4 through 6 New Hampshire ENV-A2900 Multiple pollutant annual budget

trading and

banking program SO 2 7.29 MTons annual cap for Merrimack 1 & 2, Newington 1, and Schiller 4 through 6 2007

N.J.A.C. 7:27-

27.5, 27.6, 27.7, and 27.8 Hg 90% removal of Hg content of fuel annually for all coal-fired units 95% removal of Hg content of fuel annually for all MSW incinerator units 2007

N.J. A. C. Title 7, Chapter 27, Subchapter 19, Table 1 NO x Annual rate limits in lbs/MMBtu for the following technologies: 1.0 for tangential and wall-fired wet-bottom coal boilers serving an EGU 0.60 for cyclone-fired wet-bottom coal boilers serving an EGU 0.38 for tangential dry-bottom coal boilers serving an EGU 0.45 for wall-fired dry-bottom coal boilers serving an EGU 0.55 for cyclone-fired dry-bottom coal boilers serving an EGU 0.20 for tangential oil and/or gas boilers serving an EGU 0.28 for wall-fired oil and/or gas boilers serving an EGU 0.43 for cyclone-fired oil and/or gas boilers serving an EGU 2007

New Jersey

N.J. A. C. Title 7, Chapter 27, Subchapter 19, Table 4 NO x 2.2 lbs/MWh annual GPS for gas-burning simple cycle combustion turbine units 3.0 lbs/MWh annual GPS for oil-burning simple cycle combustion turbine units 1.3 lbs/MWh annual GPS for gas-burning combined cycle CT or regenerative cycle CT units 2.0 lbs/MWh annual GPS for oil-burning combined cycle CT or regenerative cycle CT units 2007

Part 237 NO x 39.91 MTons non-ozone season cap for fossil fuel units > 25 MW 2004

Part 238 SO 2 131.36 MTons annual cap for fossil fuel units > 25 MW 2005

New York Mercury Reduction Program for Coal-Fired Electric Utility Steam Generating Units Hg 786 lbs annual cap through 2014 for all coal fired boiler or CT units >25 MW after Nov. 15, 1990. 0.60 lbs/TBtu annual rate limit for all coal units > 25 MW developed after Nov.15 1990 2010

NO x 25 MTons annual cap for Progress Energy coal plants > 25 MW and 31 MTons annual cap for Duke Energy coal plants > 25 MW 2007

North Carolina NC Clean Smokestacks Act: Statute 143-215.107D SO 2 2012: 100 MTons annual cap for Progress Energy coal plants > 25 MW and 150 MTons annual cap for Duke Energy coal plants >25MW 2013 onwards: 50 MTons annual cap for Progress Energy coal plants > 25 MW and 80 2009

### Appendix 3-2.5 State/ Region Bill Emission Type Emission Specifications Implementation Status
MTons annual cap for Duke Energy coal plants > 25 MW

Oregon Administrative Rules, Chapter 345, Division 24 CO 2 675 lbs/MWh annual rate limit for new combustion turbines burni ng natural gas with a CF >75% and all new non-base load plants (with a CE <= 75%) emitting CO 2 1997

Oregon Utility Mercury Rule - Existing Units Hg 90% removal of Hg content of fuel reduction or 0.6 lbs/TBtu limitation for all existing coal units >25 MW 2012 Oregon

Oregon Utility Mercury Rule - Potential Units Hg 25 lbs rate limit for all potential coal units > 25 MW 2009

Pacific Northwest Washington State House

Bill 3141 CO 2 $1.45/Mton cost (2004$) for all new fossil-fuel power plant 2004

SO 2 273.95 MTons cap of SO 2 for all grandfathered units built before 1971 in East Texas Region Senate Bill 7 Chapter 101 NO x Annual cap for all grandfathered units built before 1971 in MTons: 84.48 in East Texas, 18.10 in West Texas, 1.06 in El Paso Region 2003

East and Central Texas annual rate limits in lbs/MMBtu for units that came online before 1996: Gas fired units: 0.14 Coal fired units: 0.165 Stationary gas turbines: 0.14

Dallas/Fort Worth Area annual rate limit for utility boilers, auxiliary steam boilers, stationary gas turbines, and duct burners used in an electric power generating system except for CT and CC units online after 1992: 0.033 lbs/MMBtu or 0.50 lbs/MWh output or 0.0033 lbs/MMBtu on system wide heat input weighted average for large utility systems 0.06 lbs/MMBtu for small utility systems Houston/Galveston region annual Cap and Trade (MECT) for all fossil units: 17.57 MTons Texas

Chapter 117 NO x

Beaumont-Port Arthur region annual rate limits for utility boilers, auxiliary steam boilers, stationary gas turbines, and duct burners used in an electric power generating system: 0.10 lbs/MMBtu 2007

Utah R307-424 Permits: Mercury Requirements

for Electric Generating Units Hg 90% removal of Hg content of fuel annually for all coal units > 25 MW 2013

Wisconsin NR 428 Wisconsin Administration Code NO x Annual rate limits in lbs/MMBtu for coal fired boilers > 1,000 MMBtu/hr : Wall fired, tangential fired, cyclone fired, and fluidized bed: 2009: 0.15, 2013 onwards: 0.10 Arch fired: 2009 onwards: 0.18 2009

### Appendix 3-2.6 State/ Region Bill Emission Type Emission Specifications Implementation Status

Annual rate limits in lbs/MMBtu for coal fired boilers between 500 and 1,000 MMBtu/hr: Wall fired: 2009: 0.20; 2013 onwards: 0.17 in 2013 Tangential fired: 2009 onwards: 0.15 Cyclone fired: 2009: 0.20; 2013 onwards: 0.15 Fluidized bed: 2009: 0.15; 2013 onwards: 0.10 Arch fired: 2009 onwards: 0.18

Annual rate limits for CTs in lbs/MMBtu: Natural gas CTs > 50 MW: 0.11 Distillate oil CTs > 50 MW: 0.28 Biologically derived fuel CTs > 50 MW: 0.15 Natural gas CTs between 25 and 49 MW: 0.19 Distillate oil CTs between 25 and 49 MW: 0.41 Biologically derived fuel CTs between 25 and 49 MW: 0.15

Annual rate limits for CCs in lbs/MMBtu: Natural gas CCs > 25 MW: 0.04 Distillate oil CCs > 25 MW: 0.18 Biologically derived fuel CCs > 25 MWs: 0.15 Natural gas CCs between 10 and 24 MW: 0.19

Chapter NR 446. Control of Mercury Emissions Hg 2012 through 2014: 40% reduction in total Hg emissions for all coal-f ired units in electric utilities with annual Hg emissions > 100 lbs 2015 onwards: 90% removal of Hg content of fuel or 0.0080 lbs/GW-hr reduction in coal fired EGUs > 150 MW 80% removal of Hg content of fuel or 0.0080 lbs/GW-hr reduction in coal fired EGUs > 25 MW 2010Appendix 3-3.1Appendix 3-3 New Source Review (NSR) Settlements in EPA Base Case v.4.10Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceAlabama PowerJames H.MillerAlabamaUnits 3 &4Install andoperate FGDcontinuously95%12/31/2011Operate existingSCR continuously0.15/1/20080.0312/31/2006With 45 days ofsettlement entry,APC must retire7,538 SO2emissionallowances.APC shall not sell,trade, or otherwiseexchange any PlantMiller excess SO2emissionallowances outsideof the APC system1/1/2021http://www.epa.gov/compliance/resources/cases/civil/caa/alabamapower.htmlMinnkota Power CooperativeBeginning 1/01/2006, Minnkota shall not emit more than 31,000 tons of SO2/year, no more than 26,000 tons beginning 2011, no more than 11,500 tons beginning 1/01/2012. If Unit 3 is not operational by 12/31/2015, then beginning 1/01/2014, the plant wide emission shall not exceed 8,500.Unit 1Install andcontinuouslyoperate FGD95% if wetFGD, 90%if dry12/31/2011Install andcontinuously operateOver-fire AIR, orequivalenttechnology withemission rate < .360.3612/31/20090.03 ifwetFGD,.015 ifdry FGDMilton R.YoungMinnesotaUnit 2Design,upgrade, andcontinuouslyoperate FGD90%12/31/2010Install andcontinuously operateover-fire AIR, orequivalenttechnology withemission rate < .360.3612/31/20070.03Before 2008Plant will surrender4,346 allowances foreach year 2012 –2015, 8,693allowances for years2016 – 2018, 12,170allowances for year2019, and 14,886allowances/yearthereafter if Units 1 –3 are operational by12/31/2015. If onlyUnits 1 and 2 areoperationalby12/31/2015, theplant shall retire17,886 units in 2020and thereafter.Minnkota shall notsell or trade NOxallowancesallocated to Units 1,2, or 3 that wouldotherwise beavailable for sale ortrade as a result ofthe actions taken bythe settlingdefendants tocomply with therequirementshttp://www.epa.gov/compliance/resources/cases/civil/caa/minnkota.htmlSIGECOFB CulleyIndianaUnit 1Repower tonatural gas(or retire)12/31/2006The provision did notspecify an amount ofSO2allowances to besurrendered. It onlyprovided that excessallowances resultingfrom compliance withNSR settlementprovisions must beretired.http://www.epa.gov/compliance/resources/cases/civil/caa/sigecofb.htmlAppendix 3-3.2Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceUnit 2Improve andcontinuouslyoperate existingFGD (shared byUnits 2 and 3)95%6/30/2004Unit 3Improve andcontinuouslyoperate existingFGD (shared byUnits 2 and 3)95%6/30/2004Operate ExistingSCR Continuously0.19/1/2003Install andcontinuouslyoperate a Baghouse0.0156/30/2007PSEG FOSSILBergenNew JerseyUnit 2Repower tocombinedcycle12/31/2002The provision did notspecify an amount ofSO2allowances to besurrendered. It onlyprovided that excessallowances resultingfrom compliance withNSR settlementprovisions must beretired.http://www.epa.gov/compliance/resources/cases/civil/caa/psegllc.htmlHudsonNew JerseyUnit 2Install Dry FGD(or approved alt.technology) andcontinuallyoperate0.1512/31/2006Install SCR (orapproved tech) andcontinually operate0.15/1/2007Install Baghouse (orapprovedtechnology)0.01512/31/2006MercerNew JerseyUnits 1 &2Install Dry FGD(or approved alt.technology) andcontinuallyoperate0.1512/31/2010Install SCR (orapproved tech) andcontinually operate0.135/1/2006TECOUnits 1 &2ExistingScrubber(shared by Units1 & 2)95% (95%or .25)09/1/00(01/01/13)Install SCR0.15/1/2009Big BendFloridaUnit 3ExistingScrubber93% ifUnits 3 & 42000Install SCR0.15/1/2009The provision did notspecify an amount ofSO2allowances to besurrendered. It onlyprovided that excessallowances resultinghttp://www.epa.gov/compliance/resources/cases/civil/caa/teco.htmlAppendix 3-3.3Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReference(shared by Units3 & 4)areoperating(01/01/10)Unit 4ExistingScrubber(shared by Units3 & 4)93% ifUnits 3 & 4areoperating6/22/2005Install SCR0.17/1/2007from compliance withNSR settlementprovisions must beretired.GannonFloridaSix unitsRetire all sixcoal unitsandrepower atleast 550MW of coalcapacity tonatural gas12/31/2004WEPCOWEPCO shall comply with the following system wide average NOxemission rates and total NOxtonnage permissible: by 1/1/2005 an emission rate of 0.27 and 31,500 tons, by 1/1/2007 an emission rate of 0.19 and 23,400 tons, and by 1/1/2013 an emission rate of 0.17and 17, 400 tons. For SO2emissions, WEPCO will comply with: by 1/1/2005 an emission rate of 0.76 and 86,900 tons, by 1/1/2007 an emission rate of 0.61and 74,400 tons, by 1/1/2008 an emission rate of 0.45 and 55,400 tons, and by 1/1/2013 an emission rate of 0.32and 33,300 tons.http://www.epa.gov/compliance/resources/cases/civil/caa/wepco.htmlUnits 1 –4Retire orinstall SO2and NOxcontrols12/31/2012Install andcontinuouslyoperate FGD (orapproved equiv.tech)95% or 0.112/31/2012Install SCR (orapproved tech) andcontinually operate0.112/31/2012Units 5 &6Install and operatelow NOxburners12/31/2003Units 7 &8Operate existing lowNOxburners12/31/2005Install BaghousePresque IsleWisconsinUnit 9Operate existing lowNOxburners12/31/2006Install Baghouse1Install andcontinuouslyoperate FGD (orapproved controltech)95% or 0.112/31/2006Install andcontinuously operateSCR (or approvedtech)0.112/31/2006PleasantPrairieWisconsin2Install andcontinuouslyoperate FGD (orapproved controltech)95% or 0.112/31/2007Install andcontinuously operateSCR (or approvedtech)0.112/31/2003The provision did notspecify an amount ofSO2allowances to besurrendered. It onlyprovided that excessallowances resultingfrom compliance withNSR settlementprovisions must beretired.Appendix 3-3.4Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceUnits 5 &6Install andcontinuouslyoperate FGD (orapproved controltech)95% or 0.112/31/2012Install andcontinuously operateSCR (or approvedtech)0.112/31/2012Unit 7Install andcontinuouslyoperate FGD (orapproved controltech)95% or 0.112/31/2012Install andcontinuously operateSCR (or approvedtech)0.112/31/2012Oak CreekWisconsinUnit 8Install andcontinuouslyoperate FGD (orapproved controltech)95% or 0.112/31/2012Install andcontinuously operateSCR (or approvedtech)0.112/31/2012PortWashingtonWisconsinUnits 1 –4Retire12/31/04 forUnits 1 – 3.Unit 4 byentry ofconsentdecreeValleyWisconsinBoilers 1– 4Operate existing lowNOxburner30 daysafter entryof consentdecreeVEPCOThe Total Permissible NOxEmissions (in tons) from VEPCO system are: 104,000 in 2003, 95,000 in 2004, 90,000 in 2005, 83,000 in 2006, 81,000 in 2007, 63,000 in 2008 – 2010, 54,000 in 2011, 50,000 in 2012, and 30,250 each year there after. Beginning 1/1/2013 theywill have a system wide emission rate no greater then 0.15 lb/MMBtu.Mount StormWestVirginiaUnits 1 –3Construct orimprove FGD95% or0.151/1/2005Install andcontinuously operateSCR0.111/1/2008Unit 4Install andcontinuously operateSCR0.11/1/2013Unit 5Construct orimprove FGD95% or0.1310/12/2012Install andcontinuously operateSCR0.11/1/2012ChesterfieldVirginiaUnit 6Construct orimprove FGD95% or0.131/1/2010Install andcontinuously operateSCR0.11/1/2011http://www.epa.gov/compliance/resources/cases/civil/caa/vepco.htmlChesapeakeEnergyVirginiaUnits 3 &4Install andcontinuously operateSCR0.11/1/2013On or before March31 of every yearbeginning in 2013and continuingthereafter, VEPCOshall surrender45,000 SO2allowances.Appendix 3-3.5Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceCloverVirginiaUnits 1 &2Improve FGD95% or0.139/1/2003PossumPointVirginiaUnits 3 &4Retire andrepower tonatural gas5/2/2003Santee CooperSantee Cooper shall comply with the following system wide averages for NOxemission rates and combined tons for emission of: by 1/01/2005 facility shall comply with an emission rate of 0.3 and 30,000tons, by 1/1/2007 an emission rate of 0.18 and 25,000 tons, by1/1/2010 and emission rate of 0.15 and 20,000 tons. For SO2emission the company shall comply with system wide averages of: by 1/1/2005 an emission rate of 0.92 and 95,000 tons, by 1/1/2007 and emission rate of 0.75 and 85,000 tons, by 1/1/2009 an emission rateof 0.53 and 70 tons, and by 1/1/2011 and emission rate of 0.5 and 65 tons.http://www.epa.gov/compliance/resources/cases/civil/caa/santeecooper.htmlUnit 1Upgrade andcontinuouslyoperate FGD95%6/30/2006Install andcontinuously operateSCR0.15/31/2004CrossSouthCarolinaUnit 2Upgrade andcontinuouslyoperate FGD87%6/30/2006Install andContinuouslyoperate SCR0.11/0.105/31/04and05/31/07Unit 1Install andcontinuouslyoperate FGD95%12/31/2008Install andcontinuously operateSCR0.11/0.111/30/04and11/30/04Unit 2Install andcontinuouslyoperate FGD95%12/31/2008Install andcontinuously operateSCR0.1211/30/2004Unit 3Upgrade andcontinuouslyoperate existingFGD90%12/31/2008Install andcontinuously operateSCR0.14/0.1211/30/2005and11/30/08WinyahSouthCarolinaUnit 4Upgrade andcontinuouslyoperate existingFGD90%12/31/2007Install andcontinuously operateSCR0.13/0.1211/30/05and11/30/08Unit 1Operate low NOxburner or morestringent technology6/25/2004GraingerSouthCarolinaUnit 2Operate low NOxburner or morestringent technology5/1/2004The provision did notspecify an amount ofSO2allowances to besurrendered. It onlyprovided that excessallowances resultingfrom compliance withNSR settlementprovisions must beretired.Appendix 3-3.6Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceJeffriesSouthCarolinaUnits 3, 4Operate low NOxburner or morestringent technology6/25/2004Ohio EdisonOhio Edison shall achieve reductions of 2,483 tons NOxbetween 7/1/2005 and 12/31/2010 using any combination of: 1) low sulfur coal at Burger Units 4 and 5, 2) operating SCRs currently installed at Mansfield Units 1 – 3 during the months of October through April,and/or 3) emitting fewer tons than the Plant-Wide Annual Cap for NOxrequired for the Sammis Plant. Ohio Edison must reduce 24,600 tons system-wide of SO2by 12/31/2010.No later than 8/11/2005, Ohio Edison shall install and operate low NOxburners on Sammis Units 1 - 7 and overfired air on Sammis Units 1,2,3,6, and 7. No later than 12/1/2005, Ohio Edison shall install advanced combustion control optimization with software to minimizeNOxemissions from Sammis Units 1 – 5.http://www.epa.gov/compliance/resources/cases/civil/caa/ohioedison.htmlInstall Induct50%removalInstall SNCRScrubber (oror 1.1lb/MMBtu(or approvedapproved equiv.alt. tech) &control tech)operateUnit 112/31/2008continuously0.2510/31/2007Install Induct50%removalOperateScrubber (oror 1.1lb/MMBtuexisting SNCRapproved equiv.continuouslyUnit 2control tech)12/31/20080.252/15/2006Install Induct50%removalOperate low NOxburners and overfireair by 12/1/05; installSNCR12/1/2005Scrubber (oror 1.1lb/MMBtu(or approvedandapproved equiv.alt. tech) &10/31/2007control tech)operateUnit 312/31/2008continuously by12/31/070.25Install Induct50%removalInstall SNCRW.H.SammisPlantOhioUnit 4Scrubber (oror 1.1lb/MMBtu6/30/2009(or approved0.2510/31/2007Beginning on1/1/2006, OhioEdison may use, sellor transfer anyrestricted SO2only tosatisfy theOperational Needs atthe Sammis, Burgerand Mansfield Plant,or new units withinthe FirstEnergySystem that complywith a 96% removalfor SO2. Forcalendar year 2006through 2017, OhioEdison mayaccumulate SO2allowances for use atthe Sammis, Burger,and Mansfield plants,or FirstEnergy unitsequipped with SO2Emission ControlStandards.Beginning in 2018,Ohio Edison shallsurrender unusedrestricted SO2allowances.Appendix 3-3.7Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceapproved equiv.alt. tech) &control tech)operatecontinuouslyInstall Flash50%removalInstall SNCRDryer Absorberor 1.1lb/MMBtu(or approvedor ECO2(oralt. tech) &approved equiv.Operatecontrol tech) &ContinuouslyoperateUnit 5continuously6/29/20090.293/31/2008Install FGD3(orInstall SNCR"MinimumOperateapproved equiv.(or approvedExtentExistingcontrol tech) &alt. tech) &Practicable"ESPoperateoperateContinuouslyUnit 6continuously95%removal or0.13lb/MMBtu6/30/2011continuously6/30/20050.031/1/2010Install FGD (orOperate"MinimumOperateapproved equiv.existing SNCRExtentExistingcontrol tech) &ContinuouslyPracticable"ESPoperateContinuouslyUnit 7continuously95%removal or0.13lb/MMBtu6/30/20118/11/20050.031/1/2010UpgradeUnit 1existing FGD95%12/31/2005UpgradeMansfieldPlantPennsylvaniaUnit 2existing FGD95%12/31/2006Appendix 3-3.8Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceUpgradeUnit 3existing FGD95%10/31/2007Install low NOx"MinimizeEmissions totheburners, over-firedExtentEastlakeOhioUnit 5air and SNCR &operate continuouslyPracticable"12/31/2006Unit 412/31/2011BurgerOhioUnit 5Repowerwith at least80%biomassfuel, up to20% lowsulfur coal.12/31/2011MirantI1,6System-wide NOxEmission Annual Caps: 36,500 tons 2004; 33,840 tons 2005; 33,090 tons 2006; 28,920 tons 2007; 22,000 tons 2008; 19,650 tons 2009; 16,000 tons 2010 onward. System-wide NOxEmission Ozone Season Caps: 14,700 tons 2004; 13,340 tons 2005;12,590 tons 2006; 10,190 tons 2007; 6,150 tons 2008 – 2009; 5,200 tons 2010 thereafter. Beginning on 5/1/2008, and continuingfor each and every Ozone Season thereafter, the Mirant System shall not exceed a System-wide Ozone Season Emission Rate of 0.150lb/MMBtu NOx.http://www.epa.gov/compliance/resources/cases/civil/caa/mirant.htmlUnit 1Unit 2Install low NOxburners (or moreeffective tech) &Unit 3operate continuously5/1/2004Install low NOxburners (or moreeffective tech) &Unit 4operate continuously5/1/2004Install low NOxburners (or moreeffective tech) &PotomacRiver PlantVirginiaUnit 5operate continuously5/1/2004Appendix 3-3.9Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceInstall SCR(or approvedalt. tech) &Unit 1operate continuously0.15/1/2007Install SCR(or approvedalt. tech) &MorgantownPlantMarylandUnit 2operate continuously0.15/1/2008Unit 1Install andcontinuouslyoperate FGD (orequiv.technology)95%6/1/2010Chalk PointMarylandUnit 2Install andcontinuouslyoperate FGD (orequiv.technology)95%6/1/2010For each year afterMirant commencesFGD operation atChalk Point, Mirantshall surrender thenumber of SO2Allowances equal tothe amount by whichthe SO2Allowancesallocated to the Unitsat the Chalk PointPlant are greater thanthe total amount ofSO2emissionsallowed under thisSection XVIII.Illinois PowerSystem-wide NOxEmission Annual Caps: 15,000 tons 2005; 14,000 tons 2006; 13,800 tons 2007 onward. System-wide SO2Emission Annual Caps: 66,300 tons 2005 – 2006; 65,000 tons 2007; 62,000 tons 2008 – 2010; 57,000 tons 2011; 49,500 tons 2012;29,000 tons2013 onward.http://www.epa.gov/compliance/resources/cases/civil/caa/illinoispower.htmlUnits 1 &2Install wet or dryFGD (orapproved equiv.alt. tech) &operatecontinuously0.112/31/2011Operate OFA &existing SCRcontinuously0.18/11/2005Install &continuouslyoperate Baghouse0.01512/31/2010BaldwinIllinoisUnit 3Install wet or dryFGD (orapproved equiv.alt. tech) &operatecontinuously0.112/31/2011Operate OFA and/orlow NOxburners0.12 until12/30/12; 0.1from 12/31/1208/11/05and12/31/12Install &continuouslyoperate Baghouse0.01512/31/2010By year end 2008,Dynergy willsurrender 12,000SO2emissionallowances, by yearend 2009 it willsurrender 18,000, byyear end 2010 it willsurrender 24,000,any by year end 2011and each yearthereafter it willsurrender 30,000allowances. If theAppendix 3-3.10Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReference8/11/2005ForBaghouse:12/31/12;andFor ESP:12/31/05HavanaIllinoisUnit 6Install wet or dryFGD (orapproved equiv.alt. tech) &operatecontinuously1.2lb/MMBtuuntil12/30/2012; 0.1lb/MMBtufrom12/31/2012onward12/31/2012Operate OFA and/orlow NOxburners &operate existingSCR continuously0.18/11/2005Install &continuouslyoperate Baghouse,then install ESP oralt. PM equipFor Baghouse:0.015lb/MMBtu; ForESP:0.03lb/MMBtuOperate OFA"Minimumand/or low NOxburnersExtentUnit 11.27/27/2005Practicable"8/11/2005Install ESP (orequiv. alt. tech) &continuouslyoperate ESPs0.0312/31/2006Operate OFA"Minimumand/or low NOxburnersExtentHennepinIllinoisUnit 21.27/27/2005Practicable"8/11/2005Install ESP (orequiv. alt. tech) &continuouslyoperate ESPs0.0312/31/2006Operate OFA"Minimumand/or low NOxburnersExtentVermilionIllinoisUnits 1 &21.21/31/2007Practicable"8/11/2005Install ESP (orequiv. alt. tech) &continuouslyoperate ESPs0.0312/31/2010Operate OFA"Minimumand/or low NOxburnersExtentWood RiverIllinoisUnits 4 &51.27/27/2005Practicable"8/11/2005Install ESP (orequiv. alt. tech) &continuouslyoperate ESPs0.0312/31/2005surrenderedallowances result ininsufficient remainingallowances allocatedto the unitscomprising the DMGsystem, DMG canrequest to surrenderfewer SO2allowances.Kentucky Utilities CompanyEW BrownGeneratingStationKentuckyUnit 3Install FGD97% or0.10012/31/2010Install andcontinuously operateSCR by 12/31/2012,continuously operatelow NOxboiler andOFA.0.0712/31/2012Continuouslyoperate ESP0.0312/31/2010KU must surrender53,000 SO2allowances of 2008or earlier vintage byMarch 1, 2009. Allsurplus NOxallowances must besurrendered through2020.SO2and NOxallowances may notbe used forcompliance, andemissionsdecreases forpurposes ofcomplying with theConsent Decree donot earn credits.http://www.epa.gov/compliance/resources/cases/civil/caa/kucompany.htmlSalt River Project Agricultural Improvement and Power District (SRP)Appendix 3-3.11Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceUnit 1 orUnit 2Immediatelybegincontinuousoperation ofexisting FGDson both units,install new FGD.95% or0.08New FGDinstalled by1/1/2012Install andcontinuously operatelow NOxburner andSCR0.32 prior toSCRinstallation,0.080 afterLNB by06/01/2009, SCR by06/01/2014Optimizationbeginsimmediately,rate limitbegins01/01/12(date of newFGDinstallation)CoronadoGeneratingStationArizonaUnit 1 orUnit 2Install new FGD95% or0.081/1/2013Install andcontinuously operatelow NOxburner0.326/1/2011Optimization andcontinuousoperation of existingESPs.0.03Optimizationbeginsimmediately,rate limitbegins01/01/13(date of newFGDinstallation)Beginning in 2012, allsurplus SO2allowances for bothCoronado andSpringerville Unit 4must be surrenderedthrough 2020. Theallowances limited bythis condition may,however, be used forcompliance at aprospective futureplant using BACTand otherwisespecified in par. 54 ofthe consent decree.SO2and NOxallowances may notbe used forcompliance, andemissionsdecreases forpurposes ofcomplying with theConsent Decree donot earn credits.http://www.epa.gov/compliance/resources/cases/civil/caa/srp.htmlAmerican Electric PowerAnnual Cap(tons)YearAnnual Cap(tons)Year450,000201096,0002009450,000201192,5002010420,000201292,5002011350,000201385,0002012340,000201485,0002013275,000201585,0002014260,000201675,0002015235,000201772,0002016 andthereafter184,0002018Eastern System-Wide174,0002019 andthereafterNOxand SO2allowances thatwould have beenmade available byemission reductionspursuant to theConsent Decreemust be surrendered.NOxand SO2allowances may notbe used to complywith any of the limitsimposed by theConsent Decree.The ConsentDecree includes aformula forcalculating excessNOxallowancesrelative to the CAIRAllocations, andrestricts the use ofsome. See par. 74-79 for details.Reducing emissionsbelow the EasternSystem-WideAnnual TonnageLimitations for NOxand SO2earnssuper complianceallowances.http://www.epa.gov/compliance/resources/cases/civil/caa/americanelectricpower1007.htmlSpornWestVirginia1 – 4ClinchRiverVirginia1 – 3At least600MW fromvarious unitsIndianaTannersCreekRetire,retrofit, orre-power12/31/2018Appendix 3-3.12Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReference1 – 3KammerWestVirginia1 – 3Unit 1Install andcontinuouslyoperate FGD12/31/2009Install andcontinuously operateSCR1/1/2008Unit 2Install andcontinuouslyoperate FGD12/31/2010Install andcontinuously operateSCR1/1/2009AmosWestVirginiaUnit 3Install andcontinuouslyoperate FGD12/31/2009Install andcontinuously operateSCR1/1/2008Unit 1Burn only coalwith no morethan 1.75lb/MMBtuannual averageDate ofentryContinuouslyoperate low NOxburnersDate ofentryBig SandyKentuckyUnit 2Install andcontinuouslyoperate FGD12/31/2015Install andcontinuously operateSCR1/1/2009Unit 1Install andcontinuouslyoperate FGD12/31/2008Install andcontinuously operateSCR1/1/2009Continuouslyoperate ESP0.0312/31/2009Unit 2Install andcontinuouslyoperate FGD12/31/2008Install andcontinuously operateSCR1/1/2009Continuouslyoperate ESP0.0312/31/2009CardinalOhioUnit 3Install andcontinuouslyoperate FGD12/31/2012Install andcontinuously operateSCR1/1/2009Clinch RiverVirginiaUnits 1 –3Plant-wideannual cap:21,700tons from2010 to2014, then16,300after1/1/20152010 –2014, 2015andthereafterContinuouslyoperate low NOxburnersDate ofentryUnit 1Retire,retrofit, orre-powerDate ofentryConesvilleOhioUnit 2Retire,retrofit, orre-powerDate ofentryAppendix 3-3.13Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceUnit 3Retire,retrofit, orre-power12/31/2012Unit 4Install andcontinuouslyoperate FGD12/31/2010Install andcontinuously operateSCR12/31/2010Unit 5Upgradeexisting FGD95%12/31/2009Continuouslyoperate low NOxburnersDate ofentryUnit 6Upgradeexisting FGD95%12/31/2009Continuouslyoperate low NOxburnersDate ofentryUnit 1Install andcontinuouslyoperate FGDDate ofentryInstall andcontinuously operateSCR1/1/2009GavinOhioUnit 2Install andcontinuouslyoperate FGDDate ofentryInstall andcontinuously operateSCR1/1/2009Glen LynVirginiaUnits 5, 6Burn only coalwith no morethan 1.75lb/MMBtuannual averageDate ofentryContinuouslyoperate low NOxburnersDate ofentryKammerWestVirginiaUnits 1 –3Plant-wideannual cap:35,0001/1/2010Continuouslyoperate over-fire airDate ofentryKanawhaRiverWestVirginiaUnits 1, 2Burn only coalwith no morethan 1.75lb/MMBtuannual averageDate ofentryContinuouslyoperate low NOxburnersDate ofentryUnit 1Install andcontinuouslyoperate FGD12/31/2007Install andcontinuously operateSCR1/1/2009MitchellWestVirginiaUnit 2Install andcontinuouslyoperate FGD12/31/2007Install andcontinuously operateSCR1/1/2009MountaineerWestVirginiaUnit 1Install andcontinuouslyoperate FGD12/31/2007Install andcontinuously operateSCR1/1/2008MuskingumRiverOhioUnits 1 –4Retire,retrofit, orre-power12/31/2015Appendix 3-3.14Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceUnit 5Install andcontinuouslyoperate FGD12/31/2015Install andcontinuously operateSCR1/1/2008Continuouslyoperate ESP0.0312/31/2002PicwayOhioUnit 9Continuouslyoperate low NOxburnersDate ofentryUnit 1Install andcontinuouslyoperate FGD12/31/2017Install andcontinuously operateSCR12/31/2017RockportIndianaUnit 2Install andcontinuouslyoperate FGD12/31/2019Install andcontinuously operateSCR12/31/2019SpornWestVirginiaUnit 5Retire,retrofit, orre-power12/31/2013Units 1 -3Burn only coalwith no morethan 1.2lb/MMBtuannual averageDate ofentryContinuouslyoperate low NOxburnersDate ofentryTannersCreekIndianaUnit 4Burn only coalwith no morethan 1.2% sulfurcontent annualaverageDate ofentryContinuouslyoperate over-fire airDate ofentryEast Kentucky Power Cooperative Inc.By 12/31/2009, EKPC shall choose whether to: 1) install and continuously operate NOxcontrols at Cooper 2 by 12/31/2012 and SO2controls by 6/30/2012 or 2) retire Dale 3 and Dale 4 by 12/31/2012.12-monthrolling limit(tons)Start of 12-month cycle12-monthrolling limit(tons)Start of 12-monthcycle57,00010/1/200811,5001/1/200840,0007/1/20118,5001/1/2013System-wideSystem-wide 12-month rollingtonnage limitsapply28,0001/1/2013All units mustoperate low NOxboilers8,0001/1/2015PM control devicesmust be operatedcontinuouslysystem-wide, ESPsmust be optimizedwithin 270 days ofentry date, or EKPCmay choose tosubmit a PMPollution ControlUpgrade Analysis.0.031 year fromentry dateAll surplus SO2allowances must besurrendered eachyear, beginning in2008.SO2and NOxallowances may notbe used to complywith the ConsentDecree. NOxallowances thatwould becomeavailable as a resultof compliance withthe Consent Decreemay not be sold ortraded. SO2andNOxallowancesallocated to EKPCmust be used withinthe EKPC system.Allowances madeavailable due tosuper compliancemay be sold ortraded.http://www.epa.gov/compliance/resources/cases/civil/caa/nevadapower.htmlAppendix 3-3.15Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceUnit 1Install andcontinuouslyoperate FGD95% or 0.16/30/2011Continuouslyoperate SCR0.12 for Unit 1until01/01/2013, atwhich point theunit limit dropsto 0.1. Prior to01/01/2013, thecombinedaverage whenboth units areoperating mustbe no morethan 0.160 daysafter entrySpurlockKentuckyUnit 2Install andcontinuouslyoperate FGD by10/1/200895% or 0.11/1/2009Continuouslyoperate SCR andOFA0.1 for Unit 2,0.1 combinedaverage whenboth units areoperating60 daysafter entryUnit 1Install andcontinuously operatelow NOxburners by10/31/20070.461/1/2008Unit 2Install andcontinuously operatelow NOxburners by10/31/20070.461/1/2008EKPC mustsurrender 1,000 NOxallowancesimmediately underthe ARP, and 3,107under the NOxSIPCall. EKPC mustalso surrender15,311 SO2allowances.Date of entryhttp://www.epa.gov/compliance/resources/cases/civil/caa/eastkentuckypower-dale0907.htmlUnit 3EKPC maychoose toretire Dale 3and 4 in lieuof installingcontrols inCooper 212/31/2012Dale PlantKentuckyUnit 4CooperKentuckyUnit 1Appendix 3-3.16Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceUnit 2If EKPC opts toinstall controlsrather thanretiring Dale, itmust install andcontinuouslyoperate FGD orequiv.technology95% or0.10If EKPC elects toinstall controls, itmust continuouslyoperate SCR orinstall equiv.technology0.08 (or 90% ifnon-SCRtechnology isused)12/31/2012Nevada Power CompanyBeginning 1/1/2010, combined NOxemissions from Units 5, 6, 7, and 8 must be no more than 360 tons per year.Unit 55ppm 1-houraverage12/31/08(ULNBinstallation), 01/30/09(1-houraverage)Unit 65ppm 1-houraverage12/31/09(ULNBinstallation), 01/30/10(1-houraverage)Unit 75ppm 1-houraverage12/31/09(ULNBinstallation), 01/30/10(1-houraverage)ClarkGeneratingStationNevadaUnit 8Units may only firenatural gasIncrease waterinjectionimmediately, theninstall and operateultra-low NOxburners (ULNBs) orequivalenttechnology. In 2009,Units 5 and 8 maynot emit more than180 tons combined5ppm 1-houraverage12/31/08(ULNBinstallation), 01/30/09(1-houraverage)Allowances may notbe used to complywith the ConsentDecree, and noallowances madeavailable due tocompliance with theConsent Decreemay be traded orsold.http://www.epa.gov/compliance/resources/cases/civil/caa/nevadapower.htmlDayton Power & LightNon-EPA Settlement of 10/23/2008Owners may notpurchase any newcatalyst with SO2toSO3conversion rategreater than 0.5%0.17 stationwide30 daysafter entryStuartGeneratingStationOhioStation-wideCompleteinstallation ofFGDs on eachunit.96% or0.107/31/20090.17 stationwide60 daysafter entrydate0.030 lbper unit7/31/2009NOxand SO2allowances may notbe used to complywith the monthlyrates specified inthe ConsentDecree.Courtlinkdocument providedby EPA in emailAppendix 3-3.17Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReference82%includingdata fromperiods ofmalfunctions7/31/09through7/30/11Install controltechnology on oneunit0.10 on anysingle unit12/31/20120.15 stationwide7/1/201282%includingdata fromperiods ofmalfunctionsafter7/31/110.10 stationwide12/31/2014Installrigid-typeelectrodes ineachunit'sESP12/31/2015PSEG FOSSIL, Amended Consent Decree of November 2006Unit 7Retire unit1/1/2007http://www.epa.gov/compliance/resources/decrees/amended/psegfossilamended-cd.pdfKearnyNew JerseyUnit 8Retire unit1/1/2007Allowances allocatedto Kearny, Hudson,and Mercer may onlybe used for theoperational needs ofthose units, and allsurplus allowancesmust be surrendered.Within 90 days ofamended ConsentDecree, PSEG mustsurrender 1,230 NOxAllowances and8,568 SO2Allowances notalready allocated toor generated by theunits listed here.Kearny allowancesmust be surrenderedwith the shutdown ofthose units.0.1512/31/20100.112/31/2010Annual Cap(tons)YearAnnual Cap(tons)Year5,54720073,48620075,27020083,48620085,27020093,4862009HudsonNew JerseyUnit 2Install Dry FGD(or approved alt.technology) andcontinuallyoperate5,2702010Install SCR (orapproved tech) andcontinually operate3,4862010Install Baghouse (orapprovedtechnology)0.01512/31/2010Appendix 3-3.18Settlement ActionsRetire/RepowerSO2controlNOxControlPM or Mercury ControlAllowanceRetirementAllowance RestrictionCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateRetirementRestrictionEffectiveDateReferenceMercerNew JerseyUnits 1 &2Install Dry FGD(or approved alt.technology) andcontinuallyoperate0.1512/31/2010Install SCR (orapproved tech) andcontinually operate0.11/1/2007Install Baghouse (orapprovedtechnology)0.01512/31/2010Westar EnergyUnits 1, 2, and 3 have a total annual limit of6,600 tons of SO2starting 2011Units 1-3 must continuously operate Low NOxCombustion Systems by 2012 and achieve andmaintain a 30-Day Rolling Average Unit EmissionRate for NOxof no greater than 0.180 lb/MMBtu.Units 1, 2, and 3 must operate each ESP andFGD system continuously by 2011 andmaintain a 0.030 lb/MMBtu PM EmissionsRate.Units 1, 2, and 3 must all install FGDs by2011 and operate them continuously.One of the three units must install an SCR by 2015and operate it continuously to maintain a 30-DayRolling Average Unit Emission Rate for NOxof nogreater than 0.080 lb/MMBtu.Units 1 and 2’s ESPs must be rebuilt by 2014in order to meet a 0.030 lb/MMBtu PMEmissions RateJeffreyEnergyCenterKansasAll unitsFGDs must maintain a 30-Day RollingAverage Unit Removal Efficiency for SO2ofat least 97% or a 30-Day Rolling AverageUnit Emission Rate for SO2of no greaterthan 0.070 lb/MMBtu.By 2013 Westar shall elect to either (a) install asecond SCR on one of the other JEC Units by2017 or (b) meet a 0.100 lb/MMBtu Plant-Wide 12-Month Rolling Average Emission Rate for NOxby2015Duke EnergyUnits 1 &3Retire orrepower asnatural gas1/1/2012GallagherIndianaUnits 2 &4Install Drysorbent injectiontechnology80%1/1/2012Notes:1) This summary table describes New Source Review settlement actions as they are represented in EPA Base Case v.4.10. The settlement actions are simplified for representation in the model.This table is not intended to be a comprehensive description of all elements of the actualsettlement agreements.2) Settlement actions for which the required emission limits will be effective by the time of the first mapped run year (before 1/1/2012) are built into the database of units used in EPA Base Case v.4.10 ("hardwired"). However, future actions are generally modeled as individual constraints onemission rates in EPA Base Casev.4.10allowing the modeled economic situation to dictate whether and when a unit would opt to install controls versus retire.3) Some control installations that are required by theseNSR settlements have already been taken by the affected companies, even if deadlines specified in their settlement haven't occurred yet.Any controls that are already in place are built into EPABase Case v.4.104) If a settlement agreement requires installation of PM controls, then the controls are shown in this table and reflected inEPA Base Casev.4.10. If settlement requires optimization or upgrade of existing PM controls, those actions are not included inEPA Base Case v.4.10.5) For units for which an FGD is modeled as an emissions constraint in EPA Base Case v.4.10, EPA used the assumptions on removal efficiencies that are shown in Table 5-4 of this documentation report.6) For units for which an FGD is hardwired in EPA Base Case v.4.10, unless the type of FGD is specified in the settlement, EPAmodeling assumes the most cost effective FGD (wet or dry) and a corresponding 98% removal efficiency for wet and 93% for dry.7) For units for which an SCR is modeled as an emissions constraint or is hardwired in EPA Base Case v.4.10, EPA assumed an emissions rate equal to 10% of the unit's uncontrolled rate, with afloor of .06 lb/MMBtu or used the emission limit if provided.8) The applicable low NOxburner reduction efficiencies are shown in Table A 3-1:3 in the Base Case v.4.10 documentation materials.9) EPA included in EPA Base Case v.4.10 the requirements of the settlements as they existed at the second quarter of 2010.10) Some of the NSR settlements require the retirement of SO2allowances. For EPA Base Case v.4.10, EPA estimates the amount of allowances to be retired from these settlements and adjusted the total Title IV allowances accordingly.Appendix 3-4.1Appendix 3-4 State Settlements in EPA Base Case v.4.10State Enforcement ActionsRetire/RepowerSO2controlNOxControlPM ControlMercury ControlCompany andPlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffective DateEquipmentRateEffectiveDateEquipmentRateEffectiveDateAESUnit 4Install FGD90%9/1/2007Install SCR0.159/1/2007GreenidgeNew YorkUnit 3Install BACT12/31/2009Install BACT12/31/2009Unit 890%12/31/2010Install SCR0.1512/31/2010WestoverNew YorkUnit 7Install BACT12/31/2009Install BACT12/31/2009HicklingNew YorkUnits 1 &2Install BACT5/1/2007Install BACT5/1/2007JennisonNew YorkUnits 1 &2Install BACT5/1/2007Install BACT5/1/2007Niagara Mohawk PowerNRG shall comply with the below annual tonnage limitations for its Huntley and Dunkirk Stations: 2005 is 59,537 tons of SO2and 10,777 tons of NOx, 2006 is 34,230 of SO2and 6,772 of NOx, 2007 is 30,859 of SO2and 6,211 of NOx, 2008 is22,733 tons of SO2HuntleyNew YorkUnits 63– 66RetireBefore2008Public Service Co. of NMUnit 110/31/200810/31/200812/31/200912/31/2009Unit 23/31/20093/31/200912/31/200912/31/2009Unit 34/30/20084/30/20084/30/20084/30/2008San JuanNew MexicoUnit 4State-of-the-arttechnology90%10/31/2007State-of-the-arttechnology0.310/31/2007OperateBaghouse anddemistertechnology0.0210/31/2007Designactivatedcarbon injectiontechnology (orcomparabletech)10/31/2007Public Service Co of ColoradoUnits 1 &2Install andoperate FGD0.1lb/MMBtucombinedaverage7/1/2009Install low-NOxemission controls0.15lb/MMBtucombinedaverage7/1/2009Install sorbentinjectiontechnology7/1/20090.1ComancheColoradoUnit 3Install andoperate FGDlb/MMBtuInstall andoperate SCR0.08Install andoperate afabric filterdust collectionsystem0.01Install sorbentinjectiontechnologyWithin 180days of startupAppendix 3-4.2State Enforcement ActionsRetire/RepowerSO2controlNOxControlPM ControlMercury ControlCompany andPlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffective DateEquipmentRateEffectiveDateEquipmentRateEffectiveDateTVABull RunTennesseeUnit 1Complete FGDinstallation0.15lb/MMBtu,4,431 TPYFGDalreadyactive as ofdate ofentry0.08lb/MMBtu,2,295 TPYUnit 10.15lb/MMBtu,1,023 TPY0.05lb/MMBtu,372 TPYUnit 20.15lb/MMBtu,1,028 TPY0.05lb/MMBtu,374 TPYUnit 30.15lb/MMBtu,1,081 TPY0.05lb/MMBtu,389 TPYJohn SevierTennesseeUnit 4Install FGD0.15lb/MMBtu,1,000 TPY27 monthsfrom dateof entryInstall SCR0.05lb/MMBtu,360 TPY21 monthsfrom date ofentryUnit 10.15lb/MMBtu,794 TPY0.06lb/MMBtu,323 TPYUnit 20.15lb/MMBtu,785 TPY0.06lb/MMBtu,320 TPYUnit 30.15lb/MMBtu,822 TPY0.06lb/MMBtu,335 TPYUnit 40.15lb/MMBtu,800 TPY0.06lb/MMBtu,326 TPYUnit 50.15lb/MMBtu,1,021 TPY0.06lb/MMBtu,416 TPYUnit 60.15lb/MMBtu,1,095 TPY0.05lb/MMBtu,365 TPYUnit 70.15lb/MMBtu,1,040 TPY0.05lb/MMBtu,347 TPYUnit 80.15lb/MMBtu,1,048 TPY0.05lb/MMBtu,349 TPYKingstonTennesseeUnit 9Install FGD0.15lb/MMBtu,1,012 TPY27 monthsfrom dateof entryOperate existingSCR0.05lb/MMBtu,337 TPYAppendix 3-4.3State Enforcement ActionsRetire/RepowerSO2controlNOxControlPM ControlMercury ControlCompany andPlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffective DateEquipmentRateEffectiveDateEquipmentRateEffectiveDateUnit 10.15lb/MMBtu,569 TPY0.06lb/MMBtu,246 TPYUnit 20.15lb/MMBtu,608 TPY0.06lb/MMBtu,263 TPYUnit 30.15lb/MMBtu,663 TPY0.06lb/MMBtu,287 TPYUnit 40.15lb/MMBtu,602 TPY0.06lb/MMBtu,261 TPYUnit 50.15lb/MMBtu,640 TPY0.06lb/MMBtu,277 TPYUnit 6Install FGD0.15lb/MMBtu,626 TPY27 monthsfrom dateof entryInstall SCR0.06lb/MMBtu,271 TPY21 monthsfrom date ofentryUnit 70.56lb/MMBtu,8950 TPY0.06lb/MMBtu,892 TPYWidows CreekAlabamaUnit 80.30lb/MMBtu,4,508 TPY0.06lb/MMBtu,860 TPYRochester Gas & ElectricRussell PlantNew YorkUnits 1 –4RetireallunitsMirant New YorkUnit 1Retire5/7/2007Lovett PlantNew YorkUnit 2Retire4/30/2008Appendix 3-5.1Appendix 3-5 Citizen Settlements in EPA Base Case v.4.10Citizen Suits Provided by DOJRetire/RepowerSO2controlNOxControlPM ControlCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateSWEPCO (AEP)WelshTexasUnits 1-3Install andoperate CEMs12/31/2010Allegheny EnergyHatfield'sFerryPennsylvaniaUnits 1- 3Install andoperate wetFGD6/30/2010Install andoperate sulfurtrioxide injectionsystems, improveESPperformance0.1 lb/MMBtu in2006, then 0.075lbs per hour(filterable) and 0.1lb/MMBtu forparticles less thanten microns in 20102006 and6/30/2010Wisconsin Public Service CorpPulliamWisconsinUnits 3& 4Retire12/31/2007University of WisconsinCharterStreetHeatingPlantWisconsinRepower toburn 100%biomass12/31/2012Appendix 3-5.2Citizen Suits Provided by DOJRetire/RepowerSO2controlNOxControlPM ControlCompanyand PlantStateUnitActionEffectiveDateEquipmentPercentRemovalor RateEffectiveDateEquipmentRateEffectiveDateEquipmentRateEffectiveDateTucson Electric PowerUnits 1& 20.27lb/MMBtu12/31/20060.22lb/MMBtu12/31/20060.03 lb/MMBtu1/1/2006Unit 3SpringervillePlantArizonaFutureUnit 4Dry FGD,85%reductionrequiredFour-unitcap of10,662tons peryear onceunits 3 and4 areoperationalSCR, LNBFour-unitcap of8,940 tonsper yearonce units3 and 4 areoperationalBaghouse

### Appendix 3-6.1 

## Appendix 3-6 Renewable Portfolio Standards in EPA Base Case v.4.10

NEMS Region IPM Regions Covered Units 2012 2015 2020 2030 2035 - 2050 CNV CA-N and CA-S % 15.7% 17.3% 20.0% 20.0% 20.0%

ECAR MECS, RFCO, RFCP, and TVAK % 0.8% 3.0% 4.5% 5.7% 5.7%

ERCOT ERCT % 3.9% 5.0% 5.0% 5.0% 5.0% MAAC MACE, MACS, and MACW % 7.4% 10.1% 14.8% 15.4% 15.4% MAIN COMD, GWAY, and WUMS % 5.6% 8.9% 13.2% 17.5% 17.5% MAPP MRO % 3.7% 4.6% 6.1% 7.2% 7.2%

NE NENG % 7.4% 9.6% 13.4% 13.8% 13.8% NWP NWPE and PNW % 4.6% 7.3% 12.4% 13.7% 13.7%

NY DSNY, LILC, NYC, and UPNY GWh 4,838 5,233 5,097 5,236 5,369

RA AZNM, RMPA, and SNV % 3.0% 4.2% 6.0% 6.9% 6.9% SPP SPPN and SPPS % 1.9% 1.9% 3.8% 3.8% 3.8%

STV ENTG, SOU, TVA, VACA, and VAPW % 0.5% 0.9% 1.7% 1.9% 1.9%

Notes: The Renewable Portfolio Standard percentages are applied to modeled electricity sale projections. The actual renewable portfolio standard targets in GWh are implemented exactly as shown in the model.

### Appendix 3-7.1 

## Appendix 3-7 Capacity Deployment Li mits for Advanced Coal with CCS and New Nuclear in EPA Base Case v.4.10

Run Year Advanced Coal with CCS (MW) New Nuclear

(MW) 2012 0 0 2015 2,000 0 2020 9,750 7,500 2030 38,220 29,400 2040 112,367 86,436 2050 293,652 225,886 Note: The 2020 through 2050 limits for Advanced Coal with CCS and New Nuclear technologies are a joint constraint, with the maximum amount of possible development for each technology shown by run year. If the maximum amount of one technology is developed in a given run year, zero MW of the other may be developed. See the production possibility chart below.

Production Possibility Curves (Incremental Capacity in MW by Run Year)

2020 2030 2040 2050

0 50,000 100,000 150,000 200,000 250,000

0 50,000 100,000 150,000 200,000 250,000 300,000

Advanced Coal with CCSNew NuclearAppendix 3-8.1Appendix 3-8 Nuclear Capacity DeploymentConstraint in EPA Base Case v.4.10RunYearBase NewNuclearCapacityBase New NuclearCapacity DeploymentEquationPossibleAdditional New NuclearCapacity Deployment Equation1Maximum Annual Incremental New Nuclear CapacityDeployment Allowed Equation20207,5007,50007,500203014,7001.96 *2020_Base_Capacity+ 1.96 *2020_Incremental_Capacity= 1.96 * (2020_Base_Capacity + 2020_Incremental_Capacity)204028,8121.96 *2030_Base_Capacity+ 1.96 *2030_Incremental_Capacity= 1.96 * (2030_Base_Capacity + 2030_Incremental_Capacity)205056,4721.96 *2040_Base_Capacity+ 1.96 *2040_Incremental_Capacity= 1.96 * (2040_Base_Capacity + 2040_Incremental_Capacity)Maximum Possible New Nuclear Capacity Deployment AllowedDeployment Starts 2020Deployment Starts 2030Deployment Starts 2040Deployment Starts 2050RunYearIncrementalCumulativeIncrementalCumulativeIncrementalCumulativeIncrementalCumulative20207,5007,500000000203029,40036,90014,70014,7000000204086,436123,33657,62472,32428,81228,812002050225,886349,222169,415241,739112,943141,75556,47256,472Notes:No nuclear deployment is allowed before 20201Addtional new nuclear capacity deployment isonlypossible if nuclear capacity has beenbuilt in the previous run year.Appendix 3-8.2Maximum Possible Cumulative New Nuclear Capacity Each Run Year050,000100,000150,000200,000250,000300,000350,0002020203020402050Run YearsCapacity (MW)Deployment Starts 2020Deployment Starts 2030Deployment Starts 2040Deployment Starts 2050

### Appendix 3-9.1 

## Appendix 3-9 Complete Availability Assumptions in EPA Base Case v.4.10

This is a small exerpt of the data in Appendix 3-9. The complete data set in spreadsheet format can be downloaded via the link found at www.epa.gov/airmarkets/progsregs/epa-ipm/BaseCasev410.html
Please see 

### Table 3-7 for summary data

Unit ID Plant Name Plant Type Winter Availability Summer Availability Annual Availability
55522_G_CT1 Sundance Combustion Turbine 89.2 90.8 89.9

55522_G_CT10 Sundance Combustion Turbine 89.2 90.8 89.9

55522_G_CT2 Sundance Combustion Turbine 89.2 90.8 89.9

55522_G_CT3 Sundance Combustion Turbine 89.2 90.8 89.9

55522_G_CT4 Sundance Combustion Turbine 89.2 90.8 89.9

55522_G_CT5 Sundance Combustion Turbine 89.2 90.8 89.9

55522_G_CT6 Sundance Combustion Turbine 89.2 90.8 89.9

55522_G_CT7 Sundance Combustion Turbine 89.2 90.8 89.9

55522_G_CT8 Sundance Combustion Turbine 89.2 90.8 89.9

55522_G_CT9 Sundance Combustion Turbine 89.2 90.8 89.9

55257_G_1 Ina Road Water Pollution Control Fac Combustion Turbine 88.4 90.4 89.2

55257_G_2 Ina Road Water Pollution Control Fac Combustion Turbine 88.4 90.4 89.2

55257_G_3 Ina Road Water Pollution Control Fac Combustion Turbine 88.4 90.4 89.2

55257_G_4 Ina Road Water Pollution Control Fac Combustion Turbine 88.4 90.4 89.2

55257_G_5 Ina Road Water Pollution Control Fac Combustion Turbine 88.4 90.4 89.2

55257_G_6 Ina Road Water Pollution Control Fac Combustion Turbine 88.4 90.4 89.2

55257_G_7 Ina Road Water Pollution Control Fac Combustion Turbine 88.4 90.4 89.2

82755_C_1 AZNM_AZ_Combustion Turbine Combustion Turbine 89.8 92.2 90.8

6088_G_5 North Loop Combustion Turbine 89.2 90.8 89.9

118_G_GE1 Saguaro Combustion Turbine 89.8 92.2 90.8

124_G_GT2 Demoss Petrie Combustion Turbine 89.8 92.2 90.8

82757_C_1 AZNM_CA_Combustion Turbine Combustion Turbine 89.8 92.2 90.8

2468_G_6 Raton Combustion Turbine 88.4 90.4 89.2

82759_C_1 AZNM_NM_Combustion Turbine Combustion Turbine 89.8 92.2 90.8

54814_G_GENA Milagro Cogeneration Plant Combustion Turbine 89.2 90.8 89.9

### 4-1 

# 4 Generating Resources “Existing”, “planned-committed”, and “potential” ar e the three general types of generating units modeled in EPA Base Case v.4.10. Units that are currently operational in the electric industry are termed as “existing” units. Units that are not currently operating but are firmly anticipated to be operational in the future, and have either broken ground (initiated construction) or secured financing are termed “planned-committed”. “Potential” units refer to new generating options used in IPM for capacity expansion projections of t he electric industry. Existing and planned-committed units are entered as exogenous inputs to the model, whereas potential units are endogenous to the model in the sense that the model determines the location and size of all the potential units that end up in the final solution for a specific model run.

This chapter is organized into the following five sections:

(1) Section 4.1 provides background information on the National Electric Energy Data System (NEEDS), the database which serves as the repository for information on existing and planned-committed units which are modeled in the EPA Base Case v.4.10, (2) Section 4.2 provides detailed information on existing non-nuclear generating units modeled in EPA Base Case v.4.10, (3) Section 4.3 provides detailed information pertaining to planned-committed units which are assumed in EPA Base Case v.4.10, (4) Section 4.4 provides detailed information pertaining to the IPM assumptions for potential plants, and (5) Section 4.5 describes the handling of existing and potential nuclear units in EPA Base Case v.4.10


## 4.1 National Electric Energy Data System (NEEDS) EPA Base Case v.4.10 uses the NEEDS database as its source for data on all existing and planned-committed units. Table 4-1 below summarizes the resources used in developing data on existing units in NEEDS v.4.10. The data sources for planned-committed units in NEEDS are discussed below in Section 4.3. The population of existing units in NEEDS v4.10 represents generating units that were in operation th rough the end of 2006. The population of plannedcommitted includes any units online or scheduled to come online from 2007 to the end of 2011 (with two exceptions listed in the note under Table 4-2 below).



## 4.2 Existing Units EPA Base Case v.4.10 models existing units based on information contained in NEEDS. The sections below describe the procedures followed in determining the population of units in NEEDS, as well as each unit’s capacity, location, and confi guration. Details are also given on the model plant aggregation scheme and the cost and performance characteristics associated with the existing non-nuclear units represented in EPA Base Case v.4.10.

4.2.1 Population of Existing Units The population of existing units was taken primarily from EIA 860 (2006) and EIA 767 (2005). A number of rules were used to screen the various data sources. These rules helped to ensure data consistency, but also made the population data adaptable for use in IPM. Table 4-2 below summarizes the rules used in populating the NEEDS v.4.10 database.

### 4-2 Table 4-1 Data Sources for NEEDS v.4.10 for EPA Base Case v.4.10
Data Source 1 

### Data Source Documentation

DOE's Form EIA-860 DOE's Form EIA-860 is an annual survey of utility power plants at the generator level. It contains data such as summer, winter and nameplate capacity, location (state and county), status, prime mover, primary energy source and in-service year. NEEDS v.4.10 uses EIA Form 860 (2006) data as one of the primary data inputs.

DOE's Form EIA-767 DOE's Form EIA-767 is an annual survey, "Steam-Electric Plant Operation and Design Report", that contains data for utility nuclear and fossil fuel steam boilers such as fuel quantity and quality; boileridentification, location, status, and design information; and postcombustion NO X 

### control, FGD scrubber and particulate collector device information. Note that boilers in plants with less than 10 MW do not report all data elements. The relationship between boilers and generators is also provided, along with generator-level generation and nameplate capacity. Note that boilers and generators are not necessarily in a one-to-one correspondence. NEEDS v.4.10 uses EIA Form 767 (2005) data as one of the primary data inputs.

NERC Electricity Supply and Demand (ES&D) database The NERC ES&D is released annually. It contains generator-level information such as summer, winter and nameplate capacity, state, NERC region and sub-region, status, primary fuel and on-line year. NEEDS v.4.10 uses NERC ES&D (2006) data as one of the primary data inputs.

DOE’s Annual Energy Outlook (AEO) The Energy Information Administra tion (EIA) Annual Energy Outlook presents annually updated forecasts of energy supply, demand and prices covering a 20-25 year time horizon. The projections are based on results from EIA’s National Energy Modeling System (NEMS). Information from AEO such as heat rates, planned committed units, nuclear unit capacities and uprates were used in NEEDS v.4.10. Global Energy Decisions New Entrants database Global Energy’s New Entrants database has information on new power plant builds, rerates and retirements. Information on committed units is based on November 2009 dataset. EPA's Emission Tracking System (ETS 2007) The Emission Tracking System (ETS) database is updated quarterly. It contains boiler-level information such as primary fuel, heat input, SO 2 

### and NO X 

### controls, and SO 2 

### and NO X 

### emissions. Utility and RPO (Regional Planning Organizations) Comments Comments from selected U.S. utilities and RPOs regarding the population in NEEDS as well as unit characteristics were used in NEEDS v.4.10.

Note:

1 

### Shown in Table 4-1 are the primary issue dates of the indicated data sources that were used. Other vintages of these data sources were also used in instances where date were not available for the indicated issued date or where there were methodological reasons for using other vintages of the data.

### 4-3 Table 4-2 Rules Used in Populating NEEDS v.4.10 for EPA Base Case v.4.10

Scope Rule
Capacity Excluded units with reported nameplate, summer and winter capacity of zero

Status Excluded units that were out of service for two or three consecutive years (i.e., generators with status codes “OS” in the latest three reporting years and boilers with status codes “OS” in the latest two reporting years) and units that were no longer in service and not expected to be returned to service (i.e., generators or boilers with status codes of "RE"). Status of boiler(s) and associated generator(s) were taken into account for determining operation status

Planned or Committed Units Included planned units that had broken ground or secured financing and were expected to be online by the end of 2011; one biomass and one nuclear unit that are scheduled to come online after 2011 were also included

Firm/Non-firm Electric Sales Excluded non-utility onsite generators that do not produce electricity for sale to the grid. Excluded all mobile and distributed generators
Note:
1 

### The biomass unit is Mitchell, unit 3, and the nuclear unit is Watts Bar Nuclear Plant, unit 2.

As with previous versions of the database, NEEDS v.4.10 includes steam units at the boiler level and non-steam units at the generator level. A unit in NEEDS v.4.10, therefore, refers to a boiler in the case of a steam unit and a generator in the case of a non-steam unit. Table 4-3 provides a summary of the population and capacity of the existing units included in NEEDS v.4.10 through 2006. EIA Form 860 (2006) and Form 767 (2005) is the starting point and largest component of the existing unit population in NEEDS v.4.10 but the final population of existing units is supplemented based on information from other sources, including comments from utilities, submissions to EPA's Emission Tracking System , Annual Energy Outlook, and reported capacity in Global Energy’s New Entrants database.

Table 4-3 Summary Population (through 2006) of Existing Units in NEEDSv.4.10 for EPA Base Case v.4.10 Plant Type Number of Units Capacity (MW) Biomass 134 2,286 Coal Steam 1,235 305,451 Combined Cycle 1,532 179,557 Combustion Turbine 5,386 132,293 Fossil Waste 20 610 Geothermal 196 2,264 Hydro 3,754 77,713 IGCC 4 529 Landfill Gas 698 1,068 Municipal Solid Waste 176 2,098 Non-Fossil Waste 45 516 Nuclear 104 101,099 O/G Steam 682 112,371 Pumped Storage 150 20,940 Solar 19 412 Tires 3 44 Wind 330 11,637
Grand Total 14,468 950,889

### 4-4 4.2.2 Capacity The NEEDS unit capacity values implemented in EPA Base Case v.4.10 reflect net summer dependable capacity 16 

### , to the extent possible. Table 4-4 summarizes the hierarchy of primary data sources used in compiling capacity data for NEEDS v.4.10.

Table 4-4 Hierarchy of Data Sources for Capacity in NEEDS v.4.10 Sources Presented in Hierarchy

Capacity from Utility Comments 2006 EIA 860 Summer Capacity NERC ES&D 2006 Summer Capacity 2006 EIA 860 Winter Capacity NERC ES&D 2006 Winter Capacity 2006 EIA 860 Nameplate Capacity
Notes : Presented in hierarchical order that applies. If capacity is zero, unit is not included.

As noted earlier, for steam units NEEDS v.4.10 includes boiler level data, while for non-steam units NEEDS v.4.10 contains generator level data. Capacity data in EIA and NERC data sources are generator specific and not boiler specific. Therefore, it was necessary to develop an algorithm for parsing generator level capacity to the boiler level for steam producing units.

The capacity-parsing algorithm used for steam units in NEEDS v.4.10 took into account boilergenerator mapping. Fossil steam and nuclear steam electric units have boilers attached to generators that produce electricity. There are generally four types of links between boilers and generators: one boiler to one generator, one boiler to many generators, many boilers to one generator and many boilers to many generators.

The capacity-parsing algorithm used for steam units in NEEDS utilized steam flow data with the boiler-generator mapping. Under EIA 767, steam units report the maximum steam flow from theboiler to the generator. There is, however, no further data on the steam flow of each boilergenerator link. Instead, EIA 767 contains only the maximum steam flow for each boiler. Table 4-5summarizes the algorithm used for parsing capacity with data on maximum steam flow and boilergenerator mapping. In Table 4-5 MFB i 

### refers to the maximum steam flow of boiler i and MW G j 

### refers to the capacity of generator j . The algorithm uses the available data to derive the capacity of a boiler, referred to as MW B j 

### in Table 4-5.

Table 4-5 Capacity-Parsing Algorithm for Steam Units in NEEDS v.4.10 Type of Boiler-Generator Links One-to-One One-to-Many Many-to-One Many-to-Many For Boiler B 1 

### to B N 

### linked to Generators G 1 

### to G N 

### MW B i 

### = MW G j 

### MW B i 

### = Σ

j 

### MW G j 

### MW B i 

### = (MF B i 

### / Σ i 

### MF Bi 

### ) * MW G j 

### MW B i 

### = (MF B i 

### / Σ i 

### MF B i 

### ) * Σ j 

### MW G j



### Notes: MF B i 

### = maximum steam flow of boiler i MW G j 

### = electric generation capacity of generator j

16 

### As used here, net summer dependable capacity is the net capability of a generating unit in megawatts (MW) for daily planning and operation purposes during the summer peak season, after accounting for station or auxiliary services.

### 4-5 Since EPA Base Case v.4.10 uses net energy for load as demand, NEEDS v.4.10 only includes generators that sell the majority of their power to the electric grid in order to be consistent with demand. The generators that should be in NEEDS v.4.10. by this qualification are determined from the 2005 EIA Form 906 non-utility source and disposition data set.

4.2.3 Plant Location NEEDS v.4.10 uses state, county and model region data to represent the physical location of each plant.

State and County NEEDS v.4.10 used the state and county data in EIA 860 (2006)

Model Region For each unit the associated model region wa s derived based on NERC regions and sub-regions reported in NERC ES&D 2006 for that unit. For units with no NERC sub-region data, NERC region and state were used to derive associated model regions. For units with no NERC region data, state and county were used to derive associated model regions. Table 3-1 in Chapter 3 provides a summary of the mapping between NERC regions and EPA Base Case v.4.10 model regions.

4.2.4 Online Year The EPA Base Case v.4.10 uses online year to capture when the unit entered service. NEEDS includes online years for all units in the database. In NEEDS v.4.10, online years for boilers, utility and non-utility generators were primarily derived from reported in-service dates in EIA 767 2005 and EIA 860 2006 respectively.

EPA Base Case v.4.10 does not include any assumption about the retirement year for generating units, except for existing nuclear units which mu st retire when they reach age 60. (See section 3.7 for a discussion of the nuclear lifetime assumption.) EPA Base Case v.4.10 does, however, provide economic retirement options to coal , oil and gas steam, combined cycle, combustion turbines, and nuclear units. This means that the m odel may elect to retire these units if it is economical to do so. In IPM, an early retired plant ceases to incur FOM and VOM costs. However, retired units do meet capital cost obligati ons for retrofits if the model projected a retrofit on the unit prior to retirement.

4.2.5 Unit Configuration Unit configuration refers to the physical specificat ion of a unit’s design. Unit configuration in EPA Base Case v.4.10 drives model plant aggregation, modeling of pollution control options and mercury emission modification factors. NEEDS v.4.10 contains information on the firing and bottom type of coal steam boilers in the database. Great effort was taken to see that the inventory of existing and committed controls represented in EPA Base Case v.4.10 was comprehensive and as up-to-date as possible. The hierarchy of data sources used is shown in Table 4-6.

4.2.6 Model Plant Aggregation While IPM is comprehensive in representing all the units contained in NEEDS, an aggregation scheme is used to combine existing units with sim ilar characteristics into “model plants”. The aggregation scheme serves to reduce the size of the model and makes the model manageable while capturing the essential characteristics of the generating units. The EPA Base Case v.4.10 aggregation scheme is designed so that each model plant only represents generating units from a single state. This design makes it possible to obtain state-level results directly from IPM outputs. In addition, the aggregation scheme supports m odeling plant-level emission limits on fossil generation

The “model plant” aggregation scheme encompasses a variety of different classification categories including location, size, technology, heat rate, fuel choices, unit configuration, SO 2

### 4-6 emission rates and environmental regulations am ong others. Units are aggregated together only if they match on all the different categories specified for the aggregation. The 10 major categories used for the aggregation scheme in EPA Base Case v.4.10 are the following:

(1) Model Region (2) Unit Technology Type (3) Fuel Demand Region (4) Applicable Environmental Regulations (5) State (6) Unit Configuration (7) Emission Rates (8) Heat Rates (9) Fuel (10) Size

Table 4-6 Data Sources for Unit Configuration in NEEDS v.4.10 for EPA Base Case v.4.10

Unit Component Primary Data Source Secondary Data Source Tertiary Data Source Other Sources Default

Firing Type Utility/RPO Comments 2005 EIA 767 -- -- -- Bottom Type Utility/RPO Comments 2005 EIA 767 -- -- Dry

SO 2 

### Pollution Control NSR Settlement or Utility/RPO Comments EPA’s Emission Tracking System (ETS) - 2006 2005 EIA 767 See Note No Control

NO x 

### Pollution Control NSR Settlement or Utility/RPO Comments EPA’s Emission Tracking System (ETS) - 2006 2005 EIA 767 See Note No Control

Particulate Matter Control NSR Settlement or Utility/RPO Comments EPA’s Emission Tracking System (ETS) - 2006 2005 EIA 767 1999 Hg ICR --

Note: In addition to the primary, secondary and tertiary data sources listed here, the following sources were consulted and emission controls were updated when corroborating information could be found: McILVAINE Utility Upgrade Database, ICAC (Institute of Clean Air Companies), and web sites of generating unit owners and operators.

Table 4-7 provides a crosswalk between actual plants and the aggregated “model plants” used in the EPA Base Case v.4.10. For each plant type, the table shows the number of real plants and the number of model plants representing these real plants in EPA Base Case v.4.10. 17

17 

### For readers interested in the intricacies of Table 4-7, here are several observations: (1) Depending on its capacity and fuel types combusted, an existing coal steam model plant may be provided with multiple scrubber and ACI retrofit options. As a result the total number of model plants representing scrubber and ACI retrofits may exceed the total number of model plants representing existing coal steam units. (See chapt er 5 for a detailed description of the sulfur dioxide (scrubber) and mercury (ACI) retrofit opti ons.) (2) The “Number of IPM Model Plants” shown for many of the “Plant Types” in the “Retro fits” block in Table 4-7 exceeds the “Number of IPM Model Plants” shown for “Plant Type” “C oal Steam” in the block labeled “Existing and Planned/Committed Units”, because a particular retrofit “Plant Type” can include multiple technology options and multiple timing options (e .g., Technology A in Stage 1 + Technology B in Stage 2, the reverse timing, or both technologies simultaneously in Stage 1). (3) Since only a subset of coal plants is eligible for certain retrof its, many of the “Plant Types” in the “Retrofits” block that represent only a single retrofit te chnology (e.g., “Retrofit Coal with Selective Noncatalytic Reduction (SNCR)”) have a “Number of of IPM Model Plants” that is a smaller than

### 4-7 Table 4-7 Aggregation Profile of Model Plants as Provided at Set Up of EPA Base Case v.4.10 Existing and Planned/Committed Units

Plant Type Number of Units Number of IPM Model Plants
Biomass 161 71
Coal Steam 1,267 913 Combined Cycle 1,627 610
Combustion Turbine 5,474 2,225 Fossil_Other 21 14
Fuel Cell 4 4
Geothermal 211 8 Hydro 3,771 99 Import 2 2 Integrated Gas Combined Cycle 6 5
Landfill Gas 747 59 Non Fossil_Other 1 241 73
Nuclear 2 105 105
Oil/Gas Steam 685 435 Pumped Storage 151 21
Solar 92 37
Wind 458 57
Total 15,023 4,738

New Units
Plant Type Number of Units Number of IPM Model Plants
New Biomass -- 64 New Coal with Carbon Capture -- 754 New Combined Cycle -- 32
New Combustion Turbine -- 32
New Fuel Cell -- 32 New Future Technology -- 160
New Geothermal -- 26
New IGCC -- 58
New Landfill Gas -- 96
New Nuclear -- 64
New Offshore Wind -- 690
New Onshore Wind -- 600 New SPC-DryFGD_SCR_ACI 27 New SPC-WetFGD_SCR -- 27
New Solar Thermal -- 55
New Solar PV -- 32



### the “Number of IPM Model Plants” shown for “Plant Type” “Coal Steam”. (4) The total number of model plants representing different types of new units often exceeds the 32 model regions and the specific totals vary from technology to technology for several reasons. First, some technologies have multiple vintages, which must be represented by separate model plants in each IPM region. Second, some technologies are not available in particular regions (e.g., geothermal is geographically restricted to certain regions, convent ional pulverized coal is not provided as an option in CA-N).

### 4-8 Total -- 2,749

Retrofits

Plant Type Number of Units Number of IPM Model Plants Retrofit Coal with Activated Carbon Injection (ACI) -- 427
Retrofit Coal with ACI + SCR -- 1,076
Retrofit Coal with ACI + SCR + Scrubber -- 1,575
Retrofit Coal with ACI + SCR + Scrubber + CCS -- 1,708
Retrofit Coal with ACI + SNCR + Scrubber -- 357
Retrofit Coal with ACI + SNCR -- 447
Retrofit Coal with ACI + SNCR + Scrubber + CCS -- 161
Retrofit Coal with ACI + Scrubber -- 1,570
Retrofit Coal with ACI + Scrubber + CCS -- 1,372 Retrofit Coal with Selective Catalytic Reduction (SCR) -- 360
Retrofit Coal with SCR + Scrubber -- 2,281
Retrofit Coal with SCR + Scrubber + CCS -- 2,065
Retrofit Coal with Selectiv e Noncatalytic Reduction (SNCR) -- 141
Retrofit Coal with SNCR + Scrubber -- 544
Retrofit Coal with SNCR + Scrubber + CCS -- 112
Retrofit Coal with Scrubber -- 587
Retrofit Coal with Scrubber + CCS -- 1,078
Retrofit Coal with CCS -- 14
Retrofit Oil Gas with SCR -- 281
Total -- 16,156

Early Retirements
Plant Type Number of Units Number of IPM Model Plants CC Early Retirement -- 610 Coal Early Retirement -- 6,178 CT Early Retirement -- 2,225 IGCC Early Retirement -- 5 Nuke Early Retirement -- 105 O/G Early Retirement -- 435
Total -- 9,558

Grand Total (Existing and Planned/Committed + New + Retrofits + Early Retirements): 33,201

Notes:

1 Non Fossil_Other includes units whose fuel is munici pal solid waste, tires, and other non-fossil waste.

2 The 105 nuclear units include 104 currently operating units plus Watts Bar Nuclear Plant, Unit 2, which is scheduled to come online in 2014. All are listed in Appendix 4-3.

### 4-9 4.2.7 Cost and Performance Characteristics of Existing Units In EPA Base Case v.4.10 heat rates, emission rates, variable operation and maintenance cost (VOM) and fixed operation and maintenance costs (FOM) are used to characterize the cost and performance of all existing units in NEEDS v.4.10. For existing units, only the cost of maintaining (FOM) and running (VOM) the unit are modeled. Embedded costs, such as carrying capital charges, are not modeled. The section below contains a discussion of the cost and performance assumptions for existing units used in the EPA Base Case v.4.10.

Variable Operating and Maintenance Cost (VOM) VOM represents the non-fuel cost variable associated with producing a unit of electricity. If the generating unit contains pollution control equipment, VOM includes the cost of operating the control equipment. Table 4-8 below summarizes VOM assumptions used in EPA Base Case v.4.10. The values shown in this table we re obtained using a procedure developed jointly by EPA’s power sector engineering staff and ICF.

Table 4-8 VOM Assumptions (2007$) in EPA Base Case v.4.10

Capacity Type SO 2 

### Control Hg Control NO x 

### Control Variable O&M Range (mills/kWh)

No NO x 

### 2.63 - 7.67 Combined Cycle -- -- SCR 2.75 - 7.79
No NO x 

### 3.66 - 5.05

SCR 4.34 - 5.73 No Hg
SNCR 4.73 - 6.12
No NO x 

### 4.62 - 6.02

SCR 5.30 - 6.69 Scrubbed - Dry

ACI
SNCR 5.70 - 7.09
No NO x 

### 2.48 - 3.87

SCR 3.16 - 4.55 No Hg
SNCR 3.55 - 4.94
No NO x 

### 3.44 - 4.83

SCR 4.12 - 5.51 Scrubbed - Wet
ACI
SNCR 4.51 - 5.91
No NO x 

### 0.90 - 2.29

SCR 1.58 - 2.97 No Hg
SNCR 1.97 - 3.37
No NO x 

### 1.87 - 3.26

SCR 2.54 - 3.94 Coal Steam

Unscrubbed

ACI
SNCR 2.94 - 4.33 Conventional Hydroelectric -- -- -- 6.66

No NO x 

### 2.60 - 9.59 Combined Turbine -- No Hg SCR 2.73 - 9.71 Fuel Cell -- -- -- 9.7 Geothermal -- -- -- 8.3 IGCC -- -- -- 0 - 4.72 MSW/Landfill Gas -- -- -- 8.79

### 4-10 Capacity Type SO 2 

### Control Hg Control NO x 

### Control Variable O&M Range (mills/kWh)

No NO x 

### 0.94 - 5.07 Scrubbed - Wet No Hg SNCR 1.49 - 5.62
No NO x 

### 0.94 - 5.07

SCR 1.06 - 5.19 Oil/Gas Steam
Unscrubbed No Hg
SNCR 1.49 - 5.62 Pumped Storage -- -- -- 8.37 Solar Photovoltaic -- -- -- 2.09

Solar Thermal -- -- -- 2.78 Wind -- -- -- 3.18
Wood/Biomass -- -- -- 6.98

Fixed Operation and Maintenance Cost (FOM) FOM represents the annual fixed cost of maintaining a unit. FOM costs are incurred independent of achieved generation levels and signify the fix ed cost of operating and maintaining the unit for generation. Table 4-9 summarizes the FOM assumptions used in EPA Base Case v.4.10. Note that FOM varies by the age of the unit. The values appearing in this table include the cost of maintaining any associated pollution control equipment. The values in Table 4-9 are based on FERC (Federal Energy Regulatory Commission) Form 1 data.

Heat Rates Heat Rates describe the efficiency of the unit expr essed as BTUs per kWh. The treatment of heat rates in EPA Base Case v.4.10 is discussed in Section 3.8.

Lifetimes Unit lifetime assumptions in EPA Base Case v.4.10 are detailed in Sections 3.7 and 4.2.8.

SO 2 

### Rates Section 3.9.1 contains a detailed discussion of SO 2 

### rates for existing units.

NO x 

### Rates Section 3.9.2 contains a detailed discussion of NO x 

### rates for existing units.

Mercury Emission Modification Factors (EMF) Mercury EMF refers to the ratio of mercury emi ssions (mercury outlet) to the mercury content of the fuel (mercury inlet). Section 5.4.2 contains a detailed discussion of the EMF assumptions in EPA Base Case v.4.10.

4.2.8 Life Extension Costs for existing units The usable modeling time horizon in previous EPA Base Cases typically extended out only as far as 2030 and covered a period of roughly 20-25 years. In contrast, the modeling time horizon in EPA Base Case 4.10 extends to 2050 covers a period of almost 40 years. Due to this longer time horizon, provision had to be made in EPA Base Case v.4.10 for investments (beyond the routine maintenance of the power plant) that would be requir ed to extend the life of existing units over this longer time horizon. The life extension costs for units with retirement options are summarized in Table 4-10 below. These costs were based on a review of FERC Form 1 data regarding annual capital expenditures over the last 10 – 15 years of the power plan.

### 4-11 Table 4-9 FOM Assumptions Used in EPA Base Case v.4.10

Prime Mover Type Primary Fuel SO 2 Control Hg Control NO x Control Age of Unit FOM (2007$ /kW-Yr) Combined

Cycle Oil & Gas -- -- -- All Years 12.6

Conventional Hydroelectric Water -- -- -- All Years 14.3

Fuel Cell Natural Gas -- -- -- All Years 18.3

>30 years 8.8
20-30 years 8.5 Gas Turbine Oil & Gas -- -- -- 0-20 years 3.7

Geothermal Earth -- -- -- All Years 21.6

IGCC Coal -- -- -- All Years 118.4
MSW/Landfill Gas Landfill Gas -- -- -- All Years 23.6

Nuclear Nuclear -- -- -- All Years 100.5 Pumped Storage Water -- -- -- All Years 18.3

Solar Photovoltaic Sun -- -- -- All Years 17.1

Solar Thermal Sun -- -- -- All Years 22.6

0 to 20 Years 42.2

20 to 30 Years 44.2

30 to 40 Years 55.1 No NO x

Greater than 40 Years 60.8

0 to 20 Years 42.9

20 to 30 Years 44.9

30 to 40 Years 55.7 SCR

Greater than 40 Years 61.4

0 to 20 Years 42.5

20 to 30 Years 44.5

30 to 40 Years 55.3 No Hg

SNCR

Greater than 40 Years 61.1

0 to 20 Years 42.4

20 to 30 Years 44.4

30 to 40 Years 55.2 No NO x

Greater than 40 Years 60.9

0 to 20 Years 43.0

20 to 30 Years 45.0

30 to 40 Years 55.9 SCR

Greater than 40 Years 61.6

0 to 20 Years 42.6

20 to 30 Years 44.6

30 to 40 Years 55.5 Scrubbed - Dry

ACI

SNCR

Greater than 40 Years 61.2

0 to 20 Years 43.2

20 to 30 Years 45.2

30 to 40 Years 56.0 No NO x

Greater than 40 Years 61.8

0 to 20 Years 43.8 Steam Turbine Coal

Scrubbed - Wet No Hg

SCR

20 to 30 Years 45.8

### 4-12 Prime Mover Type Primary Fuel SO 2 Control Hg Control NO x Control Age of Unit FOM (2007$ /kW-Yr)
30 to 40 Years 56.7

Greater than 40 Years 62.4

0 to 20 Years 43.5

20 to 30 Years 45.5

30 to 40 Years 56.3 SNCR

Greater than 40 Years 62.0

0 to 20 Years 43.3

20 to 30 Years 45.3

30 to 40 Years 56.2 No NO x

Greater than 40 Years 61.9

0 to 20 Years 44.0

20 to 30 Years 46.0

30 to 40 Years 56.8 SCR

Greater than 40 Years 62.6

0 to 20 Years 43.6

20 to 30 Years 45.6

30 to 40 Years 56.5 ACI

SNCR

Greater than 40 Years 62.2

0 to 20 Years 32.9

20 to 30 Years 34.9

30 to 40 Years 45.8 No NO x

Greater than 40 Years 51.5

0 to 20 Years 33.6

20 to 30 Years 35.6

30 to 40 Years 46.4 SCR

Greater than 40 Years 52.2

0 to 20 Years 33.2

20 to 30 Years 35.2

30 to 40 Years 46.1 No Hg

SNCR

Greater than 40 Years 51.8

0 to 20 Years 33.1

20 to 30 Years 35.1

30 to 40 Years 45.9 No NO x

Greater than 40 Years 51.7

0 to 20 Years 33.7

20 to 30 Years 35.7

30 to 40 Years 46.6 SCR

Greater than 40 Years 52.3

0 to 20 Years 33.3

20 to 30 Years 35.3

30 to 40 Years 46.2 Unscrubbed

ACI

SNCR

Greater than 40 Years 51.9

0 to 20 Years 18.4

20 to 30 Years 21.0

30 to 40 Years 22.3 No NO x

Greater than 40 Years 28.4 Oil & Gas -- --

SCR 0 to 20 Years 19.3

### 4-13 Prime Mover Type Primary Fuel SO 2 Control Hg Control NO x Control Age of Unit FOM (2007$ /kW-Yr)
20 to 30 Years 21.9

30 to 40 Years 23.2

Greater than 40 Years 29.3

0 to 20 Years 18.6

20 to 30 Years 21.2

30 to 40 Years 22.5 SNCR

Greater than 40 Years 28.6

Wind Wind -- -- -- All Years 18.3

Wood/Biomass Biomass -- -- -- All Years 20.1



### Table 4-10 Life Extension Cost Assumptions Used in EPA Base Case v.4.10

Plant Type Lifespan without Life

Extension Expenditures Life Extension Cost as Proportion of New Unit Capital Cost (%) Capital Cost of New Unit (2007$/kW) Life Extension Cost (2007$/kW)

Coal Steam 40 7.0 2,918 204 Combined Cycle 30 9.3 976 91 Combustion Turbine & IC Engine 30 4.2 698 30

Oil/Gas Steam 40 3.4 2,699 91 IGCC 40 7.4 3,265 242 Nuclear 40 9.0 4,621 416 Note: Life extension expenditures double the lifespan of the unit.



## 4.3 Planned-Committed Units EPA Base Case v.4.10 includes all planned-committed units that are likely to come online because ground has been broken, financing obtained, or other demonstrable factors indicate a high probability that the unit will be built before 2012.

4.3.1 Population and Model Plant Aggregation Like existing units, planned-committed units are contained in NEEDS. A comprehensive update of planned-committed units contained in NEEDS was performed for EPA Base Case v.4.10 using the information sources listed in Table 4-1 . Table 4-11 summarizes the extent of inventory of planned-committed units in EPA Base Case v.4.10 indicating its generating capacity by unit types.

Due to data confidentiality restrictions, NEEDS v.4.10 does not list the planned-committed units on a unit by unit basis. Rather, all units having similar technologies and located within the samemodel region are aggregated together as one record. Table 4-12 gives a breakdown of plannedcommitted units by IPM region, unit type, number of units, and capacity included in EPA Base Case v.4.10.

### 4-14 Table 4-11 Summary of Planned-Committed Units in NEEDS v.4.10 for EPA Base Case v.4.10 Type Capacity (MW) Year Range Described Renewables/Non-conventional Biomass 495 2007 - 2012 Fuel Cell 3 2011 Geothermal 302 2007 - 2011 Hydro 91 2007 - 2011 Landfill Gas 279 2007 - 2011 Municipal Solid Waste 35 2007 - 2011 Non-Fossil Waste 235 2011 Pumped Storage 40 2011 Solar 687 2007 - 2011 Wind 26,295 2007 - 2011 Subtotal 28,462 Fossil/Conventional Coal Steam 17,055 2007 - 2011 Combined Cycle 25,088 2007 - 2011 Combustion Turbine 9,648 2007 - 2011 Fossil Waste 274 2011 IGCC 1,230 2009 - 2011 Nuclear 1,180 2014 O/G Steam 115 2008 - 2011 Subtotal 54,590
Grand Total 83,052

Table 4-12 Planned-Committed Units by Model Region in NEEDS v.4.10 for EPA Base Case v.4.10 IPM Region Plant Type Capacity (MW) Biomass 24 Coal Steam 400 Combined Cycle 94 Combustion Turbine 593 Geothermal 95 Solar 20 AZNM

Wind 130 Combined Cycle 1,279 Combustion Turbine 603 Fuel Cell 1 Geothermal 25 Landfill Gas 17 Solar 249 CA-N

Wind 2,533 Biomass 2 Combined Cycle 1,293 Combustion Turbine 454 Fuel Cell 2 Hydro 5 CA-S

Landfill Gas 8

### 4-15 IPM Region Plant Type Capacity (MW) Pumped Storage 40 Solar 255 Wind 112 Combined Cycle 573 Landfill Gas 6 COMD
Wind 282 DSNY Combined Cycle 635 Biomass 14 Coal Steam 665 ENTG Combined Cycle 1,336 Biomass 50 Coal Steam 3,250 Combined Cycle 3,266 Combustion Turbine 639 Fossil Waste 274 Landfill Gas 16 Non-Fossil Waste 10 Solar 3 ERCT

Wind 7,669 Combined Cycle 6,365 Combustion Turbine 1,191 Landfill Gas 21 FRCC

Municipal Solid Waste 16 Coal Steam 1,800 Landfill Gas 5 GWAY
Wind 1,177

LILC Combined Cycle 350 Combustion Turbine 105 Landfill Gas 27 O/G Steam 9 MACE

Solar 7 Combustion Turbine 30 Landfill Gas 5 MACS O/G Steam 100 Biomass 30 Landfill Gas 22 Municipal Solid Waste 14 Wind 480 Landfill Gas 10 MACW

Wind 480 Biomass 107 Coal Steam 1,782 Combined Cycle 1,204 Combustion Turbine 878 Hydro 11 Landfill Gas 7 MRO

Municipal Solid Waste 5

### 4-16 IPM Region Plant Type Capacity (MW) Non-Fossil Waste 25 Wind 4,598 Biomass 44 Combined Cycle 987 Combustion Turbine 301 Hydro 16 Landfill Gas 11 O/G Steam 6 Solar 1 NENG

Wind 650 Biomass 1 Coal Steam 1,112 Combined Cycle 1,062 Geothermal 182 Hydro 12 Solar 2 NWPE

Wind 266

NYC Combustion Turbine 1 Biomass 31 Combined Cycle 1,262 Combustion Turbine 175 Hydro 19 Landfill Gas 10 Non-Fossil Waste 58 Solar 2 PNW

Wind 2,584 Combined Cycle 720 IGCC 1,230 Landfill Gas 26 Non-Fossil Waste 3 RFCO

Wind 554 Coal Steam 695 Combined Cycle 580 Landfill Gas 5 Non-Fossil Waste 80 RFCP

Wind 264 Coal Steam 768 Combustion Turbine 658 Hydro 9 Landfill Gas 6 Solar 15 RMPA

Wind 1,362 Combustion Turbine 720 Solar 112 SNV
Wind 189 Biomass 116 SOU Combined Cycle 1,237

### 4-17 IPM Region Plant Type Capacity (MW) Non-Fossil Waste 18 Coal Steam 1,172 Combustion Turbine 845 SPPN
Wind 651 Coal Steam 660 Combined Cycle 1,080 Combustion Turbine 939 Non-Fossil Waste 35 Solar 8 Wind 943 Nuclear 1,180 SPPS

Wind 111 Coal Steam 1,018 Combustion Turbine 200 TVAK Landfill Gas 5 Biomass 25 Hydro 13 Landfill Gas 14 Non-Fossil Waste 7 UPNY

Wind 826 Coal Steam 1,980 Combustion Turbine 779 Hydro 6 Landfill Gas 18 Solar 12 VACA

Wind 38 Combined Cycle 1,190 Combustion Turbine 537 Landfill Gas 31 VAPW

Solar 1 Biomass 50
Coal Steam 1,753 Combined Cycle 575
Landfill Gas 8 WUMS

Wind 396

Note: Any unit that has an online year of 2007- 2011 was considered a Planned and Committed Unit

4.3.2 Capacity The capacity of planned-committed units in NEEDS v.4.10 was obtained from the information sources reported above in Table 4-1 .

4.3.3 State and Model Region State location data for the planned-committed units in NEEDS v.4.10 came from the information sources noted in Section 4.3.1. The state information was then used to assign planned-committed units to their respective model regions.

### 4-18 4.3.4 Online and Retirement Year As noted above, planned-committed units included in NEEDS v.4.10 are only those units which are likely to come on-line before 2012. All pl anned-committed units were given a default online year of end of 2011 since this is the first analysis year in EPA Base Case v.4.10. The assumptions in EPA Base Case v.4.10 do not include a lifetime for planned-committed units.

4.3.5 Unit Configuration and Cost-and-Performance All planned-committed units in NEEDS v.4.10 assume the cost, performance, and unit configuration characteristics of potential units that are available in 2012. A detailed description of potential unit assumptions is provided below in Section 4.4.



## 4.4 Potential Units The EPA Base Case v.4.10 includes options for developing a variety of potential units that may be “built” at a future date in response to electricity demand and the constraints represented in the model. Defined by region, technology, and the y ear available, potential units with an initial capacity of 0 MW are inputs into IPM. When the model is run, the capacity of certain potential units is raised from zero to meet demand and other system and operating constraints. This results in the model’s projection of new capacity.

In Table 4-7 the block labeled “New Units” gives a breakdown of the type and number of potential units provided in EPA Base Case v.4.10. The following sections describe the cost and performance assumptions for the potential units represented in the EPA Base Case v.4.10.

4.4.1 Methodology Used to Derive the Cost and Performance Characteristics of Conventional Potential Units Cost and performance assumptions for potential units in previous EPA base cases were based primarily on data from the latest available Annual Energy Outlook (AEO) published by the U.S. Department of Energy’s Energy Information Administration. However, an unprecedented run up in the costs of new generating units over the preceding 5 years prompted EPA to analyze other references in addition to the AEO for Base Case v.4.10. The cost escalation which was particularly noticeable for base load electric generating units, was generally attributed to international competition and, was increasingly seen as permanent. That is, there was a growing consensus that costs were not going to settle back to pre-2010 levels.

With this in mind, the power sector engineering staff in EPA’s Clean Air Markets Division performed comparative cost analyses based on reports and discussions with government agencies, national technical laboratories, i ndustry, academia, and various non-governmental organizations. The key sources reviewed included:

• U.S. Energy Information Administration: Annual Energy Outlook 2008, 2009, 2010 18



### • National Energy Technology Laboratory, Fo ssil Energy Power Plant Desk Reference (Bituminous Coal) 19

18 

### U.S. Department of Energy, Energy Informati on Administration, Assumptions to the Annual Energy Outlook 2008: Electric Market Module, DOE/EIA-0554(2008), June 2008. www.eia.doe.gov/oiaf/archive/aeo08/assumption/electricity.html U.S. Department of Energy, Energy Informati on Administration, Assumptions to the Annual Energy Outlook 2009: Electric Market Module, DOE/EIA-0554(2009), March 2009. www.eia.doe.gov/oiaf/archive/aeo09/assumption/electricity.html U.S. Department of Energy, Energy Informati on Administration, Assumptions to the Annual Energy Outlook 2010: Electric Market Module, #:DOE/EIA-0554(2010), April 2010. www.eia.doe.gov/oiaf/aeo/assumption/electricity.html

. 19 

### U.S. Department of Energy, Nati onal Energy Technology Laboratory, Fossil Energy Power Plant Desk Reference, Bituminous Coal and Natural Gas to Electricity Summary Sheets DOE/NETL-

### 4-19 • U.S. Environmental Protection Agency, Envi ronmental Footprints and Costs of Coal-Based Integrated Gasification Combined Cycle and Pulverized Coal Technologies 20



### • EPRI (Electric Power Research Institute): “Economic Assessment of Advanced Coal-Based Power Plants with CO 2 

### Capture” 21



### • Harvard University: “Realistic Costs of Carbon Capture” 22



### • Massachusetts Institute of Technology: ”Update on the Cost of Nuclear Power” 23



### • Union of Concerned Scientists: Climate 2030 - A National Blueprint for a Clean Energy Economy 24



### 4.4.2 Cost and Performance for Potential Conventional Units The comparative analyses described in the preceding section resulted in the cost and performance characteristics shown in Tabl e 4-13. They are based on EPA’s engineering assessments. As seen in Table 4-13, EPA Base Case v.4.10 includes cost and performance characteristics for the following potential tec hnologies: supercritical pulverized coal, advanced combined cycle, advanced combustion turbines, in tegrated gasification combined cycle (IGCC), advanced coal with carbon capture capabilities, and nuclear units. The cost and performance assumptions are based on the size (i.e., electric al generating capacity in MW) indicated in the table. However, the total new capacity that is added in a given model run for these technologies is not restricted to these capacity levels.

This table includes several components of cost. The total installed cost of developing and building a new plant is captured through the capital cost. It includes expenditures on pollution control equipment that new units are assumed to install to satisfy air regulatory requirements. The capital costs shown in Table 4-13 are typically referred to as “overnight” capital costs. They include engineering, procurement, construction, startup, and owner’s costs (for such items as land, cooling infrastructure, administration and associated buildings, site works, switchyards, project management, licenses, etc). The capital costs in Table 4-13 do not include interest during construction (IDC). IDC is added to the capital costs shown in Table 4-13 during the set-up of a run. Calculation of IDC is based on the construction profile and the discount rate. Details on the discount rates used in the EPA Base Case v.4.10 are discussed in Chapter 8 under financial assumptions.

2007/1282, May 2007. http://www.netl.doe.gov/energyanalyses/pubs/Cost%20and%20Per formance%20Baseline-012908.pdf

20 

### U.S. EPA, Environmental Footprints and Costs of Coal-Based Integrated Gasification Combined Cycle and Pulverized Coal Technologies. EPA-430/R-06/2006, July 2006.

21 

### Booras, G., Economic Assessment of Advanced Coal-Based Power Plants with CO2 Capture , a presentation at EPRI (Electric Power Research Institute) MIT Carbon Sequestration Forum IC, September 16, 2008.

22 

### Al-Juaied, M and A Whitmore, ““Realistic Costs of Carbon Capture” Harvard Kennedy School. Belfer Center Discussion Paper 2009-08, July 2009. http://belfercenter.ksg.harvard.edu/files/2009_AlJuaied_Whitmore_Realistic_Costs_of_Carbon_C apture_web.pdf

23 

### Du, Y., J.E. Parsons (2009). Update on the cost of nuclear power. MIT Center for Energy and Environmental Policy Research (CEEPR) Working Paper 09-004, May 2009. http://web.mit.edu/ceepr/www/publications/workingpapers/2009-004.pdf

24 

### Cleetus R., S. Clemmer, and D. Friedman, Climate 2030 - A National Blueprint for a Clean Energy Economy, Union of Concerned Scientists, May 2009. http://www.ucsusa.org/global_warming/solu tions/big_picture_solutions/climate-2030- blueprint.html#Download_the_Climate_2030_Blueprint_repo4-20Table 4-13 Performance and Unit Cost Assumptions for Potential (New) Capacity from Conventional Technologies in EPA Base Casev4.10AdvancedCombinedCycleAdvancedCombustionTurbineNuclearIntegratedGasificationCombinedCycle –BituminousIntegratedGasificationCombined Cycle– SubbituminousAdvancedCoal withCarbonCaptureBituminous1Advanced Coalwith CarbonCapture –Subbituminous1SupercriticalPulverizedCoal - WetBituminousSupercriticalPulverizedCoal - DrySubBituminousSize (MW)5601701350600600500500600600First YearAvailable201520122017201320132015201520132013Lead Time (Years)326444444Vintage #1(years covered)2012 -20542012 - 20542012 -20542012 - 20542012 - 20542012 - 20542015 - 20542012 - 20542012 - 2054Availability87%92%90%85%85%85%85%85%85%Vintage #1Heat Rate(Btu/kWh)6,81010,72010,4008,4248,06210,1499,7138,8748,937Capital2(2007$/kW)9766984,6213,2653,3104,7204,7852,9183,008Fixed O&M(2007$/kW/yr)14.412.392.447.948.260.561.028.928.6Variable O&M(2007$/MWh)2.573.590.771.321.151.671.463.432.27Notes:1For The term “Advanced Coal with Carbon Capture” is used here and in the output files for EPA Base Case v.4.10 to represent a variety oftechnologies that are expected to provide carbon capture capabilities. These include both supercritical steam generators withcarbon capture andintegrated gasification combined cycle (IGCC) with carbon capture. Although IGCC with carbon capture was used to define the cost andperformance parameters that are implemented in EPA Base Case v.4.10, projections of “Advanced Coal with Carbon Capture” in EPABase Casev.4.10 are not limited to this technology.2Capital cost represents overnight capital cost.

### 4-21 Table 4-13 also shows fixed operating and maintenance (FOM) and variable operating and maintenance (VOM) components of cost. FOM is the annual cost of maintaining a generating unit. It represents expenses incurred regardless of the extent that the unit is run. It is expressed in units of $ per kW per year. VOM represents the costs incurred in running an electric generating unit. It is proportional to the electrical energy pr oduced and is expressed in units of $ per MWh.

In addition to the three components of cost, Table 4-13 indicates the first year available, lead time, vintage periods, heat rate, and availability for each type of unit. Lead time represents the construction time needed for a unit to come online. Vintage periods are used to capture the cost and performance improvements resulting from technological advancement and learning-by-doing. Mature technologies and technologies whose first year available is not at the start of the modeling time horizon may have only one vintage period, w hereas newer technologies may have several vintage periods. Heat rate indicates the efficiency of the unit and is expressed in units of energy consumed (Btus) per unit of electricity generated (k Wh). Availability indicates the percentage of time that a generating unit is available to provide electricity to the grid once it has come on line. Availability takes into account estimates of the time consumed by planned maintenance and forced outages. The emission characteristics of the potential units are not presented in Table 4-13, but can be found in Table 3-11.

4.4.3 Short-Term Capital Cost Adder Besides the capital costs shown in Table 4-13 and Table 4-16 EPA Base Case v.4.10 includes a short-term capital cost adder that kicks in if the new capacity in a specific model run year exceeds certain upper bounds. This is meant to reflect the added cost incurred due to short-term competition for scarce labor and materials. Table 4-14 shows the cost adders for each type of potential unit for model run years through 2030. The adder is not imposed after 2030 on the premise that by then market adjustments will have eliminated the short term scarcity experienced in earlier years.

Here’s how these short-term adders work in Base Case v.4.10: The column labeled “Step 1” in Table 4-14 indicates the total amount of capacity of a particular plant type that can be built in a given model run year without incurring a cost adder. However, if the Step 1 upper bound is exceeded, then either the Step 2 or Step 3 cost adder is incurred. Above the Step 1 upper bound, the Step 2 cost adder applies until the cumulative capacity exceeds the Step 1 + Step 2 upper bound. Beyond that point, the Step 3 capital cost adder applies. For example, the Step 1 upper bound in 2012 for coal steam potential units is 25,301 MW. If no more than this total new coal steam capacity is built in 2012, only the capital cost shown in Table 4-13 is incurred. Between 25,301 and 42,168 MW (the sum of the Step 1 and Step 2 upper bounds, i.e., 25,301 MW + 16, 867 MW = 42,168 MW), the Step 2 cost adder of $967/ MW applies. For all the new coal capacity built in that model run year (not just the increment of new capacity above the Step 1 upper bound of 25,301 MW), this extra cost is added to the capital cost shown in Table 4-13. If the total new coal steam capacity exceeds the Step 1 + Step 2 upper bound of 42,168 MW, then the Step 3 capacity adder of $2,500/MW is incurred. To determine if the upper bounds for plant type “Coal Steam” in Table 4-14 have been reached, one must sum the capacities added in a model run year for plant types Supercritical Pulverized Coal - Wet Bituminous and Supercritical Pulverized Coal - Dry Bituminous. The upper bound for “Coal Steam” applies to the sum of the capacity added in the model run year for these two plant types.

The short-term capital cost adders shown in Table 4-14 were derived from AEO assumptions.

### 4-22 4.4.4 Regional Cost Adjustment The capital costs reported in Table 4-14 are generic. Before EPA implements these capital cost values they are converted to region-specific costs. This is done through the application of regional adjustment factors which capture regional differences in labor, material, and construction costs. The regional adjustment factors used in EPA Base Case v.4.10 are shown in Table 4-15. They were developed from AEO and are applied to both conventional technologies shown in Table 4-13 and to the renewable and non-convent ional technologies shown in Table 4-16 below.4-23Table 4-14 Short-Term Capital Cost Adders forNew Power Plants in EPA Base Case v.4.10 (2007$)2012201520202030IDNumberPlant TypeStep 1Step 2Step 3Step 1Step 2Step 3Step 1Step 2Step 3Step 1Step 2Step 3Upper Bound (MW)600400--1,8001,200--3,0002,000--6,0004,000--1BiomassAdder ($/kW)--1,4103,646--1,4103,646--1,4103,646--1,4103,646Upper Bound (MW)8,4345,622--25,30116,867--42,16828,112--84,33656,224--2Coal SteamAdder ($/kW)--9672,500--9672,500--9672,500--9672,500Upper Bound (MW)46,46930,979--139,40692,937--232,344154,896--464,687309,791--3CombinedCycleAdder ($/kW)--310801--310801--310801--310801Upper Bound (MW)24,09816,066--72,29548,197--120,49280,328--240,984160,656--4CombustionTurbineAdder ($/kW)--213551--213551--213551--213551Upper Bound (MW)600400--1,8001,200--3,0002,000--6,0004,000--5Fuel CellAdder ($/kW)--1,9875,138--1,9875,138--1,9875,138--1,9875,138Upper Bound (MW)315210--946630--1,5761,051--3,1522,102--6GeothermalAdder ($/kW)--1,9815,123--1,9815,123--1,9815,123--1,9815,123Upper Bound (MW)2,4001,600--7,2004,800--12,0008,000--24,00016,000--7IGCC andAdvancedCoal withCarbonCaptureAdder ($/kW)--1,0722,774--1,0722,774--1,0722,774--1,0722,774Upper Bound (MW)600400--1,8001,200--3,0002,000--6,0004,000--8Landfill GasAdder ($/kW)--1,1352,936--1,1352,936--1,1352,936--1,1352,936Upper Bound (MW)11,2307,487--33,69022,460--56,15137,434--112,30174,867--9NuclearAdder ($/kW)--1,5794,083--1,5794,083--1,5794,083--1,5794,083Upper Bound (MW)10670--317211--528352--1,056704--10SolarThermalAdder ($/kW)--1,6084,158--1,6084,158--1,6084,158--1,6084,158Upper Bound (MW)5436--5436--9060--180120--11Solar PVAdder ($/kW)--1,7334,483--1,7334,483--1,7334,483--1,7334,4834-242012201520202030IDNumberPlant TypeStep 1Step 2Step 3Step 1Step 2Step 3Step 1Step 2Step 3Step 1Step 2Step 3Upper Bound (MW)23,00019,670--29,50519,670--49,17432,783--98,34865,566--12OnshoreWindAdder ($/kW)--6461,672--6461,672--6461,672--6461,672Upper Bound (MW)600400--1,8001,200--3,0002,000--6,0004,000--13OffshoreWindAdder ($/kW)--1,3043,373--1,3043,373--1,3043,373--1,3043,373Note:The term “Advanced Coal with Carbon Capture” is used here and in the output files for EPA Base Case v.4.10 to represent a variety oftechnologies that are expected to provide carbon capture capabilities. These include both supercritical steam generators withcarbon capture andintegrated gasification combined cycle (IGCC) with carbon capture. Although IGCC with carbon capture was used to define the cost andperformance parameters that are implemented in EPA Base Case v.4.10 and shown inTable 4-13, projections of “Advanced Coal with CarbonCapture” in EPA Base Case v.4.10 are not limited to this technology.

### 4-25 Table 4-15 Regional Cost Adjustment Factors for Conventional and Renewable Generating Technologies in EPA Base Case v.4.10 Model Region Region Description or Reliability Council Name Regional Factor AZNM Western Electricity Coordinating Council - Arizona, New Mexico 1.003 CA-N Western Electricity Coordinating Council - California North 1.058 CA-S Western Electricity Coordinating Council - California South 1.058 COMD Commonwealth Edison 1.004 DSNY Downstate New York 1.043 ENTG Entergy 0.960 ERCT Texas Regional Entity 0.986 FRCC Florida Reliability Coordinating Council 0.961 GWAY Gateway 1.004

LILC Long Island Company 1.879 MACE Legacy Mid-Atlantic Area Council - East 0.996 MACS Legacy Mid-Atlantic Area Council - South 0.996 MACW Legacy Mid-Atlantic Area Council - West 0.996 MECS Michigan Electric Coordination System 1.004 MRO Midwest Regional Planning Organization 1.004 NENG New England Power Pool 1.145

NWPE Western Electricity Coordinating Council - Northwest Power Pool East 1.026

NYC New York City 1.989 PNW Western Electricity Coordinating Council - Pacific Northwest 1.026 RFCO Reliability First Corporation - MISO 1.004 RFCP Reliability First Corporation - PJM 1.004

RMPA Western Electricity Coordinating Council - Rocky Mountain Power Area 1.003

SNV Western Electricity Coordinating Council - Southern Nevada 1.003 SOU Southern Company 0.960 SPPN Southwest Power Pool - North 0.997 SPPS Southwest Power Pool - South 0.997 TVA Tennessee Valley Authority 0.960 TVAK Tennessee Valley Authority - MISO-KY 1.004 UPNY Upstate New York 1.043 VACA Virginia-Carolinas 0.960 VAPW Dominion Virginia Power 0.960 WUMS Wisconsin-Upper Michigan 1.004

### 4-26 4.4.5 Cost and Performance for Potential Renewable Generating and Non-Conventional Technologies The renewable and non-conventional generating tec hnologies included as potential units in the EPA Base Case v.4.10 are conventional biomass boilers, biomass gasification combined cycle (BGCC), onshore and offshore wind (shallow and deep), geothermal, fuel cells, solar photovoltaic, solar thermal, and landfill gas. Table 4-16 summarizes the cost and performance assumptions in EPA Base Case v.4.10 for these potential units. Except for biomass, the parameters shown in Table 4-16 are based on AEO 2009. The size (MW) presented in Table 4-16 represents the capacity on which unit cost estimates were developed and does not indicate the total potential capacity that the model can build of a given technology. Due to the distinctive nature of generation from renewable resources, some of the values shown in Table 4-16 are averages or ranges that are discussed in further detailed in the followi ng subsections. Also discussed below are additional types of data from sources other than AEO 2009 that play a role in the representation of these ty pes of generation in EPA Base Case v.4.10.4-27Table 4-16 Performance and Unit Cost Assumptions for Potential (New) Renewable and Non-Conventional Technology Capacity in EPABase Case v4.10Landfill GasBiomassConventionalBoiler1BiomassGasificationCombinedCycle1FuelCellsGeothermalLGHILGLoLGVLoSolarPhotovoltaicSolarThermalOnshoreWindOffshoreWindSize (MW)3512010503051005050First Year Available201320192013201420132012201320132013Lead Time (Years)343432333Vintage #1(years covered)2012 -20192012 -20192012 -20192012 - 20192012 -20192012 -20192012 -2019Vintage #2(years covered)2020 -20292020 -20292020 -20292020 - 20292020 -20292020 -20292020 -2029Vintage #3(years covered)2012 - 20542020 - 20542012 -20542012 - 20542030 -20542030 -20542030 -20542030 - 20542030 -20542030 -20542030 -2054Availability85%85%87%87%90%90%90%95%95%Generation CapabilityEconomic DispatchGeneration ProfileVintage #1Heat Rate (Btu/kWh)13,500-7,93029,655 -397,03513,64813,64813,6480000Capital (2007$/kW)24,698-6,2591,624 -20,6742,5963,2705,0355,7655,1561,9543,852 -5,085Fixed O&M (2007$/kW/yr)85.2-5.7151 - 219114.3114.3114.311.756.830.389.5Variable O&M (2007$/MWh)11.60-47.920.000.010.010.01000.000.00Vintage #2Heat Rate (Btu/kWh)-9,800--13,64813,64813,6480000Capital (2007$/kW)2-4,071--2,5053,1564,8595,3504,6411,9123,621 -4,780Fixed O&M (2007$/kW/yr)-48.3--114.3114.3114.311.756.830.389.5Variable O&M (2007$/MWh)-8.83--0.010.010.01000.000.00Vintage #3Heat Rate (Btu/kWh)----13,64813,64813,6480000Capital (2007$/kW)2----2,0192,5443,9163,7774,3831,5802,809 -3,708Fixed O&M (2007$/kW/yr)----114.3114.3114.311.756.830.389.5Variable O&M (2007$/MWh)----0.010.010.01000.000.00Note:1The biomass generating technologies shown inthis table represent new capacity designed to burn biomass only. Assumptions forbiomass co-firing at existing coal plants can befound in Table 5-14.2Capital cost represents overnight capital cost.

### 4-28 It should be noted that the short term capital cost adder in Table 4-14 and the regional cost adjustment factors in Table 4-15 apply to the renewable and non-conventional generation technologies as they did to the conventional generation technologies

Biomass Electricity Generation Two biomass generation technologies with separate vi ntage periods are offered as new (potential) units in EPA Base Case v.4.10. Conventional direct fired biomass boilers are offered in vintage period 1, i.e., 2012-2019. Based on engineering and ma rket analysis that indicated that biomass gas combined cycle (BGCC) units will become commercially available by 2020, BGCC with its much more favorable heat rate and cost characteristics is provided as a potential unit from 2020 onward. Prepared by EPA’s power sector engineering staff, the cost and performance characteristics of these two technology options are shown in Table 4-16.

Wind Generation Previous EPA base cases only represented onshore wind generation. In addition to onshore wind, EPA Base Case v.4.10 includes offshore-shallow and offshore-deep wind generation. The following sections describe four key aspects of the representation of wind generation: wind quality and resource potential, generation profiles, reserve margin contribution, and capital cost calculation.

Wind Quality and Resource Potential : Wind resources are conventionally categorized into wind quality classes, ranging from class 1 (designated to be the least productive and reliable class for wind generation) to class 7 (designated to be the most productive and reliable class for wind generation). Areas designated as wind class 3 or higher are generally suitable for commercial wind turbine applications. Whereas previous EPA base cases only included wind classes 4, 5, and 6, EPA Base Case v.4.10 also includes class 3 and 7.

EPA worked with the U.S. Department of Energy ’s (DOE) National Renewable Energy Laboratory (NREL), on a complete update of the wind resource assumptions for use in EPA Base Case v.4.10. The result is a complete representation of the potential onshore, offshore (shallow and deep) wind generating capacity (in MW) broken into four cost classes (described in greater detail below) in each IPM model region. Table 4-17, Table 4-18 , and Table 4-19 present the onshore, offshore shallow, and offshore deep wind resource assumptions that are used in EPA Base Case v.4.10.

### 4-29 Table 4-17 Onshore Regional Potential Wind Capacity (MW) by Wind and Cost Class in EPA Base Case v.4.10 Cost Class IPM Region Wind Class 1 2 3 4 3 707 1,714 44 125,761
4 42 329 27 33,741
5 6 2,028 1,612 2,355
6 214 447 264 512 AZNM

7 4 106 144 166 3 1,269 -- -- 8,646
4 560 1,069 1,476 1,539
5 79 631 746 765 6 286 -- 795 740 CA-N

7 12 352 -- 399 3 1,689 -- -- 18,014
4 1,323 1,460 1,402 2,536
5 380 614 653 831 6 118 -- 435 540 CA-S

7 46 -- 231 233 3 2 -- -- 62,549 COMD 4 19 260 -- 312 3 98 599 297 500 4 64 -- -- 268 5 -- 60 -- 89 6 -- -- 30 29 DSNY

7 -- -- -- 29 3 1 1 -- 2 ENTG 5 -- 20 -- 30 3 3,230 -- 1,198 321,950
4 9,912 32,701 2,796 51,392
5 396 1,415 899 1,484 6 207 -- 512 582 ERCT

7 5 -- -- 20 3 10 -- -- 275,467 GWAY 4 -- 621 580 922 3 -- -- 872 567 LILC 4 -- 128 -- 194 3 384 -- 925 1,264
4 9 -- -- 163 MACE
5 -- -- -- 2 3 8 -- -- 57
4 2 5 -- 10 MACS
5 -- -- -- 3 3 700 1,054 1,747 2,412 4 182 -- 636 477 5 71 -- 200 161 MACW

6 -- 26 -- 46

### 4-30 Cost Class IPM Region Wind Class 1 2 3 4 3 3,571 -- -- 35,633
4 -- -- 467 259 MECS
5 -- 11 -- 12 3 17 -- -- 2,021,548
4 7 103 163,083 1,010,547
5 2,052 22,146 68,431 156,014 MRO

6 1,310 1,985 1,380 4,489 3 1,768 -- -- 9,127
4 737 1,128 -- 1,968
5 364 442 -- 969 6 28 356 471 438 NENG

7 147 -- -- 478 3 1,913 -- -- 630,559
4 1,891 6,167 7,657 236,910
5 353 1,058 3,600 50,582 6 122 1,239 3,823 17,728 NWPE

7 205 1,517 755 1,755 3 216 902 354 74,323
4 69 322 208 15,515
5 11 86 122 3,483 6 34 87 99 1,463 PNW

7 18 116 79 190 3 12,199 22,865 8,334 28,239 RFCO 4 -- -- -- 64 3 196 289 4,841 2,976
4 82 354 -- 466
5 44 -- 169 159 6 41 -- 148 96 RFCP

7 2 24 -- 53 3 1,327 -- -- 409,831
4 3,222 4,769 8,447 216,430
5 2,546 7,038 15,851 58,990 6 3,149 8,000 11,388 14,741 RMPA

7 619 6,188 2,151 6,748 3 -- -- -- 2,707
4 -- -- -- 234
5 9 -- -- 39 SNV

6 1 -- -- 11 SOU 3 1 -- 2 2 3 1 -- -- 454,442
4 1,933 9,618 37,341 163,483 SPPN
5 -- -- -- 839

### 4-31 Cost Class IPM Region Wind Class 1 2 3 4 3 109 -- 69 452,934
4 2,945 11,388 44,398 349,741
5 2,660 13,184 -- 21,167 6 3 105 111 170 SPPS

7 -- -- -- 34 3 204 -- -- 637
4 -- 121 166 155
5 19 -- 56 62 6 3 -- 19 16 TVA

7 -- 5 -- 12 3 4 -- -- 220 4 2 -- 17 15 5 -- -- -- 5 TVAK

6 -- -- 1 -- 3 -- 2,822 3,346 3,675
4 -- 403 -- 555
5 -- 95 -- 114 UPNY

6 -- -- 35 25 3 92 272 -- 879
4 33 60 -- 124
5 8 -- 39 44 6 4 -- 30 27 VACA

7 -- 7 -- 18 3 -- 261 -- 352 4 13 40 -- 100 5 5 -- 34 35 6 5 -- 29 33 VAPW

7 -- 11 -- 17 3 371 -- -- 130,624
4 57 49 64 84 WUMS
5 -- -- -- 41

### 4-32 Table 4-18 Offshore Shallow Regional Potential Wind Capacity (MW) by Wind and Cost Class in EPA Base Case v.4.10 Cost Class IPM Region Wind Class 1 2 4 3 497 995 995
4 281 561 561
5 43 86 86 6 29 59 59 CA-N

7 1 3 3 3 280 560 560
4 139 277 277
5 139 278 278 6 114 228 228 CA-S

7 1 3 3 3 393 785 785 COMD 4 981 1,963 1,963 DSNY 3 11 22 22 3 43,021 86,041 86,041 ENTG 4 695 1,390 1,390 3 14,372 28,745 28,745
4 15,191 30,382 30,382 ERCT
5 4,457 8,915 8,915 3 43,305 86,610 86,610 FRCC 4 6,120 12,240 12,240 3 693 1,385 1,385
4 1,610 3,220 3,220
5 1,851 3,703 3,703 LILC

6 530 1,060 1,060 3 2,745 5,490 5,490
4 8,161 16,323 16,323
5 8,838 17,676 17,676 MACE

6 5,123 10,246 10,246 3 1,066 2,132 2,132 MACS 4 187 374 374 3 160 319 319
4 136 271 271
5 1,320 2,640 2,640 MACW

6 5 11 11 3 2,349 4,699 4,699 4 3,850 7,701 7,701 5 5,451 10,903 10,903 6 1,886 3,771 3,771 MECS

7 6 13 13 3 399 798 798
4 952 1,904 1,904
5 348 696 696 MRO

6 1 3 3

### 4-33 Cost Class IPM Region Wind Class 1 2 4 3 2,326 4,652 4,652
4 3,134 6,269 6,269
5 2,989 5,978 5,978 6 6,157 12,314 12,314 NENG

7 334 667 667 3 99 198 198
4 253 506 506 NYC
5 3 5 5 3 716 1,432 1,432
4 1,651 3,303 3,303
5 1,310 2,620 2,620 6 204 407 407 PNW

7 112 224 224 3 686 1,371 1,371
4 2,298 4,595 4,595
5 5,664 11,328 11,328 RFCO

6 1,963 3,925 3,925 3 8,466 16,933 16,933
4 4,903 9,806 9,806 SOU
5 311 621 621 3 884 1,768 1,768
4 859 1,718 1,718 UPNY
5 659 1,318 1,318 3 6,229 12,458 12,458
4 12,064 24,129 24,129
5 13,709 27,418 27,418 VACA

6 3,209 6,419 6,419 3 1,990 3,981 3,981
4 3,422 6,843 6,843
5 3,886 7,772 7,772 VAPW

6 3,660 7,321 7,321 3 2,272 4,544 4,544
4 2,902 5,805 5,805
5 1,832 3,664 3,664 WUMS

6 680 1,360 1,360

### 4-34 Table 4-19 Offshore Deep Regional Potential Wind Capacity (MW) by Wind and Cost Class in EPA Base Case v.4.10 Cost Class IPM Region Wind Class 1 2 4
3 4,039 8,077 8,077
4 25,846 51,693 51,693
5 9,947 19,894 19,894 6 21,282 42,565 42,565 CA-N

7 19,318 38,636 38,636 3 10,721 21,441 21,441
4 9,247 18,494 18,494
5 14,402 28,805 28,805 6 10,140 20,280 20,280 CA-S

7 169 339 339
4 1,028 2,055 2,055
5 1,100 2,200 2,200 COMD
6 950 1,900 1,900 3 13,473 26,945 26,945 ENTG 4 10,730 21,461 21,461
4 10,501 21,003 21,003 ERCT 5 6,548 13,096 13,096 3 28,019 56,037 56,037
4 53,858 107,715 107,715 FRCC
5 1,109 2,219 2,219
4 501 1,002 1,002
5 431 861 861 6 15,589 31,178 31,178 LILC

7 5 10 10 3 11 22 22
4 140 280 280
5 1,049 2,099 2,099 MACE

6 22,490 44,979 44,979 MACS 4 7 14 14
4 29 58 58 MACW 5 557 1,114 1,114 3 220 439 439
4 701 1,403 1,403
5 7,710 15,420 15,420 6 33,394 66,787 66,787 MECS

7 45 90 90 3 354 707 707 4 4,882 9,765 9,765 5 3,283 6,566 6,566 MRO

6 1,535 3,070 3,070 3 144 287 287
4 1,754 3,508 3,508
5 3,683 7,366 7,366 6 59,338 118,676 118,676 NENG

7 1,762 3,524 3,524

### 4-35 Cost Class IPM Region Wind Class 1 2 4
NYC 4 58 116 116 3 241 482 482
4 813 1,625 1,625
5 12,502 25,005 25,005 6 34,795 69,589 69,589 PNW

7 25,739 51,477 51,477
4 9 19 19
5 1,162 2,323 2,323 RFCO
6 507 1,014 1,014 3 8,765 17,529 17,529 4 15,055 30,109 30,109 SOU
5 2,169 4,339 4,339 3 50 100 100
4 1,506 3,011 3,011
5 5,993 11,986 11,986 UPNY

6 465 930 930
4 2 3 3
5 6,536 13,073 13,073 VACA
6 23,687 47,373 47,373
5 151 302 302 VAPW 6 16,150 32,300 32,300 3 395 790 790
4 3,533 7,066 7,066
5 8,073 16,145 16,145 WUMS

6 39,059 78,119 78,119

### 4-36 Generation Profiles : Unlike other renewable generation technologies, which dispatch on an economic basis subject to their availability c onstraint, wind and solar technologies can only be dispatched when the wind blows and the sun shines. To represent intermittent renewable generating sources like wind and solar, EPA Base Case v.4.10 uses generation profiles which specify hourly generation patterns for a representative day in winter and summer. Each eligible model region is provided with a distinct set of winter and summer generation profiles for wind, solar thermal, and solar photovoltaic plants.

For Hour1 through Hour 24 the generation profile indicates the amount of generation (kWh) per MW of available capacity. The wind generation pr ofiles were prepared with data from NREL. This provided the separate winter and summer generation profiles for wind classes 3-7 for onshore and offshore (shallow and deep) generation in each IPM region. (As an illustrative example Appendix 4-1 shows the generation profile for onshore wind in model region CA-N.) In IPM the seasonal average “kWh of generation per MW” (shown in the last row of the example in Appendix 4-1) is used to derive the generation from a particular wind class in a specific model region.

To obtain the seasonal generation for the units in a particular wind class in a specific region, one must multiply the installed capacity by the capacity factor (which represents the ratio of actual productivity in a time period to the theoretical maximum in the period). Capacity factor is the average “kWh of generation per MW” from the applicable generation profile multiplied by the number of days in the time period (i.e., summer or winter) to obtain the level of generation. The capacity factors for wind generation that are used in EPA Base Case v.4.10 were obtained from AEO 2010 and are shown in Table 4-20, Table 4-21, and. Table 4-22

Reserve Margin Contribution (also referred to as capacity credit) : EPA Base Case v.4.10 uses reserve margins, discussed in detail in Section 3.6, to model reliability. Each region has a reserve margin requirement which is used to determi ne the total capacity needed to reliably meet peak demand. The ability of a unit to assist a region in meeting its reliability requirements is modeled through the unit’s contribution to reserve margin. If the unit has 100 percent contribution towards reserve margin, then the entire capacity of the unit is counted towards meeting the region’s reserve margin requirement. However, if any unit has less than a 100 percent contribution towards reserve margin, then only the designated s hare of the unit’s capacity counts towards the reserve margin requirement.

All units except those that depend on intermittent resources have 100% contributions toward reserve margin. This means that all renewable resource technologies except wind and solar, have 100 percent contribution towards reserve margin in the EPA Base Case v.4.10. (Note Hydro, not considered a renewable technology, also has le ss than a 100% reserve margin contribution.)

Reserve margin contribution ratios are based on AEO 2010. Table 4-20, Table 4-21, and Table 4-22 present the reserve margin contributions apportioned to new wind plants in the EPA Base Case v.4.10 as derived from AEO 2010. The tables show the onshore and offshore (shallow and deep) reserve margins for each wind class in each model region.

### 4-37 Table 4-20 Onshore Reserve Margin Contribution an Average Capacity Factor by Wind Class and Model Region Wind Class IPM Model Region 3 4 5 6 7
AZNM 22% 24% 29% 34% 37% CA-N 14% 16% 19% 22% 24% CA-S 14% 16% 19% 22% 24% COMD 18% 20% -- -- -- DSNY 18% 20% 24% 28% 30% ENTG 26% -- 35% -- -- ERCT 19% 21% 25% 29% 32% GWAY 18% 20% -- -- --

LILC 18% 20% -- -- -- MACE 18% 20% 24% -- -- MACS 18% 20% 24% -- -- MACW 18% 20% 24% 28% -- MECS 22% 25% 30% -- -- MRO 19% 21% 26% 30% -- NENG 15% 16% 20% 23% 25% NWPE 25% 28% 33% 39% 42% PNW 25% 28% 33% 39% 42% RFCO 22% 25% -- -- -- RFCP 22% 25% 30% 35% 38% RMPA 22% 24% 29% 34% 37% SNV 22% 24% 29% 34% -- SOU 26% -- -- -- -- SPPN 24% 27% 33% -- -- SPPS 24% 27% 33% 38% 42% TVA 26% 29% 35% 41% 44% TVAK 22% 25% 30% 35% -- UPNY 18% 20% 24% 28% -- VACA 26% 29% 35% 41% 44% VAPW 26% 29% 35% 41% 44% WUMS 18% 20% 24% -- -- Average Annual Capacity Factor 29% 33% 39% 46% 50%

Table 4-21 Offshore Shallow Reserve Margin C ontribution an Average Capacity Factor by Wind Class and Model Region Wind Class IPM Model Region 3 4 5 6 7
CA-N 15% 17% 20% 24% 25% CA-S 15% 17% 20% 24% 25% COMD 19% 21% -- -- -- DSNY 19% -- -- -- -- ENTG 27% 30% -- -- -- ERCT 20% 22% 26% -- -- FRCC 19% 21% -- -- -- LILC 19% 21% 25% 29% --

### 4-38 Wind Class IPM Model Region 3 4 5 6 7
MACE 19% 21% 25% 30% -- MACS 19% 21% -- -- -- MACW 19% 21% 25% 30% -- MECS 23% 26% 31% 37% 39% MRO 20% 22% 27% 32% -- NENG 15% 17% 21% 24% 26% NYC 19% 21% 25% -- -- PNW 26% 29% 35% 41% 44% RFCO 23% 26% 31% 37% -- SOU 27% 30% 36% -- -- UPNY 19% 21% 25% -- -- VACA 27% 30% 36% 43% -- VAPW 27% 30% 36% 43% -- WUMS 19% 21% 25% 30% -- Average Annual Capacity Factor 31% 34% 41% 48% 52%

Table 4-22 Offshore Deep Reserve Margin C ontribution an Average Capacity Factor by Wind Class and Model Region Wind Class IPM Model Region 3 4 5 6 7
CA-N 15% 17% 20% 24% 26% CA-S 15% 17% 20% 24% 26% COMD -- 21% 26% 30% -- ENTG 27% 30% -- -- -- ERCT -- 22% 26% -- -- FRCC 18% 21% 25% -- -- LILC -- 21% 25% 30% 32% MACE 19% 21% 25% 30% -- MACS -- 21% -- -- -- MACW -- 21% 25% -- -- MECS 23% 26% 31% 37% 40% MRO 20% 23% 27% 32% -- NENG 15% 17% 21% 24% 26% NYC -- 21% -- -- -- PNW 26% 29% 35% 41% 45% RFCO -- 26% 31% 37% -- SOU 27% 30% 36% -- -- UPNY 19% 21% 25% 30% -- VACA -- 30% 36% 43% -- VAPW -- -- 36% 43% -- WUMS 19% 21% 26% 30% -- Average Annual Capacity Factor 31% 35% 41% 49% 53%

### 4-39 Capital cost calculation : EPA Base Case v.4.10 uses multipliers similar to the LT (long term) multipliers from the Energy Information Administration’s NEMS model 25 

### to capture differences in the capital cost of new wind capacity caused by such factors as distance from existing transmission, terrain variability, slope and other causes of resource degradation, site accessibility challenges, population proximity, competing land uses, aesthetics, and environmental factors. Four cost classes are used in EPA Base Case v.4.10 with class 1 having the lowest cost adjustment factor (1) and class 4 having the highest adjustment factor (ranging from 2.48 to 2.67 depending on the model region and whether the wind resource is onshore, offshore shallow or offshore deep), as shown in Table 4-23. To the obt ain the capital cost for a particular new wind model plant, the base capital costs shown in T able 4-16 are multiplied by the cost adjustment factor for the wind cost class applicable to the new plant.

Table 4-23 Capital Cost Adjustment Factors for New Wind Plants in Base Case v.4.10 Cost Class
1 2 3 4 Onshore 1 1.2 1.5 2.5 1 

### Offshore Deep Water 1 1.35 -- 2.5 Offshore Shallow Water 1 1.35 -- 2.5
Note:
1 

### The Cost Adjustment Factor for Cost Class 4 Onshore is 2.5 for the majority of regions. Exceptions are as follows: ERCT has a Cost Adjustment Factor for Cost Class 4 Onshore of 2.62 AZNM, RMPA, and SNV have a Cost Adjustment Factor for Cost Class 4 Onshore of 2.66 NWPE, PNW, SPPN, SPPS, and MRO have a Cost Adjustment Factor for Cost Class 4 Onshore of 2.67

Many factors figure in whether the model determines that adding wind capacity yields the greatest incremental improvement in the system-wide (leas t cost) solution available to the model at a particular point in the solution process. These factors include trade-offs between such items as the cost, capacity factor, reserve margin contribution, and dispatch capabilities and constraints on the new wind capacity relative to other choices. However, to perform its trade-off computations, the model requires the values described above.

As an illustrative example, Table 4-24 shows the calculations that would be performed to derive the potential electric generation, reserve margin contribution, and cost of new (potential) onshore capacity in wind class 7, cost class 2 in the CA-N model region in run year 2020.

25 

### Revising the Long Term Multipliers in NEMS: Quantifying the Incremental Transmission Costs Due to Wind Power, Report to EIA from Princeton Energy Resources International, LLC. May 2007.

### 4-40 Table 4-24 Example Calculations Of Wind Generation Potential, Reserve Margin Contribution, And Capital Cost For Onshore Wind In CA-N At Wind Class 7, Cost Class 2

Solar Generation Solar Resource Potential : No explicit constraint limit is placed on solar electric capacity in EPA Base Case v.4.10. However, since solar thermal is only feasible in areas with sufficient direct isolation, EPA Base Case v.4.10 includes the assumption that new solar thermal plants can only be built west of the Mississippi River. Solar photovoltaic is not limited to specific parts of the country.

Generation profiles : Like wind, solar is an intermittent renewal technology. Since it can only be dispatched when the sun shines, not on a strictly economic basis, it is represented in EPA Base Case v.4.10 with generation profiles which specify hourly generation patterns for typical winter and summer days in each eligible region. The generation profiles were prepared with data from AEO 2010, which provided separate winter and summer generation profiles for solar thermal and photovoltaic in each eligible IPM region. As an illustrative example, Appendix 4-2 shows the solar thermal and solar photovoltaic winter and summer generation profiles in model region AZNM.

Reserve margin contribution : The procedure described above for calculating the reserve margin contributions for wind generation was also used for solar generation. Table 4-25 presents the winter and summer average capacity factors (CFs) and reserve margin contributions by model Required Data

Table 4-17 Potential wind capacity ( C ) = 352 MW Appendix 4-1 Winter average generation ( G W 

### ) per available MW per hour = 559 kWh/MW Appendix 4-1 Summer average generation ( G S 

### ) per available MW per hour = 422 kWh/MW Hours in Winter ( H W 

### ) season (October – April) = 5,088 hours Hours in Summer ( H S 

### )season (May – September) = 3,672 hours

Table 4-20 Reserve Margin Contribution ( RM ) CA-N, Wind Class 7 = 24 percent

Table 4-16 Capital Cost ( Cap 2020 

### ) in vintage range for year 2020 = $1,912/kW Table 4-23 Capital Cost Adjustment Factor ( CAF ON,C2 

### ) for onshore cost class 2 = 1.2 Table 4-15 Regional Factor (RF) 1.058

Calculations



## GWh hours MWkWh MW hours MWkWh MW HGCHGC Potential Generation sS wW



## 546,1 3672/422 352 5088/559 352

= × × + × ×= × × + ××=



## MW MW CRM

84 352%24 on Contributi Margin Reserve

= ×= × =

dollars million MW CRF Cost Capital



## 548$ 352058.11.2 $1,912/MW CAF Cap C2ON, 2020



## = ××× = × × ×=

### 4-41 region for new solar thermal and photovoltaic units in EPA Base Case v.4.10. The region-specific summer and winter capacity factors included in this table are metrics that provide a shorthand depiction of the hourly specific generation profiles for each region. They are based on AEO 2010 data. The assumptions in EPA Base Case v.4.10 for capacity factors and reserve margin contributions for existing units are also based on AEO 2010.

Table 4-25 Solar Reserve Margin Contribution and Average Capacity Factor by Model Region Solar Thermal Solar Photovoltaic

Model Region Winter Average Capacity

Factor Summer Average Capacity

Factor Reserve Margin Contribution Winter Average Capacity

Factor Summer Average Capacity

Factor Reserve Margin Contribution

AZNM 30% 42% 43% 25% 28% 28% CA-N 32% 51% 53% 23% 28% 28% CA-S 32% 51% 53% 23% 28% 28% COMD 19% 23% 24% DSNY 17% 22% 22% ENTG 21% 23% 23% ERCT 26% 35% 36% 22% 24% 25% FRCC 23% 23% 23% GWAY 19% 23% 24%

LILC 17% 22% 22% MACE 18% 22% 23% MACS 18% 22% 23% MACW 18% 22% 23% MECS 17% 23% 23% MRO 18% 34% 36% 20% 23% 24% NENG 19% 22% 23% NWPE 23% 41% 18% 18% 25% 16%

NYC 17% 22% 22% PNW 23% 41% 18% 18% 25% 16% RFCO 17% 23% 23% RFCP 17% 23% 23% RMPA 30% 42% 43% 25% 28% 28% SNV 30% 42% 43% 25% 28% 28% SOU 21% 23% 23% SPPN 22% 35% 37% 22% 24% 25% SPPS 22% 35% 37% 22% 24% 25% TVA 21% 23% 23% TVAK 17% 23% 23% UPNY 17% 22% 22% VACA 21% 23% 23% VAPW 21% 23% 23% WUMS 19% 23% 24%

Geothermal Generation Geothermal Resource Potential : Six model regions in EPA Base Case v.4.10 have geothermal potential. The potential capacity in each of these r egions is shown in Table 4-26. The values are based on AEO 2010 data.

### 4-42 Table 4-26 Regional Assumptions on Potential Geothermal Electric Capacity IPM Model Region Capacity (MW)
AZNM 2,216 CA-N 662 CA-S 124 NWPE 4,555 PNW 1,336 RMPA 70
Grand Total 8,963
Note: This data is a summary of the geothermal data used in EPA Base Case v.4.10

Cost Calculation : EPA Base Case v.4.10 does not contain a single capital cost, but multiple geographically-dependent capital costs for geothermal generation. The assumptions for geothermal were developed using AEO 2010 cost and performance estimates for 88 known sites. Both dual flash and binary cycle technologies 26 

### were represented. In EPA Base Case v.4.10 the 88 sites were collapsed into 26 different options based on geographic location and cost and performance characteristics of geothermal sites in each of the six eligible IPM regions where geothermal generation opportunities exist. Table 4-27 shows the potential geothermal capacity and cost characteristics for applicable model regions.

Table 4-27 Potential Geothermal Capacity and Cost Characteristics by Model Region
IPM Region Capacity (MW) Capital Cost (2007$) FO&M (2007$/kW-yr) 1,404 4,002 185.1

196 4,675 206.8 316 5,650 201.1 294 7,744 192.2 AZNM

6 9,199 218.8
575 1,624 185.1 7 2,873 185.1 CA-N
80 4,214 206.2
71 4,957 185.1 48 5,679 185.1 CA-S
5 6,817 185.1

26 

### In dual flash systems, high temperature water (above 400 ° F) is sprayed into a tank held at a much lower pressure than the fluid. This causes so me of the fluid to “flash,” i.e., rapidly vaporize to steam. The steam is used to drive a turbine, which, in turn, drives a generator. In the binary cycle technology, moderate temperature water (less than 400 ° F) vaporizes a secondary, working fluid which drives a turbine and generator. Due to its use of more plentiful, lower temperature geothermal fluids, these systems tend to be most cost effective and are expected to be the most prevalent future geothermal technology.

### 4-43 IPM Region Capacity (MW) Capital Cost (2007$) FO&M (2007$/kW-yr)
9 6,255 164.1 24 8,337 164.1 103 9,776 168.6 1,165 11,465 179.6 3,001 20,674 181.7

137 4,523 185.1

67 5,380 183.0 12 3,428 218.8 28 4,594 185.1 NWPE

9 8,210 185.1 268 3,890 151.5

36 4,782 151.5 420 5,211 156.3 PNW

612 5,625 190.3 RMPA 70 5,820 185.1

Landfill Gas Electricity Generation Landfill Gas Resource Potential : Estimates of potential electric capacity from landfill gas are based on the AEO 2010 inventory. EPA Base Case v.4.10 represents 3 categories of potential landfill gas units; “hi”, “low”, and “very low”. The categories refer to the amount and rate of methane production from the existing landfill site. Table 4-28 summarizes potential electric capacity from landfill gas used in EPA Base Case v.4.10.

There are several things to note about Table 4-28. Since the potential electric capacity from new landfill gas units is based on AEO 2009, the limits listed in Table 4-28 apply to the NEMS (National Energy Modeling System) regions indicated in column 1. In EPA Base Case v.4.10 the sum of the new landfill gas electric capacity in the corresponding IPM regions shown in column 2 cannot exceed the limits shown in columns 3-5. As noted earlier, the capacity limits for three categories of potential landfill gas units are distinguished in this table based on the rate of methane production at three categories of landfill sites: LGHI = high rate of landfill gas production, LGLo = low rate of landfill gas production, and LGLVo = very low rate of landfill gas production. The values shown in Table 4-28 represent an upper bound on the amount of new landfill capacity that can be added in each of the indicated model regions for each of the three landfill categories.

The cost and performance assumptions for adding new capacity in each of the three landfill categories are presented in Table 4-16.

Table 4-28 Regional Assumptions on Potential Electric Capacity from New Landfill Gas Units (MW) Class NEMS Region IPM Region LGHI LGLo LGLVo ECAR RFCO, MECS, RFCP, TVAK 72 30 539 ERCOT ERCT 12 26 316 MAAC MACE, MACS, MACW 93 22 311 MAIN WUMS, COMD, GWAY 83 92 495 MAPP MRO 43 22 150

NY DSNY, LILC, NYC, UPNY 54 27 142 NE NENG 62 6 51 FL FRCC 14 26 158

### 4-44 Class NEMS Region IPM Region LGHI LGLo LGLVo

STV SOU, TVA, ENG, VACA, VAPW 68 22 447 SPP SPPN, SPPS 5 - 185 NWP PNW, NWPE 27 58 185

RA AZNM, SNV, RMPA - - 91 CNV NA-N, CA-S 131 250 749 US 664 581 3,819



## 4.5 Nuclear Units 4.5.1 Existing Nuclear Units Population, Plant Location, and Unit Configuration : To provide maximum granularity in forecasting the behavior of existing nuclear units, all 105 nuclear units in EPA Base Case v.4.10 are represented by separate model plants. As noted in Table 4-7 the 105 nuclear units include 104 currently operating units plus Watts Bar Nuclear Plant, Unit 2, which is scheduled to come online in 2014. All are listed in Appendix 4-3. The population characteristics, plant location, and unit configuration data in NEEDS, v.4.10 were obtained primarily from EIA Form 860 and AEO 2010.

Capacity : Nuclear units are baseload power plants with high fixed (capital and fixed O&M) costs and low variable (fuel and variable O&M) costs. Due to their low VOM and fuel costs, nuclear units are run to the maximum extent possible, i.e., up to their availability. Consequently, a nuclear unit's capacity factor is equivalent to its availability. Thus, EPA Base Case v.4.10 uses capacity factor assumptions to define the upper bound on gener ation from nuclear units. Nuclear capacity factor assumptions in EPA Base Case v.4.10 are based on an Annual Energy Outlook projection algorithm. The nuclear capacity factor pr ojection algorithm is described below:

• For each reactor, the capacity factor over time is dependent on the age of the reactor.

• Capacity factors increase initially due to learning, and decrease in the later years due to aging.

• For individual reactors, vintage classi fications (older and newer) are used.

• For the older vintage (start before 1982) nuclear power plants, the performance peaks at 25 years:

o Before 25 years: Performance increases by 0.5 percentage point per year;

o 25-60 years: Performance remains flat; and

• • For the newer vintage (start in or after 1982) nuclear power plants, the performance peaks at 30 years:

o Before 30 years: Performance increases by 0.7 percentage points per year;

o 30-60 years: Performance remains flat; and

• The maximum capacity factor is assumed to be 90 percent. That is, any given reactor is not allowed to grow to a capacity factor higher than 90 percent. However, if a unit began with a capacity factor above 90 percent, it is allowed to retain that capacity factor. Given historical capacity factors above 90 percent, the projected capacity factors range from 89 percent to 93 percent.

Cost and Performance : Unlike non-nuclear existing conventional units discussed in section 4.2.7, emission rates are not needed for nuclear units, since there are no SO 2 

### , NO X 

### , CO 2 

### , or mercury emissions from nuclear units.

As with other generating resources, EPA Base Case v.4.10 uses variable operation and maintenance (VOM) costs and fixed operation and maintenance (FOM) costs to characterize the cost of operating nuclear units. The heat rate, FOM, and VOM values from AEO 2010, which were used to characterize the cost and performance of existing nuclear units in EPA Base Case v.4.10 are shown in Appendix 4-03.

### 4-45 EPA Base Case v.4.10 also uses the nuclear capacity uprates from AEO 2010. These are shown in Table 4-29.

4.5.2 Potential Nuclear Units The cost and performance assumptions for nuclear pot ential units that the model has the option to build in EPA Base Case v.4.10 are shown in Table 4-13 above. The cost assumptions were updated as part of the comparative analysis performed by EPA’s power sector engineering staff. That update is described above in section 4.4.1.

Table 4-29 Nuclear Upratings (MW) as Incor porated in EPA Base Case v.4.10 from AEO 2010
Name Plant ID Unit ID Year Change in MWs Arkansas Nuclear One 8055 1 2016 50.0 Arkansas Nuclear One 8055 2 2016 59.0 Brunswick 6014 1 2014 56.3 Brunswick 6014 2 2014 56.2 Byron Generating Station 6023 1 2019 116.4 Byron Generating Station 6023 2 2019 11.4 Catawba 6036 1 2016 67.7 Catawba 6036 2 2016 67.7 Duane Arnold 1060 1 2015 34.8 Fermi 1729 2 2016 67.0 Grand Gulf 6072 1 2015 76.7 Harris 6015 1 2017 54.0 Joseph M Farley 6001 1 2017 51.0 Joseph M Farley 6001 2 2017 52.0 Limerick 6105 1 2018 113.4 Limerick 6105 2 2018 113.4 McGuire 6038 1 2014 110.0 McGuire 6038 2 2014 110.0 Oconee 3265 1 2017 51.0 Oconee 3265 2 2017 51.0 Oconee 3265 3 2017 51.0 Peach Bottom 3166 2 2014 66.7 Peach Bottom 3166 3 2014 66.7 Perry 6020 1 2016 74.0 PSEG Salem Generation 2410 1 2015 70.4 PSEG Salem Generation 2410 2 2015 67.8 Quad Cities Generation 880 1 2013 52.0 Quad Cities Generation 880 2 2013 52.0 Sequoyah 6152 1 2013 69.0 Sequoyah 6152 2 2013 68.0 South Texas Project 6251 1 2013 76.8 South Texas Project 6251 2 2013 76.8 Surry 3806 1 2015 47.9 Surry 3806 2 2015 47.9 V C Summer 6127 1 2015 58.0 Waterford 3 4270 3 2016 69.1 Wolf Creek Generation 210 1 2017 70.0
Watts Bar Nuclear Plant 7722 2 2014 1,180

### Appendix 4-1.1 

## Appendix 4-1 Representative Wind Gene ration Profiles in EPA Base Case v.4.10 Illustrative Hourly Wind Generation Profile (kWh of Generation per MW of Electricity)

Wind Class Wind Class Winter

Hour 3 4 5 6 7 Summer Hour 3 4 5 6 7 01 268 361 492 504 635 01 380 410 456 591 555 02 268 359 488 503 633 02 374 402 444 583 546 03 269 355 483 498 629 03 366 391 422 566 523 04 263 345 464 483 613 04 350 368 387 535 479 05 253 326 430 454 580 05 326 334 337 489 413 06 243 310 396 424 544 06 299 295 286 433 344 07 236 297 374 402 518 07 282 261 250 389 295 08 233 291 372 395 514 08 292 256 248 384 293 09 230 282 371 391 511 09 320 270 269 408 316 10 227 276 372 389 510 10 348 296 298 442 348 11 225 276 374 391 510 11 368 320 320 466 368 12 226 280 376 394 508 12 381 340 331 480 373 13 226 283 376 396 505 13 388 354 334 484 370 14 228 287 381 400 507 14 393 365 341 489 372 15 227 288 385 402 511 15 393 372 350 496 383 16 228 289 390 404 517 16 390 371 361 501 398 17 231 289 394 406 524 17 384 358 369 501 414 18 240 296 405 415 538 18 377 340 371 496 421 19 250 309 423 433 558 19 375 331 378 496 431 20 259 325 444 453 580 20 374 335 386 500 440 21 267 339 464 473 601 21 378 354 402 518 464 22 271 350 479 488 618 22 382 381 424 546 498 23 271 357 488 498 629 23 384 401 444 572 529 24 268 360 491 502 633 24 382 409 455 586 547
Winter Average 246 314 421 437 559 Summer Average 362 346 361 498 422

Notes: Based on Onshore Wind in Model Region CA-N. This is an example of the wind data used in EPA Base Case v.4.10

### Appendix 4-2.1 

## Appendix 4-2 Representative Solar Gene ration Profiles in EPA Base v.4.10 Illustrative Hourly Solar Generation Profile (kWh of Generation per MW of Electricity)

Winter

Hour Solar Thermal Solar Photovoltaic Summer Hour Solar Thermal Solar Photovoltaic

01 3 0 01 9 0 02 3 0 02 9 0 03 3 0 03 9 0 04 3 0 04 9 0 05 3 0 05 9 0 06 181 29 06 284 13 07 181 29 07 284 13 08 601 660 08 720 610 09 601 660 09 720 610 10 601 660 10 720 610 11 601 660 11 720 610 12 601 660 12 720 610 13 601 660 13 720 610 14 601 660 14 720 610 15 601 660 15 720 610 16 601 660 16 720 610 17 601 660 17 720 610 18 601 660 18 720 610 19 181 29 19 284 13 20 181 29 20 284 13 21 181 29 21 284 13 22 181 29 22 284 13 23 181 29 23 284 13 24 181 29 24 284 13
Winter Average 336 312 Summer Average 426 284

Notes: Based on model region AZNM. This is an example of the solar data used in EPA Base Case v.4.10Appendix 4-3.1Appendix 4-3 Characteristicsof Existing Nuclear UnitsRegionStatePlant NameORISCode_UnitIdOn-LineYearCapacity (MW)Heat Rate(Btu/kWh)FOM(2007$/kW-yr)VOM(2007mills/kWh)Palo Verde6008_119861,31110,427117.20.50Palo Verde6008_219861,35210,427117.20.55AZNMArizonaPalo Verde6008_319881,28310,427117.20.52Diablo Canyon6099_119851,12210,427132.10.71CA-NCaliforniaDiablo Canyon6099_219861,11810,427132.10.70San Onofre360_219831,07010,427208.61.00CA-SCaliforniaSan Onofre360_319841,08010,427208.61.05Braidwood Generation Station6022_119881,17810,427129.70.57Braidwood Generation Station6022_219881,15210,427129.70.59Byron Generating Station6023_119851,16410,427126.40.57Byron Generating Station6023_219871,13610,427126.40.56LaSalle Generating Station6026_119841,11810,427157.90.75LaSalle Generating Station6026_219841,12010,427157.90.76Dresden Generating Station869_2197086710,427203.60.89Dresden Generating Station869_3197186710,427203.60.95Quad Cities Generating Station880_1197286710,427177.30.83COMDIllinoisQuad Cities Generating Station880_2197286710,427177.30.83Indian Point 22497_219731,02010,427227.31.40DSNYNew YorkIndian Point 38907_319761,02510,427199.20.96Arkansas Nuclear One8055_1197483610,427152.60.63ArkansasArkansas Nuclear One8055_2198098810,427152.60.65Waterford 34270_319851,15210,427162.70.61LouisianaRiver Bend6462_1198696710,427193.71.03ENTGMississippiGrand Gulf6072_119851,26610,427134.80.54Appendix 4-3.2RegionStatePlant NameORISCode_UnitIdOn-LineYearCapacity (MW)Heat Rate(Btu/kWh)FOM(2007$/kW-yr)VOM(2007mills/kWh)Comanche Peak6145_119901,20210,240120.00.61Comanche Peak6145_219931,20210,317120.00.63South Texas Project6251_119881,28010,427121.60.59ERCTTexasSouth Texas Project6251_219891,28010,427121.60.58St Lucie6045_1197683910,427142.30.64St Lucie6045_2198371410,427142.30.71Turkey Point621_3197269310,427146.80.67Turkey Point621_4197369310,427146.80.66FRCCFloridaCrystal River628_3197785110,427181.50.81IllinoisClinton Power Station204_119871,04310,427200.10.97GWAYMissouriCallaway6153_119841,19010,427139.70.74PSEG Salem Generating Station2410_119771,17410,427159.90.77PSEG Salem Generating Station2410_219811,13010,427159.90.79Oyster Creek2388_1196961910,427255.11.17New JerseyPSEG Hope Creek Generating Station6118_119861,19610,427147.50.84Limerick6105_119861,13410,427127.60.55MACEPennsylvaniaLimerick6105_219901,13410,427127.60.54Calvert Cliffs Nuclear Power Plant6011_1197588510,427155.20.75MACSMarylandCalvert Cliffs Nuclear Power Plant6011_2197787410,427155.20.72Peach Bottom3166_219741,11210,427172.70.81Peach Bottom3166_319741,11210,427172.70.80PPL Susquehanna6103_119831,28310,427172.10.89PPL Susquehanna6103_219851,28810,427172.10.88MACWPennsylvaniaThree Mile Island8011_1197478610,427170.90.82Fermi1729_219881,12210,427155.90.80MECSMichiganPalisades1715_1197277810,427197.21.14Appendix 4-3.3RegionStatePlant NameORISCode_UnitIdOn-LineYearCapacity (MW)Heat Rate(Btu/kWh)FOM(2007$/kW-yr)VOM(2007mills/kWh)IowaDuane Arnold1060_1197558110,427208.41.11Monticello1922_1197164610,427187.81.02Prairie Island1925_1197455110,427162.30.82MinnesotaPrairie Island1925_2197454510,427162.30.83Fort Calhoun2289_1197347810,427219.41.19MRONebraskaCooper8036_1197476710,427223.61.29Millstone566_2197588210,427205.41.10ConnecticutMillstone566_319861,23610,427192.21.02MassachusettsPilgrim Nuclear Power Station1590_1197268510,427241.61.06NewHampshireSeabrook6115_119901,24410,427178.70.94NENGVermontVermont Yankee3751_1197262010,427203.81.06PNWWashingtonColumbia Generating Station371_219841,13110,427152.10.73Donald C Cook6000_119751,02910,942174.91.05MichiganDonald C Cook6000_219781,07710,848174.91.08Perry6020_119871,23111,000160.10.80RFCOOhioDavis Besse6149_1197788711,000158.10.90Beaver Valley6040_1197688710,962190.30.96RFCPPennsylvaniaBeaver Valley6040_2198788710,946190.30.90Joseph M Farley6001_1197785111,794138.50.71AlabamaJoseph M Farley6001_2198186011,650138.50.68Edwin I Hatch6051_1197587610,427146.90.77Edwin I Hatch6051_2197988310,427146.90.78Vogtle649_119871,17210,427145.60.66SOUGeorgiaVogtle649_219891,16910,427145.60.65SPPNKansasWolf Creek Generating Station210_119851,16610,427137.70.71Appendix 4-3.4RegionStatePlant NameORISCode_UnitIdOn-LineYearCapacity (MW)Heat Rate(Btu/kWh)FOM(2007$/kW-yr)VOM(2007mills/kWh)Browns Ferry46_119741,22510,55099.90.40Browns Ferry46_219751,28610,21599.90.42AlabamaBrowns Ferry46_319771,33710,21599.90.40Sequoyah6152_119811,15010,123115.40.48Sequoyah6152_219821,12710,202115.40.45Watts Bar Nuclear Plant7722_119961,12110,266139.10.64TVATennesseeWatts Bar Nuclear Plant7722_220141,18010,26692.40.49Nine Mile Point Nuclear Station2589_1196962110,427193.50.98Nine Mile Point Nuclear Station2589_219691,31110,427188.90.97James A Fitzpatrick6110_1197685210,427203.00.90UPNYNew YorkR. E. Ginna Nuclear Power Plant6122_1197049810,427205.80.92Brunswick6014_1197793810,318110.60.46Brunswick6014_2197593710,397110.60.46Harris6015_1198790010,982131.30.60McGuire6038_119811,10010,427119.60.48North CarolinaMcGuire6038_219841,10010,427119.60.50H B Robinson3251_2197171010,697119.80.59Oconee3265_1197384610,427139.60.73Oconee3265_2197484610,427139.60.65Oconee3265_3197484610,427139.60.72Catawba6036_119851,12910,427134.00.64Catawba6036_219861,12910,427134.00.64VACASouth CarolinaV C Summer6127_1198496610,427143.50.80Surry3806_1197279910,427120.70.58Surry3806_2197379910,427120.70.57North Anna6168_1197894010,42798.70.47VAPWVirginiaNorth Anna6168_2198092510,42798.70.50Appendix 4-3.5RegionStatePlant NameORISCode_UnitIdOn-LineYearCapacity (MW)Heat Rate(Btu/kWh)FOM(2007$/kW-yr)VOM(2007mills/kWh)Point Beach4046_1197059910,427202.30.97Point Beach4046_2197260110,427202.31.00WUMSWisconsinKewaunee8024_1197455610,427151.50.85

### 5-1 

# 5 Emission Control Technologies EPA Base Case v.4.10 includes a major update of emission control technology assumptions. For this base case EPA contracted with engineering firm Sargent and Lundy to perform a complete bottom-up engineering reassessment of the cost and performance assumptions for sulfur dioxide (SO 2 

### ) and nitrogen oxides (NO X 

### ) emission controls. In addition to the work by Sargent and Lundy, Base Case v.4.10 includes two Activated Carbon Injections (ACI) options (Standard and Modified) for mercury (Hg) control 27 

### . Capture and storage options for carbon dioxide (CO 2 

### ) have also been added in the new base case.

These emission control options are listed in Table 5-1. They are available in EPA Base Case v.4.10 for meeting existing and potential federal, regi onal, and state emission limits. It is important to note that, besides the emission control opti ons shown in Table 5-1 and described in this chapter, EPA Base Case v.4.10 offers other compliance options for meeting emission limits. These include fuel switching, adjustments in the dispatching of electric generating units, and the option to retire a unit.

Table 5-1 Summary of Emission Control Technology Retrofit Options in EPA Base Case v.4.10 SO 2 

### Control Technology Options NO X 

### Control Technology Options Hg Control Technology Options CO 2 

### Control Technology Options Limestone Forced Oxidation (LSFO) Scrubber Selective Catalytic Reduction (SCR) System Standard ActivatedCarbon Injection (SPACACI) System CO 2 

### Capture and Sequestration

Lime Spray Dryer (LSD) Scrubber Selective NonCatalytic Reduction

(SNCR) System Modified Activated Carbon Injection (MPAC-ACI) System

Combustion Controls SO 2 

### and NO X 

### Control Technology Removal Cobenefits



## 5.1 Sulfur Dioxide Control Technologies Two commercially available Flue Gas Desulfurizat ion (FGD) technology options for removing the SO 2 

### produced by coal-fired power plants are offered in EPA Base Case v.4.10: Limestone Forced Oxidation (LSFO) — a wet FGD technology — and Lime Spray Dryer (LSD) — a semi-dry FGD technology which employs a spray dryer absorber (SDA). In wet FGD systems, the polluted gas stream is brought into contact with a liquid alkaline sorbent (typically limestone) by forcing it through a pool of the liquid slurry or by spraying it with the liquid. In dry FGD systems the polluted gas stream is brought into contact with the alkaline sorbent in a semi-dry state through use of a spray dryer. The removal efficiency for SDA drops steadily for coals whose SO 2 

### content exceeds 3lb SO 2 

### /MMBtu, so this technology is provided only to plants which have the option to burn coals with sulfur content no greater than 3 lbs SO 2 

### /MMBtu. In EPA Base Casev.4.10 when a unit retrofits with an LSD SO 2 

### scrubber, it loses the option of burning BG, BH, and LG coals due to their high sulfur content.

In EPA Base Case v.4.10 the LSFO and LSD SO 2 

### emission control technologies are available to existing "unscrubbed" units. They are also ava ilable to existing "scrubbed" units with reported removal efficiencies of less than fifty percent. Such units are considered to have an injection technology and classified as “unscrubbed” for modeling purposes in the NEEDS database of

27 

### The mercury emission controls options and assumptions in EPA Base Case v.4.10 do not reflect mercury control updates that are currently under way at EPA in support of the Utility MACT initiative and do not make use of data collected under EPA’s 2010 Information Collection Request (ICR).

### 5-2 existing units which is used in setting up the EPA base case. The scrubber retrofit costs for these units are the same as regular unscrubbed units retrofitting with a scrubber. Scrubber efficiencies for existing units were derived from data reported in EIA Form 767. In transferring this data for use in EPA Base Case v.4.10 the following changes were made. The maximum removal efficiency was set at 98% for wet scrubbers and 93% for dry scrubber units. Existing units reporting efficiencies above these levels in Form 767 were assigned the maximum removal efficiency in NEEDS v.4.10 indicated in the previous sentence.

As shown in Table 5-2, existing units that ar e selected to be retrofitted by the model with scrubbers are given the maximum removal efficienc ies of 98% for LSFO and 93% for LSD. The procedures used to derive the cost of each scrubber type are discussed in detail in the following sections.

Table 5-2 Summary of Retrofit SO 2 

### Emission Control Performance Assumptions Performance Assumptions Limestone Forced Oxidation (LSFO) Lime Spray Dryer (LSD)

Percent Removal 98% with a floor of 0.06 lbs/MMBtu 93% with a floor of 0.065 lbs/MMBtu
Capacity Penalty Heat Rate Penalty Cost (2007$) Calculated based on characteristics of the unit: See Table 5-4 for examples Calculated based on characteristics of the unit: See Table 5-4 for examples Applicability Units ≥ 25 MW Units ≥ 25 MW
Sulfur Content Applicability Coals ≤ 3 lbs SO 2 

### /MMBtu

Applicable Coal Types BA, BB, BD, BE, BG, BH, SA, SB, SD, LD, LE, and LG BA, BB, BD, BE, SA, SB, SD, LD, and LE

Potential (new) coal-fired units built by the model are also assumed to be constructed with a scrubber achieving a removal efficiency of 98% fo r LSFO and 93% for LSD. In EPA Base Case v.4.10 the costs of potential new coal units include the cost of scrubbers.

5.1.1 Methodology for Obtaining SO 2 

### Controls Costs The Sargent and Lundy update of SO 2 

### and NO x 

### control costs is notable on several counts. First, it brought costs up to levels seen in the marketplace in 2009. Incorporating these costs into EPA’s base case carries an implicit assumption, not universally accepted, that the run up in costs seen over the preceding 5 years and largely attributed to international competition, is permanent and will not settle back to pre-2009 levels. Second, a revised methodology, based on Sargent and Lundy’s expert experience, was used to build up the capital, fixed and variable operating and maintenance components of cost. That methodology, which employed an engineering build up of each component of cost, is described here and in the following sections. Detailed example cost calculation spreadsheets for both SO 2 

### and NO x 

### controls are included in Appendices 5-1 and 5-2 respectively. The Sargent and Lundy reports in which these spreadsheets appeared can be downloaded via links to the Appendices 5-1A, 5-1B, 5-2A, and 5-2B links found at www.epa.gov/airmarkets/progsregs/epaipm/BaseCasev410.html

.
Capital Costs : In building up capital costs three separate cost modules were included for LSD and four for LSFO: absorber island, reagent preparation, waste handling (LSFO only), and everything else (also called “balance of plant”) with the latter constituting the largest cost module, consisting of fans, new wet chimney, piping, ductwork, minor waste water treatment, and other costs required for treatment. For each of the four modul es the cost of foundations, buildings, electrical equipment, installation, minor, physical and chemical wastewater treatment, and average retrofit difficulty were taken into account.

### 5-3 The governing cost variables for each module are indicated in Table 5-3. The major variables affecting capital cost are unit size and the SO 2 

### content of the fuel with the latter having the greatest impact on the reagent and waste handling facilities. In addition, heat rate affects the amount of flue gas produced and consequently the size of each of the modules. The quantity of flue gas is also a function of coal rank since different coals have different typical heating values.

Table 5-3 Capital Cost Modules and Their Governing Variables for SO 2 

### and NO x 

### Emission Controls

Module Retrofit Difficulty (1 = average) Coal Rank Factor (Bi t 

### = 1, PRB = 1.05, Lignite = 1.07) Heat Rate (Btu/kWh) SO 2 

### Rate (lb/MMBtu) NO x 

### Rate (lb/MMBtu) 5 

### Unit Size (MW)

SO 2 

### Emission Controls – Wet FGD and SDA FGD

Absorber Island X X X X X

Reagent Preparation X X X X

Waste Handling X X X X

Balance of Plant 1 

### X X X X

NO x 

### Emission Controls – SCR and SNCR

SCR/SNCR Island 2 

### X X X X 3 

### X

Reagent Preparation 3 

### X

Air Heater Modification 4 

### X X X X X

Balance of Plant 5 

### – SCR X X X X

Balance of Plant 1



### – SNCR X X

Notes:
1 

### “Balance of plant” costs include such cost items as ID and booster fans, new wet chimneys, piping, ductwork, minor waste water treatment, auxiliary power modifications, and other electrical and site upgrades.
2 

### The SCR island module includes the cost of inlet ductwork, reactor, and bypass. The SNCR island module includes cost of injectors, blower s, distributed control system (DCS), and reagent system.
3 

### Only applies to SCR.
4 

### On generating units that burn bituminous coal whose SO 2 

### and content exceeds 3 lbs/MMBtu, air heater modifications used to control SO 3 

### are needed in conjunction with the operation of SCR and SNCR.
5 

### For SCR, the NO x 

### rate is frequently expressed through the calculated NO x 

### removal efficiency.

### 5-4 Once the key variables that figure in the cost of the four modules are identified, they are used to derive costs for each base module in equations developed by Sargent and Lundy based on their experience with multiple engineering projects. The base module costs are summed to obtain total bare module costs. This total is increased by 30% to account for additional engineering and construction fees. The resulting value is the capital, engineering, and construction cost (CECC) subtotal. To obtain the total project cost (TPC), the CECC subtotal is increased by 5% to account for owner’s home office costs, i.e., owner’s engineering, management, and procurement costs. The resulting sum is then increased by another 10% to build in an Allowance for Funds used During Construction (AFUDC) over the 3-year engi neering and construction cycle. The resulting value, expressed in $/kW, is the capital cost factor that is used in EPA Base Case v.4.10.

Variable Operating and Maintenance Costs (VOM) : These are the costs incurred in running the emission control device. They are proportional to the electrical energy produced and are expressed in units of $ per MWh. For FGD, Sargent and Lundy identified four components of VOM: (a) costs for reagent usage, (b) costs for waste generation, (c) make up water costs, and (d) cost of additional power required to run the c ontrol (often called the “parasitic load”). For a given coal rank and a pre-specified SO 2 

### removal efficiency, each of these components of VOM cost is a function of the generating unit’s heat rate (Btu/kWh) and the sulfur content (lb SO 2 

### /MMBtu) of the coal (also referred to as the SO 2 

### feed rate). For purposes of modeling, the total VOM includes the first three of these component costs. The last component – cost of additional power – is factored into IPM, not in the VOM value, but through a capacity and heat rate penalty as described in the next paragraph. Due to the differences in the removal processes, the per MWh cost for waste handling, makeup water, and auxiliary power tend to be higher for LSFO while reagent usage cost and total VOM (excluding parasitic load) are higher for LSD.

Capacity and Heat Rate Penalty : The amount of electrical power required to operate the FGD device is represented through a reduction in the amount of electricity that is available for sale to the grid. For example, if 1.6% of the unit’ s electrical generation is needed to operate the scrubber, the generating unit’s capacity is reduced by 1.6%. This is the “capacity penalty.” At the same time, to capture the total fuel used in generation both for sale to the grid and for internal load (i.e., for operating the FGD device), the unit’s heat rate is scaled up such that a comparable reduction (1.6% in the previous example) in the new higher heat rate yields the original heat rate 28 

### . The factor used to scale up the original heat rate is called “heat rate penalty.” It is a modeling procedure only and does not represent an increase in the unit’s actual heat rate (i.e., a decrease in the unit’s generation efficiency). Unlike previous base cases, which assumed a generic heat rate and capacity penalties for all installations, in EPA Base Case v.4.10 specific LSFO and LSD heat rate and capacity penalties are calculated for each installation based on equations developed by Sargent and Lundy that take into account the rank of coal burned, its SO 2 

### rate, and the heat rate of the model plant.

Fixed Operating and Maintenance Costs (FOM) : These are the annual costs of maintaining a unit. They represent expenses incurred regardless of the extent to which the emission control system is run. They are expressed in units of $ per kW per year. In calculating FOM Sargent and Lundy took into account labor and materials costs associated with operations, maintenance, and administrative functions. The following assumptions were made:

28 

### Mathematically, the relationship of the heat rate and capacity penalties (both expressed as positive percentage values) can be represented as follows:



### 1001

100 Penalty Capacity 1 1 Penalty RateHeat × ⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜

⎝ ⎛

− ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

− =

### 5-5 • FOM for operations is based on the number of operators needed which is a function of the size (i.e., MW capacity) of the generating uni t and the type of FGD control. For LSFO 12 additional operators were assumed to be required for a 500 MW or smaller installation and 16 for a unit larger than 500 MW. For LSD 8 additional operators were assumed to be needed.

• FOM for maintenance is a direct function of the FGD capital cost

• FOM for administration is a function of the FOM for operations and maintenance.

Table 5-4 presents the capital, VOM, and FOM costs as well as the capacity and heat rate penalty for the two SO 2 

### emission control technologies (LSFO and LSD) included in EPA Base Case v.4.10 for an illustrative set of generating units with a representative range of capacities and heat rates.5-6Table 5-4 Illustrative Scrubber Costs (2007$) for RepresentativeSizes and Heat Rates under the Assumptions in EPA Base Case v.4.10Capacity (MW)1003005007001000Scrubber TypeHeat Rate(Btu/kWh)CapacityPenalty(%)HeatRatePenalty(%)VariableO&M(mills/kWh)CapitalCost($/kW)FixedO&M($/kW-yr)CapitalCost($/kW)FixedO&M($/kW-yr)CapitalCost($/kW)FixedO&M($/kW-yr)CapitalCost($/kW)Fixed O&M($/kW-yr)CapitalCost($/kW)FixedO&M($/kW-yr)LSFOMinimum Cutoff:≥25 MW9,000-1.51.531.6674722.554710.54737.84307.23885.9Maximum Cutoff:None10,000-1.671.71.8478322.857310.84968.04517.44076.1Assuming 3lb/MMBtu SO2ContentBituminous Coal11,000-1.841.872.0381723.259811.05178.24707.64256.3LSDMinimum Cutoff:≥25 MW9,000-1.181.22.1364116.44698.14066.13855.33854.9Maximum Cutoff:None10,000-1.321.332.3667016.74918.34246.34035.54035.1Assuming 2lb/MMBtu SO2ContentBituminous Coal11,000-1.451.472.6069817.05118.54426.54205.74205.2

### 5-7 

## 5.2 Nitrogen Oxides Control Technology The EPA Base Case v.4.10 includes two categories of NO x 

### reduction technologies: combustion and post-combustion controls. Combustion controls reduce NO x 

### emissions during the combustionprocess by regulating flame characteristics such as temperature and fuel-air mixing. Postcombustion controls operate downstream of the combustion process and remove NO x 

### emissions from the flue gas. All the specific combustion and post-combustion technologies included in EPA Base Case v.4.10 are commercially available and currently in use in numerous power plants.

5.2.1 Combustion Controls The EPA Base Case v.4.10 representation of combustion controls uses equations that are tailored to the boiler type, coal type, and combustion controls already in place and allow appropriate additional combustion controls to be exogenously applied to generating units based on the NO x 

### emission limits they face. Characterizations of the emission reductions provided by combustion controls are presented in Table 3-1.3 in Appendix 3-1. The EPA Base Case v.4.10 cost assumptions for NO x 

### Combustion Controls are summarized in Table 5-5. Table 5-6 provides a mapping of existing coal unit configurations and incremental combustion controls applied in EPA Base Case v.4.10 to achieve state-of-the-art combustion control configuration.

Table 5-5 Cost (2007$) of NO x 

### Combustion Controls for Coal Boilers (300 MW Size)

Boiler Type Technology Capital ($/kW) Fixed O&M($/kWyr) Variable O&M (mills/kWh)

Low NO x 

### Burner without Overfire Air (LNB without OFA) 45 0.3 0.07Dry Bottom WallFired Low NO x 

### Burner with Overfire Air (LNB with OFA) 61 0.4 0.09

Low NO x 

### Coal-and-Air Nozzles with Close-Coupled Overfire Air (LNC1) 24 0.2 0.00

Low NO x 

### Coal-and-Air Nozzles with Separated Overfire Air (LNC2) 33 0.2 0.03 Tangentially- Fired Low NO x 

### Coal-and-Air Nozzles with Close-Coupled and Separated Overfire Air (LNC3) 38 0.3 0.03

Vertically-Fired NO x 

### Combustion Control 29 0.2 0.06 Scaling Factor The following scaling factor is used to obtain the capital and fixed operating and maintenance costs applicable to the capacity (in MW) of the unit taking on combustion controls. No scaling factor is applied in calculating the variable operating and maintenance cost. LNB without OFA & LNB with OFA = ($ for X MW Unit) = ($ for 300 MW Unit) x (300/ X

) 0.359 

### LNC1, LNC2 and LNC3 = ($ for X MW Unit) = ($ for 300 MW Unit) x (300/ X

) 0.359 

### Vertically-Fired = ($ for X MW Unit) = ($ for 300 MW Unit) x (300/ X

) 0.553


### where ($ for 300 MW Unit) is the val ue obtained using the factors shown in the above table and X

is the capacity (in MW) of the unit taking on combustion controls.

### 5-8 Table 5-6 Incremental Combustion NO x 

### Controls in EPA Base Case v.4.10
Boiler Type Existing NO x 

### Combustion Control Incremental Combustional Control

LNB OFA Cell NGR LNB AND OFA Cyclone -- OFA Stoker/SPR -- OFA

-- LNC3
LA LNC3
LNB CONVERSION FROM LNC1 TO LNC3 LNB + OFA CONVERSION FROM LNC1 TO LNC3 LNC1 CONVERSION FROM LNC1 TO LNC3 LNC2 CONVERSION FROM LNC2 TO LNC3 OFA LNC1 Tangential

ROFA LNB

Vertical -- NO x 

### Combustion Control - Vertically Fired Units

-- LNB AND OFA
LA LNB AND OFA
LNB OFA
LNF OFA Wall

OFA LNB

5.2.2 Post-combustion Controls The EPA Base Case v.4.10 includes two post-combustion retrofit control technologies for existing coal units: Selective Catalytic Reduction (SCR) and Selective Non-Catalytic Reduction (SNCR). In EPA Base Case v.4.10 oil/gas steam units are eligible for SCR only. NO x 

### reduction in an SCR system takes place by injecting ammonia (NH 3 

### ) vapor into the flue gas stream where the NO x 

### is reduced to nitrogen (N2) and water H 2 

### O abetted by passing over a catalyst bed typically containing titanium, vanadium oxides, molybdenum, and/or tungsten. As its name implies, SNCR operates without a catalyst. In SNCR a nitrogenous reducing agent (reagent), typically ammonia or urea, is injected into, and mixed with, hot flue gas where it reacts with the NO x 

### in the gas stream reducing it to nitrogen gas and water vapor. Due to the presence of a catalyst, SCR can achieve greater NO x 

### reductions than SNCR. However, SCR costs are higher.

Table 5-7 summarizes the performance and applicability assumptions in EPA Base Case v.4.10 for each NO x 

### post-combustion control technology and provides a cross reference to information on cost assumptions.

Table 5-7 Summary of Retrofit NO x 

### Emission Control Performance Assumptions

Control Performance Assumptions Selective Catalytic Reduction (SCR) Selective Non-Catalytic Reduction

(SNCR) Unit Type Coal Oil/Gas Coal Pulverized Coal: 35% Percent Removal 90% down to 0.06 lb/MMBtu 80% Fluidized Bed: 50%
Size Applicability Units ≥ 25 MW Units ≥ 25 MW Units ≥ 25 MW

Costs (2007$) See Table 5-8 See Table 5-9 See Table 5-8

### 5-9 Potential (new) coal-fired, combined cycle, and IGCC units are modeled to be constructed with SCR systems and designed to have emission rates ranging between 0.01 and 0.06 lb NO x 

### /MMBtu. EPA Base Case v.4.10 cost assumptions for these units include the cost of SCR

5.2.3 Methodology for Obtaining SCR Costs for Coal Units As with the update of SO 2 

### control costs, Sargent and Lundy employed an engineering build-up ofthe capital, fixed and variable operating and maintenance components of cost to update postcombustion NO x 

### control costs. This section describes the approach used for SCR. The next section treats SNCR. Detailed example cost ca lculation spreadsheets for both technologies can be found in Appendix 5-2.

For cost calculation purposes the Sargent and Lundy methodology calculates plant specific NO x 

### removal efficiencies, i.e., the percent difference between the uncontrolled NO x 

### rate 29 

### for a model plant and the cost calculation floor NO x 

### rate corresponding to the predominant coal rank used at the plant ( 0.07 lb/MMBtu for bituminous and 0.05 lb/MMBtu for subbitumionus and lignite coals). For example, a plant that burns subbitumionus coal with an uncontrolled NO x 

### rate of 0.1667 lb/MMBtu, and a cost calculation floor NO x 

### rate of 0.05 lb/MMBtu would have a removal efficiency of 70%, i.e., (0.1667 – 0.05)/0.1667 = 0.1167/0.1667 = .70. The NO x 

### removal efficiency so obtained figures in the capital, VOM, and FOM components of SCR cost.

Capital Costs : In building up SCR capital costs, four separate cost modules were included: SCR island (e.g., inlet ductwork, reactor, and bypass), reagent preparation, air pre-heater modification, and balance of plan (e.g., ID or booster fans, pipi ng, and auxiliary power modification). Air preheater modification cost only applies for pl ants that burn bituminous coal whose SO 2 

### content is 3 lbs/MMBtu or greater, where SO 3 

### control is necessary. Otherwise, there is no air pre-heat cost. For each of the four modules the cost of foundati ons, buildings, electrical equipment, installation, and average retrofit difficulty were taken into account.

The governing cost variables for each module are indi cated in Table 5-3. All four capital cost modules, except reagent preparation, are functions of retrofit difficulty, coal rank, heat rate, and unit size. NO x 

### rate (expressed via the NO x 

### removal efficiency) affects the SCR and reagent preparation cost modules. Not shown in Table 5- 3, heat input (in Btu/hr) also impacts reagent preparation costs. As noted above, the SO 2 

### rate becomes a factor in SCR cost for plants that combust bituminous coal with 3 lbs SO 2 

### /MMBtu or greater, where air pre-heater modifications are needed for SO 3 

### control.

As with FGD capital costs, the base module costs for SCR are summed to obtain total bare module costs. This total is increased by 30% to account for additional engineering and construction fees. The resulting value is the capital, engineering, and construction cost (CECC) subtotal. To obtain the total project cost (TPC) the CECC subtotal is increased by 5% to account for owner’s home office costs, i.e., owner’s engineering, management, and procurement costs. Whereas the resulting sum is then increased by another 10% for FGD, for SCR it is increased by 6% to factor in an Allowance for Funds used During Construction (AFUDC) over the 2-year engineering and construction cycle (in contrast to the 3-year cycle assumed for FGD). The resulting value, expressed in $/MW, is the capital cost factor that is used in EPA Base Case v.4.10.

Variable Operating and Maintenance Costs (VOM) : For SCR Sargent and Lundy identified four components of VOM: (a) costs for the urea reagent, (b) costs of catalyst replacement and disposal, (c) cost of required steam, and (d) cost of additional power required to run the control

29 

### More precisely, the uncontrolled NO X 

### rate for a model plant in EPA Base Case v.4.10 is the capacity weighted average of the Mode 1 NO X 

### rates of the generating units comprising the model plant. The meaning of “Mode 1 NO X 

### rate” is discussed in section 3.9.2 and Appendix 3-1 (“NO X 

### Rate Development in EPA Base Case v.4.10).

### 5-10 (i.e., the “parasitic load”). As was the case for FGD, the last component – cost of additional power – is factored into IPM, not in the VOM value, but through a capacity and heat rate penalty as described earlier. Of the first three of these component costs, reagent cost and catalyst replacement are predominant while steam cost is much lower in magnitude. NO x 

### rates and heat rates are key determinates of reagent and steam costs, while NO x 

### rate (via removal efficiency), capacity factor, and coal rank are key drivers of catalyst replacement costs.

Capacity and Heat Rate Penalty : Unlike previous base cases, which assumed a generic heat rate and capacity penalties for all installations, in EPA Base Case v.4.10 specific SCR heat rate and capacity penalties are calculated for each installation based on equations developed by Sargent and Lundy that take into account the rank of coal burned, its SO 2 

### rate, and the heat rate of the model plant.

Fixed Operating and Maintenance Costs (FOM) : For SCR the following assumptions were made:

• FOM for operations is based on the assumption that one additional operator working half-time is required.

• FOM for maintenance is assumed to $193,585 (in 2007$) for generating units less than 500 MW and $290,377 (in 2007$) for generating units 500 MW or greater

• There was assumed to be no FOM for administration for SCR.

Table 5-8 presents the SCR and SNCR capital, VOM, and FOM costs and capacity and heat rate penalties for an illustrative set of coal generating units with a representative range of capacities, heat rates, and NO x 

### removal efficiencies. The illustrations include and identify plants that do and do not burn bituminous coal with 3 lbs SO 2 

### /MMBtu or greater.5-11Table 5-8 Illustrative Post Combustion NOXControls for Coal Plants Costs (2007$) for Representative Sizes and Heat Rates under theAssu Assumptions in EPA Base Case v.4.10Capacity (MW)1003005007001000Control TypeHeat Rate(Btu/kWh)CapacityPenalty(%)HeatRatePenalty(%)VariableO&M(mills/kWh)CapitalCost($/kW)FixedO&M($/kWyr)CapitalCost($/kW)FixedO&M($/kWyr)CapitalCost($/kW)FixedO&M($/kWyr)CapitalCost($/kW)FixedO&M($/kWyr)CapitalCost($/kW)FixedO&M($/kWyr)SCRMinimum Cutoff:≥25 MW9,000-0.540.541.152212.51770.81630.71550.51470.4Maximum Cutoff: NoneAssuming Bituminous Coal10,000-0.560.561.242402.51930.81780.71690.51620.4NOxrate: 0.5 lb/MMBtuSO2rate: 2.0 lb/MMBtu11,000-0.580.591.332582.52090.81930.71840.51760.4SNCR - Non-FBCMinimum Cutoff:≥25 MW9,0000.88451Maximum Cutoff: NoneAssuming Bituminous Coal10,0000.98471NOxrate: 0.5 lb/MMBtuSO2rate: 2.0 lb/MMBtu11,000-0.050.051.08481Size Not ModeledSNCR - Fluidized BedMinimum Cutoff:≥25 MW9,0000.88340.9180.4140.2110.290.1Maximum Cutoff: NoneAssuming Bituminous Coal10,0000.98350.9190.4140.2120.2100.1NOxrate: 0.5 lb/MMBtuSO2rate: 2.0 lb/MMBtu11,000-0.050.051.08360.9190.4140.2120.2100.1Note:If a coal plant burns bituminous coal with a SO2content above 3.0 lb/MMBtu then the capital costs will increasedue to the required air preheatermodification. For example, a100MW coal boiler with an SCR burning bituminous coal at a heat rate of 11,000 Btu/kWh and an SO2rate of 4.0 lb/MMBtu will have a capital cost of 296 $/kW, a 36 $/kW increase incapital costs from an identicalboiler burning coal with an SO2rate of 2.0 lb/MMBtu.

### 5-12 5.2.4 Methodology for Obtaining SCR Costs for Oil/Gas Steam units The cost calculations for SCR described in secti on 5.2.3 apply to coal units. For SCR on oil/gas steam units the cost calculation procedure employed in EPA’s most recent previous base case was used. However, capital costs were scaled up by 2.13 to account for increases in the component costs that had occurred since the assumptions were incorporated in that base case. All costs were expressed in constant 2007$ for consistency with the dollar year cost basis used throughout EPA Base Case v4.10. Table 5-9 shows that resulting capital, FOM, and VOM cost assumptions for SCR on oil/gas steam units. T he scaling factor for capital and fixed operating and maintenance costs, described in footnote 1, applies to all size units from 25 MW and up.

Table 5-9 Post-Combustion NO X 

### Controls for Oil/Gas Steam Units in EPA Base Case v.4.10
Post-Combustion Control Technology Capital ($/kW) Fixed O&M ($/kW-yr) Variable O&M (mills/kWh) Percent Removal
SCR 1 

### 75 1.08 0.12 80%
Notes: The “Coefficients” in the table above are multip lied by the terms below to determine costs. “MW” in the terms below is the unit’s capacity in megawatts. This data is used in the generation of EPA Base Case v.4.0

1 

### SCR Cost Equations: SCR Capital Cost and Fixed O&M: (200/MW) 0.35 

### The scaling factors shown above apply up to 500 MW. The cost obtained for a 500 MW unit applies for units larger than 500 MW. Example for 275 MW unit: SCR Capital Cost ($/kW) = 75 * (200/275) 0.35 

### ≈ 67 $/kW SCR FOM Cost ($/kW-yr) = 1.08 * (200/275) 0.35 

### ≈ 0.97 $/kW-yr SCR VOM Cost (mills/kWh) = 0.12 mills/kWh Reference: Cost Estimates for Selected Applications of NO X



### Control Technologies on Stationary Combustion Boilers , Bechtel Power Corporation for US EPA, June 1997

5.2.5 Methodology for Obtaining SNCR Costs In the Sargent and Lundy cost update for SNCR a generic NO X 

### removal efficiency of 25% is assumed. However, the capital, fixed and variable operating and maintenance costs of SNCR on circulating fluidized bed (CFB) uni ts are distinguished from the corresponding costs for other boiler types (e.g. cyclone, and wall fired).

Capital Costs : Due to the absence of a catalyst and, with it, the elimination of the need for more extensive reagent preparation, the Sargent and Lundy engineering build up of SNCR capital costs includes three rather than four separate cost m odules: SNCR (injectors, blowers, distributive control system, reagent system), air pre-heater modification, and balance of plan (e.g., ID or booster fans, piping, and auxiliary power modifica tion). For CFB units, the SNCR and balance of plan module costs are 75% of what they are on other boiler types. The air pre-heater modification cost module is the same as for SCR and there is no cost difference between CFB and other boiler types. As with SCR the air heater modification cost only applies for plants that burn bituminous coal whose SO 2 

### content is 3 lbs/MMBtu or greater, where SO 3 

### control is necessary. Otherwise, there is no air pre-heat cost. For each of the three modules the cost of foundations, buildings, electrical equipment, installation, and average retrofit difficulty were taken into account.

The governing cost variables for each module are indicated in Table 5-3. Unit size affects all three modules. Retrofit difficulty, coal rank, and heat rate impact the SNCR and air heater modification modules. The SO 2 

### rate impacts the air pre-heater modification module. NO X 

### rate

### 5-13 (expressed via the NO X 

### removal efficiency) and heat input (not shown in Table 5-3) affect the balance of plan module.

The base module costs for SNCR are summed to obtain total bare module costs. This total is increased by 30% to account for additional engineeri ng and construction fees. The resulting value is the capital, engineering, and construction cost (CECC) subtotal. To obtain the total project cost (TPC) the CECC subtotal is increased by 5% to account for owner’s home office costs, i.e., owner’s engineering, management, and procurement costs. Since SNCR projects are typically completed in less than a year, there is no Allowance for Funds used During Construction (AFUDC) in the SNCR capital cost factor that is used in EPA Base Case v.4.10.

Variable Operating and Maintenance Costs (VOM) : Sargent and Lundy identified two components of VOM for SNCR: (a) cost for the urea reagent and (b) the cost of dilution water. The magnitude of the reagent cost predominates the VOM with the cost of dilution water at times near zero. There is no capacity or heat rate penalty associated with SNCR since the only impact on power are compressed air or blower required for urea injection and the reagent supply system.

Capacity and Heat Rate Penalty : Unlike previous base cases, which assumed a generic heat rate and capacity penalties for all installations, in EPA Base Case v.4.10 specific SNCR heat rate and capacity penalties are calculated for each installation based on equations developed by Sargent and Lundy that take into account the rank of coal burned, its SO 2 

### rate, and the heat rate of the model plant.

Fixed Operating and Maintenance Costs (FOM) : The assumptions for FOM for operations and for administration are the same for SNCR as for SCR, i.e.,

• FOM for operations is based on the assumption that one additional operator working half-time is required.

• There was assumed to be no FOM for administration for SCR.

FOM for maintenance materials and labor was assumed to be a direct function of base module cost, specifically, 1.2% of those costs divided by the capacity of the generating unit expressed in kilowatts.

Detailed example cost calculation spreadsheets for SNCR can be found in Appendix 5-2.

5.2.6 SO 2 

### and NO x 

### Controls for Units with Capacities from 25 MW to 100 MW (25 M ≤ capacity < 100 MW) In EPA Base Case v.4.10 coal units with capacities between 25 MW and 100 MW are offered the same SO 2 

### and NO x 

### emission control options as larger units. However, for purposes of modeling, the costs of controls for these units are assumed to be equivalent to that of a 100 MW unit. This assumption is based on several considerations. First, to achieve economies of scale, several units in this size range are likely to be ducted to share a single common control, so the 100 MW cost equivalency assumption, though generic, would be technically plausible. Second, single units in this size range that are not grouped to achieve economies of scale are likely to have the option of hybrid multi-pollutant controls currently under development. 30 

### These hybrid controls achieve cost economies by combining SO 2 

### , NO X 

### and particulate controls into a single control unit. Singly, the costs of the individual control would be hi gher for units below 100 MW than for a 100 MW unit,

30 

### See, for example, the Greenidge Multi-Pollutant Control Project, which was part of the U.S. Department of Energy, National Energy Technology Lab’s Power Plant Improvement Initiative. A joint effort of CONSOL Energy Inc. AES Greenidge LLC, and Babcock Power Environmental, Inc., information on the project can be found at www.netl.doe.gov/technologies/coalpower/cctc/PPII/b ibliography/demonstration/environmental/bib _greenidge.html

.

### 5-14 but when combined in the Multi-Pollutant Technologies (MPTs) their costs would be roughly equivalent to the cost of individual controls on a 100 MW unit. While MPTs are not explicitly represented in EPA Base Case v.4.10, single units in the 25-100 MW range that take on combinations of SO 2 

### and NO X 

### controls in a model run can be thought of as being retrofit with an MPT.

Illustrative scrubber, SCR, and SNCR costs for 25-100 MW coal units with a range heats rates can be found by referring to the 100 MW “Capital Costs ($/kW)” and “Fixed O&M” columns in Table 5-4 and Table 5-8. The Variable O&M cost component, which applies to units regardless of size, can be found in the fifth column in these tables.



## 5.3 Biomass Co-firing Under most climate policies currently being discussed, biomass is treated as “carbon neutral,” i.e., a zero contributor of CO 2 

### to the atmosphere. The reasoning is that the CO 2 

### emitted in the combustion of biomass will be reabsorbed via photosynthesis in plants grown to replace the biomass that was combusted. Consequently, if a power plant can co-fire biomass and thereby replace a portion of fossil fuel, it reduces its CO 2 

### emissions by approximately the same proportion, although combustion efficiency losses may so mewhat diminish the proportion of CO 2 

### reduction. Roughly speaking, by co-firing enough biomass to produce 10% of a coal plant’s power output, a co-fired plant can realize close to an effective 10% reduction in CO 2 

### emitted.

Biomass co-firing is provided as a fuel choice for all coal-fired power plants in EPA Base Case v.4.10. However, logistics and boiler engineering considerations place limits on the extent of biomass that can be fired. The logistic cons iderations arise because it is only economic to transport biomass a limited distance from where it is grown. In addition, the extent of storage that can be devoted at a power plant to this relatively low density fuel is another limiting factor. Boiler efficiency and other engineering considerations, la rgely due to the relatively higher moisture content and lower heat content of biomass compared to fossil fuel, also plays a role in limiting the level of co-firing.

In EPA Base Case v.4.10 the limit on biomass co-firing is expressed as the percentage of the facility level power output that is produced from biomass. Based on analysis by EPA’s power sector engineering staff, a maximum of 10% of the facility level power output (not to exceed 50 MW) can be fired by biomass. In EPA Base Case v.4.10 “facility level” is defined as the set of generating units which share the same ORIS code 31 

### in NEEDS v.4.10.

The capital and FOM costs associated with biomass co-firing are summarized in Table 5-10. Developed by EPA’s power sector engineering staff 32 

### , they are on the same cost basis as the

31 

### The ORIS plant locator code is a unique identif ying number (originally assigned by the Office of Regulatory Information Systems from which the ac ronym derived). The ORIS code is given to power plants by EIA and remains unchanged under ownership changes.

32 

### Among the studies consulted in developing these costs were: (a) Briggs, J. and J. M. Adams, Biomass Combustion Options for Steam Generation , Presented at Power-Gen 97, Dallas, TX, December 9 – 11, 1997. (b) Grusha, J and S. Woldehanna, K. McCarthy, and G. Heinz, Long Term Results from the First US Low NOx Conversion of a Tangential Lignite Fired Unit , presented at 24th International Technical Conference on Coal & Fuel Systems, Clearwater, FL., March 8 – 11, 1999. (c) EPRI, Biomass Cofiring: Field Test Results: Summary of Results of the Bailly and Seward Demonstrations , Palo Alto, CA, supported by U.S. Depar tment of Energy Division of Energy Efficiency and Renewable Energy, Wa shington D.C.; U.S. Department of Energy Division Federal Energy Technology Center, Pittsburgh PA; Northern Indiana Public Service Company, Merrillville, IN; and GPU Generation, Inc., Johnstown, PA: 1999. TR-113903. (d) Laux S., J. Grusha, and D. Tillman, Co-firing of Biomass and Opportunity Fuels in Low NOx

### 5-15 costs shown in Table 4-16 which resulted from EPA’s comparative analysis of electricity sector costs as described in Chapter 4.

Table 5-10 Biomass Cofiring for Coal Plants
Size of Biomass Unit (MW) 5 10 15 20 25 30 35 40 45 50 Capital Cost (2007$/kW From Biomass) 488 411 371 345 327 312 300 290 282 275 Fixed O&M (2007$/kW-yr) 24.2 16.2 11.7 9.4 8.0 11.1 9.9 8.9 8.1 7.5



### The capital and FOM costs were implemented by ICF in EPA Base Case v.4.10 as a $/MMBtu biomass fuel cost adder. The procedure followed to implement this was first to represent the discrete costs shown in Table 5-10 as continuous exponential cost functions showing the FOM and capital costs for all size coal generating uni ts between 0 and 50 MW in size. Then, for every coal generating unit represented in EPA Base Case 4.10, the annual payment to capital for the biomass co-firing capability was derived by multiplying the total capital cost obtained from the capital cost exponential function by an 11% capital c harge rate. (This is the capital charge rate for environmental retrofits found in Table 8-1 and discussed in Chapter 8.) The resulting value was added to the annual FOM cost obtained from the FOM exponential function to obtain the total annual cost for the biomass co-firing for each generating unit.

Then, the annual amount of fuel (in MMBtus) required for each generating unit was derived by multiplying the size of a unit (in MW) by its heat rate (in Btu/kWh) by its capacity factor (in percent) by 8,760 hours (i.e., the number of hours in a year). Dividing the resulting value by 1000 yielded the annual fuel required by the generating unit in MMB tus. Dividing this number into the previously calculated total annual cost for biomass co-firing resulted in the cost of biomass co-firing per MMBtu of biomass combusted. This was represented in IPM as a fuel cost adder incurred when a coal units co-fires biomass.



## 5.4 Mercury Control Technologies As previously noted, the mercury emission controls options and assumptions in EPA Base Case v.4.10 do not reflect mercury control updates that are currently under way at EPA in support of the Utility MACT initiative and do not make use of data collected under EPA’s 2010 Information Collection Request (ICR). The following discussion is based on EPA’s earlier work on mercury controls.

For any power plant, mercury emissions depend on the mercury content of the fuel used, the combustion and physical characteristics of the unit, and the emission control technologies deployed. In the absence of emission policies that would require the installation of mercury emission controls, mercury emission reductions below the mercury content of the fuel are strictlydue to characteristics of the combustion process and incidental removal resulting from nonmercury control technologies, i.e., the SO 2 

### , NO X 

### , and particulate controls. While the base case itself does not include any federal mercury control policies, it does include some State mercury reduction requirements. IPM has the capability to model mercury controls that might be installed in response to such State mercury control policies. These same controls come into play in model runs that analyze possible federal mercury policies relative to the base case. The technology specifically designated for mercury control in such policy runs is Activated Carbon Injection (ACI) downstream of the combustion process.

Burners, PowerGen 2000 - Orlando, FL, www.fwc.com/publications/tech_papers/powgen/pdfs/clrw_bio.pdf

. Tillman, D. A., Cofiring Biomass for Greenhouse Gas Mitigation , presented at Power-Gen 99, New Orleans, LA, November 30 – December 1, 1999. (e) Tillman, D. A. and P. Hus, Blending Opportunity Fuels with Coal for Efficiency and Environmental Benefit , presented at 25th International Technical Conference on Coal Utilization & Fuel Systems, Clearwater, FL., March 6 – 9, 2000

### 5-16 The following discussion is divided into three parts. Sections 5.4.1 and 5.4.2 treat the two factors that figure into the unregulated mercury emissions resulting under EPA Base Case v.4.10. Section 5.4.1 discusses how mercury content of fuel is modeled in EPA Base Case v.4.10. Section 5.4.2 looks at the procedure used in t he base case to capture the mercury reductions resulting from different unit and (non-mercury) cont rol configurations. Section 5.4.3 explains the mercury emission control options that are available under EPA Base Case v.4.10. A major focus is on the cost and performance features of Activated Carbon Injection. Each section indicates the data sources and methodology used.

5.4.1 Mercury Content of Fuels Coal : The assumptions in EPA Base Case v.4.10 on the mercury content of coal (and the majority of emission modification factors discussed below in Section 5.4.2) are derived from EPA’s “Information Collection Request for Electric Utility Steam Generating Unit Mercury Emissions Information Collection Effort” (ICR). 33 

### A two-year effort initiated in 1998 and completed in 2000, the ICR had three main components: (1) identifying all coal-fired units owned and operated by publicly-owned utility companies, Federal power agencies, rural electric cooperatives, and investor-owned utility generating companies, (2) obtaining “accurate information on the amount of mercury contained in the as-fired coal used by eac h electric utility steam generating unit . . . with a capacity greater than 25 megawatts electric [MWe]), as well as accurate information on the total amount of coal burned by each such unit,” and (3) obtaining data by coal sampling and stack testing at selected units to characterize mercury reductions from representative unit configurations.

The ICR second component resulted in more than 40,000 data points indicating the coal type, sulfur content, mercury content and other characte ristics of coal burned at coal-fired utility units greater than 25 MW. To make this data usable in EPA Base Case v.4.10, these data points were first grouped by IPM coal types and IPM coal supply regions. (IPM coal types divide bituminous, sub-bituminous, and lignite coal into different gr ades based on sulfur content. See Table 5-11.) Next, a clustering analysis was performed on the data using the SAS statistical software package. Clustering analysis places objects into groups or cl usters, such that data in a given cluster tend to be similar to each other and dissimilar to data in other clusters. The clustering analysis involved two steps. First, the number of clusters of me rcury concentrations for each IPM coal type was determined based on the range of mercury and SO 2 

### concentrations for that coal type. Each coal type used one, two or three clusters. To the great est extent possible the total number of clusters for each coal type was limited to keep the model size and run time within feasible limits. Second, the clustering procedure was used to group each coal type within each IPM coal supply region into the previously determined number of clusters and show the resulting mercury concentration for each cluster. The average of each cluster is the mercury content of coal finally used in EPA Base Case v.4.10 for estimating mercury emissions. IPM input files retain the mapping between different coal type-supply region combinations and the mercury clusters. Table 5-11 below provides a summary by coal type of the number of clusters and their mercury concentrations.

33 

### Data from the ICR can be found at http://www.epa.gov/ttn/atw/combust/utiltox/mercury.html

.

### 5-17 Table 5-11 Mercury Clusters and Mercury Content of Coal by IPM Coal Types
Mercury Emission Factors by Coal Sulfur Grades (lbs/TBtu) Coal Type by Sulfur Grade

Cluster #1 Cluster #2 Cluster #3 Low Sulfur Easter Bituminous (BA) 3.19 4.37 -- Low Sulfur Western Bituminous (BB) 1.82 4.86 -- Low Medium Sulfur Bituminous (BD) 5.38 8.94 21.67 Medium Sulfur Bituminous (BE) 19.53 8.42 -- High Sulfur Bituminous (BG) 7.10 20.04 14.31 High Sulfur Bituminous (BH) 7.38 13.93 34.71 Low Sulfur Subbituminous (SA) 4.24 5.61 -- Low Sulfur Subbituminous (SB) 6.44 -- -- Low Medium Sulfur Subbituminous (SD) 4.43 -- -- Low Medium Sulfur Lignite (LD) 7.51 12.00 -- Medium Sulfur Lignite (LE) 13.55 7.81 -- High Sulfur Lignite (LG) 14.88 -- --

Oil, natural gas, and waste fuels : The EPA Base Case v.4.10 also includes assumptions on the mercury content for oil, gas and waste fuels, which were based on data derived from previous EPA analysis of mercury emissions from power plants. 34 

### Table 5-12 provides a summary of the assumptions on the mercury content for oil, gas and waste fuels included in EPA Base Case v.4.10.

Table 5-12 Assumptions on Mercury Concentr ation in Non-Coal Fuel in EPA Base Case v.4.10 Fuel Type Mercury Concentration (lbs/TBtu) Oil 0.48 Natural Gas 0.00 1


### Petroleum Coke 23.18 Biomass 0.57 Municipal Solid Waste 71.85

Geothermal

Resource 2.97 - 3.7

Note:

1 

### The values appearing in this table are rounded to two decimal places. The zero value shown for natural gas is based on an EPA study that found a mercury content of 0.00014 lbs/TBtu. Values for geothermal resources represent a range.

5.4.2 Mercury Emission Modification Factors Emission Modification Factors (EMFs) represent the mercury reductions attributable to the specific burner type and configuration of SO 2 

### , NO X 

### , and particulate matter control devices at an electric generating unit. An EMF is the ratio of outlet mercury concentration to inlet mercury concentration, and depends on the unit's burner type, particulate control device, post-combustion NO X 

### control and SO 2 

### scrubber control. In other words, the mercury reduction achieved (relative to

34 

### “Analysis of Emission Reduction Options for t he Electric Power Industry,” Office of Air and Radiation, US EPA, March 1999.

### 5-18 the inlet) during combustion and flue-gas treatment process is (1-EMF). The EMF varies by the type of coal (bituminous, sub-bituminous, and li gnite) used during the combustion process.

Deriving EMFs involves obtaining mercury inlet data by coal sampling and mercury emission data by stack testing at a representation set of coal units. As noted above, EPA's EMFs were initially based on 1999 mercury ICR emission test data. More recent testing conducted by the EPA, DOE, and industry participants 35 

### has provided a better understanding of mercury emissions from electric generating units and mercury capture in pollution control devices. Overall the 1999 ICR data revealed higher levels of mercury capture for bituminous coal-fired plants than for subbitumionus and lignite coal-fired plants, and significant captur e of ionic Hg in wet-FGD scrubbers. Additional mercury testing indicates that for bituminous coals, SCR systems have the ability to convert elemental Hg into ionic Hg and thus allow easier capture in a downstream wet-FGD scrubber. This improved understanding of mercury capture with SCRs was incorporated in EPA Base Case v.4.10 mercury EMFs for unit configurations with SCR and wet scrubbers.

Table 5-13 below provides a summary of EMFs used in EPA Base Case v.4.10. Table 5-14 provides definitions of acronyms for existing controls that appear in Table 5-13. Table 5-15 provides a key to the burner type designations appearing in Table 5-13.

5.4.3 Mercury Control Capabilities EPA Base Case v.4.10 offers two options for meeting mercury reduction requirements: (1) combinations of SO 2 

### , NO X 

### , and particulate controls which deliver mercury reductions as a cobenefit and (2) Activated Carbon Injection (ACI), a retrofit option specifically designed for mercury control. These two options are discussed below.

35 

### For a detailed summary of emissions test dat a see Control of Emissions from Coal-Fired Electric Utility Boilers: An Update, EPA/Office of Research and Development, February 2005. This report can be found at www.epa.gov/ttnatw01/utility/hgwhitepaperfinal.pdf

.

### 5-19 Table 5-13 Mercury Emission Modification Factors Used in EPA Base Case v.4.10

Burner Type Particulate Control Post Combustion

Control – NO X Post Combustion

Control -

SO 2 Bituminous EMF Subbitumionus EMF Lignite EMF

Cyclone Cold Side ESP SNCR None 0.64 0.97 0.93 Cyclone Cold Side ESP SNCR Wet FGD 0.46 0.84 0.58 Cyclone Cold Side ESP SNCR Dry FGD 0.64 0.65 0.93 Cyclone Cold Side ESP SCR None 0.64 0.97 0.93 Cyclone Cold Side ESP SCR Wet FGD 0.1 0.84 0.58 Cyclone Cold Side ESP SCR Dry FGD 0.64 0.65 0.93 Cyclone Cold Side ESP None Wet FGD 0.46 0.84 0.58 Cyclone Cold Side ESP None Dry FGD 0.64 0.65 0.93 Cyclone Cold Side ESP None None 0.64 0.97 0.93 Cyclone Cold Side ESP + FF SNCR Wet FGD 0.1 0.27 0.58 Cyclone Cold Side ESP + FF SCR None 0.11 0.27 1 Cyclone Cold Side ESP + FF SCR Wet FGD 0.1 0.27 0.58 Cyclone Cold Side ESP + FF SCR Dry FGD 0.4 0.95 0.91 Cyclone Cold Side ESP + FF None Wet FGD 0.1 0.27 0.58 Cyclone Cold Side ESP + FF None Dry FGD 0.4 0.95 0.91 Cyclone Cold Side ESP + FF None None 0.11 0.27 1 Cyclone Cold Side ESP + FGC SNCR None 0.64 0.97 0.93 Cyclone Cold Side ESP + FGC SNCR Wet FGD 0.46 0.84 0.58 Cyclone Cold Side ESP + FGC SNCR Dry FGD 0.64 0.65 0.93 Cyclone Cold Side ESP + FGC SCR None 0.64 0.97 0.93 Cyclone Cold Side ESP + FGC SCR Wet FGD 0.1 0.84 0.58 Cyclone Cold Side ESP + FGC SCR Dry FGD 0.64 0.65 0.93 Cyclone Cold Side ESP + FGC None Wet FGD 0.46 0.84 0.58 Cyclone Cold Side ESP + FGC None Dry FGD 0.64 0.65 0.93 Cyclone Cold Side ESP + FGC None None 0.64 0.97 0.93 Cyclone Cold Side ESP + FGC + FF SCR None 0.11 0.27 1 Cyclone Cold Side ESP + FGC + FF SCR Wet FGD 0.1 0.27 0.58 Cyclone Cold Side ESP + FGC + FF SCR Dry FGD 0.4 0.95 0.91 Cyclone Cold Side ESP + FGC + FF None Wet FGD 0.1 0.27 0.58 Cyclone Cold Side ESP + FGC + FF None Dry FGD 0.4 0.95 0.91 Cyclone Cold Side ESP + FGC + FF None None 0.11 0.27 1 Cyclone Fabric Filter SNCR None 0.11 0.27 1 Cyclone Fabric Filter SNCR Wet FGD 0.03 0.27 0.58 Cyclone Fabric Filter SNCR Dry FGD 0.4 0.95 0.91 Cyclone Fabric Filter SCR None 0.11 0.27 1 Cyclone Fabric Filter SCR Wet FGD 0.1 0.27 0.58 Cyclone Fabric Filter SCR Dry FGD 0.4 0.95 0.91 Cyclone Fabric Filter None Wet FGD 0.1 0.27 0.58 Cyclone Fabric Filter None Dry FGD 0.4 0.95 0.91 Cyclone Fabric Filter None None 0.11 0.27 1 Cyclone Hot Side ESP SNCR None 0.9 1 1 Cyclone Hot Side ESP SNCR Wet FGD 0.58 0.6 1 Cyclone Hot Side ESP SNCR Dry FGD 0.9 1 1 Cyclone Hot Side ESP SCR None 0.9 1 1 Cyclone Hot Side ESP SCR Wet FGD 0.1 0.8 1 Cyclone Hot Side ESP SCR Dry FGD 0.9 1 1

### 5-20 Burner Type Particulate Control Post Combustion

Control – NO X Post Combustion

Control -

SO 2 Bituminous EMF Subbitumionus EMF Lignite EMF

Cyclone Hot Side ESP None Wet FGD 0.58 0.6 1 Cyclone Hot Side ESP None Dry FGD 0.9 1 1 Cyclone Hot Side ESP None None 0.9 1 1 Cyclone Hot Side ESP + FF None None 0.11 0.27 1 Cyclone Hot Side ESP + FGC SNCR None 0.9 1 1 Cyclone Hot Side ESP + FGC SNCR Wet FGD 0.58 0.6 1 Cyclone Hot Side ESP + FGC SNCR Dry FGD 0.9 1 1 Cyclone Hot Side ESP + FGC SCR None 0.9 1 1 Cyclone Hot Side ESP + FGC SCR Wet FGD 0.1 0.8 1 Cyclone Hot Side ESP + FGC SCR Dry FGD 0.9 1 1 Cyclone Hot Side ESP + FGC None Wet FGD 0.58 0.6 1 Cyclone Hot Side ESP + FGC None Dry FGD 0.9 1 1 Cyclone Hot Side ESP + FGC None None 0.9 1 1 Cyclone No Control SNCR None 1 1 1 Cyclone No Control SNCR Wet FGD 0.45 0.6 1 Cyclone No Control SNCR Dry FGD 1 1 1 Cyclone No Control SCR None 1 1 1 Cyclone No Control SCR Wet FGD 0.1 0.7 1 Cyclone No Control SCR Dry FGD 1 1 1 Cyclone No Control None Wet FGD 0.45 0.6 1 Cyclone No Control None Dry FGD 1 1 1 Cyclone No Control None None 1 1 1 Cyclone PM Scrubber None None 0.8 1 1

FBC Cold Side ESP SNCR None 0.65 0.65 0.62

FBC Cold Side ESP SNCR Wet FGD 0.65 0.65 0.62

FBC Cold Side ESP SCR Wet FGD 0.1 0.84 0.62

FBC Cold Side ESP None Wet FGD 0.65 0.65 0.62

FBC Cold Side ESP None Dry FGD 0.45 0.45 1

FBC Cold Side ESP None None 0.65 0.65 0.62

FBC Cold Side ESP + FF SNCR None 0.05 0.43 0.43

FBC Cold Side ESP + FF SNCR Dry FGD 0.05 0.43 0.43

FBC Cold Side ESP + FF None Dry FGD 0.05 0.43 0.43

FBC Cold Side ESP + FF None None 0.05 0.43 0.43

FBC Cold Side ESP + FGC SNCR None 0.65 0.65 0.62

FBC Cold Side ESP + FGC SNCR Wet FGD 0.65 0.65 0.62

FBC Cold Side ESP + FGC SCR Wet FGD 0.1 0.84 0.62

FBC Cold Side ESP + FGC None Wet FGD 0.65 0.65 0.62

FBC Cold Side ESP + FGC None Dry FGD 0.45 0.45 1

FBC Cold Side ESP + FGC None None 0.65 0.65 0.62

FBC Cold Side ESP + FGC + FF SNCR None 0.05 0.43 0.43

FBC Cold Side ESP + FGC + FF SNCR Dry FGD 0.05 0.43 0.43

FBC Cold Side ESP + FGC + FF None Dry FGD 0.05 0.43 0.43

FBC Cold Side ESP + FGC + FF None None 0.05 0.43 0.43

FBC Fabric Filter SNCR None 0.05 0.43 0.43

FBC Fabric Filter SNCR Wet FGD 0.05 0.43 0.43

FBC Fabric Filter SNCR Dry FGD 0.05 0.43 0.43

FBC Fabric Filter SCR None 0.05 0.43 0.43

### 5-21 Burner Type Particulate Control Post Combustion

Control – NO X Post Combustion

Control -

SO 2 Bituminous EMF Subbitumionus EMF Lignite EMF

FBC Fabric Filter SCR Wet FGD 0.05 0.27 0.43

FBC Fabric Filter SCR Dry FGD 0.05 0.43 0.43

FBC Fabric Filter None Wet FGD 0.1 0.43 0.43

FBC Fabric Filter None Dry FGD 0.05 0.43 0.43

FBC Fabric Filter None None 0.05 0.43 0.43

FBC Hot Side ESP SNCR None 1 1 1

FBC Hot Side ESP SNCR Dry FGD 0.45 0.45 1

FBC Hot Side ESP None Dry FGD 0.45 0.45 1

FBC Hot Side ESP None None 1 1 1

FBC Hot Side ESP + FGC SNCR None 1 1 1

FBC Hot Side ESP + FGC SNCR Dry FGD 0.45 0.45 1

FBC Hot Side ESP + FGC None Dry FGD 0.45 0.45 1

FBC Hot Side ESP + FGC None None 1 1 1

FBC No Control SNCR None 1 1 1

FBC No Control SNCR Wet FGD 1 1 1

FBC No Control SNCR Dry FGD 0.45 0.45 1

FBC No Control SCR None 1 1 1

FBC No Control SCR Wet FGD 0.1 0.7 1

FBC No Control SCR Dry FGD 0.45 0.45 1

FBC No Control None Wet FGD 1 1 1

FBC No Control None Dry FGD 0.45 0.45 1

FBC No Control None None 1 1 1

PC Cold Side ESP SNCR None 0.64 0.97 1

PC Cold Side ESP SNCR Wet FGD 0.34 0.65 0.56

PC Cold Side ESP SNCR Dry FGD 0.64 0.65 1

PC Cold Side ESP SCR None 0.64 0.97 1

PC Cold Side ESP SCR Wet FGD 0.1 0.84 0.56

PC Cold Side ESP SCR Dry FGD 0.64 0.65 1

PC Cold Side ESP None Wet FGD 0.34 0.84 0.56

PC Cold Side ESP None Dry FGD 0.64 0.65 1

PC Cold Side ESP None None 0.64 0.97 1

PC Cold Side ESP + FF SNCR None 0.2 0.75 1

PC Cold Side ESP + FF SNCR Wet FGD 0.1 0.3 0.56

PC Cold Side ESP + FF SNCR Dry FGD 0.05 0.75 1

PC Cold Side ESP + FF SCR None 0.2 0.75 1

PC Cold Side ESP + FF SCR Wet FGD 0.1 0.3 0.56

PC Cold Side ESP + FF SCR Dry FGD 0.05 0.75 1

PC Cold Side ESP + FF None Wet FGD 0.3 0.3 0.56

PC Cold Side ESP + FF None Dry FGD 0.05 0.75 1

PC Cold Side ESP + FF None None 0.2 0.75 1

PC Cold Side ESP + FGC SNCR None 0.64 0.97 1

PC Cold Side ESP + FGC SNCR Wet FGD 0.34 0.65 0.56

PC Cold Side ESP + FGC SNCR Dry FGD 0.64 0.65 1

PC Cold Side ESP + FGC SCR None 0.64 0.97 1

PC Cold Side ESP + FGC SCR Wet FGD 0.1 0.84 0.56

PC Cold Side ESP + FGC SCR Dry FGD 0.64 0.65 1

PC Cold Side ESP + FGC None Wet FGD 0.34 0.84 0.56

### 5-22 Burner Type Particulate Control Post Combustion

Control – NO X Post Combustion

Control -

SO 2 Bituminous EMF Subbitumionus EMF Lignite EMF

PC Cold Side ESP + FGC None Dry FGD 0.64 0.65 1

PC Cold Side ESP + FGC None None 0.64 0.97 1

PC Cold Side ESP + FGC + FF SNCR None 0.2 0.75 1

PC Cold Side ESP + FGC + FF SNCR Wet FGD 0.1 0.3 0.56

PC Cold Side ESP + FGC + FF SNCR Dry FGD 0.05 0.75 1

PC Cold Side ESP + FGC + FF SCR None 0.2 0.75 1

PC Cold Side ESP + FGC + FF SCR Wet FGD 0.1 0.3 0.56

PC Cold Side ESP + FGC + FF SCR Dry FGD 0.05 0.75 1

PC Cold Side ESP + FGC + FF None Wet FGD 0.3 0.3 0.56

PC Cold Side ESP + FGC + FF None Dry FGD 0.05 0.75 1

PC Cold Side ESP + FGC + FF None None 0.2 0.75 1

PC Fabric Filter SNCR None 0.11 0.27 1

PC Fabric Filter SNCR Wet FGD 0.03 0.27 0.56

PC Fabric Filter SNCR Dry FGD 0.05 0.75 1

PC Fabric Filter SCR None 0.11 0.27 1

PC Fabric Filter SCR Wet FGD 0.1 0.27 0.56

PC Fabric Filter SCR Dry FGD 0.05 0.75 1

PC Fabric Filter None Wet FGD 0.1 0.27 0.56

PC Fabric Filter None Dry FGD 0.05 0.75 1

PC Fabric Filter None None 0.11 0.27 1

PC Hot Side ESP SNCR None 0.9 0.9 1

PC Hot Side ESP SNCR Wet FGD 0.58 0.75 1

PC Hot Side ESP SNCR Dry FGD 0.6 0.85 1

PC Hot Side ESP SCR None 0.9 0.9 1

PC Hot Side ESP SCR Wet FGD 0.1 0.8 1

PC Hot Side ESP SCR Dry FGD 0.6 0.85 1

PC Hot Side ESP None Wet FGD 0.58 0.8 1

PC Hot Side ESP None Dry FGD 0.6 0.85 1

PC Hot Side ESP None None 0.9 0.94 1

PC Hot Side ESP + FF SNCR None 0.11 0.27 1

PC Hot Side ESP + FF SNCR Wet FGD 0.03 0.27 0.56

PC Hot Side ESP + FF SNCR Dry FGD 0.05 0.75 1

PC Hot Side ESP + FF SCR None 0.11 0.27 1

PC Hot Side ESP + FF SCR Wet FGD 0.1 0.15 0.56

PC Hot Side ESP + FF SCR Dry FGD 0.05 0.75 1

PC Hot Side ESP + FF None Wet FGD 0.03 0.27 0.56

PC Hot Side ESP + FF None Dry FGD 0.05 0.75 1

PC Hot Side ESP + FF None None 0.11 0.27 1

PC Hot Side ESP + FGC SNCR None 0.9 0.9 1

PC Hot Side ESP + FGC SNCR Wet FGD 0.58 0.75 1

PC Hot Side ESP + FGC SNCR Dry FGD 0.6 0.85 1

PC Hot Side ESP + FGC SCR None 0.9 0.9 1

PC Hot Side ESP + FGC SCR Wet FGD 0.1 0.8 1

PC Hot Side ESP + FGC SCR Dry FGD 0.6 0.85 1

PC Hot Side ESP + FGC None Wet FGD 0.58 0.8 1

PC Hot Side ESP + FGC None Dry FGD 0.6 0.85 1

PC Hot Side ESP + FGC None None 0.9 0.94 1

### 5-23 Burner Type Particulate Control Post Combustion

Control – NO X Post Combustion

Control -

SO 2 Bituminous EMF Subbitumionus EMF Lignite EMF

PC Hot Side ESP + FGC + FF SNCR None 0.11 0.27 1

PC Hot Side ESP + FGC + FF SNCR Wet FGD 0.03 0.27 0.56

PC Hot Side ESP + FGC + FF SNCR Dry FGD 0.05 0.75 1

PC Hot Side ESP + FGC + FF SCR None 0.11 0.27 1

PC Hot Side ESP + FGC + FF SCR Wet FGD 0.1 0.15 0.56

PC Hot Side ESP + FGC + FF SCR Dry FGD 0.05 0.75 1

PC Hot Side ESP + FGC + FF None Wet FGD 0.03 0.27 0.56

PC Hot Side ESP + FGC + FF None Dry FGD 0.05 0.75 1

PC Hot Side ESP + FGC + FF None None 0.11 0.27 1

PC No Control SNCR None 1 1 1

PC No Control SNCR Wet FGD 0.58 0.7 1

PC No Control SNCR Dry FGD 0.6 0.85 1

PC No Control SCR None 1 1 1

PC No Control SCR Wet FGD 0.1 0.7 1

PC No Control SCR Dry FGD 0.6 0.85 1

PC No Control None Wet FGD 0.58 0.7 1

PC No Control None Dry FGD 0.6 0.85 1

PC No Control None None 1 1 1

PC PM Scrubber SNCR None 0.9 0.91 1

PC PM Scrubber SCR None 0.9 1 1

PC PM Scrubber None None 0.9 0.91 1

Stoker Cold Side ESP SNCR None 0.65 0.97 1

Stoker Cold Side ESP SNCR Wet FGD 0.34 0.73 0.56

Stoker Cold Side ESP SNCR Dry FGD 0.65 0.65 1

Stoker Cold Side ESP SCR None 0.65 0.97 1

Stoker Cold Side ESP SCR Wet FGD 0.1 0.84 0.56

Stoker Cold Side ESP SCR Dry FGD 0.65 0.65 1

Stoker Cold Side ESP None Wet FGD 0.34 0.84 0.56

Stoker Cold Side ESP None Dry FGD 0.65 0.65 1

Stoker Cold Side ESP None None 0.65 0.97 1

Stoker Cold Side ESP + FGC SNCR None 0.65 0.97 1

Stoker Cold Side ESP + FGC SNCR Wet FGD 0.34 0.73 0.56

Stoker Cold Side ESP + FGC SNCR Dry FGD 0.65 0.65 1

Stoker Cold Side ESP + FGC SCR None 0.65 0.97 1

Stoker Cold Side ESP + FGC SCR Wet FGD 0.1 0.84 0.56

Stoker Cold Side ESP + FGC SCR Dry FGD 0.65 0.65 1

Stoker Cold Side ESP + FGC None Wet FGD 0.34 0.84 0.56

Stoker Cold Side ESP + FGC None Dry FGD 0.65 0.65 1

Stoker Cold Side ESP + FGC None None 0.65 0.97 1

Stoker Fabric Filter SNCR None 0.11 0.27 1

Stoker Fabric Filter SNCR Wet FGD 0.03 0.27 0.56

Stoker Fabric Filter SNCR Dry FGD 0.1 0.75 1

Stoker Fabric Filter SCR None 0.11 0.27 1

Stoker Fabric Filter SCR Wet FGD 0.1 0.27 0.56

Stoker Fabric Filter SCR Dry FGD 0.1 0.75 1

Stoker Fabric Filter None Wet FGD 0.1 0.27 0.56

Stoker Fabric Filter None Dry FGD 0.1 0.75 1

### 5-24 Burner Type Particulate Control Post Combustion

Control – NO X Post Combustion

Control -

SO 2 Bituminous EMF Subbitumionus EMF Lignite EMF

Stoker Fabric Filter None None 0.11 0.27 1

Stoker Hot Side ESP SNCR None 1 1 1

Stoker Hot Side ESP SNCR Wet FGD 0.58 1 1

Stoker Hot Side ESP SNCR Dry FGD 1 1 1

Stoker Hot Side ESP SCR None 1 1 1

Stoker Hot Side ESP SCR Wet FGD 0.1 0.8 1

Stoker Hot Side ESP SCR Dry FGD 1 1 1

Stoker Hot Side ESP None Wet FGD 0.58 1 1

Stoker Hot Side ESP None Dry FGD 1 1 1

Stoker Hot Side ESP None None 1 1 1

Stoker Hot Side ESP + FGC SNCR None 1 1 1

Stoker Hot Side ESP + FGC SNCR Wet FGD 0.58 1 1

Stoker Hot Side ESP + FGC SNCR Dry FGD 1 1 1

Stoker Hot Side ESP + FGC SCR None 1 1 1

Stoker Hot Side ESP + FGC SCR Wet FGD 0.1 0.8 1

Stoker Hot Side ESP + FGC SCR Dry FGD 1 1 1

Stoker Hot Side ESP + FGC None Wet FGD 0.58 1 1

Stoker Hot Side ESP + FGC None Dry FGD 1 1 1

Stoker Hot Side ESP + FGC None None 1 1 1

Stoker No Control SNCR None 1 1 1

Stoker No Control SNCR Wet FGD 0.58 1 1

Stoker No Control SNCR Dry FGD 1 1 1

Stoker No Control SCR None 1 1 1

Stoker No Control SCR Wet FGD 0.1 0.7 1

Stoker No Control SCR Dry FGD 1 1 1

Stoker No Control None Wet FGD 0.58 1 1

Stoker No Control None Dry FGD 1 1 1

Stoker No Control None None 1 1 1

Stoker PM Scrubber None None 1 1 1

Other Cold Side ESP SNCR None 0.64 0.97 1

Other Cold Side ESP SNCR Wet FGD 0.34 0.73 0.56

Other Cold Side ESP SNCR Dry FGD 0.64 0.65 1

Other Cold Side ESP SCR None 0.64 0.97 1

Other Cold Side ESP SCR Wet FGD 0.1 0.84 0.56

Other Cold Side ESP SCR Dry FGD 0.64 0.65 1

Other Cold Side ESP None Wet FGD 0.34 0.84 0.56

Other Cold Side ESP None Dry FGD 0.64 0.65 1

Other Cold Side ESP None None 0.64 0.97 1

Other Cold Side ESP + FGC SNCR None 0.64 0.97 1

Other Cold Side ESP + FGC SNCR Wet FGD 0.34 0.73 0.56

Other Cold Side ESP + FGC SNCR Dry FGD 0.64 0.65 1

Other Cold Side ESP + FGC SCR None 0.64 0.97 1

Other Cold Side ESP + FGC SCR Wet FGD 0.1 0.84 0.56

Other Cold Side ESP + FGC SCR Dry FGD 0.64 0.65 1

Other Cold Side ESP + FGC None Wet FGD 0.34 0.84 0.56

Other Cold Side ESP + FGC None Dry FGD 0.64 0.65 1

Other Cold Side ESP + FGC None None 0.64 0.97 1

### 5-25 Burner Type Particulate Control Post Combustion

Control – NO X Post Combustion

Control -

SO 2 Bituminous EMF Subbitumionus EMF Lignite EMF

Other Fabric Filter SNCR None 0.45 0.75 1

Other Fabric Filter SNCR Wet FGD 0.03 0.27 0.56

Other Fabric Filter SNCR Dry FGD 0.4 0.75 1

Other Fabric Filter SCR None 0.11 0.27 1

Other Fabric Filter SCR Wet FGD 0.1 0.27 0.56

Other Fabric Filter SCR Dry FGD 0.4 0.75 1

Other Fabric Filter None Wet FGD 0.1 0.27 0.56

Other Fabric Filter None Dry FGD 0.4 0.75 1

Other Fabric Filter None None 0.11 0.27 1

Other Hot Side ESP SNCR None 1 1 1

Other Hot Side ESP SNCR Wet FGD 0.58 1 1

Other Hot Side ESP SNCR Dry FGD 1 1 1

Other Hot Side ESP SCR None 1 1 1

Other Hot Side ESP SCR Wet FGD 0.1 0.8 1

Other Hot Side ESP SCR Dry FGD 1 1 1

Other Hot Side ESP None Wet FGD 0.58 1 1

Other Hot Side ESP None Dry FGD 1 1 1

Other Hot Side ESP None None 1 1 1

Other Hot Side ESP + FF None None 0.11 0.27 1

Other Hot Side ESP + FGC SNCR None 1 1 1

Other Hot Side ESP + FGC SNCR Wet FGD 0.58 1 1

Other Hot Side ESP + FGC SNCR Dry FGD 1 1 1

Other Hot Side ESP + FGC SCR None 1 1 1

Other Hot Side ESP + FGC SCR Wet FGD 0.1 0.8 1

Other Hot Side ESP + FGC SCR Dry FGD 1 1 1

Other Hot Side ESP + FGC None Wet FGD 0.58 1 1

Other Hot Side ESP + FGC None Dry FGD 1 1 1

Other Hot Side ESP + FGC None None 1 1 1

Other Hot Side ESP + FGC + FF None None 0.11 0.27 1

Other No Control SNCR None 1 1 1

Other No Control SNCR Wet FGD 0.58 0.7 1

Other No Control SNCR Dry FGD 1 1 1

Other No Control SCR None 1 1 1

Other No Control SCR Wet FGD 0.1 0.7 1

Other No Control SCR Dry FGD 1 1 1

Other No Control None Wet FGD 0.58 0.7 1

Other No Control None Dry FGD 1 1 1

Other No Control None None 1 1 1

Other PM Scrubber None None 0.9 0.91 1

### 5-26 Table 5-14 Definition of Acronyms for Existing Controls Acronym Description ESP Electro Static Precipitator - Cold Side HESP Electro Static Precipitator - Hot Side ESP/O Electro Static Precipitator - Other

FF Fabric Filter FGD Flue Gas Desulfurization - Wet DS Flue Gas Desulfurization - Dry SCR Selective Catalytic Reduction PMSCRUB Particulate Matter Scrubber

Table 5-15 Key to Burner Type Designations in Table 5-13 “PC” refers to conventional pulverized coal boilers. Typical configurations include wall-fired and tangentially fired boilers (also called T-fired boilers). In wall-fired boilers the burner’s coal and air nozzles are mounted on a single wall or opposing walls. In tangentially fired boilers the burner’s coal and air nozzles are mounted in each corner of the boiler.

“Cyclone” refers to cyclone boilers where air and crushed coal are injected tangentially into the boiler through a “cyclone burner” and “cyclone barrel” which create a swirling motion allowing smaller coal particles to be burned in suspension and larger coal particles to be captured on the cyclone barrel wall where they are burned in molten slag.

“Stoker” refers to stoker boilers where lump coal is fed continuously onto a moving grate or chain which moves the coal into the combustion zone in which air is drawn through the grate and ignition takes place. The carbon gradually burns off, leaving ash which drops off at the end into a receptacle, from which it is removed for disposal.

“FBC" refers to “fluidized bed combustion” where solid fuels are suspended on upward-blowing jets of air, resulting in a turbulent mixing of gas and solids and a tumbling action which provides especially effective chemical reactions and heat transfer during the combustion process.

“Other" refers to miscellaneous burner types including cell burners and arch- , roof- , and vertically-fired burner configurations

.

### 5-27 Mercury Control through SO 2 

### and NO X 

### Retrofits In EPA Base Case v.4.10, units that install SO 2 

### , NO X 

### , and particulate controls, reduce mercury emissions as a byproduct of these retrofits. Section 5.4.2 described how EMFs are used in the base case to capture the unregulated mercury emissions depending on the rank of coal burned, the generating unit’s combustion characteristi cs, and the specific configuration of SO 2 

### , NO X 

### , and particulate controls (i.e., hot and cold-side electrostatic precipitators (ESPs), fabric filters (also called “baghouses”) and particulate matter (PM) scrubbers). These same EMFs would be available in mercury policy runs to characterize the mercury reductions that can be achieved by retrofitting a unit with SCR, SNCR, SO 2 

### scrubbers and particulate controls. The absence of a federal mercury emission reduction policy means t hat these controls appear in the base case in response to SO 2 

### , NO X 

### , or particulate limits or state-level mercury emission requirements. However, in future model runs where mercury limits are present these same SO 2 

### and NO X 

### controls could be deliberately installed for mercury control if they provide the least cost option for meeting mercury policy limits.

Activated Carbon Injection (ACI) The technology specifically designated for mercur y control is Activated Carbon Injection (ACI) downstream of the combustion process in coal fired units. A comprehensive ACI update, which will incorporate the latest field experience through 2010, is being prepared by Sargent and Lundy (the same engineering firm that developed the SO 2 

### and NO X 

### control assumptions used in EPA Base Case v.4.10). It will be incorporated in a future EPA base case. The ACI assumptions in the current base case release are the result of a 2007 internal EPA engineering study.

Based on this study, it is assume that 90% removal from the level of mercury in the coal is achievable with the application of one of three al ternative ACI configurations: Standard Powered Activated Carbon (SPAC), Modified Powered Activated Carbon (MPAC), or SPAC in combination with a fabric filter. The MPAC option exploits t he discovery that by converting elemental mercury to oxidized mercury, halogens (like chlorine, i odine, and bromine) can make activated carbon more effective in capturing the mercury at the high temperatures found in industrial processes like power generation. In the MPAC system, a small amount of bromine is chemically bonded to the powdered carbon which is then injected into the flue gas stream either upstream of both the particulate control device (ESP or fabric filter) and the air pre-heater (APH), between the APH and the particulate control device, or downstream of both the pre-existing APH and particulate control devices but ahead of a new dedicated pulsed-jet fabric filter. (The latter is known as the TOXECON TM 

### approach, an air pollution control process patented by EPRI.)

Table 5-16 presents the capital, FOM, and VOM costs as well as the capacity and heat rate penalty for the five Hg emission control technologies included in EPA Base Case v.4.10 for an illustrative set of generating units with a representative range of capacities.5-28Table 5-16 Illustrative Activated Carbon Injection Costs (2007$)for Representative Sizes under the Assumptions in EPA Base Casev.4.10Capacity (MW)100300500700Control TypeCapacityPenalty(%)HeatRatePenalty(%)CapitalCost($/kW)FixedO&M($/kWyr)VariableO&M cost(mills/kWh)CapitalCost($/kW)FixedO&M($/kW-yr)VariableO&M cost(mills/kWh)CapitalCost($/kW)FixedO&M($/kW-yr)VariableO&M cost(mills/kWh)CapitalCost($/kW)FixedO&M($/kW-yr)VariableO&M cost(mills/kWh)MPAC_BaghouseMinimum Cutoff:≥25 MWMaximum Cutoff: NoneAssuming BituminousCoal-0.430.4330.10.1620.050.1720.040.1720.030.16MPAC_CESPMinimum Cutoff:≥25 MWMaximum Cutoff: NoneAssuming BituminousCoal-0.430.4380.10.5760.10.6150.10.6150.10.59SPAC_BaghouseMinimum Cutoff:≥25 MWMaximum Cutoff: NoneAssuming BituminousCoal-0.430.4350.10.2240.10.2330.10.2330.10.23SPAC_ESPMinimum Cutoff:≥25 MWMaximum Cutoff: NoneAssuming BituminousCoal-0.430.43270.52.29210.32.46180.32.44170.32.39SPAC_ESP+ToxeconMinimum Cutoff:≥25 MWMaximum Cutoff: NoneAssuming BituminousCoal-0.430.432694.32.442022.52.611762.12.591612.02.54

### 5-29 The applicable ACI option depends on the coal type burned, its SO 2 

### content, the boiler and particulate control type and, in some instances, consideration of whether an SO 2 

### scrubber (FGD) system or SCR NO x 

### post-combustion control are present. Table 5-17 shows the ACI assignment scheme used in EPA Base Case v.4.10 to achieve 90% mercury removal.

Table 5-17 Assignment Scheme for Mercury Emissions Control Using Activated Carbon Injection (ACI) in EPA Base Case v.4.10 Applicability of Activated Carbon Injection

Coal Type SO 2 in Coal (lb/MMBtu) Boiler Type Particulate Control Type FGD System SCR System Toxecon Required? ACI Type With 90% Hg

Reduction Bit/Sub-bit/ Lig < 1.6 Non-CFB CS-ESP or BH (no FGC) -- No No MPAC

Bit/Sub-bit/ Lig -- Non-CFB CS-ESP or BH (no FGC) LSD -- No MPAC

Bit/Subbit/Lig -- CFB CS-ESP or BH (no FGC) -- -- No MPAC

Bit < 1.6 Non-CFB CS-ESP Non-LSD Yes No SPAC
Bit ≥ 1.6 Non-CFB CS-ESP or BH -- -- No SPAC Sub-bit/Lig ≥ 1.6 Non-CFB CS-ESP -- -- Yes SPAC Sub-bit/Lig ≥ 1.6 Non-CFB BH -- -- No SPAC Bit/Sub-bit/ Lig -- Non-CFB HESP -- -- Yes SPAC

Bit/Sub-bit/ Lig -- -- HESP or CSESP (with FGC) -- -- Yes SPAC

Bit/Sub-bit/ Lig < 1.6 Non-CFB BH No Yes No MPAC

Bit/Sub-bit/ Lig < 1.6 Non-CFB CS-ESP (no FGC) No Yes No MPAC

Bit/Sub-bit/ Lig -- -- No Control -- -- Yes SPAC

Bit/Sub-bit/ Lig < 1.6 -- BH Non-LSD Yes No SPAC

Sub-bit/ Lig < 1.6 -- CS-ESP (no FGC) Non-LSD Yes Yes SPAC

Bit/Subbit/Lig -- -- Cyclone -- -- Yes SPAC

Notes: Legends:
ACI Activated carbon injection If the existing equipment provides 90% Hg removal, no ACI system is required.

BH Baghouse
Bit Bituminous coal "--" means that the categor y type has no effect on the ACI application.
CFB Circulating fluidized-bed boiler

CS-ESP Cold side electrostatic precipitator FGC Flue gas conditioning
HESP Hot electrostatic precipitator

Lig Lignite

MPAC Modified powdered activated carbon

SPAC Standard powdered activated carbon Sub-bit Subbituminous coal

### Appendix 5-1.1 

## Appendix 5-1 Example Cost Ca lculation Worksheets for SO 2



## Control Technologies in EPA Base Case v.4.10

### Appendix 5-1.2

### Appendix 5-1.3

### Appendix 5-1.4

### Appendix 5-2.1 

## Appendix 5-2 Example Cost Ca lculation Worksheets for NO x



## PostCombustion Control Technologies in EPA Base Case v.4.10

### Appendix 5-2.2

### Appendix 5-2.3

### Appendix 5-2.4

### 6-1 

# 6 CO 2 

# Capture, Transport, and Storage

6.1 CO 2 

## Capture Among the potential (new) units that the model can build in EPA Base Case v.4.10 are advanced coal-fired units with CO 2 

### capture (carbon capture). 1 

### The cost and performance characteristics of these units are shown in Table 4-13 and are discussed in Chapter 4.

Besides offering carbon capture capabilities on potential units that the model builds from scratch, EPA Base Case v.4.10 also provides carbon capture as a retrofit option for existing pulverized coal plants. The incremental costs and performance assumptions for these retrofits are shown in Table 6-1.

Table 6-1 Performance and Unit Cost Assumptions for Carbon Capture Retrofits on Pulverized Coal Plants Applicability (Original MW Size) 450-750 MW > 750 MW Incremental 1 

### Capital Cost (2007 $/kW) 1,972 1,599 Incremental 1 

### FOM (2007 $/kW-yr) 3.00 1.98 Incremental 1 

### VOM (2007 (mills/kWh) 2.35 2.35 Capacity Penalty (%) -25% -25% Heat Rate Penalty (%) 33% 33% CO 2 

### Removal (%) 90% 90% Note:
1 

### Incremental costs are applied to the derated (after retrofit) MW size.

The capital costs shown in Table 6-1 are based on the costs reported for Case 1 in a study 2 

### performed for the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) by a team consisting of Alstom Power, Inc., American Electric Power (AEP), ABB Global, and the Ohio Coal Development Office. For Case 1 this comprehensive engineering study, conducted from 1999-2001, evaluated the impacts on plant performance and the required cost to add facilities to capture greater than 90% of the CO 2 

### emitted by AEP’s Conesville Ohio Unit #5. This is a 450 MW subcritical 3 

### pulverized bituminous coal plant with a lime based FGD, and an electrostatic precipitator for particulate control. The carbon capture method that was evaluated was an amine-based scrubber using the Kerr-McGee/ABB Lummus Global commercially available monoethanolamine (MEA) process. In this system the flue gas leaves the FGD (which has been modified to reduce the SO 2 

### concentration as required by the MEA process) and is cooled and ducted to the MEA system where more than 96% of the CO 2 

### can be removed. For use in EPA Base Case v.4.10 the capital cost was scaled to be applicable to the MW capacity sizes shown in Table 6-1 and converted to constant 2007$ from the 2006$ costs reported in the NETL study.

1 

### The term “New Advanced Coal with CCS” encompasses various technologies that can provide carbon capture. These include supercritical steam generators with carbon capture and integrated gasification combined cycle (IGCC) with carbon capt ure. For purposes of characterizing the cost and performance characteristics of advanced coal with carbon capture, IGCC with carbon capture was used in Table 4-13.

2“ 

### Carbon Dioxide Capture from Existing Coal-Fir ed Power Plants” DOE/NETL-401/110907. Final Report (Original Issue Date, December 2006) Revision Date, November 2007 ( http://www.netl.doe.gov/energyanalyses/pubs/CO2%20Retrofi t%20From%20Existing%20Plant s%20Revised%20November%202 007.pdf . A summary of costs for each of the cases appears in Table 3-65 (p. 139).

3 

### “Subcritical” refers to thermal power plants t hat operate below the ”critical temperature” and “critical pressure” (220 bar) where boiling, i.e., the formation of steam bubbles in water, no longer occurs. Such units are less efficient than “supercritical” and “ultra supercritical” steam generators.

### 6-2 A capacity derating penalty of 25% was assumed, based on reported research and field experience as of the summer of 2010. The corresponding heat rate penalty was 33%. (For an explanation of the capacity and heat rate penalti es and how they are calculated, see the discussion under VOM in section 5.1.1.)

Since the fixed (FOM) and variable operating and maintenance (VOM) costs from the Conesville study were given without documentation, another NETL study 4 

### which fully documented these costs was used to obtain the FOM and VOM values shown in Table 6-1. For FOM and VOM, the cost differential was calculated between Case 9, a 550 MW subcritical pulverized coal plant with CO 2 

### capture, and Case 10, a comparable unit but without CO 2 

### capture. These differentials provided the VOM and FOM costs for the “450-750 MW” case in Table 6-1. For the “greater than 750 MW” case these costs were scaled up by the ratio of a unit with an effective capacity of 750 MW to the effective capacity of the 450-750 MW case raised to a power (k), where k reflects the elasticity of the costs due to economies of scale. (The same approach was used to scale the capital costs.) For capital, FOM, and VOM, the value of k was 0.65, 0.30, and 1 respectively.



## 6.2 CO 2 

## Storage The capacity and cost assumptions for CO 2 

### storage in EPA Base Case v.4.10 are based on GeoCAT (Geosequestration Cost Analysis Tool), a spreadsheet model developed for EPA by ICF International in support of EPA’s draft Federal Requirements Under the Underground Injection Control (UIC) Program for Carbon Dioxide Geologic Storage Wells. 5 

### The GeoCAT model combines detailed characteristics of sequestrat ion capacity by state and geologic setting for the U.S. with costing algorithms for individual components of geologic sequestration of CO 2 

### . The outputs of the model are regional sequestration co st curves that indicate how much potential storage capacity is available at different CO 2 

### storage cost points.

The GeoCAT model includes three modules: a unit cost specification module, a project scenario costing module, and a geologic and regional cost curve module. The unit cost module includes data and assumptions for 120 unit cost elements falling within the following cost categories:

• Geologic Site Characterization

• Monitoring the movement of CO 2 

### in the subsurface

• Injection Well Construction

• Area of Review and Corrective Action (incl uding fluid flow and reservoir modeling during and after injection and identification, evaluation, and remediation of existing wells within the area of review)

• Well Operation

• Mechanical Integrity Testing

• Financial Responsibility (to maintain sufficient resources for activities related to closing and remediation of the site)

• General and Administrative

4 

### “Cost and Performance Baseline for Fossil Energy Plants” DOE/NETL-2007/1281, Volume 1: Bituminous Coal and Natural Gas to Electricity, Final Report (Original Issue Date, May 2007) Revision 1, August 2007 ( http://www.netl.doe.gov/energyanalyses/pubs/Bituminous%20Baseline_Final%20Report.pdf ). The VOM and FOM cost calculations for Case 9 appear in Exhibits 4-14 (p. 349) and for Case 10 in Exhibit 4-24 (p. 373).

5 

### “Federal Requirements Under the Underground Injection Control (UIC) Program for Carbon Dioxide (CO 2 

### ) Geologic Sequestration (GS) Wells,” Federal Register , July 25, 2008 (Volume 73,Number 144), pp. 43491-43541. www.epa.gov/fedrgstr/EPA-WATER/2008/July/Day25/w16626.htm and www.epa.gov/safewater/uic/w ells_sequestration.html#regdevelopment.

### 6-3 Of the ten cost categories for geologic CO 2 

### sequestration listed above, the largest cost drivers (in roughly descending order of magnitude) are well operation, injection well construction, and monitoring.

The costs derived in the unit cost specification module are used in the GeoCAT project scenario costing module to develop commercial scale costs for seven sequestration scenarios of geologic settings:

• saline reservoirs,

• depleted gas fields,

• depleted oil fields,

• enhanced oil recovery,

• enhanced coal bed methane recovery,

• enhanced shale gas, and

• basalt

EPA’s application of GeoCAT includes only storage capacity for the first four scenarios. The last three reservoir types are not included because they are considered technically uncertain and minor for the foreseeable future.

The results of the project scenario costing module are taken as inputs into the geologic and regional cost curve module of GeoCAT whic h generates national and regional “cost curves” indicating the volume of sequestration capacity in each region and state in the U.S. as a function of cost. This module contains a database of sequestration capacity by state and geologic reservoir type. It incorporates assessments from the U.S. Department of Energy’s “Carbon Sequestration Atlas of the United States and Canada” 6 

### , enhanced by ICF International to include assessments of the Gulf of Mexico, shale gas s equestration potential, and the use of distribution of proved oil and gas recovery by region to estimate CO 2 

### potential in areas not covered in the DOE atlas. The geologic and regional cost curv e module also has a characterization of regionalized costs, drilling depths, and other factors that go into the regional cost curves. 7



### For EPA Base Case v.4.10 GeoCAT identified storage opportunities in 33 of the lower 48 continental states and storage cost curves were developed for each of them. 8 

### The storage curve for California is designated as California offshore. Louisiana and Texas have both onshore and offshore storage cost curves. In addition, there are Atlantic offshore and Pacific offshore storage

6 

### “Carbon Sequestration Atlas of the United States and Canada”, U.S. Department of Energy, National Energy Technology Laborator y, Morgantown, WV, March, 2007.

7 

### Detailed discussions of the GeoCAT model and its application for EPA can be found in U.S. Environmental Protection Agency, Office of Water, “Geologic CO 2 

### Sequestration Technology and Cost Analysis, Technical Support Document” (EPA 816-B-08-009) June 2008, http://www.epa.gov/ogwdw000/uic/pdfs/suppor t_uic_co2_technologyandcostanalysis.pdf and Harry Vidas, Robert Hugman and Christa Clapp, “Analysis of Geologic Sequestration Costs for the United States and Implications for Climate Change Mitigation,” Science Digest, Energy Procedia, Volume 1, Issue 1, February 2009, Pages 4281-4288. Available online at www.sciencedirect.com

. 8 

### The states without identified storage opportunities in EPA Base Case v.4.10 are Connecticut, Delaware, Idaho, Iowa, Maine, Maryland, Massachusetts, Minnesota, Missouri, New Hampshire, New Jersey, North Carolina, Rhode Island, Vermont, and Wisconsin. This implies that these states did not present storage opportunities for the four sequestration scenarios included in EPA’s inventory, i.e., saline reservoirs, depleted gas fields, depleted oil fields, and enhanced oil recovery.

### 6-4 cost curves. The result is a total of 37 storage cost curves which are shown in Appendix 6-1 (“CO 2 

### Storage Cost Curves in EPA Base Case”). 9



### The cost curves shown in Appendix 6-1 are in the form of step functions. This implies that in any given year a specified amount of storage is ava ilable at a particular step price until either the annual storage limit (column 4) or the total storage capacity (column 5) is reached. In determining whether the total storage capacity has been reached, the model tracks the cumulative storage used up through the current year. Once the cumulative storage used equals the total storage capacity, no more storage is available going forward at the particular step price.

There are several additional things to note about t he cost curves in Appendix 6-1. First, besides electric sector demand for CO 2 

### storage, there is expected to be demand from the industrial sector as well. Therefore, before being incorporated into EPA Base Case v.4.10, the original CO 2 

### storage capacity in each storage region was reduced by an estimate of the storage required for CO 2 

### generated by industrial sector sources. To do this, ICF first estimated the level of industrial demand in each CO 2 

### storage region expected at an allowance price of $150 per ton. 10 

### (An allowance price of $150/ton was chosen to provi de a conservative estimate of the amount of storage available to the electricity sector, since under most CO 2 

### policies that would be analyzed with EPA Base Case v.4.1.0 the allowance price is expected to be below $150/ton.) Then, for each region ICF calculated the ratio of the industr ial demand to total storage capacity available for less than $10/ton. (An upper limit of $10/ton was chosen because the considerable amount storage available up to that price could be expec ted to exhaust the industrial demand.) Converting this to a percent value and subtracting from 100%. ICF obtained the percent of storage capacity available to the electricity sector for le ss than $10/ton. Finally, the “Annual Step Bound (MMTons)” and “Total Storage Capacity (MMTons)” was multiplied by this percentage value for each step below $10/ton in the cost curves for the region to obtain the reduced storage capacity that went into the storage cost curves for the electric sector in EPA Base Case v.4.10. Thus, the values shown in Table 6-1 represent the storage available to the electric sector.

Second, price steps from region to region are the same. (That is, CO 2 

### STEP5 (column 2) has a step cost value of $4.54/Ton (column 3) across a ll storage regions (column 1). This across-region price equivalency holds for every step.) However, the amount of storage available in any given year (labeled “Annual Step Bound (MMTons)” in column 4) and the total storage available over all years (labeled “Total Storage Capacity (MMTons)” in column 5) vary from region to region. Third, in any given region, the cost curves are the same for every year. Thus, the step cost, step bound, and total storage capacity shown in columns 4 and 5 in Appendix 6-1 remain the same from year to year. This feature implies the assumption that over the modeling time horizon, i.e., 2012-2050, no new storage will need to be added to augment the storage that is in today’s storage inventory. This assumption is not meant to imply that additional storage is unavailable. It only implies that for purposes of modeling the assumption will only be revisited if model runs requiring storage exhaust key components in the storage inventory.

Finally, in each storage cost curve included in Appendix 6-1, CO 2 

### STEP1 through CO 2 

### STEP3 show a negative cost, and CO 2 

### STEP4 shows a zero cost. These steps in the cost curves represent storage available from enhanced oil recovery (EOR) where oil producers either pay or

9 

### For consistency across the emission costs repres ented in v.4.10, the costs shown in Appendices 9-1 and 9-2 are expressed in units of dollars per short ton. In IPM documentation and outputs the convention is to use the word “tons” to indicate short tons and the word “tonnes” to indicate metric tons. In discussing CO 2 

### outside of the modeling framework, the international convention is to use metric tons. To obtain the $/tonne equivalent mu ltiply the $/ton values shown In Appendices 9-1 and 9-2 by 1.1023.

10 

### The approach that ICF employed to estimate industrial demand for CO 2 

### storage is described in ICF International, “Methodology and Results for Initial Forecast of Industrial CCS Volumes,” January 2009.

### 6-5 offer free storage for CO 2 

### which they inject into mature oil wells to enhance the amount of oil recovered. 11



## 6.3 CO 2 

## Transport Each of the 32 IPM model regions can send CO 2 

### to the 37 regions represented by the storage cost curves in Appendix 6-1. The associated transport costs (in 2007$/Ton) are shown in Appendix 6-2 (CO 2 

### Transportation Matrix in EPA Base Case v.4.0).

These costs were derived by ICF International by first calculating the pipeline distance from each of the CO 2 

### Production Regions to each of the CO 2 

### Storage Regions listed in Appendix 6-2. (For example, the distance from MACS to Louisiana Onshore was estimated as 997 miles.) Since there are large economies of scale for pipelines, CO 2 

### transportation costs would depend on how many power plants and industrial CO 2 

### sources could share a pipeline over a given distance. Consequently, ICF’s method assumes that the longer the distance from the source of the CO 2 

### (in our example MACS) to the sink for the CO 2 

### (in our example Louisiana Onshore), the more chance there is for other sources to share in the transportation costs with the pipeline diameter growing with distance as more sources are fed into the same system. Cost components include pipeline costs (in $/inch-mile) and cost of service (in $/ton per 75 miles). These cost components in turn are functions of the required diameter and thickness of the pipeline and the flow capacity of the pipeline, which themselves are functions of the assumed number of power plants using the pipeline. Table 6-2 illustrates the build-up of cost for the MACS to Louisiana Onshore example.

This example comes to $20.11 per ton of CO 2 

### (in 2007$) for the overall miles pipeline distance traveled. (This is the short ton equivalent to the $22.17 metric tonne value shown in Table 6-2.)

Table 6-2 CO 2 

### Transport Cost Calculation Example – MACS to Louisiana Onshore

Outside

Dia. Inches Inside Dia. Inches Wall Thickness

Inches Pipeline Cost in $/Inch-Mile Total Cost of Service in $/metric ton per 75 miles or 121 km Flow Capacity in

metric tons/day Flow Capacity in million standard cubic feet per day (60 degrees F and 14.73 psi) Number of 500 MW IGCC plants

accomodated

12.75 12.0 0.39 75,000 $ $4.36 10,775 203 0.97 16 15.0 0.49 78,116 $ $3.25 19,139 361 1.73 24 22.5 0.73 84,119 $ $2.02 53,385 1,007 4.83 30 28.2 0.92 86,399 $ $1.56 93,887 1,771 8.49 36 33.8 1.10 88,678 $ $1.27 148,913 2,808 13.46 42 39.4 1.28 90,958 $ $1.10 219,942 4,148 19.88

Note: 500 MW IGCC plant would produce 512 metric tonnes of CO 2 per hour. Of this, 90% or 461 tonnes would be captured. Maximum CO2 tranport needs would be 11,064 tonnes per power plant per day. Cost of service based on 7 cents per kWh electricity

.

Miles $/Mile per Tonne Cost per Tonne Annual Cost per Power Plant @85 Utilization Rate
Single Power Plant Pipeline (12 inch, small gathering) distance in miles 25 $0.058 $1.45 $4,986,315 Two Power Plant Pipeline (16 inch, la rge gathering) distance in miles 25 $0.043 $1.08 $3,717,211 Eight Power Plant Pipeline (30 inch, mainline) distance in miles 947 $0.021 $19.64 $67,400,166 Total Distance & Costs 997 $0.022 $22.17 $76,103,692 CARBON DIOXIDE PIPELINES

Example Spacial Assumptions

11 

### There is also a market for CO 2 

### injection in enhanced coal bed methane (ECBM) production. ECBM is excluded from EPA’s inventory as discussed earlier.

### Appendix 6-2.1 

## Appendix 6-1 CO 2



## Storage Cost Curves in EPA Base Case 4.10
Note: The curves for each region are app licable in each model run year 2012 - 2050.

CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP1 -13.61 1 45
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 6
STEP5 4.54 31 1,568
STEP6 9.07 39 1,967
STEP7 13.61 38 1,895
STEP8 18.14 0 9
STEP9 22.68 4 186
STEP10 27.22 13 639
STEP11 31.75 0 7
STEP12 36.29 0 14
STEP13 40.82 0 0
STEP14 45.36 1 68
STEP15 49.90 0 0
STEP16 54.43 0 14
STEP17 58.97 0 0
STEP18 63.50 0 0 Alabama

STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 121 6,026
STEP6 9.07 145 7,275
STEP7 13.61 113 5,659
STEP8 18.14 0 0
STEP9 22.68 38 1,887
STEP10 27.22 0 1
STEP11 31.75 0 0
STEP12 36.29 0 0
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0
STEP18 63.50 0 0 Arizona

STEP19 68.04 0 0
STEP1 -13.61 1 62
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 146 7,297 Arkansas

STEP6 9.07 177 8,863

### Appendix 6-2.2 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP7 13.61 142 7,110
STEP8 18.14 1 35
STEP9 22.68 51 2,568
STEP10 27.22 3 128
STEP11 31.75 0 0
STEP12 36.29 1 53
STEP13 40.82 0 0
STEP14 45.36 1 71
STEP15 49.90 0 0
STEP16 54.43 1 53
STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 1,832 91,580
STEP6 9.07 2,211 110,528
STEP7 13.61 1,706 85,311
STEP8 18.14 0 0
STEP9 22.68 0 0
STEP10 27.22 569 28,437
STEP11 31.75 0 0
STEP12 36.29 0 0
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0
STEP18 63.50 0 0 Atlantic Offshore

STEP19 68.04 0 0
STEP1 -13.61 19 941
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 2 121
STEP5 4.54 1,227 61,357
STEP6 9.07 1,966 98,304
STEP7 13.61 367 18,335
STEP8 18.14 11 531
STEP9 22.68 70 3,516
STEP10 27.22 30 1,507
STEP11 31.75 0 0
STEP12 36.29 16 797
STEP13 40.82 0 0 California Onshore

STEP14 45.36 21 1,063

### Appendix 6-2.3 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP15 49.90 0 0
STEP16 54.43 16 797
STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 0 0
STEP1 -13.61 3 136
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 13 627
STEP6 9.07 16 801
STEP7 13.61 16 804
STEP8 18.14 1 35
STEP9 22.68 7 353
STEP10 27.22 1 59
STEP11 31.75 0 0
STEP12 36.29 0 22
STEP13 40.82 0 0
STEP14 45.36 1 30
STEP15 49.90 0 0
STEP16 54.43 0 22
STEP17 58.97 0 0
STEP18 63.50 0 0 Colorado

STEP19 68.04 0 0
STEP1 -13.61 2 105
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 456 22,813
STEP6 9.07 550 27,479
STEP7 13.61 426 21,317
STEP8 18.14 0 13
STEP9 22.68 143 7,172
STEP10 27.22 1 33
STEP11 31.75 0 0
STEP12 36.29 0 20
STEP13 40.82 0 0
STEP14 45.36 1 26
STEP15 49.90 0 0
STEP16 54.43 0 20
STEP17 58.97 0 0
STEP18 63.50 0 0 Florida

STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0 Georgia

STEP3 -4.54 0 0

### Appendix 6-2.4 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP4 0.00 0 0
STEP5 4.54 42 2,117
STEP6 9.07 51 2,555
STEP7 13.61 40 2,000
STEP8 18.14 0 0
STEP9 22.68 0 0
STEP10 27.22 13 667
STEP11 31.75 0 0
STEP12 36.29 0 0
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 0 0
STEP1 -13.61 3 165
STEP2 -9.07 0 16
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 309 15,455
STEP6 9.07 373 18,653
STEP7 13.61 303 15,168
STEP8 18.14 1 73
STEP9 22.68 108 5,420
STEP10 27.22 4 180
STEP11 31.75 0 0
STEP12 36.29 2 109
STEP13 40.82 0 0
STEP14 45.36 3 144
STEP15 49.90 0 0
STEP16 54.43 2 108
STEP17 58.97 0 0
STEP18 63.50 0 0 Illinois

STEP19 68.04 0 0
STEP1 -13.61 0 17
STEP2 -9.07 0 2
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 164 8,195
STEP6 9.07 198 9,890
STEP7 13.61 167 8,332
STEP8 18.14 0 1
STEP9 22.68 56 2,781
STEP10 27.22 0 2 Indiana

STEP11 31.75 0 0

### Appendix 6-2.5 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP12 36.29 0 1
STEP13 40.82 0 0
STEP14 45.36 0 1
STEP15 49.90 0 0
STEP16 54.43 0 1
STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 0 0
STEP1 -13.61 6 287
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 37 1,863
STEP6 9.07 50 2,513
STEP7 13.61 66 3,323
STEP8 18.14 14 685
STEP9 22.68 32 1,620
STEP10 27.22 0 0
STEP11 31.75 0 0
STEP12 36.29 12 620
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0
STEP18 63.50 0 0 Kansas

STEP19 68.04 0 0
STEP1 -13.61 0 7
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 54 2,694
STEP6 9.07 86 4,310
STEP7 13.61 16 808
STEP8 18.14 1 26
STEP9 22.68 4 208
STEP10 27.22 1 64
STEP11 31.75 0 0
STEP12 36.29 4 182
STEP13 40.82 0 0
STEP14 45.36 1 52
STEP15 49.90 0 0
STEP16 54.43 1 39
STEP17 58.97 0 0
STEP18 63.50 0 0 Kentucky

STEP19 68.04 2 86

### Appendix 6-2.6 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP1 -13.61 20 1,012
STEP2 -9.07 0 0
STEP3 -4.54 3 130
STEP4 0.00 0 0
STEP5 4.54 918 45,891
STEP6 9.07 1,127 56,334
STEP7 13.61 949 47,463
STEP8 18.14 7 353
STEP9 22.68 47 2,342
STEP10 27.22 334 16,704
STEP11 31.75 0 0
STEP12 36.29 8 397
STEP13 40.82 0 0
STEP14 45.36 26 1,292
STEP15 49.90 3 134
STEP16 54.43 11 530
STEP17 58.97 0 0
STEP18 63.50 0 0 Louisiana Onshore

STEP19 68.04 8 397
STEP1 -13.61 23 1,128
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 0 0
STEP6 9.07 0 0
STEP7 13.61 661 33,069
STEP8 18.14 2,535 126,766
STEP9 22.68 0 0
STEP10 27.22 677 33,829
STEP11 31.75 2,208 110,376
STEP12 36.29 992 49,604
STEP13 40.82 20 1,012
STEP14 45.36 1,695 84,765
STEP15 49.90 0 0
STEP16 54.43 1,338 66,898
STEP17 58.97 0 0
STEP18 63.50 0 0 Louisiana Offshore

STEP19 68.04 1,159 57,975
STEP1 -13.61 1 62
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 339 16,935
STEP6 9.07 409 20,471
STEP7 13.61 323 16,130 Michigan

STEP8 18.14 0 4

### Appendix 6-2.7 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP9 22.68 108 5,380
STEP10 27.22 0 7
STEP11 31.75 0 6
STEP12 36.29 0 4
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 2
STEP16 54.43 0 0
STEP17 58.97 0 3
STEP18 63.50 0 0
STEP19 68.04 0 2
STEP1 -13.61 2 117
STEP2 -9.07 0 0
STEP3 -4.54 0 18
STEP4 0.00 0 0
STEP5 4.54 552 27,623
STEP6 9.07 668 33,410
STEP7 13.61 522 26,080
STEP8 18.14 1 63
STEP9 22.68 4 221
STEP10 27.22 178 8,877
STEP11 31.75 0 0
STEP12 36.29 1 71
STEP13 40.82 0 0
STEP14 45.36 3 157
STEP15 49.90 0 0
STEP16 54.43 2 95
STEP17 58.97 0 0
STEP18 63.50 0 0 Mississippi

STEP19 68.04 1 71
STEP1 -13.61 4 194
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 1,608 80,381
STEP6 9.07 1,941 97,053
STEP7 13.61 1,505 75,253
STEP8 18.14 8 396
STEP9 22.68 513 25,652
STEP10 27.22 23 1,131
STEP11 31.75 0 0
STEP12 36.29 8 391
STEP13 40.82 0 0
STEP14 45.36 13 652
STEP15 49.90 0 0 Montana

STEP16 54.43 10 522

### Appendix 6-2.8 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 8 391
STEP1 -13.61 5 241
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 91 4,549
STEP6 9.07 110 5,499
STEP7 13.61 91 4,538
STEP8 18.14 8 376
STEP9 22.68 41 2,068
STEP10 27.22 22 1,110
STEP11 31.75 0 0
STEP12 36.29 8 384
STEP13 40.82 0 0
STEP14 45.36 13 640
STEP15 49.90 0 0
STEP16 54.43 10 512
STEP17 58.97 0 0
STEP18 63.50 0 0 North Dakota

STEP19 68.04 8 384
STEP1 -13.61 0 11
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 15 734
STEP6 9.07 18 881
STEP7 13.61 17 859
STEP8 18.14 0 11
STEP9 22.68 7 337
STEP10 27.22 1 25
STEP11 31.75 0 0
STEP12 36.29 0 15
STEP13 40.82 0 0
STEP14 45.36 0 20
STEP15 49.90 0 0
STEP16 54.43 0 15
STEP17 58.97 0 0
STEP18 63.50 0 0 Nebraska

STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0 Nevada

STEP5 4.54 60 3,024

### Appendix 6-2.9 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP6 9.07 73 3,650
STEP7 13.61 56 2,821
STEP8 18.14 0 0
STEP9 22.68 19 940
STEP10 27.22 0 0
STEP11 31.75 0 0
STEP12 36.29 0 0
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 0 0
STEP1 -13.61 13 672
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 36 1,791
STEP6 9.07 67 3,338
STEP7 13.61 103 5,130
STEP8 18.14 6 285
STEP9 22.68 59 2,960
STEP10 27.22 21 1,033
STEP11 31.75 0 0
STEP12 36.29 6 293
STEP13 40.82 0 0
STEP14 45.36 8 391
STEP15 49.90 0 0
STEP16 54.43 6 293
STEP17 58.97 0 0
STEP18 63.50 0 0 New Mexico

STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 0 0
STEP6 9.07 0 8
STEP7 13.61 1 54
STEP8 18.14 0 15
STEP9 22.68 1 39
STEP10 27.22 0 7
STEP11 31.75 0 0
STEP12 36.29 0 23 New York

STEP13 40.82 0 0

### Appendix 6-2.10 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 213 10,634
STEP6 9.07 257 12,835
STEP7 13.61 206 10,320
STEP8 18.14 0 13
STEP9 22.68 71 3,551
STEP10 27.22 1 33
STEP11 31.75 0 0
STEP12 36.29 3 130
STEP13 40.82 0 0
STEP14 45.36 1 26
STEP15 49.90 0 0
STEP16 54.43 0 20
STEP17 58.97 0 0
STEP18 63.50 0 0 Ohio

STEP19 68.04 1 66
STEP1 -13.61 18 898
STEP2 -9.07 2 116
STEP3 -4.54 3 154
STEP4 0.00 0 0
STEP5 4.54 2 117
STEP6 9.07 10 502
STEP7 13.61 41 2,070
STEP8 18.14 8 387
STEP9 22.68 35 1,767
STEP10 27.22 36 1,779
STEP11 31.75 0 0
STEP12 36.29 9 436
STEP13 40.82 0 0
STEP14 45.36 22 1,116
STEP15 49.90 0 0
STEP16 54.43 12 581
STEP17 58.97 0 0
STEP18 63.50 0 0 Oklahoma

STEP19 68.04 9 436
STEP1 -13.61 0 0 Oregon
STEP2 -9.07 0 0

### Appendix 6-2.11 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 171 8,530
STEP6 9.07 206 10,294
STEP7 13.61 161 8,036
STEP8 18.14 0 0
STEP9 22.68 54 2,679
STEP10 27.22 0 0
STEP11 31.75 0 0
STEP12 36.29 0 0
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 0 0
STEP6 9.07 0 0
STEP7 13.61 309 15,432
STEP8 18.14 331 16,535
STEP9 22.68 573 28,660
STEP10 27.22 0 2
STEP11 31.75 0 3
STEP12 36.29 198 9,921
STEP13 40.82 331 16,537
STEP14 45.36 265 13,234
STEP15 49.90 0 0
STEP16 54.43 2 83
STEP17 58.97 0 8
STEP18 63.50 2 110 Pacific Offshore

STEP19 68.04 222 11,123
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 52 2,611
STEP6 9.07 84 4,178
STEP7 13.61 19 972
STEP8 18.14 1 34
STEP9 22.68 5 262 Pennsylvania

STEP10 27.22 2 86

### Appendix 6-2.12 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP11 31.75 0 0
STEP12 36.29 4 206
STEP13 40.82 0 0
STEP14 45.36 1 69
STEP15 49.90 0 0
STEP16 54.43 1 52
STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 2 93
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 43 2,126
STEP6 9.07 51 2,565
STEP7 13.61 40 2,000
STEP8 18.14 0 0
STEP9 22.68 13 667
STEP10 27.22 0 0
STEP11 31.75 0 0
STEP12 36.29 0 0
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0
STEP18 63.50 0 0 South Carolina

STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 219 10,933
STEP6 9.07 264 13,196
STEP7 13.61 206 10,313
STEP8 18.14 0 0
STEP9 22.68 69 3,438
STEP10 27.22 0 1
STEP11 31.75 0 0
STEP12 36.29 0 0
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0 South Dakota

STEP18 63.50 0 0

### Appendix 6-2.13 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 21 1,035
STEP6 9.07 33 1,657
STEP7 13.61 6 319
STEP8 18.14 0 0
STEP9 22.68 0 0
STEP10 27.22 0 0
STEP11 31.75 0 0
STEP12 36.29 0 0
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0
STEP18 63.50 0 0 Tennessee

STEP19 68.04 0 0
STEP1 -13.61 113 5,633
STEP2 -9.07 0 0
STEP3 -4.54 14 724
STEP4 0.00 0 0
STEP5 4.54 1,800 90,016
STEP6 9.07 2,195 109,766
STEP7 13.61 1,913 95,669
STEP8 18.14 25 1,258
STEP9 22.68 228 11,406
STEP10 27.22 690 34,524
STEP11 31.75 0 0
STEP12 36.29 38 1,887
STEP13 40.82 0 0
STEP14 45.36 81 4,041
STEP15 49.90 0 0
STEP16 54.43 38 1,887
STEP17 58.97 0 0
STEP18 63.50 0 0 Texas Onshore

STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 0 0
STEP6 9.07 0 0 Texas Offshore

STEP7 13.61 397 19,842

### Appendix 6-2.14 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP8 18.14 1,521 76,059
STEP9 22.68 0 0
STEP10 27.22 400 19,999
STEP11 31.75 1,324 66,192
STEP12 36.29 595 29,762
STEP13 40.82 4 197
STEP14 45.36 1,000 49,998
STEP15 49.90 0 0
STEP16 54.43 796 39,801
STEP17 58.97 3 133
STEP18 63.50 0 0
STEP19 68.04 627 31,338
STEP1 -13.61 4 195
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 2 106
STEP6 9.07 4 184
STEP7 13.61 5 251
STEP8 18.14 1 36
STEP9 22.68 3 137
STEP10 27.22 1 46
STEP11 31.75 0 0
STEP12 36.29 0 12
STEP13 40.82 0 0
STEP14 45.36 0 16
STEP15 49.90 0 0
STEP16 54.43 0 12
STEP17 58.97 0 0
STEP18 63.50 0 0 Utah

STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 0 0
STEP6 9.07 0 0
STEP7 13.61 0 21
STEP8 18.14 0 9
STEP9 22.68 1 53
STEP10 27.22 0 23
STEP11 31.75 0 0
STEP12 36.29 0 14
STEP13 40.82 0 0
STEP14 45.36 0 18 Virginia

STEP15 49.90 0 0

### Appendix 6-2.15 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP16 54.43 0 14
STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 162 8,101
STEP6 9.07 196 9,777
STEP7 13.61 155 7,738
STEP8 18.14 0 0
STEP9 22.68 52 2,579
STEP10 27.22 0 0
STEP11 31.75 0 0
STEP12 36.29 0 0
STEP13 40.82 0 0
STEP14 45.36 0 0
STEP15 49.90 0 0
STEP16 54.43 0 0
STEP17 58.97 0 0
STEP18 63.50 0 0 Washington

STEP19 68.04 0 0
STEP1 -13.61 0 0
STEP2 -9.07 0 0
STEP3 -4.54 0 0
STEP4 0.00 0 0
STEP5 4.54 47 2,351
STEP6 9.07 75 3,761
STEP7 13.61 14 721
STEP8 18.14 1 38
STEP9 22.68 5 227
STEP10 27.22 2 94
STEP11 31.75 0 0
STEP12 36.29 1 73
STEP13 40.82 0 0
STEP14 45.36 2 75
STEP15 49.90 0 0
STEP16 54.43 1 57
STEP17 58.97 0 0
STEP18 63.50 0 0 West Virginia

STEP19 68.04 0 10
STEP1 -13.61 6 324
STEP2 -9.07 0 0
STEP3 -4.54 0 0 Wyoming

STEP4 0.00 0 0

### Appendix 6-2.16 CO 2 Storage Region Step Name CO 2 Storage Step Cost (2007$/Ton) Annual Step Bound (MMTons) Total Storage Capacity (MMTons)

STEP5 4.54 2,644 132,195
STEP6 9.07 3,198 159,909
STEP7 13.61 2,486 124,304
STEP8 18.14 2 100
STEP9 22.68 836 41,794
STEP10 27.22 10 496
STEP11 31.75 0 0
STEP12 36.29 1 65
STEP13 40.82 0 0
STEP14 45.36 7 339
STEP15 49.90 0 0
STEP16 54.43 2 87
STEP17 58.97 0 0
STEP18 63.50 0 0
STEP19 68.04 1 65

### Appendix 6-2.1 

## Appendix 6-2 CO 2



## Transportation Matrix in EPA Base Case v.4.10

CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Alabama 26.30
Arizona 3.50
Arkansas 19.34
Atlantic Offshore 35.66
California Onshore 13.36
Colorado 9.43

Florida 34.30 Georgia 32.10
Illinois 23.54
Indiana 25.54
Kansas 11.13 Kentucky 29.06
Louisiana Onshore 22.58
Louisiana Offshore 23.19 Michigan 28.53 Mississippi 23.21

Montana 18.29
North Dakota 18.73

Nebraska 11.58
Nevada 9.20
New Mexico 3.30

New York 34.34

Ohio 30.66
Oklahoma 12.63

Oregon 20.63
Pacific Offshore 12.18

Pennsylvania 33.49
South Carolina 32.35
South Dakota 16.24

Tennessee 27.12
Texas Onshore 15.99
Texas Offshore 21.22
Utah 7.90 Virginia 32.84 West Virginia 30.89 AZNM

Wyoming 12.76
Alabama 37.36
Arizona 11.27
Arkansas 30.35
Atlantic Offshore 46.55
California Onshore 4.67
Colorado 17.69

Florida 45.34 Georgia 43.10
Illinois 33.75 CA-N

Indiana 35.78

### Appendix 6-2.2 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Kansas 21.53 Kentucky 39.54
Louisiana Onshore 33.61
Louisiana Offshore 34.04 Michigan 37.98 Mississippi 34.28

Montana 22.73
North Dakota 23.59

Nebraska 19.92
Nevada 5.18
New Mexico 13.42

New York 43.93

Ohio 40.66
Oklahoma 23.52

Oregon 14.68
Pacific Offshore 5.49

Pennsylvania 43.47
South Carolina 43.30
South Dakota 22.18

Tennessee 37.87
Texas Onshore 26.74
Texas Offshore 31.95
Utah 12.50 Virginia 43.36 West Virginia 41.28

Wyoming 18.13
Alabama 33.45
Arizona 7.46
Arkansas 26.55
Atlantic Offshore 42.90
California Onshore 6.99
Colorado 15.09

Florida 41.31 Georgia 39.32
Illinois 30.57
Indiana 32.59
Kansas 18.18 Kentucky 36.20
Louisiana Onshore 29.58
Louisiana Offshore 29.90 Michigan 35.22 Mississippi 30.34

Montana 21.82
North Dakota 22.54

Nebraska 17.41
Nevada 4.60 CA-S

New Mexico 9.72

### Appendix 6-2.3 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

New York 41.12

Ohio 37.62
Oklahoma 19.87

Oregon 17.43
Pacific Offshore 5.27

Pennsylvania 40.45
South Carolina 39.59
South Dakota 20.67

Tennessee 34.35
Texas Onshore 22.59
Texas Offshore 27.79
Utah 10.69 Virginia 40.00 West Virginia 38.01

Wyoming 16.62
Alabama 15.38
Arizona 26.11
Arkansas 13.19
Atlantic Offshore 16.86
California Onshore 34.86
Colorado 18.43

Florida 22.02 Georgia 16.05
Illinois 4.75
Indiana 4.82
Kansas 15.43 Kentucky 8.40
Louisiana Onshore 16.98
Louisiana Offshore 20.13 Michigan 4.81 Mississippi 15.10

Montana 18.69
North Dakota 17.74

Nebraska 16.28
Nevada 30.94
New Mexico 23.94

New York 10.57

Ohio 7.45
Oklahoma 15.12

Oregon 34.88
Pacific Offshore 35.76

Pennsylvania 10.14
South Carolina 15.15
South Dakota 16.60

Tennessee 9.56
Texas Onshore 20.39 COMD

Texas Offshore 20.75

### Appendix 6-2.4 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Utah 23.86 Virginia 11.68 West Virginia 9.22

Wyoming 19.55
Alabama 20.59
Arizona 39.27
Arkansas 23.19
Atlantic Offshore 13.07
California Onshore 48.11
Colorado 31.74

Florida 22.09 Georgia 16.20
Illinois 16.14
Indiana 14.22
Kansas 28.47 Kentucky 11.74
Louisiana Onshore 24.54
Louisiana Offshore 27.30 Michigan 11.58 Mississippi 22.44

Montana 30.66
North Dakota 29.63

Nebraska 29.57
Nevada 44.25
New Mexico 37.07

New York 5.72

Ohio 8.97
Oklahoma 27.58

Oregon 47.39
Pacific Offshore 48.99

Pennsylvania 6.18
South Carolina 14.74
South Dakota 29.17

Tennessee 14.78
Texas Onshore 30.61
Texas Offshore 28.92
Utah 37.14 Virginia 9.36 West Virginia 9.81 DSNY

Wyoming 32.53
Alabama 7.84
Arizona 21.51
Arkansas 3.44
Atlantic Offshore 16.70
California Onshore 31.99
Colorado 17.06 ENTG

Florida 16.30

### Appendix 6-2.5 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton) Georgia 13.09
Illinois 8.81
Indiana 9.87
Kansas 11.85 Kentucky 11.53
Louisiana Onshore 6.63
Louisiana Offshore 9.61 Michigan 15.04 Mississippi 5.45

Montana 22.95
North Dakota 22.36

Nebraska 16.04
Nevada 27.84
New Mexico 19.32

New York 19.12

Ohio 14.70
Oklahoma 9.24

Oregon 35.90
Pacific Offshore 31.01

Pennsylvania 17.17
South Carolina 13.34
South Dakota 19.94

Tennessee 8.76
Texas Onshore 10.31
Texas Offshore 9.93
Utah 22.29 Virginia 14.87 West Virginia 13.49

Wyoming 20.78
Alabama 11.70
Arizona 17.85
Arkansas 6.89
Atlantic Offshore 22.16
California Onshore 28.61
Colorado 16.48

Florida 19.08 Georgia 18.02
Illinois 15.08
Indiana 16.40
Kansas 11.44 Kentucky 18.11
Louisiana Onshore 7.38
Louisiana Offshore 7.88 Michigan 21.40 Mississippi 8.61

Montana 24.73
North Dakota 24.42 ERCT

Nebraska 16.44

### Appendix 6-2.6 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Nevada 24.59
New Mexico 15.94

New York 25.74

Ohio 21.33
Oklahoma 8.37

Oregon 34.69
Pacific Offshore 26.55

Pennsylvania 23.80
South Carolina 18.68
South Dakota 21.63

Tennessee 15.19
Texas Onshore 4.33
Texas Offshore 6.27
Utah 20.59 Virginia 21.28 West Virginia 20.06

Wyoming 21.08
Alabama 7.71
Arizona 33.74
Arkansas 14.94
Atlantic Offshore 8.78
California Onshore 44.39
Colorado 29.83

Florida 4.12 Georgia 5.33
Illinois 16.83
Indiana 15.92
Kansas 24.61 Kentucky 13.10
Louisiana Onshore 11.59
Louisiana Offshore 12.45 Michigan 20.06 Mississippi 10.73

Montana 34.81
North Dakota 34.07

Nebraska 28.72
Nevada 40.31
New Mexico 31.64

New York 20.09

Ohio 16.78
Oklahoma 21.91

Oregon 48.66
Pacific Offshore 42.80

Pennsylvania 17.31
South Carolina 6.76
South Dakota 31.99

Tennessee 11.23 FRCC

Texas Onshore 19.12

### Appendix 6-2.7 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Texas Offshore 14.75
Utah 35.06 Virginia 12.94 West Virginia 13.74

Wyoming 33.33
Alabama 12.18
Arizona 23.91
Arkansas 9.40
Atlantic Offshore 16.05
California Onshore 33.36
Colorado 17.01

Florida 19.59 Georgia 14.19
Illinois 3.25
Indiana 4.03
Kansas 13.08 Kentucky 7.95
Louisiana Onshore 13.27
Louisiana Offshore 16.40 Michigan 8.25 Mississippi 11.47

Montana 19.47
North Dakota 18.64

Nebraska 15.15
Nevada 29.31
New Mexico 21.68

New York 13.25

Ohio 9.20
Oklahoma 12.12

Oregon 34.74
Pacific Offshore 33.66

Pennsylvania 12.01
South Carolina 13.61
South Dakota 16.90

Tennessee 7.49
Texas Onshore 16.70
Texas Offshore 16.94
Utah 22.59 Virginia 11.76 West Virginia 9.55 GWAY

Wyoming 19.15
Alabama 20.91
Arizona 40.31
Arkansas 23.89
Atlantic Offshore 12.75
California Onshore 49.26
Colorado 32.87 LILC

Florida 21.86

### Appendix 6-2.8 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton) Georgia 16.18
Illinois 17.12
Indiana 15.16
Kansas 29.50 Kentucky 12.40
Louisiana Onshore 24.99
Louisiana Offshore 27.68 Michigan 12.79 Mississippi 22.91

Montana 31.94
North Dakota 30.91

Nebraska 30.72
Nevada 45.37
New Mexico 38.10

New York 6.99

Ohio 9.97
Oklahoma 28.52

Oregon 48.65
Pacific Offshore 50.04

Pennsylvania 7.14
South Carolina 14.74
South Dakota 30.42

Tennessee 15.36
Texas Onshore 31.26
Texas Offshore 29.36
Utah 38.30 Virginia 9.62 West Virginia 10.45

Wyoming 33.74
Alabama 18.55
Arizona 38.48
Arkansas 21.68
Atlantic Offshore 10.69
California Onshore 47.66
Colorado 31.26

Florida 19.72 Georgia 13.89
Illinois 15.24
Indiana 13.24
Kansas 27.66 Kentucky 10.19
Louisiana Onshore 22.64
Louisiana Offshore 25.32 Michigan 11.60 Mississippi 20.57

Montana 30.99
North Dakota 29.97 MACE

Nebraska 29.18

### Appendix 6-2.9 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Nevada 43.73
New Mexico 36.26

New York 6.45

Ohio 8.24
Oklahoma 26.53

Oregon 47.58
Pacific Offshore 48.24

Pennsylvania 5.49
South Carolina 12.45
South Dakota 29.27

Tennessee 13.07
Texas Onshore 29.00
Texas Offshore 27.02
Utah 36.75 Virginia 7.30 West Virginia 8.23

Wyoming 32.43
Alabama 16.44
Arizona 36.43
Arkansas 19.46
Atlantic Offshore 9.47
California Onshore 45.76
Colorado 29.37

Florida 18.22 Georgia 12.14
Illinois 13.21
Indiana 11.19
Kansas 25.61 Kentucky 7.98
Louisiana Onshore 20.47
Louisiana Offshore 23.18 Michigan 10.24 Mississippi 18.39

Montana 29.61
North Dakota 28.61

Nebraska 27.33
Nevada 41.79
New Mexico 34.20

New York 6.22

Ohio 6.46
Oklahoma 24.39

Oregon 46.05
Pacific Offshore 46.19

Pennsylvania 4.10
South Carolina 10.68
South Dakota 27.73

Tennessee 10.85 MACS

Texas Onshore 26.78

### Appendix 6-2.10 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Texas Offshore 24.84
Utah 34.89 Virginia 5.40 West Virginia 6.02

Wyoming 30.75
Alabama 18.02
Arizona 36.52
Arkansas 20.34
Atlantic Offshore 11.63
California Onshore 45.53
Colorado 29.13

Florida 20.31 Georgia 14.14
Illinois 13.33
Indiana 11.38
Kansas 25.71 Kentucky 8.92
Louisiana Onshore 21.83
Louisiana Offshore 24.66 Michigan 9.25 Mississippi 19.72

Montana 28.62
North Dakota 27.60

Nebraska 27.00
Nevada 41.62
New Mexico 34.31

New York 4.24

Ohio 6.18
Oklahoma 24.75

Oregon 45.23
Pacific Offshore 46.26

Pennsylvania 3.35
South Carolina 12.68
South Dakota 26.94

Tennessee 12.01
Texas Onshore 27.78
Texas Offshore 26.20
Utah 34.58 Virginia 7.16 West Virginia 7.03 MACW

Wyoming 30.15
Alabama 16.68
Arizona 29.60
Arkansas 15.92
Atlantic Offshore 15.62
California Onshore 38.19 MECS

Colorado 21.83

### Appendix 6-2.11 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Florida 22.17 Georgia 15.84
Illinois 7.49
Indiana 6.33
Kansas 18.94 Kentucky 7.85
Louisiana Onshore 19.10
Louisiana Offshore 22.23 Michigan 3.25 Mississippi 17.07

Montana 21.14
North Dakota 20.13

Nebraska 19.62
Nevada 34.33
New Mexico 27.44

New York 7.11

Ohio 4.97
Oklahoma 18.56

Oregon 37.67
Pacific Offshore 39.24

Pennsylvania 7.16
South Carolina 14.68
South Dakota 19.37

Tennessee 10.21
Texas Onshore 23.33
Texas Offshore 23.15
Utah 27.18 Virginia 10.10 West Virginia 7.73

Wyoming 22.60
Alabama 19.01
Arizona 19.94
Arkansas 13.69
Atlantic Offshore 23.49
California Onshore 27.89
Colorado 11.66

Florida 26.95 Georgia 21.76
Illinois 9.29
Indiana 10.96
Kansas 10.29 Kentucky 15.09
Louisiana Onshore 18.68
Louisiana Offshore 21.50 Michigan 11.53 Mississippi 17.42 MRO

Montana 11.90

### Appendix 6-2.12 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

North Dakota 11.07

Nebraska 9.34
Nevada 24.07
New Mexico 17.93

New York 17.48

Ohio 14.76
Oklahoma 11.48

Oregon 27.61
Pacific Offshore 29.28

Pennsylvania 17.42
South Carolina 21.18
South Dakota 9.43

Tennessee 15.05
Texas Onshore 18.97
Texas Offshore 21.26
Utah 16.81 Virginia 18.68 West Virginia 16.31

Wyoming 12.27
Alabama 23.42
Arizona 42.41
Arkansas 26.33
Atlantic Offshore 14.98
California Onshore 51.13
Colorado 34.79

Florida 24.11 Georgia 18.58
Illinois 19.32
Indiana 17.41
Kansas 31.63 Kentucky 14.85
Louisiana Onshore 27.50
Louisiana Offshore 30.19 Michigan 14.53 Mississippi 25.42

Montana 33.26
North Dakota 32.21

Nebraska 32.59
Nevada 47.30
New Mexico 40.21

New York 8.58

Ohio 12.16
Oklahoma 30.77

Oregon 50.04
Pacific Offshore 52.11

Pennsylvania 9.37 NENG

South Carolina 17.16

### Appendix 6-2.13 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

South Dakota 31.93

Tennessee 17.84
Texas Onshore 33.71
Texas Offshore 31.88
Utah 40.15 Virginia 12.11 West Virginia 12.91

Wyoming 35.40
Alabama 27.91
Arizona 9.77
Arkansas 20.78
Atlantic Offshore 35.44
California Onshore 14.70
Colorado 5.96

Florida 36.36 Georgia 32.67
Illinois 21.61
Indiana 23.58
Kansas 11.09 Kentucky 27.62
Louisiana Onshore 25.29
Louisiana Offshore 26.89 Michigan 25.00 Mississippi 25.18

Montana 10.32
North Dakota 10.93

Nebraska 7.21
Nevada 10.98
New Mexico 9.06

New York 30.95

Ohio 28.06
Oklahoma 13.98

Oregon 15.84
Pacific Offshore 16.89

Pennsylvania 30.80
South Carolina 32.53
South Dakota 8.96

Tennessee 26.57
Texas Onshore 20.66
Texas Offshore 25.42
Utah 3.71 Virginia 31.39 West Virginia 29.15 NWPE

Wyoming 5.44
Alabama 20.30
Arizona 39.63 NYC

Arkansas 23.21

### Appendix 6-2.14 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Atlantic Offshore 12.36
California Onshore 48.61
Colorado 32.22

Florida 21.44 Georgia 15.67
Illinois 16.43
Indiana 14.47
Kansas 28.82 Kentucky 11.72
Louisiana Onshore 24.35
Louisiana Offshore 27.05 Michigan 12.18 Mississippi 22.27

Montana 31.39
North Dakota 30.36

Nebraska 30.08
Nevada 44.72
New Mexico 37.42

New York 6.46

Ohio 9.29
Oklahoma 27.83

Oregon 48.09
Pacific Offshore 49.37

Pennsylvania 6.46
South Carolina 14.22
South Dakota 29.83

Tennessee 14.69
Texas Onshore 30.58
Texas Offshore 28.72
Utah 37.65 Virginia 9.02 West Virginia 9.77

Wyoming 33.13
Alabama 42.14
Arizona 20.11
Arkansas 35.04
Atlantic Offshore 48.98
California Onshore 13.81
Colorado 20.04

Florida 50.59 Georgia 46.59
Illinois 34.78
Indiana 36.61
Kansas 25.36 Kentucky 40.75
Louisiana Onshore 39.59
Louisiana Offshore 41.10 PNW

Michigan 36.69

### Appendix 6-2.15 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton) Mississippi 39.46

Montana 17.43
North Dakota 18.47

Nebraska 21.01
Nevada 14.85
New Mexico 21.07

New York 42.34

Ohio 40.36
Oklahoma 28.28

Oregon 3.25
Pacific Offshore 19.55

Pennsylvania 42.86
South Carolina 46.30
South Dakota 19.07

Tennessee 40.20
Texas Onshore 34.51
Texas Offshore 39.51
Utah 15.41 Virginia 44.36 West Virginia 42.03

Wyoming 16.74
Alabama 13.04
Arizona 28.50
Arkansas 12.94
Atlantic Offshore 13.05
California Onshore 37.78
Colorado 21.38

Florida 18.74 Georgia 12.54
Illinois 5.27
Indiana 3.35
Kansas 17.68 Kentucky 4.66
Louisiana Onshore 15.63
Louisiana Offshore 18.73 Michigan 5.43 Mississippi 13.56

Montana 22.47
North Dakota 21.53

Nebraska 19.37
Nevada 33.79
New Mexico 26.28

New York 8.87

Ohio 4.60
Oklahoma 16.70

Oregon 38.47
Pacific Offshore 38.25 RFCO

Pennsylvania 7.40

### Appendix 6-2.16 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

South Carolina 11.52
South Dakota 20.26

Tennessee 6.58
Texas Onshore 20.46
Texas Offshore 19.79
Utah 26.91 Virginia 7.97 West Virginia 5.45

Wyoming 23.00
Alabama 13.89
Arizona 32.04
Arkansas 15.58
Atlantic Offshore 10.62
California Onshore 41.38
Colorado 24.98

Florida 17.85 Georgia 11.42
Illinois 8.80
Indiana 6.79
Kansas 21.22 Kentucky 4.32
Louisiana Onshore 17.34
Louisiana Offshore 20.29 Michigan 6.87 Mississippi 15.22

Montana 25.71
North Dakota 24.74

Nebraska 22.97
Nevada 37.39
New Mexico 29.81

New York 6.63

Ohio 3.25
Oklahoma 20.08

Oregon 41.93
Pacific Offshore 41.80

Pennsylvania 4.25
South Carolina 10.11
South Dakota 23.65

Tennessee 7.49
Texas Onshore 23.05
Texas Offshore 21.68
Utah 30.52 Virginia 5.46 West Virginia 3.25 RFCP

Wyoming 26.52
Alabama 23.85 RMPA
Arizona 9.55

### Appendix 6-2.17 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Arkansas 16.71
Atlantic Offshore 31.63
California Onshore 17.46
Colorado 3.49

Florida 32.29 Georgia 28.72
Illinois 18.09
Indiana 20.10
Kansas 7.05 Kentucky 24.03
Louisiana Onshore 21.22
Louisiana Offshore 22.89 Michigan 22.12 Mississippi 21.10

Montana 11.42
North Dakota 11.64

Nebraska 4.18
Nevada 13.38
New Mexico 7.86

New York 28.07

Ohio 24.84
Oklahoma 9.92

Oregon 19.92
Pacific Offshore 18.51

Pennsylvania 27.64
South Carolina 28.64
South Dakota 8.93

Tennessee 22.77
Texas Onshore 16.93
Texas Offshore 21.49
Utah 6.49 Virginia 27.84 West Virginia 25.67

Wyoming 6.65
Alabama 32.28
Arizona 6.22
Arkansas 25.26
Atlantic Offshore 41.46
California Onshore 7.58
Colorado 13.07

Florida 40.30 Georgia 38.00
Illinois 28.82
Indiana 30.84
Kansas 16.51 Kentucky 34.55 SNV

Louisiana Onshore 28.58

### Appendix 6-2.18 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Louisiana Offshore 29.13 Michigan 33.30 Mississippi 29.20

Montana 19.63
North Dakota 20.35

Nebraska 15.37
Nevada 3.47
New Mexico 8.34

New York 39.22

Ohio 35.81
Oklahoma 18.42

Oregon 16.35
Pacific Offshore 7.40

Pennsylvania 38.63
South Carolina 38.20
South Dakota 18.48

Tennessee 32.81
Texas Onshore 21.87
Texas Offshore 27.10
Utah 8.50 Virginia 38.36 West Virginia 36.31

Wyoming 14.43
Alabama 4.18
Arizona 27.42
Arkansas 8.43
Atlantic Offshore 10.90
California Onshore 37.87
Colorado 22.64

Florida 11.34 Georgia 7.18
Illinois 9.70
Indiana 9.30
Kansas 17.56 Kentucky 8.20
Louisiana Onshore 7.81
Louisiana Offshore 10.55 Michigan 14.28 Mississippi 5.77

Montana 27.31
North Dakota 26.58

Nebraska 21.36
Nevada 33.74
New Mexico 25.23

New York 16.41

Ohio 12.17 SOU

Oklahoma 15.13

### Appendix 6-2.19 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton) Oregon 41.36
Pacific Offshore 36.88

Pennsylvania 13.91
South Carolina 7.47
South Dakota 24.49

Tennessee 5.17
Texas Onshore 14.74
Texas Offshore 12.18
Utah 28.01 Virginia 10.43 West Virginia 9.83

Wyoming 25.93
Alabama 13.64
Arizona 18.28
Arkansas 7.41
Atlantic Offshore 20.61
California Onshore 28.02
Colorado 11.97

Florida 22.01 Georgia 17.82
Illinois 7.70
Indiana 9.68
Kansas 7.49 Kentucky 13.26
Louisiana Onshore 12.47
Louisiana Offshore 15.15 Michigan 13.35 Mississippi 11.49

Montana 16.91
North Dakota 16.32

Nebraska 10.49
Nevada 23.89
New Mexico 16.05

New York 18.80

Ohio 14.87
Oklahoma 6.58

Oregon 30.49
Pacific Offshore 28.03

Pennsylvania 17.68
South Carolina 17.65
South Dakota 13.92

Tennessee 11.76
Texas Onshore 12.94
Texas Offshore 14.83
Utah 17.50 Virginia 17.08 SPPN

West Virginia 15.04

### Appendix 6-2.20 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton) Wyoming 15.10

Alabama 11.20
Arizona 17.67
Arkansas 4.43
Atlantic Offshore 20.65
California Onshore 28.28
Colorado 14.16

Florida 19.48 Georgia 16.93
Illinois 11.33
Indiana 12.87
Kansas 8.86 Kentucky 15.21
Louisiana Onshore 8.21
Louisiana Offshore 10.25 Michigan 17.63 Mississippi 8.21

Montana 21.41
North Dakota 20.99

Nebraska 13.59
Nevada 24.13
New Mexico 15.52

New York 22.34

Ohio 18.01
Oklahoma 5.82

Oregon 32.92
Pacific Offshore 27.08

Pennsylvania 20.64
South Carolina 17.26
South Dakota 18.31

Tennessee 12.63
Texas Onshore 7.79
Texas Offshore 9.48
Utah 19.01 Virginia 18.71 West Virginia 17.18 SPPS

Wyoming 18.39
Alabama 8.35
Arizona 27.00
Arkansas 9.27
Atlantic Offshore 11.57
California Onshore 37.00
Colorado 21.01

Florida 14.92 Georgia 9.38 TVA

Illinois 5.89

### Appendix 6-2.21 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Indiana 5.11
Kansas 16.43 Kentucky 4.98
Louisiana Onshore 11.03
Louisiana Offshore 14.09 Michigan 10.09 Mississippi 8.93

Montana 24.27
North Dakota 23.45

Nebraska 19.38
Nevada 32.90
New Mexico 24.77

New York 12.91

Ohio 8.49
Oklahoma 14.64

Oregon 39.25
Pacific Offshore 36.71

Pennsylvania 10.73
South Carolina 8.86
South Dakota 21.65

Tennessee 3.25
Texas Onshore 16.66
Texas Offshore 15.30
Utah 26.56 Virginia 8.43 West Virginia 6.94

Wyoming 23.64
Alabama 10.73
Arizona 27.75
Arkansas 11.08
Atlantic Offshore 12.03
California Onshore 37.41
Colorado 21.13

Florida 16.77 Georgia 10.78
Illinois 4.94
Indiana 3.25
Kansas 16.98 Kentucky 3.82
Louisiana Onshore 13.38
Louisiana Offshore 16.47 Michigan 7.71 Mississippi 11.30

Montana 23.31
North Dakota 22.43

Nebraska 19.30 TVAK

Nevada 33.35

### Appendix 6-2.22 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

New Mexico 25.51

New York 10.73

Ohio 6.29
Oklahoma 15.60

Oregon 38.86
Pacific Offshore 37.51

Pennsylvania 8.79
South Carolina 9.94
South Dakota 20.88

Tennessee 4.40
Texas Onshore 18.59
Texas Offshore 17.60
Utah 26.71 Virginia 7.66 West Virginia 5.56

Wyoming 23.27
Alabama 20.03
Arizona 36.09
Arkansas 21.28
Atlantic Offshore 14.72
California Onshore 44.58
Colorado 28.28

Florida 23.17 Georgia 16.86
Illinois 13.43
Indiana 11.72
Kansas 25.39 Kentucky 10.47
Louisiana Onshore 23.46
Louisiana Offshore 26.43 Michigan 7.98 Mississippi 21.34

Montana 26.70
North Dakota 25.66

Nebraska 26.05
Nevada 40.78
New Mexico 33.92

New York 3.25

Ohio 6.70
Oklahoma 24.84

Oregon 43.46
Pacific Offshore 45.73

Pennsylvania 4.90
South Carolina 15.43
South Dakota 25.31

Tennessee 13.63
Texas Onshore 28.78 UPNY

Texas Offshore 27.77

### Appendix 6-2.23 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Utah 33.58 Virginia 9.76 West Virginia 8.90

Wyoming 28.77
Alabama 10.50
Arizona 34.30
Arkansas 15.62
Atlantic Offshore 4.12
California Onshore 44.44
Colorado 28.53

Florida 11.33 Georgia 5.19
Illinois 12.80
Indiana 11.12
Kansas 23.89 Kentucky 7.14
Louisiana Onshore 14.99
Louisiana Offshore 17.26 Michigan 13.65 Mississippi 13.12

Montana 31.22
North Dakota 30.33

Nebraska 26.88
Nevada 40.35
New Mexico 32.07

New York 12.49

Ohio 9.80
Oklahoma 21.89

Oregon 46.66
Pacific Offshore 43.93

Pennsylvania 9.72
South Carolina 3.74
South Dakota 28.79

Tennessee 7.53
Texas Onshore 22.16
Texas Offshore 19.21
Utah 34.09 Virginia 5.81 West Virginia 6.77 VACA

Wyoming 31.03
Alabama 14.71
Arizona 36.20
Arkansas 18.50
Atlantic Offshore 7.13
California Onshore 45.84
Colorado 29.54 VAPW

Florida 15.91

### Appendix 6-2.24 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton) Georgia 9.93
Illinois 13.24
Indiana 11.24
Kansas 25.45 Kentucky 7.42
Louisiana Onshore 18.94
Louisiana Offshore 21.50 Michigan 11.51 Mississippi 16.92

Montana 30.58
North Dakota 29.61

Nebraska 27.61
Nevada 41.82
New Mexico 33.96

New York 8.42

Ohio 7.43
Oklahoma 23.95

Oregon 46.77
Pacific Offshore 45.95

Pennsylvania 5.91
South Carolina 8.48
South Dakota 28.52

Tennessee 9.77
Texas Onshore 25.61
Texas Offshore 23.29
Utah 35.10 Virginia 4.65 West Virginia 5.67

Wyoming 31.30
Alabama 18.36
Arizona 26.16
Arkansas 15.53
Atlantic Offshore 19.56
California Onshore 34.18
Colorado 18.00

Florida 25.05 Georgia 19.02
Illinois 7.54
Indiana 7.86
Kansas 15.92 Kentucky 11.21
Louisiana Onshore 19.69
Louisiana Offshore 22.81 Michigan 5.49 Mississippi 17.89

Montana 16.61 WUMS

North Dakota 15.61

### Appendix 6-2.25 CO 2 Production Region CO 2 Storage Region Cost (2007$/Ton)

Nebraska 15.69
Nevada 30.42
New Mexico 24.08

New York 11.28

Ohio 9.37
Oklahoma 16.23

Oregon 33.18
Pacific Offshore 35.61

Pennsylvania 11.68
South Carolina 18.07
South Dakota 14.90

Tennessee 12.58
Texas Onshore 22.36
Texas Offshore 23.26
Utah 23.14 Virginia 14.13 West Virginia 11.73

Wyoming 18.28 

### Note: Production Regions are equal to IPM model regions

### 7-1 

# 7 Set-up Parameters and Rules
The EPA Base Case v.4.10 includes a number of assumptions that affect the way IPM treats the analysis time horizon, retrofit assignments, and environmental specifications for trading and banking. This section provides an overview of those assumptions.



## 7.1 Run Year Mapping Although IPM is capable of representing every i ndividual year in an analysis time horizon, individual years are typically grouped into model run years to increase the speed of modeling. While the model makes decisions only for run years, information on non-run years can be captured by mapping run years to the individual years they represent.

The analysis time horizon for EPA Base Case v.4.10 extends from 2012 through 2054 with IPM seeking the least cost solution that meets all c onstraints and minimizes net present values over this 43-year period. The six years designated as “model run years” and the mapping of calendar years to run years is shown in Table 7-1.

Table 7-1 Run Years and Analysis Year Mapping Used in the EPA Base Case v.4.10 Run Year Years Represented 2012 2012 - 2013 2015 2014 - 2016 2020 2017 - 2024 2030 2025 - 2034 2040 2035 - 2045 2050 2046 - 2054



## 7.2 Retrofit Assignments In IPM, model plants that represent existing gener ating units have the option of maintaining their current system configuration, retrofitting with pollu tion controls, or retiring early. The decision to retrofit or retire is endogenous to IPM and based on the least cost approach to meeting the system and other operating constraints included in the EPA Base Case v.4.10. IPM is capable of modeling retrofits and early retirements in two stages, enabling model plants to install two different sets of retrofits incrementally at different points in time. At each stage a retrofit set may consist of a single retrofit (e.g. LSFO Scrubber) or pre-specif ied combinations of retrofits (e.g., ACI + LSFO Scrubber +SCR). In EPA Base Case v.4.10 first stage retrofit options are provided to existing coal-steam and oil/gas steam plants. These plant s, as well as combined cycle plants, combustion turbines, and nuclear plants, are also given early retirement as an option in stage 1. Second stage retrofit options are provided to coal-steam plants only.

Table 7-2 and Table 7-3 present the first and second stage retrofit options respectively. The costs of multiple retrofits on the same model plant, whether installed in one or two stages, are assumed to be additive. In linear programming models like IPM, projections of pollution control equipment capacity and early retirements that can occur over the modeling time horizon are limited to those retrofit and retirement options that have been pre-specified when setting up the modeled scenario. While the model decides endogenously whether and how much of each retrofit option to install, it cannot provide a retrofit that was not pre-specified before the modeling scenario was run. Table 7-2 and Table 7-3 show all the retrofit options available in EPA Base Case v.4.10.

### 7-2 Table 7-2 First Stage Retrofit Assignment Scheme in EPA Base Case v.4.10 Plant Type Retrofit Option 1 st 

### Stage Criteria Coal Early Retirement All coal steam boilers

Coal Steam SCR All coal steam boilers that are 25 MW or larger and do not possess an existing SCR control option

Coal Steam SNCR – Cyclone Boilers All cyclone coal steam boilers that are 25 MW or larger and smaller than 100 MW, and do not possess an existing post combustion NO x 

### control option

Coal Steam SNCR – Non Cyclone Boilers and Non FBC Boilers All non cyclone and non FBC coal steam boilers that are 25 MW or larger and smaller than 100 MW, and do not possess an existing post combustion NO x 

### control option

Coal Steam SNCR – FBC Boilers All coal FBC units that are 25 MW or larger and do not possess an existing post combustion NO x 

### control option

LSD Scrubber All unscrubbed and non FBC coal steam boilers 25 MW or larger and burning less than 3 lbs/MMBtu SO 2 

### coal

LSFO Scrubber All unscrubbed and non FBC coal steam boilers 25 MW or larger CO 2 

### Capture and Storage All scrubbed coal steam boilers 400 MW or larger

ACI - Hg Control Option (MPAC/ SPAC/ SPAC+ Toxecon) All coal steam boilers larger than 25 MW that do not have an ACI and have an Hg EMF greater than 0.1. Actual ACI technology type will be based on the boilers fuel and technology configuration. See discussion in Chapter 5. LSD Scrubber + SCR LSD Scrubber + SNCR LSFO Scrubber + SCR LSFO Scrubber + SNCR ACI + SCR ACI + SNCR ACI + LSD Scrubber ACI + LSFO Scrubber ACI + LSD Scrubber + SCR ACI + LSFO Scrubber + SCR ACI + LSD Scrubber + SNCR Coal Steam

ACI + LSFO Scrubber + SNCR Combination options – Individual technology level restrictions apply

Combined Cycle CC Early Retirement All combined cycle units

Combustion Turbine CT Early Retirement All combustion turbine units

Nuclear Nuclear Early Retirement All nuclear power units Oil/Gas Early Retirement All O/G steam boilers Oil and Gas Steam Oil and Gas Steam SCR All O/G steam boilers 25 MW or larger that do not possess an existing post combustion NO x 

### control option

### 7-3 Table 7-3 Second Stage Retrofit Assignment Scheme in EPA Base Case v.4.10 Plant Type Retrofit Option 1 st 

### Stage Retrofit Option 2 nd 

### Stage 5



### NO x 

### Control Option 1 

### SO 2 

### Control Option and/or Hg Control Option and/or CO 2 

### Control Option

SO 2 

### Control Option 2 

### NO x 

### Control Option and/or Hg Control Option and/or CO 2 

### Control Option Hg Control Option 3 

### CO 2 

### Control Option

CO 2 

### Control Option 4 

### None

NO x 

### Control Option 1 

### + SO 2 

### Control Option 2 

### Hg Control Option

NO x 

### Control Option 1 

### + Hg Control Option 3 

### CO 2 

### Control Option

SO 2 

### Control Option 2 

### + Hg Control Option 3 

### CO 2 

### Control Option Coal Steam

NO x 

### Control Option 1 

### + SO 2 

### Control Option 2 

### + Hg Control Option 3 

### CO 2 

### Control Option

Notes:

1 

### "NO x 

### Control Option" implies that a model plant may be retrofitted with one of the following NO x 

### control technologies: SCR, SNCR - cyclone, SNCR - non-cyclone, or SNCR - FBC

2 

### "SO 2 

### Control Option" implies that a model plant may be retrofitted with one of the following SO 2 

### control technologies: LSFO scrubber or LSD scrubber

3 

### "Hg Control Option" implies that a model plant may be retrofitted with one of the following activated carbon injection technology opti ons for reduction of mercury emissions: MPAC, SPAC, or SPAC + Toxecon

4 

### "CO 2 

### Control Option" implies that a model plant may be retrofitted with carbon capture and storage technology

5 

### Retrofits with multiple 2 nd 

### stage options may install any combination of the listed options.



## 7.3 Trading and Banking Four regional or national environmental air regulations included in EPA Base Case v.4.10 involve trading and banking of emission allowances 47 

### : NO x 

### SIP Call program, the Title IV SO 2 

### program, the West Region Air Partnership’s (WRAP) program regulating SO 2 

### (as part of the federal Regional Haze Rule), and the Regional Greenhouse Gas Initiative (RGGI) for CO 2 

### . Table 7-4 below summarizes the key parameters of these four trading and banking programs as incorporated in EPA Base Case v.4.10. Trading and banking are modeled on a U.S. system-wide basis for the Title IV SO 2 

### program and on a regional basis for the other three programs. EPA Base Case v.4.10 does not include any explicit assumptions on the allocation of emission allowances among model plants under any of the four programs.

47 

### For a detailed discussion of the assumptions of all the environmental air regulations included in the EPA Base Case v.4.10, see chapter 3.

### 7-4 Table 7-4 Trading and Banking Rules in EPA Base Case v.4.10 SIP Call -

Ozone Seasons NO x 

### Title IV - SO 2 

### WRAP- SO 2 

### RGGI - CO 2



### Coverage All fossil units > 25 MW 1 

### All fossil units > 25 MW All fossil units > 25 MW 2 

### All fossil units > 25 MW 3



### Timing Ozone Season (May - September) Annual Annual Annual

Size of Initial Bank The bank starting in 2012 is assumed to be zero The bank starting in 2012 is assumed to be 11 million tons The bank starting in 2018 is assumed to be zero The bank starting in 2012 is assumed to be zero

Total Allowances

(MTons) 2012 - 2054: 527.5 2012: 19,679 2013: 8,407 2014: 8,397 2015: 8,327 2016: 8,312 2017: 8,287 2018: 8,169 2019: 8,155 2020 - 2054: 8,153 2018 - 2054: 144.7 2012 - 2014: 188,077 2015: 183,375 2016: 178,673 2017: 173,971 2018 - 2054: 169,269

Notes:

1 

### Alabama, Connecticut, Delaware, District of Columbia, Illinois, Indiana, Kentucky, Maryland, Massachusetts, Michigan, Missouri, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Virginia, West Virginia

2 

### Arizona, New Mexico, Oregon, Utah, Wyoming

3 

### Connecticut, Delaware, Maine, New Hampsh ire, New Jersey, New York, Vermont, Rhode Island, Massachusetts, Maryland


## 7.4 Post-2030 Modeling Assumptions and Capabilities Previous EPA base cases had at most a usable modeling time horizon out to year 2030. EPA Base Case v.4.10 has the capability to model out to 2050. However, bottom up models like IPM, which is used to build the EPA base case, require input data at the finest possible level of granularity. Preferably, such data woul d be based on gathered information obtained through regulatory submittals, surveys, and scient ific, engineering, economic, and commercial assessments specifically related to the particular characteristics of the issue being modeled. Past 2030 or 2035 such information is rarely available.

As a result, a two tiered approach was taken to the inputs used to build EPA Base Case v.4.10. Prior to 2030 assumptions would be based to the gr eatest extent possible on verifiable empirical data gathered from the best available sources ve tted using cross-checks against alternative data sources. Beyond 2030 a pragmatic approach was taken. Where credible empirical data was available, it would be used. Where empirical data was not available, technically plausible, explicitly articulated assumptions would be us ed to extrapolate pre-2030 assumptions out to 2050. While perhaps not optimal, such an approach was seen as potentially valuable, if only because it would focus attention on the long-range assumptions needed for bottom-up modeling and, in doing so, elicit comments from the interested public and technical experts. This could lead to future improvements in the long-range inputs with possible side benefits for all projections whether based on bottom-up, top-down or hybrid modeling approaches.

A corollary of this two tiered approach to input assumptions is that the modeling results past 2030 should be viewed somewhat differently from those prior to 2030. The pre-2030 modeling results

### 7-5 are expected to bear scrutiny at a fine level of granularity (answering questions like the plausibility of a particular generating unit being retrofit with a dry SO 2 

### scrubber and a SCR in a particular model run year or of another generating unit being retired by IPM in a certain year).

The post-2030 modeling results are not intended to be examined at such a fine grain level. Instead, the post-2030 modeling capability is designed to serve two purposes: The first purpose is to ensure that EPA Base Case v.4.10 takes into account the potential impact of post-2030 policy provisions on pre-2030 modeling results. For exampl e, it would be useful to have the capability to project the impact on pre-2030 modeling results of a provision in a Climate Change bill that takes effect in 2042. The second purpose is to give a broad sense of directional trends beyond 2030. For example, using current technology cost and performance assumptions, the long-range modeling capability could provide a picture of the lik ely composition of the power sector in 2040 or 2050 with and without policy intervention. To take fuller advantage of this capability, five generic placeholder future generation technologies have been included in EPA Base Case v.4.10. While not playing a role in the base case itself, their presence allows a user to define their cost and performance characteristics at a later time and to perform sensitivity analysis to see the possible impact of new technologies on post-2030 trends.

Table 7-5 shows the underlying post-2030 modeling assumptions incorporated in EPA Base Case v.4.10 for key modeling parameters.

Table 7-5 Post-2030 Assumptions in EPA Base Case v.4.10 Topic Post-2030 Assumptions

POWER SYSTEM OPERATION

Model Regions Same as pre-2030 Electric Load Modeling

Electric Load Growth Post 2035 growth rate is based on AEO 2010 2025-2035 growth rate
Net Internal Demand (Peak Demand) Post 2035 growth rate is based on AEO 2010 2025-2035 growth rate

Load Duration Curves (LDCs) 2007 load curves adjusted to post 2030 energy and peak load projections. LDCs include six segments per season in run years 2012, 2015, 2020, and 2030 and four segments in 2040 and 2050. Transmission Interregional Transmission Capability Same as pre-2030 Transmission Link Wheeling Charge Same as pre-2030 Transmission Losses Same as pre-2030 International Imports Mexico Same as 2030 Canada Endogenously Modeled Capacity, and Dispatch

Availability Same as pre-2030 for all plant types except nuclear Same as 2030 for nuclear Capacity Factor Same as 2030 Turndown Same as pre-2030 Reserve Margins Same as pre-2030 Power Plant Lifetimes Same as pre-2030 Existing Environmental Regulation
SO 2 

### Regulations Same as 2030

### 7-6 Topic Post-2030 Assumptions
NO x 

### Regulations Same as 2030 State Specific Environmental Regulations Same as 2030 New Source Review (NSR) Same as 2030 Emission Assumptions for Potential (New) Units Same as pre-2030 Capacity Deployment Constraints Run year specific

GENERATING RESOURCES
National Electric Energy Data System (NEEDS) Same as pre-2030 Existing Units and Planned/Committed Units Population of Existing Units Same as pre-2030 Capacity Same as pre-2030 Plant Location Same as pre-2030 Online Year Same as pre-2030 Unit Configuration Same as pre-2030 Model Plant Aggregation Same as pre-2030 Cost and Performance of Existing Units Same as pre-2030 Heat Rates Same as pre-2030
NO x 

### Rates Same as pre-2030
Potential Units Cost and Performance of Potential Conventional Technologies Same as pre-2030

Cost and Performance of Potential Renewable Technologies Biomass Same as 2030 Wind Same as 2030 Solar Same as 2030 Geothermal Same as pre-2030 Landfill Gas Same as 2030 Short Term Cost Adder None Regional Adjustment Factor Same as pre-2030 Nuclear Units Existing Nuclear Units VOM and FOM Cost Assumptions for Nuclear Units Same as pre-2030 (adjusted for life extension costs) Nuclear Upratings (MW) None Nuclear Scheduled Retirements (MW) Retirement at age 60 years Potential Nuclear Units Same as 2030

EMISSION CONTROL TECHNOLOGIES
Sulfur Dioxide Control Technologies Limestone Forced Oxidation (LSFO) Same as pre-2030 Lime Spray Drying (LSD) Same as pre-2030 FGD Engineering Cost Equations Same as pre-2030 Nitrogen Oxides Control Technology Combustion Controls Same as pre-2030 Post-combustion Controls Same as pre-2030 SCR and SNCR Engineering Cost Equations Same as pre-2030 Mercury Control Technologies Mercury Content of Fuels Same as pre-2030

### 7-7 Topic Post-2030 Assumptions Mercury Emission Modification Factors Same as pre-2030 Mercury Control Capabilities Same as pre-2030 ACI Engineering Cost Equations Same as pre-2030 CO 2 

### Sequestration
CO 2 

### capture Same as pre-2030

CO 2 

### transport Same as pre-2030

CO 2 

### storage cost curves Same as pre-2030

SETUP PARAMETERS AND RULES

Run Year Mapping Run year specific Retrofit Assignments Same as pre-2030

FINANCIAL ASSUMPTIONS

Methodology Capital Charge Rates for Investments Same as 2030 Discount Rate for Capital and Non-Capital Costs Same as pre-2030

FUEL ASSUMPTIONS

Coal Coal Markets Same as pre-2030 Coal Supply Curves 2030 cost-adjusted for labor productivity Coal Transportation Costs 2030 cost-adjusted for fuel price changes Coal Assignments Same as pre-2030 Emission Factors Same as pre-2030 Natural Gas Resources Data and Reservoir Description Field Development and Production Forecast Methodology Same as pre-2030

Lower 48 States U.S. Resources Same growth as pre-2030 Canada Resources Same growth as pre-2030 Treatment of Frontier Resources Alaska North Slope starts 2040 Exploration and Production (E&P) Technology Characterization Same as 2030

End Use Demand Characterization Same growth as pre-2030 Pipeline and Transport Existing pipelines Same as pre-2030 Potential pipeline costs Same growth as pre-2030 Emission Factors Same as pre-2030 Fuel Oil Prices Same as 2035 Emission Factors Same as pre-2030 Biomass Biomass Supply Curves Same as 2035 Emission Factors Same as pre-2030 Nuclear Fuel Prices Same as 2030

### 8-1 

# 8 Financial Assumptions
This chapter presents the financial assumptions used in EPA Base Case v.4.10. The first section gives a summary of each of the key financial parameters. The remainder of the chapter presents an in-depth explanation of the theoretical underpinnings and methods used to develop the two most important financial parameters – the discount rate and capital charge rate.



## 8.1 Summary of Key Financial Parameters 8.1.1 Capital Charge Rate, Book Life, and Discount Rate for Capital Expenditures EPA Base Case v.4.10 models a diverse set of generation and emission control technologies, each of which requires financing. 1 

### Table 8-1 presents the capital charge rate, discount rate, and book life assumptions for the technologies in base case v.4.10. As will be discussed more fully later in this chapter, the capital charge rate is used to convert the capital cost of a technology into a stream of levelized annual payments that ensure capital recovery. The discount rate is used to translate future cash flows into current dollars by taking into account factors (like inflation and the ability to earn interest), which make one dollar tomorrow worth less than one dollar today. The discount rate allows inter-temporal tradeoffs and r epresents the risk adjusted time value of money. The book life is the payback period on an investment.

There are several things to note about Table 8-1. The technology differentiated capital charge rates are used in v.4.10 to derive the associated capital charge rates shown in the table. However, while the technology-differentiated discount rates appearing in the table were used in deriving these capital charge rates, in EPA Base Case v.4.10 a single U.S. discount rate of 6.15% is used across all technologies.

1 

### The capital charge rates discussed here apply to new (potential) units and environmental retrofits that IPM installs. The capital co st of existing and planned/committed generating units and the emission controls already on these units ar e considered “sunk costs” and are not represented in the model.

### 8-2 Table 8-1 U.S. Discount Rates and Capital Charge Rates in EPA Base Case v4.10
Investment Technology Capital Charge Rate Discount Rate Book Life Environmental Retrofits 11.3% 5.5% 30 Advanced Combined Cycle 12.1% 6.2% 30 Advanced Combustion Turbine 12.9% 6.9% 30 Supercritical Pulverized Coal and Integrated Gasification Combined Cycle without Carbon Capture 1 

### 14.1% 7.8% 40
Advanced Coal with Carbon Capture 11.1% 5.5% 40 Nuclear without Production Tax Credit (PTC) 10.8% 5.5% 40 Nuclear with Production Tax Credit (PTC) 2 

### 9.1% 5.5% 40 Biomass with ARRA Loan Guarantees 3 

### 9.3% 4.6% 40 Biomass without ARRA Loan Guarantees 11.1% 6.2% 40 Wind and Landfill Gas with ARRA Loan Guarantees 2 

### 10.1% 4.6% 20 Wind and Landfill Gas without ARRA Loan Guarantees 12.2% 6.2% 20 Solar and Geothermal with ARRA Loan Guarantees 2 

### 10.1% 4.6% 20 Solar and Geothermal without ARRA Loan Guarantees 12.2% 6.2% 20 Notes: The discount rates appearing in the table were used in deriving these capital charge rates. However, as noted in the text, a single U.S. discount rate of 6.15% is used across all technologies in EPA Base Case v.4.10.
1 

### The capital charge rate for these technologi es includes a 3% climate change uncertainty adder. (See text.)
2 

### The capital charge rate for this technology reflects the impact of the PTC provided under the Energy Policy Act of 2005. (See text.)
3 

### The capital charge rate for these technologies reflects the impact of ARRA loan guarantees. (See text.)

Capital Cost Adder for Climate Change Uncertainty: Adopting the procedure followed in EIA’s Annual Energy Outlook 2010, the capital charge rates shown in Table 8-1 for Supercritical Pulverized Coal and Integrated Gasification Combined Cycle (IGCC) without Carbon Capture include a 3% adder to the cost of debt and equity (see section 8.2.3 for discussion of debt and equity). This capital cost adder reflects increased financing costs for investment decisions involving coal plants without carbon capture c aused by uncertainty surrounding possible future climate change policies that could limit CO 2 

### emissions from the power sector

ARRA Loan Guarantees for Renewables and Biofuels: The American Recovery and Reinvestment Act (ARRA) of 2009 (Public Law 111-5) provides loan guarantees for renewables and biofuels. Following the procedure implemented in AEO 2010, these loan guarantees are reflected in a reduction of 2% in the cost of debt and equity for biomass, wind, landfill gas, solar, and geothermal technologies. Capital charge rates with and without the 2% reduction appear in Table 8-1 because the loan guarantees expires in 2016.

Energy Policy Act Production Tax Credit for Nuclear: The Energy Policy Act of 2005 (Sections 1301, 1306, and 1307) provides a producti on tax credit (PTC) of 18 mills/kWh for 8 years up to 6,000 MW of new nuclear capacity. T he financial impact of the credit is reflected in the capital charge rate shown in Table 8-1 for “Nuclear with Production Tax Credit (PTC).”

8.1.2 ARRA Production and Investment Tax Credit (PTC and ITC) for Renewables In addition to the loan guarantees that are reflec ted in the capital charge rates for renewables shown in Table 8-1, ARRA 2009 (Division B, Title I, Sec. 1101, 1102, and 1603) also provides extensions of the PTC and 30 percent ITC to renewables. These are represented in EPA Base Case, v.4.10 as a 30% reduction in the capital cost of these technologies in 2012.

### 8-3 8.1.3 Discount Rate for Non-Capital Expenditures The discount rate for non-capital expenditures (e.g., annual fuel, variable operations and maintenance, and fixed operations and maintenance costs) in EPA Base Case v.4.10 is assumed to be 6.15%. This serves as the default discount rate for all non-capital expenditures.

8.1.4 Inter-temporal Allowance Price Calculation Under a perfectly competitive cap-and-trade program that allows banking, the allowance price always increases by the discount rate between periods if affected sources have allowances banked between those two periods. This is a standard economic result for cap-and-trade programs and prevents sources from profiting by arbitraging allowances between two periods.

The EPA Base Case v.4.10 uses the default discount rate of 6.15 percent in computing the increase in allowance price for cap-and-trade programs when banking is engaged as a compliance strategy.

8.1.5 Nominal and Real Dollars EPA Base Case v.4.10 uses real 2007 dollars as its real dollar baseline. See Chapter 2 for further discussion on how IPM uses the real dollars for inter-temporal analysis.



## 8.2 Development of the Financial Assumptions for EPA Base Case v.4.10 This section explains the method used to derive the capital charge rate and discount rate as well as the assumptions underlying these financial parameters.

8.2.1 Introduction As noted in section 8.1, the discount rate and the capital charge rate are the two parameters that encapsulate the financing assumptions for an investment option in EPA Base Case, v.4.10. The discount rate allows inter-temporal tradeoffs and r epresents the risk adjusted time value of money. The capital charge rate is used to convert the capital cost into a stream of levelized annual payments that ensures capital recovery of an investment.

Discount Rate The discount rate is a function of the following parameters:

• Capital structure (Share of Equity vs. Debt)

• Post-tax cost of debt (Pre-tax cost of debt*(1-tax rate))

• Post-tax cost of equity

The weighted average cost of capital (WACC) is used as the discount rate and is calculated as follows:

WACC = [Share of Equity * Cost of Equity] + [Share of Preferred Stock * Cost of Preferred Stock] + [Share of Debt *After Tax Cost of Debt]

The focal point is on debt and equity (common stock) because preferred stock is generally a small share of capital structures.

Capital Charge Rate The capital charge rate is a function of the parameters that overlap in part with the discount rate, but also include parameters related to the amortization of capital:

• Capital structure (Debt/Equity shares of an investment)

• Pre-tax debt rate (or interest cost)

• Debt Life

### 8-4 • Post-tax Return on Equity (ROE) (or cost of equity)

• Other costs such as property taxes and insurance

• State and Federal corporate income taxes

• Depreciation Schedule

• Book Life

The capital charge rate is calculated by solving for earnings before interest, taxes, and depreciation (EBITDA) or pure operating earnings such that the project is able to recover the cost of equity as the internal rate of return over the lifetime of the project. The sum of discounted cash flows to the equity holders over the lifetime of t he project, discounted at the cost of equity is set equal to the initial investment. The capital charge rate so calculated is defined as follows:

• Capital Charge Rate = EBITDA/Total Investment In other words, the capital charge rate is the rate of return required on invested capital, resulting from pure operations.

8.2.2 Method for Deriving Discount Rate and Capital Charge Rate in EPA Base Case v 4.10
Introduction to Risk The risk of an investment in the power sector is heavily dependent on market structure risks. The range of risks has increased due to deregulation which has resulted in approximately 35 percent to 40 percent of capacity being deregulated IPP (Independent Power Producer) capacity. For example, merchant IPP's selling into spot market have more market risk than regulated plants or IPP’s having long-term, known-price contracts with credit worthy counter parties. There are also technology risks and financing structure risks (corporat e vs. project financings). Lastly, there is financial risk related to the extent of leverage.

The risk, especially to the extent it is correlated with overall market conditions, is an importantdriver of financing costs. Other risks are handled in the cash flows and are treated as noncorrelated with the market. This emphasis on correlated market risk is based on the Capital Asset Pricing model and associated financial theory. This analysis takes into account differences in technology and market structure risks.

Differences between corporate and project financi ngs are highlighted but no specific adjustment has been made for them.

Market Structure Risks The power sector in North America can be divided into the traditional regulated sector (as known as “cost of service” sector) and deregulated merchant sector (as known as “competitive” sector).

• Traditional Regulated - The traditional regulated market structure is typical of the vertically integrated utilities where generation (and trans mission and distribution, abbreviated T&D) investments are approved through a regulatory process and the investment is provided a regulated rate of return. Returns on investment in this form of market structure are cost plus regulated returns that are administratively determined. Returns are affected by market conditions due to regulatory lag and other imperfections in the process, but overall are less exposed to the market than deregulated investments, all else equal. In this report, we use the term “utility financing” to refer to this type of market structure. A closely related market structure is the situation where a plant is built under a power purchase agreement (PPA) with a utility with known pricing that allows for a very high degree of investment amortization during the contract period. In such an arrangement, the risks are more credit and performance related and much less market related.

• Deregulated Merchant - In a deregulated merchant market structure, investments bear the full or a very high degree of market risk as the price that they can sell electricity at is dependent on what the short-term markets will bear. Return on investment in this form of market structure is not only dependent on the state of the economy, but also on commodity

### 8-5 prices, as well as on capital investment cycles and remaining price related regulation, e.g., FERC price caps on capacity prices. The capital investment cycle can create a “boom and bust” cycle which imparts source risk or uncertain ty to the sector. The operational cash flows from investments in this sector are more volatile as compared to in the traditional regulated sector, and hence, carry more business or market risk. In this report, we use the term “merchant financing” to refer to this type of market structure.

Technology Risks The selection of new technology investment options is partially driven by the risk profile of these technology investments. For instance, in a deregulated merchant market, an investment in a combustion turbine is likely to be much more risky than an investment in a combined cycle unit because while a combustion turbine operates as a peaking unit and is able to generate revenues only in times of high demand, a combined cycle unit is able to generate revenues over a much larger number of hours in a year. An investor in a combined cycle unit, therefore, would require a lower risk premium than an investor in a combustion turbine.

Financing Structure Risks and Approach While investments in new units differ based on market structure and technology risks, differences also may occur because of financing schemes available. There are two major types of financing schemes:

• Corporate finance is a category of financing where a developer raises capital on the strength of the balance sheet of a company rather than a single project. In this type of financing, the debtors have recourse to the entire company’s assets.

• Project finance, as this category of financing is often labeled, allows developers to seek financing using only the project as recourse for the loan. For instance, a project developer may wish to develop a new combined cycle unit, but will seek to use project financing in such a way that if it defaults on the loan, debtors have recourse only to the project itself and not against the larger holdings of the project dev eloper. This approach can be more risky than corporate finance, all else being equal, becaus e there is less diversification than the corporation which can be thought as a collection of projects. However, there are some projects more suitable for project financing because: (1) they may have a self-sustaining revenue stream that is greater than the corporate average, or (2) risk is reduced through a long-term PPA with a credit worthy counterparty such as a vertically integrated utility or a regulated affiliate of a merchant company.

There are many benefits of a project financing structure but there are also costs associated with it. A project financing structure typically has higher transaction costs (and even higher debt costs as debt financing is largely privately placed), but it also solves some of the agency problems and underinvestment issues that corporate financed structures face1.

However, as noted above, this analysis does not make an effort to quantify the relative costs and benefits of one structure over the other. Rather, the approach used is based on the premise that regardless of financial structure, each project has its own risks based on market structure and technology. Further, because corporate financing is more observable than project financing2 and has evolved in the power sector to the level of making key risk inferences possible (e.g., IPP and utility stock trades), assessment of market correlated risks should be based on IPP and utility corporate financing.

1 

### For more information on project financing, see paper titled “The Economic Motivations for Using Project Finance” by Benjamin C. Esty, Harvard Business School, Feb 2003

2 

### Project Financing data is less observable as it’s not explicitly traded. Also, often key financing parameters are unavailable due to confidentiality reasons.

### 8-6 Approach to Market Structure Risk In EPA Base Case 4.10, a hybrid financing model is used, i.e., it is assumed that future development activity would be roughly evenly split between utility financings and pure merchant financings. This is designed to reflect a shift in the market in ownership and risk profiles for power generation assets, and recent development trends and emphasis on long term contracts3. In this approach, for modeling purposes, we assume that new units are financed as a weighted average of utility and merchant financing parameters. For new units we assume that both utility and merchant components get equal weights. For retrofits, we assume that utility component gets 2/3 and merchant component gets 1/3 weight4.

• Example 1: The debt to equity capital structure of a combustion turbine is 55/45 under utility financing and 30/70 under merchant financing. Under the assumption that utility and merchant components get equal weights, the debt to equity ratio under hybrid financing is D = (55 + 30)/2 = 42.5 / E = (45 + 70)/2 = 57.5.

• Example 2: The debt to equity capital structure of a retrofit is 55/45 under utility financing and 45/55 under merchant financing. Under the assumption of a 2/3, 1/3, utility/merchant weighting, the debt to equity ratio under hybrid financing is D = (55 * 2/3) + (45 * 1/3) = 51.6 / E = (45 * 2/3) + (55 * 1/3) = 48.3. A full summary for all technologies appears in Table 8-2 below.

Capital Charge Rate – A More Detailed Description The capital charge rate is calculated by solving for earnings before interest, taxes, and depreciation (EBITDA) or pure operating earnings such that the project is able to recover the cost of equity as the internal rate of return over the lifetime of the project. The sum of discounted cash flows to the equity holders over the lifetime of t he project, discounted at the cost of equity is set equal to the initial investment. The capital charge rate so calculated is defined as follows:

Table 8-2 Capital Structure Assumptions for EPA Base Case v4.10 Technology Utility Merchant Hybrid
Combustion Turbine 55/45 30/70 42.5/57.5

Combined Cycle 55/45 45/55 50/50
Coal & Nuclear 55/45 60/40 57.5/42.5

Renewables 55/45 45/55 50/50

Retrofits 55/45 45/55 51.7/48.3

• Capital Charge Rate = EBITDA/Total Investment In other words, the capital charge rate is the rate of return required on invested capital, resulting from pure operations.

3 

### An alternate approach is to categorize the United States into the two previously discussed financial regions – Cost-of-service and competitive. The cost-of-service region will have capital charge rates based on utility financial assumptions and the competitive region will have capital charge rates based on merchant financial assumptions. Such an approach could result in overbuilding in the cost-of-service region due to lower capital charge rates in the absence of regulatory prohibitions or external sales. This is similar to the public vs. IOU financing arbitrage problem, i.e. what stops government utilities from supplying all power? In fact, there are formal and informal limits, and because fully characterizing these limits is extremely complex, a hybrid approach is used.

4 

### Retrofits are largely associated with coal fired plants and most coal fired plants are currently owned and operated by utilities in a “utility financing” structure. Moreover, since the magnitude of the retrofit investment is not as large as a plant investment, the financing of a retrofit is more likely to happen from a firm’s internal cash flows instead of external financing.

### 8-7 The discounted cash flow to the equity holders of the project is characterized in terms of the Free Cash Flow to Equity (FCFE). FCFE is a valuat ion technique to estimate cash flows paid to the equity shareholders of a company after all expenses, reinvestment, and debt repayment have been made. The FCFE approach is suited for valuati on of assets that have finite economic lives and where debt levels vary from year to year. In the FCFE approach, it is assumed that the asset has a finite life and debt reduces over time based on a mortgage-style repayment structure.

Specifically the cash flows to the equity5 are calculated as follows:

Cash Flows to Equity = EBIT (1-tax rate) - Interest (1-tax rate) + Depreciation - Capital Expenditures - Working Capital Change - Principal Payments + New Debt Issued

Discount Rates and Capital Charge Rates Based on the above approach, the discount rates and capital charge rates summarized in Table 8-1 above were obtained for investments in the U.S. The procedures and assumptions used to calculate the rates in Table 8-1 are discussed in the sections below.

8.2.3 Calculation of the Hybrid Capital Charge Rates
ROE The first step was the calculation of a return on equity (ROE) using an average ROE under utility financing (10.3%) and merchant financing (15.2%) assuming a 50:50 debt/equity ratio. This resulted in a ROE of 12.75%6. This ROE is kept the same across each technology7 but the risk differences across technologies are implemented through the capital structure. See the discussion of capital structures in this subsection under “Debt Equity Share” and the discussion of “Debt and Equity Shares and Technology Risk” in section 8.2.4.

Note that a 50:50 mix (corresponding to the hybrid capital structure for a combined cycle – see Table 8-2) has been chosen. This is because we assumed that the overall IPP risk was on average reflective of the risk profile of combined cycle units which in turn was assumed to be intermediate between base load and peaking. The combined cycle technology is considered to have “average” market risk being an intermediate type technology. Also, in the aggregate, the five selected IPP companies 8 

### have more combined cycle capacity in their supply mix than any other technology. Additionally, going forward, it is expected that gas will continue to play an increasingly important role in the supply mix of both utilities and merchant companies, with combined cycle technology playing a dominant role. For all of these reasons, it was considered appropriate to use the ROE corresponding to a combined cycle facility.

5 

### An alternative definition of free cash flow to equity is as follows: Net Income + Depreciation – capital expenditures – working capital change – Principal Payments + New Debt Issued

6 

### The Pennsylvania-New Jersey-Maryland Interconnec tion (PJM) uses a 12% ROE for a combined cycle at a 50:50 debt/equity ratio to calculate the cost of new entry (CONE) for their capacity markets. Hence, the estimates are fairly close to those used by a major RTO.

7 

### Even though ROE is kept the same across all technologies, it doesn’t mean that the market risks are considered the same for each technology. See capital structure discussion in the next paragraph.

8 

### Our merchant parameters are derived from market observations of five IPP companies – see discussion on development of merchant financed parameters.

### 8-8 Debt Equity Share Second, the capital structures (D/E)9 for the various technology types were calculated using an average of utility and merchant financing. The utility debt capacity (and returns) is assumed to be independent of technology type based on the theoretic al assumption that regulation will provide an average return to the entire rate base. The me rchant debt capacity is based on market risk where a base load plant is likely to have a higher debt capacity than a combustion turbine plant. Table 8-2 presents the capital structure assumptions used in EPA Base Case v4.10.

The risk differences across technologies are implem ented by varying the capital structure. As shown in Table 8-2 and discussed above, a peaki ng unit such as a combustion turbine is estimated to have a capital structure of 42.5/57. 5 while a base load unit such as nuclear and coal is assumed to have a capital structure of 57.5/42.5. This is based on the intuition that less risky technologies can carry more leverage. As debt is less expensive than equity, this will automatically translate into a lower capital charge rate (and a lower discount rate) for base load technologies and a higher capital charge rate ( and a higher discount rate) for peaking technology, assuming other components of the capital charge rate calculation remain the same.

Cost of Debt Third, the cost of debt was adjusted to reflect the average cost of debt for utility and merchant financings for each technology. The utility cost of debt is assumed to be the same across all technologies while the merchant cost of debt is higher than utility and is higher for a combustion turbine unit than for a combined cycle or a coal plant. Table 8-3 summarizes the debt rates used in EPA Base Case v4.10.

Table 8-3 Debt Rates for EPA Base Case v4.10 Technology Utility Merchant Hybrid
Combustion Turbine 6.25% 9% 7.63%

Combined Cycle 6.25% 8% 7.13% Coal & Nuclear 6.25% 8% 7.13% Wind 6.25% 8% 7.13% Retrofits 6.25% 8% 6.83%

These financing parameters were then combined with taxation assumptions and technology specific assumptions on depreciation, book life and debt life to yield the capital charge rates outlined in Table 8-1 above.

8.2.4 Development of Merchant Financing Parameters
Merchant ROE The Independent Power Producer (IPP) return on equity parameter was estimated to be 15.2% 10



### . This was based on empirical analysis of stock pr ice data of five pure play comparable merchant generation companies, namely Reliant, NRG, Dynegy, Mirant, and Calpine. First, levered betas 11 

### (a measure of total corporate risk which includes business and financial risk) for the five companies were calculated using five years of historical stock price data. Second, unlevered betas (a measure of business risk, i.e., those affected by a firm’s investment decisions) were

9 

### A project’s capital structure is the appropriate debt capacity given a certain level of equity, commonly represented as “D/E,” i.e., debt/equity. T he debt is the sum of all interest bearing short term and long term liabilities while equity is the amount that the project sponsors inject as equity capital.

10 

### Merchant ROE, as additionally observed by EIA, have risen over time.

11 

### Levered beta is directly measured from the company’s stock returns with no adjustment made for the debt financing undertaken by the company. It is also known as equity beta. Hence, levered beta incorporates both business and financing risk undertaken by the company.

### 8-9 calculated using the estimated levered beta, t he companies’ market debt/equity ratio, and the riskiness of debt. As most comparables historically had periods of financial distress, the unlevering 12 

### approach was modified to include the riskiness of debt, instead of purely using the Hamada equation. 13 

### The unlevered betas were then relevered 14 

### at the target debt/equity ratio of 50/50 to get the relevered equity betas and return on equity. The return on equity was determined using the Capital Asset Pricing Model (CAPM).

The CAPM parameters used to estimate the ROE are as follows:

• Risk Free Rate based on 20 year T bond rate: 4.9% 15



### • Market Risk Premium 1926-2006 16: 

### 7.1%

• Size Premium: 0.65% 17



### The estimation of the IPP ROE described here is fairly close to what EIA has published. EIA estimates a 2010 ROE of roughly 14.8% at a D/E ratio of 45/55 and increasing to approximately 16% by 2012. 18



### Merchant Cost of Debt The cost of debt for the merchant sector was computed assuming an average 3-month LIBOR (London Interbank Offered Rate) of 4.9-5% and an average spread of 3% reflecting the low medium grade or low grade nature of merchant bonds. An additional one percent spread was assumed for combustion turbine units due to the high price risk for combustion turbine units.

Debt and Equity Shares and Technology Risk The capitalization structure for merchant financi ngs was estimated to be 45/55 based on empirical analyses. This capitalization structure was assu med to be on average reflective of the combined cycle for reasons discussed earlier in this document.

Each generation technology was considered to have its own risk profile. Some evidence indicated that the greater the base load share, the lower the asset risk. This is consistent with having revenues that are more related to variable cost advantages as well as the value of capacity as opposed to peaking units which are more dependent on scarcity or capacity revenue, which, in turn, has very high systemic risk.

There are two main mechanisms for reflecting the greater risk for peakers and the lower risk for base load. First, the ROE could have been adjusted such that for a given target leverage, the ROE would be higher for peakers, and lower for base load. For example, an unlevered beta and

12 

### The unlevering process removes a company’s financ ing decision from the beta calculation. The calculation therefore, attempts to isolat e the business (operating risk) of the firm.

13 

### The Hamada equation is described at http://www.answers.com/topic/hamada-equation as “A fundamental analysis method of analyzing a firm's co sts of capital as it uses additional financial leverage, and how that relates to the overall riskiness of the firm. The measure is used to summarize the effects this type of leverage has on a firm's cost of capital (over and above the cost of capital as if the firm had no debt).”

14 

### The relevering process estimates the levered beta of the firm given a target capital structure and the pure business risks of the firm as determined from the unlevering process

15 

### Federal Reserve Statistical Release (H15 data), May 2007

16 

### Source: Stocks, Bonds, Bills, and Inflat ion, 2007 Yearbook Valuation Edition, Morningstar/Ibbotson’s Associates

17 

### Source: Stocks, Bonds, Bills, and Inflat ion, 2007 Yearbook Valuation Edition, Morningstar/Ibbotson’s Associates

18 

### See Electricity Market Module of NE MS, EIA Annual Energy Outlook, May 2009

### 8-10 ROE (which assumes zero leverage) could have been calculated using the risk differentiated capital structures and then relevered at some tar get leverage. This would have yielded a different ROE for each technology but the same capital structure across all technologies.

The second option was to keep the same ROE while varying the capital structure. This was the adopted approach. Thus, even though the leverage of peakers was lowered, the ROE was not lowered. This raised the weighted average cost of capital and the resulting capital charge rate. This effectively also raised the unlevered beta for peakers relative to combined cycle. For base load, leverage was raised without raising ROE, effectively lowering the unlevered beta and the cost of capital.

8.2.5 Development of Utility Financing Parameters Utility ROE The utility return on equity was calculated to be 10.3%. This was based on empirical analysis of correlation of returns on the S&P utility Index vs. the broader S&P 500 market index for the last five years to determine the levered beta and then unlevering and relevering based on a process similar to that for merchant sector. The ROE is also consistent with what state commissions have awarded the shareholder-owned electric utilities recently. 19



### Utility Cost of Debt The cost of debt for the utility sector was estimated to be 6.25%. The utility bonds were categorized into various credit rating rungs based on information obtained from the Edison Electric Institute. 20 

### Historical yields were analyzed for t he various rungs based on data obtained from Bloomberg. A weighted average cost of debt wa s then determined based on the historical yields and percentages of utility credit rating classes. The estimated value was also corroborated against the yield to maturity of Moody’s average utility bond index.

Debt and Equity Shares The target capitalization structure for utilities wa s determined using US utility capitalization ratios derived from Bloomberg data. Similar CAPM parameters were used to estimate the ROE of the utility sector. The capitalization structure for utility financings was estimated to be 55/45 based on empirical analyses and this capitalization structure was assumed to be on average reflective of all technologies.

Technology Risks For the utility financing, we assumed that the required returns for regulated utilities are independent of technology. This assumption was a simplifying assumption, and further empirical work may be warranted here.

8.2.6 Development of Other Parameters
Taxation and Insurance Costs Corporate and State Income Taxes: The maximum US corporate income tax rate is 35%. 21 

### State taxes vary but on a national average basis, the state taxes are 6.50%. 22 

### This yields a net effective tax rate of 39.3%.

19 

### See page 35, 2006 Financial Review, Annual Repor t of the U.S Shareholder Owned Electric Utility Industry

20 

### 2006 Financial Review, Annual Report of the U. S Shareholder Owned Electric Utility Industry

21 

### Internal Revenue Service, Publication 542.

22 

### Population weighted average based on 2008 state taxe s information available at Federation of Tax Administrators, ttp://www.taxadmin.org/fta/rate/corp_inc.html.

### 8-11 State Property Taxes: US state property taxes are approximately 0.9% based on a national average basis. This is based on extensive pr imary and secondary research conducted by ICF using property tax rates obtained from various state agencies.

Insurance Costs: Insurance costs are approximately 0.3%. This is based on estimates of insurance costs on a national average basis.

Inflation The inflation rate of 2.25% is based on an assessment of implied inflation from an analysis of yields on 5 year and 10 year Treasury securities and Treasury Inflation Protected Securities (TIPS).

Book life, Debt Life and Depreciation Table 8-4 presents a summary of various assumed lives at the national level.

Table 8-4 Book Life, Debt Life and Depreciation Schedules for EPA Base Case v. 4.10
Technology Book Life (Years) Debt Life (Years) US - MACRS Depreciation Schedule Combined Cycle 30 20 20 Combustion Turbine 30 15 15 Coal Steam and IGCC 40 20 20 Nuclear 40 20 15 Solar and Geothermal 20 20 5 Biomass 40 20 7 Wind and Landfill Gas 20 20 7 Retrofits 30 20 20

Book Life or Useful Life of Plant: The book life or useful life of a plant was estimated based on researching financial statements of utility and merchant generation companies. The financial statements 23 

### typically list the period over which long lived assets are depreciated for financial reporting purposes. The research conducted broadly supports the numbers outlined in the table above.

Debt Life : The debt life is assumed to be on a 20 years schedule except in the case of combustion turbine where debt life is lower.

Depreciation Schedule: The US MACRS 24 

### depreciation schedules were obtained from IRS Publication 946 25 

### that lists the schedules based on asset classes. The document specifies a 5 years depreciation schedule for wind energy projects and 20 years for Electric Utility Steam Production plants. These exclude combustion turbines which have a separate listing at 15 years. Nuclear Power Plants are separately listed at 15 years as well.

23 

### SEC 10K filings of electric utilities and pure me rchant companies. For example, Calpine’s 10K lists 35 years of useful life for base load plants, DTE energy uses 40 years for generation equipment; Dynegy gives a range of 20-40 years for power generation facilities; Mirant reports 14- 35 years for power production equipment; Reliant: 10-35 years

24 

### MACRS refers to the Modified Accelerated Cost Recovery System, issued after the release of the Tax Reform Act of 1986. It allowed faster depreciation than with previous methods.

25 

### IRS Publication 946, “How to Depreciate Pr operty”, Table B-2, Class Lives and Recovery Periods.

### 9-1 

# 9 Coal
The next three chapters cover the representation and underlying assumptions for fuels in EPA Base Case v.4.10. The current chapter focuses on coal, chapter 10 on natural gas, and chapter 11 on other fuels (fuel oil, biomass, nuclear fuel, and waste fuels) represented in the base case.

This chapter presents four main topics. The first is a description of how the coal market is represented in EPA Base Case v.4.10. This includes a discussion of coal supply and demand regions, coal quality characteristics, and the assignment of coals to power plants.

The next topic is the coal supply curves which were developed for EPA Base Case v.4.10 and the painstaking bottom-up, mine-based approach used to develop curves that would depict the coal choices and associated prices that power plants will face over the modeling time horizon. Included are discussions of the methods and data used to quantify the economically recoverable coal reserves, characterize their cost, and build the 84 coal supply curves that are implemented in EPA Base Case v.4.10. Illustrative examples are included of the step-by-step approach employed in developing a supply curves.

The third topic is coal transportation. It includes a description of the transport network, the methodology used to assign costs to the links in the network, and a discussion of the geographic, infrastructure, and regulatory considerations that come into play in developing specific rail, bargeand truck transport rates. The last topic covered in this chapter is coal exports, imports, and nonelectric sector demand.

The assumptions for the coal supply curves and coal transportation were finalized in September 2008, and were developed through a collaborative process with EPA supported by the following team of coal experts (with key areas of responsibility noted in parenthesis): PA Consulting Group (coal transportation and team coordination), W ood Mackenzie (coal supply curve development), Hellerworx (coal transportation and third party re view), and ICF Consulting (representation in IPM). The coal supply curves and transportation matrix implemented in EPA Base Case v.4.10 are included in appendices at the end of this chapter.



## 9.1 Coal Market Representation in EPA Base Case v.4.10 Coal supply, coal demand, coal quality, and the assi gnment of specific types of coals to individual coal fired generating units are the four key components of the endogenous coal market modeling framework in EPA Base Case v.4.10. The modeling representation attempts to realistically reflect the actual options available to each existi ng coal fired power plant while aggregating data sufficiently to keep the model size and solution time within acceptable bounds.

Each coal power plant modeled is assigned to one of 151 coal demand regions. The demand regions are defined to reflect the coal transportation options (rail, barge, truck, conveyer belt) that are available to the plants that they serve. These demand regions are interconnected by a transportation network to at least one of the 31 geographically dispersed coal supply regions. The model’s supply-demand region links reflect actual on-the-ground transportation configurations. Every coal supply region can produce and eac h coal demand region can demand at least one grade of coal. Based on historical and engineering data (as described in Section 9.1.5 below), each coal fired unit is also assigned several coal grades which it may use if that coal type is available within its demand region.

In EPA Base Case v.4.10 the endogenous demand for coal is generated by coal fired power plants interacting with a set of exogenous supply curves (see Appendix 9-4 for coal supply curve data) for each coal grade in each supply region. The curves show the supply of coal (by coal supply region and coal grade) that is available to meet demand at a given price. The supply of and demand for each grade of coal is linked to and affected by the supply of and demand for every other coal grade across supply and demand regions. The transportation network or matrix (see Appendix 9-3 for coal transportation matrix data) also factors into the final determination of

### 9-2 delivered coal prices, given coal demand and supply. IPM derives the equilibrium coal consumption and prices that result when all el ectric system operating, emission, and other requirements are met and total electric system costs over the modeling time horizon are minimized.

9.1.1 Coal Supply Regions There are 31 coal supply regions in EPA Base Case v.4.10, each representing geographic aggregations of coal-mining areas that supply one or more coal grades. Coal supply regions may differ from one another in the types and quality of c oal they can supply. Table 9-1 lists the coal supply regions included in EPA Base Case v.4.10. Figure 9-1 provides a map showing the location of both the coal supply regions lis ted in Table 9-1 and the broader supply basins commonly used when referring to U.S. coal reserves.

Table 9-1 Coal Supply Regions in EPA Base Case Region State Supply Region Central Appalachia Kentucky, East KE Central Appalachia Tennessee TN Central Appalachia Virginia VA Central Appalachia West Virginia, South WS Dakota Lignite Montana, East ME Dakota Lignite North Dakota ND

East Interior Illinois IL East Interior Indiana IN East Interior Kentucky, West KW East Interior Mississippi MS Gulf Lignite Louisiana LA Gulf Lignite Texas TX Northern Appalachia Maryland MD Northern Appalachia Ohio OH Northern Appalachia Pennsylvania, Central PC Northern Appalachia Pennsylvania, West PW Northern Appalachia West Virginia, North WN

Rocky Mountains Colorado, Green River CG Rocky Mountains Colorado, Raton CR Rocky Mountains Colorado, Uinta CU Rocky Mountains Utah UT Southern Appalachia Alabama AL Southwest Arizona AZ Southwest New Mexico, San Juan NS West Interior Kansas KS West Interior Oklahoma OK Western Montana Montana, Bull Mountains MT Western Montana Montana, Powder River MP Western Wyoming Wyoming, Green River WG Wyoming Northern PRB Wyoming, Powder River Basin WH Wyoming Southern PRB Wyoming, Powder River Basin WL

### 9-3 Figure 9-1 Map of the Coal Supply Regions in EPA Base Case v.4.10

9.1.2 Coal Demand Regions Coal demand regions are designed to reflect coal tr ansportation options available to power plants. Existing coal plants with similar transportation infrastructures (i.e., rail, barge, or truck/conveyor belt), proximity to mine (i.e., mine mouth or not mine mouth), transportation competitiveness levels (i.e., non-competitive, low-cost competitive, or high-cost competitive), and within the same geographic area are grouped into a coal demand regi on. Table 9-2 below lists the 135 coal demand regions used in EPA Base Case v.4.10 by code and descriptive name.

When IPM is run, it determines the amount and type of new generation capacity to add within each of IPM’s 32 model regions. These model r egions reflect the administrative, operational, and transmission geographic structure of the electricit y grid. Since the coal demand regions do not typically coincide or overlap with the IPM model regions, new coal plants that IPM “builds” in specific model regions must be assigned to a par ticular coal demand region. The IPM-region-tocoal-demand-region assignments for new coal generating capacity are indicated in column 3 of Table 9-2. Also shown in the last column of Table 9-2 are instances where only one existing power plant is contained in a coal demand region. Forty-seven of the coal demand regions contain only one power plant.

Table 9-2 Coal Demand Regions in EPA Base Case

Coal Demand Region Codes Descriptive Name IPM Model Regions with Potential Plants Assigned to this

Coal Demand Region Plant Name when Coal Demand Region Just Includes One Plant

ALR1 Alabama High-Cost Competitive_Not Mine Mouth_Rail --

ALR2 Alabama Low-Cost Competitive_Not Mine Mouth_Barge -- Greene County Plant

ALR3 Alabama Low-Cost Competitive_Not Mine Mouth_Rail -- E C Gaston Plant

AMM1 New Mexico High-Cost Competitive_Mine Mouth_Rail --

### 9-4 Coal Demand Region Codes Descriptive Name IPM Model Regions with Potential Plants Assigned to this

Coal Demand Region Plant Name when Coal Demand Region Just Includes One Plant

AMM2 Arizona, New Mexico High-Cost Competitive_Not Mine Mouth_Rail -- Navajo Plant

AMM4 New Mexico Low-Cost Competitive_Mine Mouth_Truck/Conveyor Belt -- San Juan Plant

AMM5 New Mexico Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt -- Raton Plant

AMN1 Arizona High-Cost Competitive_Not Mine Mouth_Rail -- Apache Station

AMN2 Arizona Low-Cost Competitive_Not Mine Mouth_Rail -- H Wilson Sundt Generating Station

AMN3 Arizona Non-Competitive_Not Mine Mouth_Rail AZNM

CAI1 Virginia High-Cost Competitive_Not Mine Mouth_Rail --

CAI2 Kentucky Low-Cost Competitive_Not Mine Mouth_Rail --

CAI3 Kentucky Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt -- Tyrone Plant

CAR1 North and South Carolina High-Cost Competitive_Not Mine Mouth_Rail --

CAR2 North and South Carolina Low-Cost Competitive_Not Mine Mouth_Rail --

CAR3 North and South Carolina NonCompetitive_Not Mine Mouth_Rail VACA

CC1 Colorado High-Cost Competitive_Not Mine Mouth_Rail --

CC2 Colorado Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt --

CC3 Colorado Non-Competitive_Not Mine Mouth_Rail RMPA

CU1 Utah High-Cost Competitive_Not Mine Mouth_Rail -- KUCC Plant

CU2 Utah Low-Cost Competitive_Mine Mouth_Truck/Conveyor Belt -- Huntington Plant

CU4 Utah Non-Competitive_Not Mine Mouth_Rail --

DAL1 North Dakota High-Cost Competitive_Mine Mouth_Truck/Conveyor Belt -- Milton R Young Plant

DAL2 Montana, North Dakota Low-Cost Competitive_Mine Mouth_Truck/Conveyor Belt --

DAL4 North Dakota Non-Competitive_Not Mine Mouth_Rail --

EIM1 Iowa, Missouri High-Cost Competitive_Not Mine Mouth_Rail -- Prairie Creek Plant

EIM2 Iowa Low-Cost Competitive_Not Mine -- Fair Station Plant

### 9-5 Coal Demand Region Codes Descriptive Name IPM Model Regions with Potential Plants Assigned to this

Coal Demand Region Plant Name when Coal Demand Region Just Includes One Plant

Mouth_Barge

EIM3 Iowa, Missouri Low-Cost Competitive_Not Mine Mouth_Rail --

EIM4 Iowa Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt -- Pella Plant

EIM5 Iowa, Missouri Non-Competitive_Not Mine Mouth_Rail --

FL1 Florida High-Cost Competitive_Not Mine Mouth_Rail FRCC

FL2 Florida Low-Cost Competitive_Not Mine Mouth_Barge --

FL3 Florida Low-Cost Competitive_Not Mine Mouth_Rail --

GAR1 Georgia, Mississippi Low-Cost Competitive_Not Mine Mouth_Rail --

GAR2 Georgia Non-Competitive_Not Mine Mouth_Rail SOU

GFB1 Alabama Low Cost Competitive_Not Mine Mouth_Rail -- Barry Plant

GFB3 Mississippi Low-Cost Competitive_Not Mine Mouth_Barge -- Jack Watson Plant

GFB4 Mississippi Non-Competitive_Not Mine Mouth_Rail -- Victor J Daniel Jr Plant

GFR1 Mississippi, Texas High-Cost Competitive_Not Mine Mouth_Rail ERCT

GFR2 Arkansas, Louisiana, Texas Low-Cost Competitive_Not Mine Mouth_Rail --

GFR3 Arkansas, Louisiana, Texas NonCompetitive_Not Mine Mouth_Rail ENTG

GWAY Illinois, Mine Mouth GWAY

IBB1 Kentucky High-Cost Competitive_Not Mine Mouth_Rail -- Cane Run Plant

IBB2 Kentucky Low Cost Competitive_Not Mine Mouth_Rail -- Mill Creek Plant

IBB3 Indiana, Kentucky Low-Cost Competitive_Not Mine Mouth_Barge --

IBB4 Illinois, Indiana, Kentucky Low-Cost Competitive_Not Mine Mouth_Rail --

III1 Illinois, Indiana High-Cost Competitive_Not Mine Mouth_Rail --

III2 Kentucky High-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt -- Green River Plant

III3 Illinois, Indiana, Kentucky Low-Cost Competitive_Not Mine Mouth_Barge --

III4 Illinois, Indiana Low-Cost Competitive_Not Mine Mouth_Rail COMD & RFCO

### 9-6 Coal Demand Region Codes Descriptive Name IPM Model Regions with Potential Plants Assigned to this

Coal Demand Region Plant Name when Coal Demand Region Just Includes One Plant

III5 Illinois, Indiana, Kentucky Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt --

III6 Indiana Non-Competitive_Mine Mouth_Truck/Conveyor Belt -- Rank E Ratts Plant

III7 Illinois, Indiana, Kentucky NonCompetitive_Not Mine Mouth_Rail TVAK

IMB1 Illinois, Iowa, Missouri High-Cost Competitive_Not Mine Mouth_Rail MRO

IMB2 Iowa, Missouri Low-Cost Competitive_Not Mine Mouth_Rail --

IMB3 Missouri Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt --

IMB4 Iowa Non-Competitive_Not Mine Mouth_Rail --

MA-1 Maryland Low-Cost Competitive_Not Mine Mouth_Rail --

MA-2 Maryland Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt --

MA-3 Maryland Non-Competitive_Not Mine Mouth_Rail MACS

MAB1 Maryland Low-Cost Competitive_Not Mine Mouth_Rail --

MIB1 Michigan High-Cost Competitive_Not Mine Mouth_Rail --

MIB2 Michigan, Wisconsin Low-Cost Competitive_Not Mine Mouth_Barge --

MIB3 Michigan, Wisconsin Low-Cost Competitive_Not Mine Mouth_Rail MECS

MIB4 Michigan Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt -- Endicott Station

MNR1 Minnesota, South Dakota High-Cost Competitive_Not Mine Mouth_Rail --

MNR2 Minnesota Low-Cost Competitive_Not Mine Mouth_Barge -- Silver Bay Power Plant

MNR3 Minnesota Low-Cost Competitive_Not Mine Mouth_Rail --

MNR5 Minnesota, South Dakota NonCompetitive_Not Mine Mouth_Rail --

MWR1 Iowa, Kansas, Missouri, Nebraska, Oklahoma High-Cost Competitive_Not Mine Mouth_Rail SPPN

MWR2 Kansas, Missouri High-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt --

MWR3 Kansas, Missouri, Nebraska, Oklahoma Low-Cost Competitive_Not Mine Mouth_Rail SPPS

### 9-7 Coal Demand Region Codes Descriptive Name IPM Model Regions with Potential Plants Assigned to this

Coal Demand Region Plant Name when Coal Demand Region Just Includes One Plant

MWR5 Kansas, Missouri Non-Competitive_Not Mine Mouth_Rail --

NAI1 West Virginia High-Cost Competitive_Mine Mouth_Rail RFCP

NAI2 West Virginia High-Cost Competitive_Not Mine Mouth_Rail -- Mt Storm Plant

NAI3 Pennsylvania, West Virginia Low-Cost Competitive_Not Mine Mouth_Barge --

NAI4 West Virginia Low-Cost Competitive_Not Mine Mouth_Rail -- Willow Island Plant

NAI5 West Virginia Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt -- Albright Plant

NAI6 Ohio Non-Competitive_Not Mine Mouth_Rail -- Muskingum River Plant

NE1 Maine, Massachusetts, New Hampshire, New Jersey High-Cost Competitive_Not Mine Mouth_Rail --

NE2 Connecticut, Massachusetts, New Hampshire, New Jersey Low-Cost Competitive_Not Mine Mouth_Barge --

NE3 Connecticut, New York Low-Cost Competitive_Not Mine Mouth_Rail DSNY

NII1 Indiana High-Cost Competitive_Not Mine Mouth_Rail --

NII2 Illinois Low-Cost Competitive_Not Mine Mouth_Barge --

NII3 Illinois, Indiana Low-Cost Competitive_Not Mine Mouth_Rail --

NNR1 Nevada Non-Competitive_Not Mine Mouth_Rail -- North Valmy Plant

NOR1 Ohio High-Cost Competitive_Not Mine Mouth_Rail --

NOR2 Ohio Low-Cost Competitive_Mine Mouth_Truck/Conveyor Belt -- Conesville Plant

NOR3 Ohio Low-Cost Competitive_Not Mine Mouth_Barge --

NOR4 Ohio Low-Cost Competitive_Not Mine Mouth_Rail -- Hiles Plant

NOR5 Ohio Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt --

NOR6 Ohio Non-Competitive_Not Mine Mouth_Rail -- O H Hutchings Plant

NU1 New York High-Cost Competitive_Not Mine Mouth_Rail UPNY

NU2 New York Low-Cost Competitive_Not Mine Mouth_Rail -- AES Westover Plant

ORP1 Ohio, Pennsylvania, West Virginia High- --

### 9-8 Coal Demand Region Codes Descriptive Name IPM Model Regions with Potential Plants Assigned to this

Coal Demand Region Plant Name when Coal Demand Region Just Includes One Plant

Cost Competitive_Not Mine Mouth_Rail

ORP2 Ohio, Pennsylvania, West Virginia Low Cost Competitive_Not Mine Mouth_Rail --

ORP3 Ohio, West Virginia Low-Cost Competitive_Not Mine Mouth_Barge --

ORP4 Ohio, Pennsylvania, West Virginia LowCost Competitive_Not Mine Mouth_Rail --

PC1 Pennsylvania High-Cost Competitive_Not Mine Mouth_Rail MACW

PC2 Pennsylvania High-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt -- Homer City Station

PC3 Pennsylvania Low-Cost Competitive_Not Mine Mouth_Barge --

PC4 Pennsylvania Low-Cost Competitive_Not Mine Mouth_Rail -- P H Glatfelter Plant

PC6 Pennsylvania Non-Competitive_Not Mine Mouth_Rail -- PPL Montour Plant

PE1 New Jersey, Pennsylvania High-Cost Competitive_Not Mine Mouth_Rail --

PE2 Pennsylvania Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt -- Shawville Plant

PE3 Delaware, New Jersey, Pennsylvania Non-Competitive_Not Mine Mouth_Rail MACE

PRB1 Wyoming High-Cost Competitive_Mine Mouth_Truck/Conveyor Belt --

PRB3 Montana Low-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt -- Colstrip Plant

PRB4 Wyoming Non-Competitive_Not Mine Mouth_Rail --

SNR1 Nevada Non-Competitive_Not Mine Mouth_Rail SNV Reid Gardner

TAB1 Alabama High-Cost Competitive_Not Mine Mouth_Rail -- Charles R Lowman

TAB2 Alabama Low Cost Competitive_Not Mine Mouth_Rail -- Widows Creek

TAB3 Alabama, Tennessee Low-Cost Competitive_Not Mine Mouth_Barge --

TKI1 Tennessee Low-Cost Competitive_Not Mine Mouth_Rail --

TKI2 Tennessee Non-Competitive_Not Mine Mouth_Rail TVA

TXL1 Mississippi, Texas High-Cost Competitive_Mine Mouth_Rail --

TXL2 Texas High-Cost Competitive_Not Mine Mouth_Rail --

### 9-9 Coal Demand Region Codes Descriptive Name IPM Model Regions with Potential Plants Assigned to this

Coal Demand Region Plant Name when Coal Demand Region Just Includes One Plant

TXL3 Texas High-Cost Competitive_Not Mine Mouth_Truck/Conveyor Belt -- Twin Oaks Power One Plant

TXL4 Louisiana, Texas Low-Cost Competitive_Mine Mouth_Truck/Conveyor Belt --

TXL5 Texas Non-Competitive_Not Mine Mouth_Rail -- Gibbons Creek

VAPW Virginia, Mine Mouth VAPW

VEP1 South Carolina, Virginia High-Cost Competitive_Not Mine Mouth_Rail --

VEP2 Virginia Non-Competitive_Not Mine Mouth_Rail --

WIR1 Wisconsin High-Cost Competitive_Not Mine Mouth_Rail --

WIR2 Wisconsin Low-Cost Competitive_Not Mine Mouth_Rail WUMS

WIR4 Wisconsin Non-Competitive_Not Mine Mouth_Rail --

WOM1 Michigan Low-Cost Competitive_Not Mine Mouth_Rail -- Eckert Station

WOM2 Michigan Non-Competitive_Not Mine Mouth_Rail -- Erickson Station

WON1 California High-Cost Competitive_Not Mine Mouth_Rail --

WON2 California Low-Cost Competitive_Not Mine Mouth_Rail -- ACE Cogeneration Facility

WON3 Montana, Oregon, Washington NonCompetitive_Not Mine Mouth_Rail PNW

WYG1 Wyoming High-Cost Competitive_Mine Mouth_Truck/Conveyor Belt NWPE

WYG2 Wyoming High-Cost Competitive_Not Mine Mouth_Rail -- Osage Plant

WYG3 Wyoming Low-Cost Competitive_Mine Mouth_Truck/Conveyor Belt --

WYG4 Wyoming Non-Competitive_Mine Mouth_Rail -- Jim Bridger

9.1.3 Coal Quality Characteristics Coal varies by heat content, SO 2 

### content and mercury content among other characteristics. To capture differences in the sulfur and heat content of coal, a two letter “coal grade” nomenclature is used. The first letter indicates the “coal rank” (bituminous, subbitumionus, or lignite) with their associated heat content ranges (as shown in Table 9-3). The second letter indicates their “sulfur grade,” i.e., the SO 2 

### ranges associated with a given type of coal. (The sulfur grades and associated SO 2 

### ranges are shown in Table 9-4.)

### 9-10 Table 9-3 Coal Rank Heat Content Ranges Coal Type Heat Content (Btu/lb) Classification
Bituminous >10,260 – 13,000 B

Sub-bituminous > 7,500 – 10,260 S
Lignite less than 7,500 L

Table 9-4 Coal Grade SO 2 

### Content Ranges

SO 2 

### Grade SO 2 

### Content Range (lbs/MMBtu)

A 0.00 – 0.80

B 0.81 – 1.20

D 1.21 – 1.66

E 1.67 – 3.34

G 3.35 – 5.00

H > 5.00

The assumptions in EPA Base Case v.4.10 on the heat, mercury, SO 2 

### , and ash content of coal are derived from EPA’s “Information Collection Request for Electric Utility Steam Generating Unit Mercury Emissions Information Collection Effort” (ICR) 1. 

### A two-year effort initiated in 1998 and completed in 2000, the ICR had three main components: (1) identifying all coal-fired units owned and operated by publicly-owned utility companies, Federal power agencies, rural electric cooperatives, and investor-owned utility generating companies, (2) obtaining “accurate information on the amount of mercury contained in the as-fired coal used by each electric utility steam generating unit... with a capacity greater than 25 megawatts electric, as well as accurate information on the total amount of coal burned by each such unit,”, and (3) obtaining data by coal sampling and stack testing at selected units to characterize mercury reductions from representative unit configurations. Data regarding the SO 2 

### and ash content of the coal used was obtained along with mercury content.

The ICR resulted in more than 40,000 data points indicating the coal type, sulfur content, mercury content, ash content, and other characteristics of coal burned at coal-fired utility units greater than 25 MW.

9.1.4 Emission Factors To make this data usable in EPA Base Case v.4.10, the ICR data points were first grouped by IPM coal grades and IPM coal supply regions. Using the grouped ICR data, the average heat, SO 2 

### , mercury, and ash content were calculated for each coal grade/supply region combination. In instances where no data were available for a particu lar coal grade in a specific supply region, the national average SO 2 

### and mercury values for the coal gr ade were used as the region’s values. The resulting values are shown in Table 9-5.

1 

### Data from the ICR can be found at http://www.epa.gov/ttn/atw/combust/utiltox/mercury.html.

### 9-11 Table 9-5 Coal Quality Characteristics by Supply Region and Coal Grade
Coal Supply Region Coal Grade Heat Content (MMBtu/Ton) SO 2 

### Content (lbs/MMBtu) Mercury Content (lbs/TBtu) Ash Content (lbs/MMBtu) Cluster Number

BB 24.82 1.1 4.2 9.8 2
BD 24 1.4 7.3 10.8 2 AL
BE 23.82 2.7 12.6 10.7 2
AZ BB 24.64 1.1 5.3 7.9 2
BA 21.49 0.7 3.1 7.3 1 CG BB 22.01 0.9 4.1 8.4 2
BA 25.5 0.7 3.5 7 1 CR BD 22.2 1.4 7 8.3 1
BA 23.8 0.7 2.6 6.3 1
BB 23.22 0.9 4 7.8 2 CU
BD 23.21 1.3 3.1 8.1 1
BE 23 2.2 6.5 6.6 2
BG 23.01 4.6 6.5 8.1 1 IL
BH 22.19 5.6 5.4 9.1 1
BD 22.62 1.4 3.8 7.4 1
BE 23.43 2.3 5.2 8 2
BG 23.37 4.3 7.2 8.2 1 IN

BH 23.41 6.1 7.1 8.6 1
BA 25.32 0.7 3 6.1 1
BB 25.79 1 4.8 6.4 2
BD 25.33 1.4 6 7.4 1
BE 25.14 2.1 7.9 7.7 2 KE

BG 24.09 3.8 12 10.2 3
KS BG 25.32 4.8 4.1 8.5 1
BD 24.23 1.6 5.6 6.2 1
BE 24.45 2.8 7.1 7.4 2
BG 23.93 4.5 6.9 8 1 KW

BH 22.84 5.7 8.2 10.2 1
LA LE 14.09 2.5 7.3 17.1 2
BB 24.64 1.1 5.3 7.9 2
BD 26.32 1.6 7.8 9.5 2
BE 24.85 2.8 15.6 11.7 1 MD

BG 23.26 3.6 16.6 16.6 3
ME LD 13.36 1.4 8.6 11.3 1
SA 18.9 0.6 4.2 4 1 MP SD 17.23 1.5 4.5 10.1 1
MS LE 13.19 2.8 12.4 21.5 1
MT BB 21 1.1 5.3 7.9 2
LD 13.7 1.5 6.4 10.7 1 ND LE 13.46 2.3 8.3 12.8 1
BB 26.4 1.1 5.3 7.9 2 NS
BD 18.1 1.6 5.5 19.6 1

### 9-12 Coal Supply Region Coal Grade Heat Content (MMBtu/Ton) SO 2 

### Content (lbs/MMBtu) Mercury Content (lbs/TBtu) Ash Content (lbs/MMBtu) Cluster Number

BE 18.1 1.8 8.2 18.8 2
BB 24.68 1.1 5.7 9.8 1
BD 25.55 1.4 6.4 10.3 1
BE 25.24 3.1 18.7 7.1 1
BG 24.34 4 18.5 8 2 OH

BH 23.92 6.4 13.9 9.1 2
OK BE 22.15 2.7 25.8 11.3 1
BD 25.06 1.4 21.7 49.3 3
BE 25.66 2.6 18 9.2 1
BG 25.33 3.8 21.5 9.6 2 PC

BH 23.39 6.3 34.7 13.9 3
BD 24.26 1.6 11.2 10 2
BE 26.22 2.5 8.4 5.4 2 PW
BG 25.86 3.7 8.6 6.5 1
BB 24.18 1.1 3.8 10.4 2
BD 23.91 1.3 6.3 10.4 1 TN
BE 26.75 2.1 8.4 6.5 2
LD 13.06 1.6 12 22.3 2
LE 13.22 3 14.7 25.6 1 TX
LG 12.27 3.9 14.9 25.5 1
BA 23.68 0.7 4.4 7.4 2
BB 23.23 0.9 3.9 8.6 2
BD 23.05 1.4 4.4 10.5 1 UT

BE 25.06 2.3 9.2 7.4 2
BA 22.7 0.7 3.5 7 1
BB 25.97 1 4.6 7 2
BD 25.76 1.4 5.7 8 1 VA

BE 26.03 2.1 8.4 8.1 2
BB 21.67 1.1 1.8 5.6 1 WG SD 18.5 1.3 4.3 10 1
SA 17.43 0.6 5.6 5.5 2 WH SB 17.43 0.9 6.4 6.5 1
WL SB 17.15 0.9 6.4 6.5 1
BD 25.01 1.5 10.3 9.2 2
BE 25.67 2.5 10.3 7.9 2
BG 26.03 4 9.3 6.9 1 WN

BH 25.15 6.1 8.8 9.6 1
BA 26.2 0.7 3.5 7 1
BB 24.73 1.1 5.7 9.2 2
BD 24.64 1.3 8.1 9.3 2
BE 24.38 1.9 8.8 9.9 2 WS

BG 25.64 4.7 7.1 6.4 1

### 9-13 Next, a clustering algorithm was used to further aggregate the data in EPA Base Case v.4.10 for model size management purposes. The clusteri ng analysis was performed on the mercury and SO 2 

### data shown in Table 9-5 using the SAS statistical software package. Clustering analysis places objects into groups or clusters, such that data in a given cluster tend to be similar to each other and dissimilar to data in other clusters. The clustering analysis involved two steps. (In the following write-up BG coal is used to illustrate how the procedure worked.) First, the number of clusters of mercury and SO 2 

### concentrations for each IPM coal type was determined based on the range in average mercury and SO 2 

### concentrations across all coal supply regions for a specific coal type. Each coal type used either one or two clusters. The total number of clusters for each coal grade was limited to keep the model size and run time within feasible limits. (Two clusters were used for BG coal.) Second, for each coal grade the clustering procedure was applied to all the regional SO 2 

### and mercury values shown in Table 9-5 for that coal grade. (In the BG coal example there are 11 such regional SO 2 

### and mercury values.) Using the SAS cluster procedure, each of the constituent regional values was assigned to a cluster and the cluster average SO 2 

### and mercury values were recorded. The resulting val ues are shown in Table 9-6 and Table 9-7. (For BG coal the Cluster #1 average SO 2 

### and mercury values are 4.36 lb/MMBtu and 7.10 lb/TBtu respectively. The Cluster #2 average SO 2 

### and mercury values are 3.89 lb/MMBtu and 20.04 lb/TBtu respectively. The Cluster #3 average SO 2 

### and mercury values are 3.68 lb/MMBtu and 14.31 lb/TBtu respectively.) Although not used in determining the clusters, ash and CO 2 

### values were calculated for each of the clusters. These va lues are shown in Table 9-8 and Table 9-9. (The CO 2 

### values were derived from data in the Energy Information Administration’s Annual Energy Outlook 2009 (AEO 2009), not from data collected in the ICR.)

IPM input files retain the mapping between diffe rent coal grade/supply region combinations and the clusters. The mapping can be seen in the last column of Table 9-5 which shows the cluster number associated with the coal grade/supply region combination indicated in the first and second columns of this table. (For BG coal, the SAS cluster procedure mapped supply regions IL, IN, KS, KW, PW, WN, and WS to Cluster #1, supply regions OH and PC to Cluster #2, and MD and KE to Cluster #3. See Figure 9-2 for an illustration of th is mapping.) Table 9-6 to Table 9-9 show the mercury, SO 2 

### , ash, and CO 2 

### values assigned to coal grades and regions based on this cluster mapping. The values shown in Table 9-6 to Table 9-9 are used in EPA Base Case v.4.10 for calculating emissions.

Table 9-6 SO 2 

### Emission Factors of Coal Used in EPA Base Case v.4.10 Sulfur Emission Factors (lbs/MMBtu) Coal Type by Sulfur Grade Cluster #1 Cluster #2 Cluster # 3 Low Sulfur Eastern Bituminous (BA) 0.7 0.67 -- Low Sulfur Western Bituminous (BB) 1.13 1.03 -- Low Medium Sulfur Bituminous (BD) 1.43 1.45 1.42 Medium Sulfur Bituminous (BE) 2.78 2.3 -- High Sulfur Bituminous (BG) 4.36 3.89 3.68 High Sulfur Bituminous (BH) 5.89 6.43 6.29 Low Sulfur Subbituminous (SA) 0.62 0.58 -- Low Sulfur Subbituminous (SB) 0.94 -- -- Low Medium Sulfur Subbituminous (SD) 1.41 -- -- Low Medium Sulfur Lignite (LD) 1.46 1.61 -- Medium Sulfur Lignite (LE) 2.88 2.38 -- High Sulfur Lignite (LG) 3.91 -- --

### 9-14 Table 9-7 Mercury Emission Factors of Coal Used in EPA Base Case v.4.10 Mercury Emission Factors (lbs/TBtu) Coal Type by Sulfur Grade Cluster #1 Cluster #2 Cluster #3 Low Sulfur Eastern Bituminous (BA) 3.19 4.37 -- Low Sulfur Western Bituminous (BB) 1.82 4.86 -- Low Medium Sulfur Bituminous (BD) 5.38 8.94 21.67 Medium Sulfur Bituminous (BE) 19.53 8.42 -- High Sulfur Bituminous (BG) 7.1 20.04 14.31 High Sulfur Bituminous (BH) 7.38 13.93 34.71 Low Sulfur Subbituminous (SA) 4.24 5.61 -- Low Sulfur Subbituminous (SB) 6.44 -- -- Low Medium Sulfur Subbituminous (SD) 4.43 -- -- Low Medium Sulfur Lignite (LD) 7.51 12 -- Medium Sulfur Lignite (LE) 13.55 7.81 -- High Sulfur Lignite (LG) 14.88 -- --

Table 9-8 Ash Emission Factors of Coal Used in EPA Base Case v.4.10 Ash Emission Factors by Coal Sulfur Grades (lbs/MMBtu) Coal Type by Sulfur Grade Cluster #1 Cluster #2 Cluster #3 Low Sulfur Eastern Bituminous (BA) 6.77 7.39 -- Low Sulfur Western Bituminous (BB) 5.59 8.1 -- Low Medium Sulfur Bituminous (BD) 9.64 9.77 49.31 Medium Sulfur Bituminous (BE) 9.84 8.69 -- High Sulfur Bituminous (BG) 7.51 8.8 13.41 High Sulfur Bituminous (BH) 9.38 9.13 13.89 Low Sulfur Subbituminous (SA) 3.98 5.47 -- Low Sulfur Subbituminous (SB) 6.5 -- -- Low Medium Sulfur Subbituminous (SD) 10.08 -- -- Low Medium Sulfur Lignite (LD) 11.01 22.33 -- Medium Sulfur Lignite (LE) 23.58 15 -- High Sulfur Lignite (LG) 25.51 -- --

### 9-15 Table 9-9 CO 2 

### Emission Factors of Coal Used in EPA Base Case v.4.10 CO 2 

### Emission Factors by Coal

Sulfur Grades (lbs/MMBtu) Coal Type by Sulfur Grade Cluster #1 Cluster #2 Cluster #3 Low Sulfur Eastern Bituminous (BA) 205.4 205.4 -- Low Sulfur Western Bituminous (BB) 205.8 205.8 -- Low Medium Sulfur Bituminous (BD) 206.6 206.6 206.6 Medium Sulfur Bituminous (BE) 206.3 206.3 -- High Sulfur Bituminous (BG) 205.2 205.2 205.2 High Sulfur Bituminous (BH) 205.2 205.2 205.2 Low Sulfur Subbituminous (SA) 213.1 213.1 -- Low Sulfur Subbituminous (SB) 212.7 -- -- Low Medium Sulfur Subbituminous (SD) 213.1 -- -- Low Medium Sulfur Lignite (LD) 217 217 -- Medium Sulfur Lignite (LE) 214.8 214.8 -- High Sulfur Lignite (LG) 213.5 -- --

Figure 9-2 Cluster Mapping Example --- BG Coal

9.1.5 Coal Grade Assignments The grades of coal that may be used by specif ic generating units were determined by an expert assessment of the ranks of coal that a unit had used in the past, the removal efficiency of the installed FGD, and the SO 2 

### permit rate of the unit. Examples of the coal grade assignments made for individual plants in EPA Base Case v.4.10 are shown in Table 9-10. Not all of the coal grades

### 9-16 allowed to a plant by the coal grade assignment are necessarily available in the plant’s assigned coal demand region (due to transportation limitat ions). IPM endogenously selects the coal burned by a plant by taking into account both the constr aint of the plant’s coal grade assignment and the constraint of the coals actually available within a plant’s coal demand region.

Table 9-10 Example of Coal Assignments Made in EPA Base Case

Plant Name Unique ID SIP SO 2 

### Limit (lbs/MMBtu) Scrubber? Fuels Allowed

Salem Harbor 1626_B_1 1.2 No BA,BB Dickerson 1572_B_3 2.8 No BA,BB,BD,BE Glen Lyn 3776_B_51 1.75 No BA,BB,BD Danskammer Generating Station 2480_B_3 1.1 No BA,BB
R E Burger 2864_B_5 9.02 No BA,BB,BD,BE,BG,BH
Moutaineer 6264_B_1 1 Yes BA,BB,BD,BE,BG,BH, SA,SB,SD Big Brown 3497_B_1 3 No LD,LE,SA,SB,SD Black River Generation 10464_B_E0001 3.8 Yes BA,BB,BD,BE,BG,BH

E D Edwards 856_B_1 4.71 No BA,BB,BD,BE,BG,SA, SB,SD
R Gallagher 1008_B_1 4.71 No BA,BB,BD,BE,BG,SA, SB,SD



## 9.2 Coal Supply Curves 9.2.1 Nature of Supply Curves Developed for EPA Base Case v.4.10 In keeping with IPM’s data-driven bottom-up modeling framework, a bottom-up approach (relying heavily on detailed economic and resource geology data and assessments) was used to prepare the coal supply curves for EPA Base Case v.4.10. Wood Mackenzie was chosen to develop the curves based on their extensive experience in preparing mine-by-mine estimates of cash operating costs for operating mines in the U.S., their access to both public and proprietary data sources, and their active updating of the data both through research and interviews.

In order to establish consistent nomenclature, W ood Mackenzie first mapped its internal list of coal regions and qualities to EPA’s 31 coal supply regions (described above in sections 9.1.1) and 12 coal grades (described above in section 9.1.3). The combined code list is shown in Table 9-11 below with the IPM supply regions appearing in t he rows and the coal grades in the columns. Wood Mackenzie then created supply curves for each region and coal-grade combination (indicated by the “x” in Table 9-11) for forecast years 2012, 2015, 2020, 2030, and 2040.

### 9-17 Table 9-11 Basin-Level Groupings Used in Preparing v.4.0 Coal Supply Curves

9.2.2 Procedure Employed in Determining Mining Costs Wood Mackenzie estimates mine production costs on a mine-by-mine basis utilizing proprietary bottom-up engineering cost models. A mine’s cash costs are the sum of its direct operating costs (DOCs), royalty tax, severance tax, property tax, reclamation tax and black lung fees. Using these mine costing models, costs curves are devel oped by summing the individual and incremental costs that make up mine cash-costs and assuming a built-in 10% discounted rate-of-return. As an illustration of the break-down of costs included in the mine costing models, Figure 9-3 lists the cost components included and calculations performed for a Powder River Basin mine supply curve. Appendix 9-1 contains a more detailed illu stration of the procedure used to derive a supply curve from its constituent mine costing models.

Coal Supply Regions and Coal Grades in EPA IPM, v.4.0
Coal Cod e 1 2 3 4 5 6 7 8 9 10 11 12 13 Geo. Region Geo. Sub-Region Region Code B A BB BD BE BG BH S A SB SD LD L E LG LH Appalachia Southern Appalachia 1 A L XXXX West Southwest 2 A Z X West Rocky Mountain 3 CG X X West Rocky Mountain 4 C R X X West Rocky Mountain 5 CU XXX Interior East Interior (Illinois Basin) 6 IL XXX Interior East Interior (Illinois Basin) 7 IN XXXX Interior Gulf Lignite 8 L A X Appalachia Northern Appalachia 9 MD XXXX West Dakota Lignite 10 M E X Appalachia Central Appalachia 11 KE XXXXX Interior West Interior 12 KS X Interior East Interior (Illinois Basin) 13 KW XXXX West Western Montana 14 MP X X Gulf Gulf Coast Lignite 15 M S X West Western Montana 16 MT X West Dakota Lignite 17 ND X X West Southwest 18 NS XXX Appalachia Northern Appalachia 19 OH XXXXX Interior West Interior 20 OK X Appalachia Northern Appalachia 21 PC XXXX Appalachia Northern Appalachia 22 PW XXX Appalachia Central Appalachia 23 TN XXX Gulf Gulf Coast Lignite 24 TX XXXX West Rocky Mountain 25 UT XXXX Appalachia Central Appalachia 26 VA XXXX West Western Wyoming 27 W G X X West Wyoming Powder River Basin 28 WH X X West Wyoming Powder River Basin 29 WL X Appalachia Northern Appalachia 30 WN XXXX Appalachia Central Appalachia 31 WS XXXXX BITUMINOUS SUB-BITUMINOU S LIGNITE

### 9-18 Figure 9-3 Cost Calculations Included When Developing Coal Supply Curves (based on a Powder River Basin Mine Supply Curve Example)

10% DCFROR All cost curves assume mine operators are able to earn a minimum 10% discounted rate-of-return.

Cash Cost

Royalty = Revenue * Royalty % Severance Tax = (Revenue - Royalty) * Sevr Tax Rate 1 * % Adjustment if needed Property Tax = (Revenue - Royalty)* property tax rate Reclamation Tax = Production * reclamation rate 2 Black Lung = Production * (the lesser of 0.55, or 4.4% * sales price of coal)

DOCs (surface mines) Labor = Production * (Wages + Benefits) / Productivity Unit Removal Costs

= Cost per yard by equipment type * Amount removed Added coal haul = year-over-year mileage increase * haul rate * production On-going reclamation = production * reclamation rate (as a function of coal depth)

< OR > DOCs (deep mines) Labor = Production * (Wages + Benefits) / Productivity Materials & Supplies Trucking costs = haul distance from mine to load-out * haul rate Mine overhead = Cost associated with mine support Division overhead = Company dependent costs for operations Pension = company's portion of employee retirement

General Definitions:

Revenues = Tons coal produced * sale price/ton Productivity (TPMH) = Tons coal produced / man hours worked in reporting period Stripping Ratio = Overburden Yard / Coal Production Production = Amount of coal removed from the mine in a given period

Cash cost: is the sum of a mine's Direct Operating Costs (DOCs), Royalty tax, Severance Tax, Property Tax, Reclamation Tax and Black Lung fees.

1 Severance Tax Rate is state specific

2 Reclamation Tax used was 0.15 for Deep Mines & 0.35 for Surface Mines



### It is important to note that although the formula for calculating mine costs is consistent across regions, some tax rates and fees vary by state and mine type. In general, there are two mine types: underground (deep) or surface mines. Undergr ound mining is categorized as being either a longwall (LW) or a continuous room-and-pillar mi ne (CM). Geologic conditions and characteristics of the coal seams determine which method will be used. Surface mines are typically categorized by the type of mining equipment used in their operati on such as draglines (DL), or truck & shovels (TS). These distinctions are important because the equipment used by the mine affects productivity measures and ultimately mine costs.

Several methods are employed for cost estimation depending on the availability of information and the diversity of mining operati ons. When possible, Wood Mackenzie analysts develop detailed lists of mine related costs. Costs such as employee wages & benefits, diesel fuel, spare parts, roof bolts and explosives among a host of others are summed to form a mine’s direct operating costs.

### 9-19 Direct costs categories include: mine labor, salaries, material and supplies, and mine overhead. The costs are estimated based on labor productivity and mining methods. Labor productivity is used to calculate mine labor and salaries by applying an average cost per employee hour to the labor productivity figure reported by MSHA or estimated based on comparable mines. For surfacemines, material and supply costs are estimated based on the mining method (dragline, truckshovel and other) and the number of yards of overburden 1 

### moved by each method. A cost per yard moved is estimated for each mining met hod and mining region. Where coal is washed, washing costs are based on the type of plant being used and the average washing cost per ton for the mining region. Overhead costs are estimated based on mine size.

Labor costs are estimated based on employment data reported to MSHA. MSHA data provides employment numbers, employee hours worked and tons of coal produced. These data are combined with labor rate estimates from various sources such as union contracts, census data and other sources such as state employment websites to determine a cost per ton for mine labor. Hourly labor costs vary between United Mine Workers of America (UMWA) and non-union mines, and include benefits and payroll taxes. Employees assigned to preparation plants, surface activities, and offices are excluded from this category and are accounted for under coal washing costs and mine overhead. These preparation plants may be located at the mine site or a central preparation plant that washes coal from a number of mines. If the coal is transported to an offsite location for washing, transportation costs to the plant are included in the total costs.

Supply costs are adjusted annually to reflect movem ents in the price of steel, diesel, natural gas and other commodities. Cost adjustments are averaged on an annual basis and analyzed to ensure that anomalous spikes in commodity prices are not carried forward in the cost analysis.

Royalties, severance taxes, black lung fees, reclamation taxes and property taxes are estimated using federal, state and local parameters.

In the Western United States, capital requirements are estimated for each mine and a life-of-mine discounted cash flow analysis is used to dete rmine the price required to yield a 10% DCFROR 2



### , including income taxes. In the Eastern United States, the required price is estimated based on operating costs and production levels.

Where information is incomplete, cost items are grouped into categories that can be compared with industry averages by mine type and location. These averages can be adjusted up or down based on new information or added assumptions. The adjustments take the form of cost multipliers or parameter values. Specific cost multipliers are developed with the aid of industry experts and proprietary formulas. This method is at times used to convert materials and supplies, on-site trucking costs and mine and division overhead categories into unit removal costs by equipment type. (This was done in the example shown in Figure 9-3 above.) To check the accuracy of these cost estimates, cash flow analysis of publicly traded companies is used. Mine cash-costs are extracted from corporate cash flows and compared with the initial estimates. Adjustments for discrepancies are made on a case-by-case basis.

Many of the cost assumptions associated with labor and productivity were taken from the Mine Safety Health Administration (MSHA) database. All active mines report information specific to production levels, number of employees and employee hours worked. Wood Mackenzie supplements the basic MSHA data with informati on obtained from mine personnel interviews and industry contacts. Phone conversations and conferences with industry professionals provide

1 

### Overburden refers to the surface soil and rock that must be removed to uncover the coal.

2 

### DCFROR stands for discounted cash flow rate of return (also called “internal rate of return” (IRR) and “rate of return”). It is the annualized effective coupounded return that can be earned on invested capital.

### 9-20 additional non-reported information such as work schedules, equipment types, percentages of washed coal, and trucking distances from the mine to wash-plants and load-out terminals.

For each active or proposed mine, Wood Mackenzie r eports the estimated cost to take coal from the mine to a logical point-of-sale. The logical point-of-sale may be a truck or railcar load-out or even a barge facility. This is done to produce a consistent cost comparison between mines. Any transport costs beyond the point-of-sale terminal are not part of this analysis and are not reflected in the supply curves themselves. (Transport costs are taken into account using a separate procedure which is described below in section 9.3.)

In cases where new mines are planned or recoverable reserves are available to support new mines (see sections 9.2.6 and 9.2.7 below), Wood Mackenzie uses nearby mines with similar geography and geology to estimate mine operating co sts and productivity levels. Production levels for new mines are estimated based on known rese rves, historic precedent, and region specific knowledge.

9.2.3 Supply Curve Development Once costs are estimated for all new or existing mines, they are sorted by cash cost, lowest to highest, and plotted cumulatively by production to form a supply curve. The supply curve then represents all mines – new or existing as well as both underground and surface mines– irrespective of market demand. Mines located toward the bottom of the curve have the lowest cost and are most likely to be developed while the mines at the top of the curve are higher cost and will likely wait to be developed. The process for developing a cumulative supply curve is illustrated in Figure 9-4 and Figure 9-5 below.

Figure 9-4 Illustration of Preliminary Step in Developing a Cumulative Coal Supply Curve

•
In the table and graph above, mine costs and production are sorted alphabetically by mine name.

To develop a supply curve from the above table t he values must be sorted by mine costs from lowest to highest. A new column for cumulative production is added, and then a supply curve graph is created which shows the costs on the ‘Y’ axis and the cumulative production on the ‘X’ axis. Notice below that the curve contains a ll mines – new or existing as well as both underground and surface mines. The resulting curve is a cont inuous supply curve but can be modified to show costs as a stepped supply curve. (Supply cu rves in stepped format are used in linear programming models like IPM.) See Figure 9-6 fo r a stepped version of the supply curve example shown in Figure 9-5. Here each step represents an individual mine, the width of the step reflects the mine’s production, and its height shows the cost of production. (See Appendix 9-1 for a more detailed example of how a supply curve is der ived from constituent mine costing models.)

### 9-21 Figure 9-5 Illustration of Final Step in Developing a Cumulative Coal Supply Curve

•
Figure 9-6 Example Coal Supply Curve in Stepped Format

•

9.2.4 Data Sources Used to Build the Curves

For active mines, data relating to labor and produc tivity is taken from MSHA databases. MSHA reports on individual mine production, number of employees and employee-hours worked. Corporate financial statements of publicly traded companies are listed with the Securities and Exchange Commission (SEC). Supplemental information on work schedules, equipment, percentages of washed coal, trucking distances between mine and preparation plants is obtained

### 9-22 in interviews performed for Wood Mackenzie ’s annually published county-by-county studies. Information on recoverable reserves comes from several sources such as Environmental Impact Statements (EIS), company annual reports, applications filed at state permitting offices or Lease by Application (LBA) filings for mines on federal lands.

For areas where public information is not available or is incomplete, reserves are estimated using geologic reports of nearby properties and some extrapolation by mining engineers and geologists.

9.2.5 Procedure Used In Determining Mine Productivity Projected production and stripping ratios 3 

### are the key determinants of surface mine productivity. Wood Mackenzie assumes mining costs increase as stripping ratios increase. The stripping ratio is the quantity of overburden removed relative to the quantity of coal recovered. Assuming that reserves are developed where they are easiest to mine and deliver to market, general theory suggests that as the easy reserves are depleted, greater amounts of overburden must be handled for the same amount of coal production; thus causing a decrease in mining productivity. However, this productivity loss is often offset by te chnology improvements in labor saving equipment.

While an understanding of the forces affecting productivity is important, no attempt is made to develop a complex algorithm that tries to bal ance increased stripping ratios with added technology improvements. Instead, Wood Mackenzie uses reported aggregate productivity (in tons per employee hour) provided by MSHA as a starting point and divides the production by the productivity calculation to obtain aggregate em ployee-hours. Allocating aggregate employee hours among specific mines, production forecasts for these mines can be converted back into minespecific productivity forecasts. These foreca sts are then examined on a mine-by-mine basis by an industry expert with region specific knowledge.

A similar approach is used for underground mines. First, as background, the specific factors affecting productivity at such mines are ident ified. For example, underground mines do not have stripping ratios. Productivity estimates for these mines largely depend on the type of mining technique used (which is a function of the region’s geology). For instance, longwall-mines can produce a high volume of low cost coal but geologic constraints like small reserve blocks and the occurrence of faulting tends to limit this technique to certain regions. In addition to geologic constraints, there are many variables that can impact underground-mine productivity but they are often difficult to quantify and forecast.

These factors are not used directly but provide a backdrop for deriving productivity estimates. As with surface mines Wood Mackenzie relies on MSHA data for its productivity estimates. Productivity estimates for underground mines st art with the MSHA estimates and are carried forward into the forecast years without adjustment.

9.2.6 Procedure to Determine Total Recoverable Reserves by Region and Type Before mine operators are allowed to mine coal , they must request various permits, conduct environmental impact studies (EIS) and, in many ca ses, notify corporate shareholders. In each of these instances, mine operators are asked to estimate annual production and total recoverable reserves. Wood Mackenzie uses the mine operat ors’ statements as the starting point for production and reserves forecasts. If no other material is available, interviews with company personnel will provide an estimate.

Region and coal type determinations for unlisted reserves are based on public information reported for similarly located mines. Classifying re serves this way means considering not only a

3 

### Stripping ratio is the amount of waste material (rock and/or soil) that must be removed to recover one unit (commonly expressed in short tons) of coal. For example, a stripping ratio of 9.8 means that to recover 1 ton of coal you must remove 9.8 tons of waste material. A lower stripping ratio means that less waste has to be removed.

### 9-23 mine’s geographic location but also its geologic c onditions such as depth and type of overburden and the specific identity of the coal seam(s) being mined. For areas where public information is not available or is incomplete, Wood Mackenzie engineers and geologists estimate reserve amounts based on land surveys and reports of coal depth and seam thickness provided by the U.S. Geologic Service (USGS). This information is then used to extrapolate reserve estimates from known coal sources to unknown sources. Coal quality determinations for unknown reserves are assigned in much the same way.

Once a mine becomes active, actual production numbers reported in corporate SEC filings and MSHA reports are subtracted from the total reserve number to arrive at current reserve amounts. Wood Mackenzie consistently updates the re serves database when announcements of new or amended reserves are made public. As a final che ck, the Wood Mackenzie supply estimates are balanced against the Demonstrated Reserve Base (DRB) 4 

### estimates to ensure that they do not exceed the DRB estimates.

9.2.7 New Mine Assumptions New mines have been included based on information that Wood Mackenzie maintains on each supply region. They include announced projects, c oal lease applications and unassigned reserves reported by mining companies. Where additional reserves are known to exist, additional incremental steps have been added. These incremental steps were added based on characteristics of the specific region, typical mine size, and cost trends. They do not necessarily imply a specific mine or mine type.

9.2.8 Other Notable Procedures Cost Rounding For simplification, the estimated mine costs were rounded so that costs less than 20 $/Ton were rounded to the nearest $0.25. Costs that fell between 20 and 50 $/Ton were rounded to the nearest $0.50 and costs greater than $50 were rounded to the nearest $1.00.

Future Cost Adjustments For consistency with the cost basis used in EPA Base Case v.4.10, costs are converted to real 2007$. Wood Mackenzie has assumed that improved productivity will lead to cost reductions in all regions except Central Appalachia where deplet ing reserves will lead to falling productivity and increased costs. Costs for all regions except Central Appalachia have been reduced at a rate of 0.4%/year. Central Appalachia’s costs have been in creased at a rate of 0.4%/year based on the assumption that labor costs on average account for 40% of the Cost of Production

. These regional cost adjustments are derived from specific factors affecting costs in regions based on information maintained by Wood Mackenzie and their on-going dialog with industry professionals.

9.2.9 Region Specific Assumptions and Outlooks Powder River Basin (PRB) Powder River Basin cost curves are based on cost models run for each of the identified projects in Wood Mackenzie’s coal supply database. These cost models are run on five year increments over a 20 year mine life. These models assume that federal lease tracts are acquired as necessary to achieve a 20 year mine life. In preparing the curves for EPA, it was assumed that existing mines would operate at projected levels through 2011 and re serves were adjusted to reflect remaining reserves at that time. Ten years additional rese rves were then added to the remaining reserves

4 

### Posted by the Energy Information Administra tion (EIA) in its January, 2007 Coal Production Report. <=$20, round to nearest $0.25 >$20 and <=$50, round to nearest $0.50 >$50, round to nearest $1.00

### 9-24 based on the assumption that additional reserves would be available for lease and the mines would continue to operate. Costs for the added reserves were increased by $3.00/ton to reflect increased coal leasing costs and increasing mining ratios in the PRB.

One existing mine, Decker, is expected to close prior to 2012 and was therefore not included in the cost curves.

A MT region was added to accommodate two proposed mines in Montana that do not fit the quality specifications for the ME or MP regions.

There is an annual limit of 612 million tons per year on Wyoming PRB coal production (i.e., coal codes WH and WL in Table 9-11) based on expec ted maximum production from existing mines plus potential production from four identified projects.

Western Bituminous Coalfields The Western Bituminous Coalfields include the Co lorado, Utah, southern Wyoming, New Mexico and Arizona coalfields. Life-of-mine costs are used for all operating mines and identified projects. Reserves have been reduced based on projected production through 2011 for existing mines and mines that are expected to go into production before 2012. Unlike the PRB no additional reserves have been added to the reserve base for existing mines.

Arizona mine costs are FOB 5 

### mine.

In Utah all costs except Deer Creek’s include transportation to a loadout facility. Deer Creek costs are based on delivery to the Huntington Canyon power plant.

In New Mexico, all costs are FOB rail or FOB mine. It is assumed that the Navajo, San JuanUnderground and the proposed Navajo South/Desert Rock mine will serve their respective minemouth power plants. One existing mine, McKinley, will be mined out by 2012. The proposed Carrizozo mine in SE New Mexico is included in the curve.

In Colorado all costs are FOB mine. This includes the Deserado mine which currently ships all of its production to the Bonanza power plant via a private railroad. A CR region has been added to accommodate two proposed mines in or near the Raton Basin.

Louisiana (LA)/ Mississippi (MS) Louisiana cost curves are based on Wood Mackenz ie cost models for existing and planned mines in Louisiana. Where a mine’s reserves exceed producti on requirements over the forecast period, it has been assumed that a second mine could be opened on the reserves. A high cost mine with cost of $50/ton was added.

A Mississippi cost curve was added.

Montana Lignite (ME) Montana Lignite curves are based on Wood Mackenzie cost estimates for GNP lignite properties. All costs are FOB mine. Because lignite typically does not ship well, it is assumed that any new mines will be developed to serve mine mouth customers.

5 

### FOB stands for “Free On Board” or “Freight on Board.” It indicates the point at which responsibility and costs for a goods is transferred from the seller (or shipper) to the buyer. “FOB mine” implies that the price includes costs up to the mine and that the buyer assumes costs beyond the mine. “FOB rail” implies the prices includes the cost of loading the coal onto a rail car.

### 9-25 North Dakota Lignite (ND) North Dakota curves are based on Wood Mackenzie cost estimates for the region. Where a mine’s reserves exceed production requirements over the forecast period, it has been assumed that a second mine could be opened on the reserves. Costs for the new mines were increased by $2.00/ton. A GNP project was added as a high cost new mine.

Oklahoma/Kansas (OK, KS) The 2006 set of coal supply curves developed for EPA by Hill and Associates (now a unit of Wood Mackenzie) was updated by increasing costs 30% and adding a high cost mine with a cost of $100/ton.

Texas Lignite Texas Lignite cost curves are based on cost models run for each of the existing mines and identified projects in Wood Mackenzie’s coal supply database. Big Brown was deleted for the existing mines as its reserves will be depleted before 2011.

Illinois Basin (IL, IN, KW) Illinois Basin cost curves are based on cost models run for each of the existing mines and identified projects in Wood Mackenzie’s coal supply database. Where additional reserves are known to be available, additional next step mi nes were added based on information maintained by Wood Mackenzie on the region.

Appalachia (AL, OH, PC, PW, TN, VA, WN & WS) Appalachian cost curves are based on Wood Ma ckenzie’s extensive database of existing and planned mines. 2012 production and cost curves were prepared for each region and coal grade using the regional model to estimate cost, production and reserve data for existing mines. To protect the proprietary nature of the regional curves, mine data were aggregated to produce a curve similar to the regional curve without disclosing specific mine data. Cost and production estimates for each of the next step mines we re prepared for each region and coal type based on information maintained by Wood Mackenzie’s knowledge for each region.

9.2.10 Explanation of Coal Supply Curve Extensions to 2040 Wood Mackenzie added additional reserves at in creased costs on a mine-by-mine basis for each region based on its extensive coal resource database and knowledge of the coal industry. A list of the mines added by region and coal grade can be f ound in Appendix 9-2. For modeling purposes the 2040 coal supply curves are used in 2050 as well.



## 9.3 Coal Transportation
Within the United States, steam coal for use in coal-fired power plants is shipped via a variety of transportation modes, including barge, conveyor belt, rail, truck, and lake/ocean vessel. A given coal-fired plant typically only has access to a fe w of these transportation options and, in some cases, only has access to a single type. The number of transportation options that a plant has when soliciting coal deliveries influences transporta tion rate levels that plant owners are able to negotiate with transportation providers.

Within the Eastern United States, rail service is provided predominately by two major rail carriers in the region, Norfolk Southern (NS) and CSX Transportation (CSX). Within the Western United States, rail service is also provided predominatel y by two major rail carriers, Burlington Northern Santa Fe (BNSF) and Union Pacific (UP). Plant s in the Midwestern United States may have access to rail service from BNSF, CSX, NS, UP, the Canadian National (CN), Canadian Pacific (CP), or short-line railroads. Barge, truck, and vessel service is provided by multiple firms, and conveyor service is only applicable to coal-fired plants directly located next to mining operations (e.g., mine-mouth plants).

### 9-26 In recent years, transportation rates (especially rail rates) have increased significantly due to significant increases in input costs (including fuel prices, steel prices and labor costs), as well as a number of Surface Transportation Board (STB) rail rate case decisions that have allowed higher rail rates to be charged at plants that are served only by a single railroad.

The transportation methodology and rates presented below reflect expected long-run equilibrium transportation rates as of September 2008, when the coal transportation rate assumptions for EPA Base Case v.4.10 were finalized. It is important to remember that these rates do not reflect rates that transporters and coal-fired owners may currently be negotiating in the near-term (pre-2012). Instead, these rates reflect expected long-term equilibrium levels, including the long-term market dynamics that will drive these pricing levels.

All rates are represented in 2007 real dollars.

9.3.1 Coal Transportation Matrix Overview Description In order to model coal transportation rates within the EPA’s modeling construct, a coal transportation matrix was developed, which represents a matrix of all the coal demand regions and coal supply regions modeled within the IPM model for EPA Base Case v.4.10. The matrix includes the associated transportation costs between these supply and demand nodes. Each coal demand region covers a set of coal plants having similar transportation infrastructure, transportation competitiveness levels, and geographic location; in addition to these criteria, coal demand regions are also classified as either “mi ne-mouth” or “non mine-mouth” regions. Coal supply regions are represented by the major coal mining basins modeled in IPM; a more detailed discussion on these regions can be found in previous sections.

Methodology Each coal supply region and coal demand region is connected via a transportation link, which can include multiple transportation modes. For each transportation link, cost estimates, in terms of $/ton, were calculated utilizing mode-based transportation cost factors, analysis of the competitive nature of the moves, and overall distance that the coal type must move over each applicable mode. An example of the calculation methodology for movements including multiple transportation modes is shown in Figure 9-7.

Figure 9-7 Calculation of Multi-M ode Transportation Costs (Example)

9.3.2 Calculation of Supply/Demand Region Distances Definition of applicable supply/demand regions Coal demand regions are linked to coal supply regi ons based on historical coal deliveries, as well as based on the potential for new coal supplies to serve a demand region going forward. A demand region may have transportation links with more than one supply region, depending on the various coal types that can be physically delivered and burned by generators within a given coal demand region. Barge Cost ($/ton) = Loading Cost ($/ton) + Barge Mill Rate (mills/ton-mile) x Barge Mileage 

# + Transloading Cost ($/ton) Rail Cost ($/ton) = Rail Mill Rate (mills/ton-mile) x Rail Mileage 

# + Barge Cost ($/ton) = Loading Cost ($/ton) + Barge Mill Rate (mills/ton-mile) x Barge Mileage 

# + Transloading Cost ($/ton) Rail Cost ($/ton) = Rail Mill Rate (mills/ton-mile) x Rail Mileage 

# +

### 9-27 Figure 9-8 Coal Demand Region with Multiple Coal Supply Regions

Transportation Links for Existing Coal-Fired Plants Transportation routings for given coal suppl y regions and coal demand regions were developed based on third-party software 6 

### and other industry knowledge available to Hellerworx and PA Consulting Group. Origins for each coal supply r egion were based on significant mines or other significant delivery points within the supply region, and destinations for each coal demand region were based on geographical points located near, and with similar key delivery transportation characteristics, as the coal plants located withi n the given coal demand region. For routes utilizing multiple modes (e.g. rail-to-barge, truck-to-rail, etc.), distances were developed separately for each transportation mode.

Transportation Links for New Coal-Fired Plants Within each coal demand region, coal transportation links representative of the typical transportation costs expected to be incurred by new coal plants within that region were developed. For coal demand regions where new coal plant construction is not expected to include minemouth plants, the transportation links for new plants are based on transportation links in the existing coal demand region that are expected to hav e costs most similar to those incurred by new plants. For coal demand regions where new coal plant construction is expected to consist primarily of mine-mouth plants, new transportati on links reflecting short-distance transportation of local coal supplies were created as needed to properly represent the transportation costs incurred by new plants. This methodology helps ensure that coal transportation costs for new coal plants are properly integrated with and assessed fairly vis-à-vis existing coal-fired assets within the IPM modeling structure.

9.3.3 Overview of Rail Rates Competition within the railroad industry is limited. Two major railroads in the Western U.S. (BNSF and UP) and two major railroads in the Eastern U.S. (CSX and NS) currently originate most of the U.S. coal traffic that moves by rail.

In recent years, railroads have increased coal tr ansportation rates in real terms wherever they have the opportunity. However, rail rates at plants captive to a single rail carrier are now close to the maximum levels prescribed by the STB, which limits the potential for further real increases in these rates. Moreover, between 2004 and 2008, the differential between rates at captive plants and rates at competitively-served plants narrowed. For all of the coal supply regions except the

6 

### Rail routing and mileage calculations utilize ALK Technologies PC*Miler software. 

### Coal Demand Region Coal Supply Region #1

Coal Supply

Region #2

Coal Supply

Region #3 Coal Demand Region Coal Supply Region #1

Coal Supply

Region #2

Coal Supply

Region #3

### 9-28 Powder River Basin (PRB), the current relatively small differentials between captive and competitive rates are expected to persist over the long-term.

All of the rail rates discussed below include railcar costs, and include fuel surcharges at expected 2012 fuel price levels.

Overview of Rail Competition Definitions Within the transportation matrix, rail rates are classified as being either captive or competitive, depending on the ability of a given coal demand region to solicit supplies from multiple suppliers. Competitive rail rates are further subdivided into high- and low-cost competitive subcategories. Competition levels are affected both by the ability to take delivery of coal supplies from multiple rail carriers, the use of multiple rail carriers to deliver coal from a single source (e.g., BNSF/UP transfer to NS/CSX for PRB coal moving east), or the option to take delivery of coal via alternative transportation modes (e.g., barge, truck or vessel).

Table 9-12 Rail Competition Definitions Competition Type Definition

Captive Demand source can only access coal supplies through a single provider; demand source has limited power when negotiating rates with railroads.

High-Cost Competitive Demand source has some, albeit still limited, negotiating power with rail providers; definition typically applies to demand sources that have the option of taking delivery from either of the two major railroads in the region.

Low-Cost Competitive Demand source has a strong position when negotiating with railroads; typically, these demand sources also have the option of taking coal supplies via modes other than rail (e.g., barge, truck, or lake/ocean vessel).

Rail RatesAs previously discussed, rail rates are subdivided into three competitive categories: captive, highcost competitive, and low-cost competitive. Moves are further subdivided based on the distance that the coal supply must move over rail lines: <200 miles, 200-299 miles, 300-399 miles, 400-699 miles, and 700+ miles. Within the Western U.S., mileages are only subdivided into two categories (<300 miles and 300+ miles), given the longer distances that these coal supplies typically move.

Initial rate level assumptions were determined based on an analysis of recent rate movements, current rate levels in relation to maximum limits prescribed by the STB, expected coal demand, diesel prices, recent capital expenditures by ra ilroads, and projected productivity improvements. In general, shorter moves result in higher applicable rail rates due to the lesser distance over which fixed costs can be spread. As previously discussed, rail rates reflect anticipated 2012 costs in 2007 real dollars.

Rates applicable to Eastern moves Rail movements within the Eastern U.S. are handled predominately by the region’s two major carriers, NS and CSX. Some short movements are handled by a variety of short-line railroads. Most plants in the Eastern U.S. are served solely by a single railroad (i.e., they are captive plants).

### 9-29 Table 9-13 Assumed Eastern Rail Rates (2007 mills/ton-mile) Mileage Block Captive High-Cost Competitive Low-Cost Competitive
< 200 72 72 58 200-299 55 55 43 300-399 55 55 43 400-699 37 37 30

700+ 35 35 28

Rates applicable to Midwestern moves Plants in the Midwestern U. S. may be served by BNSF, CN, CP, CSX, NS, UP or short-line railroads. However, the rail network in the Midwestern U.S. is very complex, and most plants are served by only one of these railroads. The Midweste rn U.S. also includes a higher proportion of barge-served and truck-served plants than is the case in the Eastern or Western U.S.

Table 9-14 Assumed Midwestern Rail Rates (2007 mills/ton-mile) Mileage Block Captive High-Cost Competitive Low-Cost Competitive
< 200 72 72 58 200-299 55 55 43 300-399 55 55 43 400-699 36 36 29

700+ 34 34 27

Rates applicable to Western moves Rail moves within the Western U.S. are handled predominately by BNSF and UP. In addition to these incumbent carriers, CP acquired the Dakota, Minnesota and Eastern Railroad (DM&E) in October 2007, which increases the probability that DM&E’s project to build a third rail line into the PRB will be completed. The analysis assumes that DM&E’s entry into the market will influence transportation rates for PRB coal by 2012, 7 

### and the rail rate forecast for PRB coal shipments to competitively-served destinations reflects this expectation.

Rates for Western coal shipments from the PRB are forecast separately from rates for Western coal shipments from regions other than the PRB. This reflects the fact that in many cases coal shipments from the PRB are subject to competition between BNSF and UP (with the possible future entry of DM&E as a third competitor withi n this region), while rail movements of Western coal from regions other than the PRB consist primarily of Colorado and Utah coal shipments that originate on UP, and New Mexico coal shipments that originate on BNSF. PRB coal shipments also typically involve longer trains moving over longer average distances than coal shipments from the other Western U.S. coal supply regions, which means these shipments typically have lower costs per ton-mile than non-PRB coal shipments.

7 

### Note that this is not equivalent to assuming that DM&E’s PRB project would be operational by 2012. Construction of the DM&E’s PRB project is expected to be a three-year process. However, in order to obtain financing and begin construction, DM&E would likely have to negotiate some rail contracts for PRB coal shipments. Thus, DM&E’s presence as a third competitor in the market for PRB coal shipments might affect rail rates well before DM&E’s additional rail line into the PRB became operational

### 9-30 Non-PRB coal moves Table 9-15 Assumed Non-PRB West ern Rail Rates (2007 mills/ton-mile)
Mileage Block Captive High-Cost Competitive Low-Cost Competitive

< 300 43 43 35 300+ 25 21 21

PRB moves confined to BNSF/UP rail lines Table 9-16 Assumed PRB Western Rail Rates (2007 mills/ton-mile) Mileage Block Captive High-Cost Competitive Low-Cost Competitive

< 300 43 43 35 300+ 23 14 14

PRB moves transferring to Eastern railroadsFor PRB coal moving west-to-east, the coal transportation matrix assumes that the applicable lowcost competitive assumption is applied to the BNSF/UP portion of the rail mileage, and an assumption of either $2.16 per ton or 28 mills per ton-mile (whichever is higher) is applied to the portion of the movement that occurs on railroads other than BNSF and UP. (The $2.16 per ton assumption is a minimum rate for short-distance movements of PRB coal on Eastern railroads.)

9.3.4 Truck rates Truck rates include loading and transport components, and all trucking flows are considered competitive because highway access is open to any trucking firm. The truck rates shown in Table 9-17 are expected long-term equilibrium levels reflective of current rates as of September 2008, and expected changes in labor costs, fuel prices, and steel prices. The slightly higher truck rates in Utah reflect market conditions specific to that market, which is relatively remote.

Table 9-17 Assumed Truck Rates (2007 Real Dollars) Market Loading Cost ($/ton) Transport (mills/ton-mile)
Outside of Utah Market 1.03 134
Utah Market 1.54 134

9.3.5 Barge and Lake Vessel Rates As with truck rates, barge rates include loading and transport components, and all flows are considered competitive because river access is open to all barge firms. The transportation matrix subdivides barge moves into three categories, which are based on the direction of the movement (upstream vs. downstream) and the size of barges t hat can be utilized on a given river. As with the other types of transportation rates forecast in this analysis, the barge rate levels shown in Table 9-18 are expected long-term equilibrium levels reflective of current rates as of September 2008, and expected changes in labor costs, fuel prices, and steel prices.

Rates for transportation of coal by lake vessel on the Great Lakes were forecast on a plantspecific basis, taking into account the lake vessel distances applicable to each movement, the expected backhaul economics applicable to each mo vement (if any), and the expected changes in labor costs and fuel and steel prices over the long-term.

### 9-31 Table 9-18 Assumed Barge Rates (2007 Real Dollars)
Type of Barge Movement Loading Cost ($/ton) Transport (mills/ton-mile)
Mississippi River, and Downstream on the Ohio River 1.75 7.9

Smaller Size Barges (Allegheny, Kanawha, and Monongahela Rivers) 1.85 6.9

Upstream on Ohio River 1.95 10.4

9.3.6 Transportation Rates for Imported Coal Transportation rates for imported coal reflect expectations regarding the long-term equilibrium level for ocean vessel rates, taking into account expected long-run equilibrium levels for fuel and steel prices, and expected continued strong demand for shipment of dry bulk commodities (especially coal and iron ore) from China and other Asian nations.

In EPA Base Case v.4.10, it is assumed that imported coal is likely to be used only at plants that can receive this coal by direct water delivery (i.e., via ocean vessel or barge delivery to the plant). This is based on an assessment of recent transportation market dynamics, which suggests thatrailroads are unlikely to quote rail rates that will allow imported coal to be cost-competitive at railserved plants. Moreover, import rates are higher for the Alabama and Florida plants than for New England plants because many of the Alabama and Florida plants are barge-served (which requires the coal to be transloaded from ocean vessel to barge at an ocean terminal, and then moved by barge to the plant), whereas most of the New England plants can take imported coal directly by vessel. Transportation rates for imported coal moving to each coal demand region are shown in Table 9-19.

Table 9-19 Assumed Transportation Rat es for Imported Coal (2007 Real Dollars)
Coal Demand Region Coal Demand Region Description Transportation Rate ($/ton)

ALR2 Alabama rail plants (ALRL)_Low-Cost Competitive Not Mine Mouth Barge 15.43

FL2 Florida plants (FL)_Low-Cost Competitive Not Mine Mouth Barge 15.43

FL3 Florida plants (FL)_Low-Cost Competitive Not Mine Mouth Rail 15.43

GFB1 Gulf barge plants (GFBG)_Low Cost Competitive Not Mine Mouth Rail 15.43

GFB3 Gulf barge plants (GFBG)_Low-Cost Competitive Not Mine Mouth Barge 15.43

NE2 New England / Hudson River plants / Hudson plant (NE)_Low-Cost Competitive Not Mine Mouth Barge 11.32

NE3 New England / Hudson River plants / Hudson plant (NE)_Low-Cost Competitive Not Mine Mouth Rail 13.37

TAB1 Tennessee and Northern Alabama river plants (TABG)_High-Cost Competitive Not Mine Mouth Rail 15.43

### 9-32 9.3.7 Other Transportation Costs In addition to the transportation rates already discussed, the transportation matrix assumes various other rates that are applied on a case-by-case basis, depending on the logistical nature of a move. These charges apply when coal must be moved between different transportation modes (e.g., rail-to-barge or truck-to-barge) – see Table 9-20.

Table 9-20 Assumed Other Transportation Rates (2007 Real Dollars)
Coal Demand Region Rate ($/ton)

Rail-to-Barge 1.23 Rail-to-Vessel 1.23

Rail-to-Rail 1.44 Truck-to-Barge 1.49 Conveyor 1.13

9.3.8 Long-Term Escalation of Transportation Rates Overview of market drivers According to data published by the Association of American Railroads (AAR), labor costs accounted for about 34% of the rail industry’s operating costs in 2006, and fuel accounted for an additional 19%. The remaining 47% of the rail industry’s costs relate primarily to locomotive and railcar ownership and maintenance, and track construction and maintenance.

The RCAF 8 

### Unadjusted for Productivity (RCAF-U), which tracks operating expenses for the rail industry, has increased at an annualized rate of 6.2%/year over the past five years, more than double the increase of 2.8%/year in general inflati on (GDP-IPD) over the same period. However, this largely resulted from steep increases in dies el fuel prices. Excluding fuel, the rail industry’s operating costs (as measured by the All-Inclusive Index Less Fuel, or AII-LF), increased by only 3.5%/year.

8 

### The Rail Cost Adjustment Factor (RCAF) refers to several indices created for regulatory purposes by the STB, calculated by the AAR, and submitted to the STB for approval. The indices are intended to serve as measures of the rate of inflation in rail inputs. The meaning of various RCAF acronyms that appear in this section can be found in the insert in Figure 9-9.

### 9-33 Figure 9-9 Rail Cost Indices Performance (2Q2003-2Q2008)

In addition to diesel fuel price increases, world pric es for steel (which is a significant component of locomotive, railcar, and track construction and maintenance costs) also increased steeply over the past five years, rising from about $290/metric tonne as of June 2003 to about $650/metric tonne as of June 2007, and over $1,000/metric tonne as of June 2008. However, during the previous five years (2Q1998-2Q2003), when steel prices were less volatile, the AII-LF closely tracked general inflation, rising at about 2.1%/year compared with 2.0%/year for the GDP-IPD.

Additionally, over the past five years, the rate of increase in the rail industry’s labor costs (3.0%/year) has closely tracked the increase in the GDP-IPD (2.8%/year.)

The other major transportation modes used to ship coal (barge and truck) have cost drivers broadly similar to those for rail transportation (labor costs, fuel costs, and equipment costs). However, a significant difference in cost drivers between the transportation modes relates to the relative weighting of fuel costs for the different transportation modes. Estimates show that, at 2006 fuel prices, fuel costs accounted for about 20% of long-run marginal costs for the rail industry, 35% of long-run marginal costs for barges, and 50% of long-run marginal costs for trucks.

### 9-34 Figure 9-10 Long-Run Marginal Cost Breakdown by Transportation Mode

9.3.9 Market Drivers Moving Forward Diesel fuel prices The Energy Information Administration’s (EIA’s) Annual Energy Outlook (AEO) 9 

### forecast of longterm equilibrium prices for diesel fuel shows expected prices ranging from about $2.62/gallon in 2012 to about $2.77/gallon in 2030 (2007 real dollars). This range of prices is comparable to the actual average on-highway diesel fuel price for 2006 which was $2.78/gallon (2007 real dollars). The coal transportation rate forecast for EPA Base Case v.4.10 assumes that diesel fuel prices will return to these long-run equilibrium levels by 2012.

Table 9-21 EIA AEO Diesel Fuel Forecast, 2012-2030 (2007 Real Dollars)
Year Rate ($/gallon)
2012 2.62 2015 2.49 2020 2.57 2025 2.61 2030 2.77 Annualized % Change, 2025-2030 1.10% Source: EIA

9 

### As noted at the beginning of this section, the coal transportation rate assumptions for EPA Base Case v.4.10 were finalized in September 2008. At that time, the Annual Energy Outlook 2008 forecast was the latest available. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Rail Barge Truck

Fuel Other

### 9-35 Iron ore prices ABARE’s 10 

### forecast of iron ore prices shows an expectation that iron ore prices will return to late 2007/early 2008 levels (i.e., to the levels prevailing before the most recent price spike) by 2012, and to decline by about 18% in real terms for thei r 5-year forecast period (2008-2013) as a whole.

Table 9-22 ABARE Forecast of Iron Ore Prices 2007 $/metric tonne ABARE Estimate of Average Price for Australian Iron Ore Exports, Year Ending (YE) March 2008 67.72

ABARE Forecast for YE Mar 2009 98.68 ABARE Forecast for YE Mar 2010 91.19 ABARE Forecast for YE Mar 2011 73.73 ABARE Forecast for YE Mar 2012 61.84 ABARE Forecast for YE Mar 2013 55.52 Total Percent Change (2008-2013) -18.00% Source: ABARE, Australian Commodities, vol. 15 no. 1 and 2, March and June Quarters 2008

Labor costs Labor costs for the rail industry are expected to continue to escalate at approximately the same rate as overall inflation (i.e., labor costs are ex pected to be approximately flat in real terms). Due to the fact that competition is stronger in the barge and trucking industries than in the rail industry, labor costs in the barge and truck industries are likely to increase at rates similar to or slightly slower than the increase in rail labor costs.

Productivity gains The most recent data published by AAR (coveri ng 2002-2006) shows that rail industry productivity increased at an annualized rate of approximately 1.2% per year during this period. However, due to limited competition in the rail industry, these productivity gains were generally not passed through to shippers. In addition, the potential for significant productivity gains in the trucking industry is relatively limited since truck load sizes, operating speeds, and truck driver hours are all regulated by law.

Long-Term Escalation of Coal Transportation Rates Based on the foregoing discussion, the transportation rate forecast assumes flat real escalation of rail and truck rates, and a 1% per year real decline in barge and lake vessel rates, which reflects some pass-through of productivity gains in those highly competitive industries, over the 2012-2025 period.

However, EIA’s forecast of diesel fuel prices, which is essentially flat in real terms during the 2012-2025 period, predicts a relatively steep rise in diesel fuel prices (annualized increase of 1.1%/year) between 2025 and 2030. Because of this, coal transportation rates are assumed to escalate as follows during the 2026-2062 period:

• Rail: 1.1% annual real increase in fuel prices x 20% fuel cost weighting = real rate increase of 0.2%/year.

• Truck: 1.1% annual real increase in fuel prices x 50% fuel cost weighting = real rate increase of 0.5%/year.

10 

### ABARE is a branch of the Australian government that forecasts prices and trade volumes for a wide variety of commodities that Australia exports. Australia is a major exporter of iron ore, accounting for about 32% of total worldwide iron ore exports in 2007. See www.abareconomics.com

### 9-36 • Barge and lake vessel: 1.1% annual real increase in fuel prices x 35% fuel cost weighting = real input cost increase of 0.4%/year, less 1% per year for pass-through of productivity gains = real rate decline of 0.6%/year.

• Conveyor: no change in real terms throughout the study period.

9.3.10 Other Considerations Transportation constraints limiti ng the growth of PRB coal use The rate at which coal shipments from the PRB (Montana and Wyoming) regions can be increased is somewhat limited in the near-term by the capacity of the rail lines that transport the coal from these regions. Hence, the following limits on the growth of PRB coal production are implemented in IPM:

• Wyoming PRB coal growth is limited to 15 million short tons additional production capacity each year.

• Montana coal growth is limited to 2 million s hort tons additional production capacity each year.

Table 9-23 Assumed Production Growth Rates Coal Supply Source Grow th/Year (MM short tons) Wyoming PRB 15 Montana 2

Other transportation constraints This analysis does not consider the Febr uary 10, 2009 announcement by Norfolk Southern and Canadian National Railway to share rail lines and enhance the efficiency of service out of the Illinois Basin coal supply region (Mid-American Corridor). This announcement has the potential to alleviate an existing bottleneck (both rate-based and logistical-based) that has historically prevented large volumes of coal moving from the I llinois Basin to Southeastern coal-fired facilities, although it is too early to know the full impact of this arrangement.

Global recession The analysis underlying the coal transportation assumptions as described above was completed prior to experiencing the full impact of the cu rrent global recession on the energy industry. In addition to downward pressure on fuel and steel pr ices, the recession led to large declines in industrial coal and electricity demand. Coal-fired plants, already faced with a glut of stockpiled coal, saw excess supply further balloon due to relatively mild temperatures throughout 2009 andhistorically low gas prices that led to some gas-fired facilities displacing more marginal, highercost, coal-fired facilities. In the face of declining demand, coal transporters saw movements fall dramatically as contracted tonnage was deferred and contracts were reworked. However, it remains to be seen how, or if, transporters will adjust rates in the face of changing demand dynamics. For example, railroads demonstrated little desire to lower rates in recent contract negotiations even as coal volumes shipped by rail were off 9.3% from year-to-date 2008 levels as of September 12, 2009.



## 9.4 Coal Exports, Imports, and Non-Electric Sectors Demand The coal supply curves used in EPA Base Case v.4.10 represent the total steam coal supply in the United States. While the U.S. power sector is the largest consumer of native coal – roughly 93% of mined U.S. coal in 2007 was used in el ectricity generation – non-electric demand must also be taken into consideration in IPM modeling in order to determine the market clearing price. Furthermore, some coal mined within the U.S. is exported out of the domestic market, and some foreign coal is imported for use in electricit y generation, and these changes in the coal supply must also be detailed in the modeling of the c oal supply available to coal power plants. The projections for imports, exports, and non-electric sector coal demand are based on EIA’s AEO 2010.

### 9-37 In EPA Base Case v.4.10 coal exports and coal-serving residential, commercial and industrial demand are designed to correspond as closely as po ssible to the projections in AEO 2010 both in terms of the coal supply regions and coal grades that meet this demand. The projections used exclude exports to Canada, as the Canadian ma rket is modeled endogenously within IPM. First, the subset of coal supply regions and coal grades in EPA Base Case v.4.10 are identified that are contained in or overlap geographically with those EIA Coal Market Module (CMM) supply regions and coal grades that are projected as servi ng exports and non-electric sector demand in AEO 2010. Next, coal for exports and non-electric demand are constrained by CMM supply region and coal grade to meet the levels projected in AEO 2010. These levels are shown in Table 9-24 and Table 9-25. (Since the AEO 2010 time horizon extends to 2035 and EPA Base Case v.4.10 to 2050, the AEO projected levels for 2035 are maintained through 2050.). IPM then endogenously determines which IPM coal supply region(s) and coal grade(s) will be selected to meet the required export or non-electric sector coal demand as part of the cost-minimization coal market equilibrium. Since there are more coal supply regions and coal grades in EPA Base Case v.4.10 than in AEO 2010 the specific regions and coal grades that serve export and non-electric sector demand are not pre-specified but modeled.

Imported coal is assumed to cost 30.81 2007$/Ton, and is only available to plants in the eight demand regions which are eligible to receive imported coal. The eight coal demand regions which may receive imported coal, along with the cost of transporting this coal to the demand regions, are summarized in Table 9-19. The total US imports of steam coal are limited as shown in Table 9-26.

Table 9-24 Coal Exports
Name 2012 2015 2020 2030 2040 -2050 Rocky Mountains - Bituminous Low Sulfur 1.24 0.97 0.45 0.84 1.08 Central Appalachia - Bituminous Medi um Sulfur 6.69 7.74 7.4 7.31 6.96 East Interior - Bituminous Medium Sulfur 6.46 4.29 0 0 0 Northern Appalachia - Bituminous Medium Sulfur 4.57 3.02 0 0 0 Wyoming Southern PRB - Subbituminous Low Sulfur 0.03 0.04 0.05 0.09 0.12

### 9-38 Table 9-25 Residential, Commercial, and Industrial Demand

Name 2012 2015 2020 2030 2040 - 2050 East Interior - Bituminous High Sulfur 6.84 7.06 7.1 6.97 6.88 Northern Appalachia - Bituminous High Sulfur 1.12 1.16 1.16 1.14 1.12 West Interior - Bituminous High Sulfur 1.59 1.85 1.99 1.98 1.95 Central Appalachia - Bituminous Low Sulfur 4.8 4.98 5.01 4.89 4.8 Southern Appalachia - Bituminous Low Sulfur 0.21 0.22 0.22 0.21 0.21 Rocky Mountain - Bituminous Low Sulfur 3.78 4.03 4.1 4.06 3.97 Arizona/New Mexico - Bituminous Low Sulfur 0.41 0.43 0.44 0.44 0.43 Central Appalachia - Bituminous Medi um Sulfur 13.3 13.84 13.93 13.66 13.42 East Interior - Bituminous Medium Sulfur 0.82 0.84 0.84 0.83 0.82 Northern Appalachia - Bituminous Medium Sulfur 4.02 4.16 4.17 4.05 3.97 Southern Appalachia - Bituminous Medi um Sulfur 1.36 1.41 1.42 1.4 1.37 Gulf Lignite - High Sulfur 2.46 2.57 2.59 2.54 2.49 Dakota Lignite - Medium Sulfur 5.26 5.43 5.46 5.36 5.3 Western Montana - Subbituminous Low Sulfur 0.43 0.19 0.03 0 0 Western Wyoming - Subbituminous Low Sulfur 0.92 0.98 1 1 0.98 Wyoming Northern PRB - Subbituminous Low Sulfur 3.85 3.99 4.01 3.94 3.88 Western Wyoming - Subbituminous Medium Sulfur 1.03 1.11 1.14 1.13 1.1 Arizona/New Mexico - Subbituminous Medium Sulfur 0.1 0.11 0.11 0.11 0.11

Table 9-26 Coal Import Limits

2012 2015 2020 2030 2040 - 2050 Annual Coal Imports Cap (Million S hort Tons) 30.0 28.9 36.0 36.2 51.5

### Appendix 9-1.1 

## Appendix 9-1. Illustrative Example of Wood Mackenzie Costing Procedure Used in Developing EPA’s Coal Supply Curves

To further demonstrate the procedures used in preparing Wood Mackenzie’s cost tables, a sample was prepared for the Colorado, Green River Basin, 0.81-1.20 lbs. SO 2 

### /MMbtu coals. This region was selected because it contains both surfac e and underground mines as well as existing (E) and planned new mines (N).

The initial step was to prepare cost models for the selected mines. The mine names have been replaced with mine step designations: E01, E 02, E03, N01, N02 and N03. Production and productivity assumptions for the mines have been modified to mask the mines selected for this sample procedure. Cost models were prepared for E01, E02, E03, N02 and N03. N01 was not modeled because it is a proposed replacement for E03 and is expected to have a cost structure very similar to E03. The cost model was run in 2008$ and used to solve for the sales price required to return a 10% DCFROR for the mine.

In the following pages of this appendix individual cost models (in the form of a series of spreadsheets) are provided for mines E 01_S, EO2_UG, EO3_UG, NO2_UG, and NO3_UG, Each mine’s spreadsheet consists of 4 pages that capture the costs and cash flows for the mines over the 2008 – 2035 time period. The first page in each spreadsheet model provides production and productivity data for the mine. The second page shows its capital requirements. The third page contains a summary of capital expenditures. The fourth page pulls together all costs and cash flows and (in the third row from the top) derives the required sales price on the assumption of a 10% rate of return.

The model results obtained for each mine were then loaded into the “EPA Cost Curve Worksheet,” which appears on the last page of this appendix. (The “sales prices ($/ton)” from the mine costing models can be seen in the 11 th 

### column (“Cost of Production (2008$)”) of the cost curve worksheet. This worksheet also contains EPA’s coal r egion and coal type data as well as Wood Mackenzie region and type codes. (The Wood Mackenzie codes were included to assist in the proper assignment of costs for areas where cost data was prepared and grouped based on Wood Mackenzie codes.) Additional data includes mine names, step names, codes for existing or new mines, mine type (used for western mines), heat content, production rate and reserves.

In conjunction with transferring the costs generated for each mine to the “EPA Cost Curve Worksheet,” a number of adjustments were made to support their use in EPA’s IPM electric sector model:

(1) There are a number of differences in the values appearing in the 11 th 

### column and the corresponding “sales price” values that appeared in the individual mine costing models. For example, it was assumed the N01 production costs would be the same as the mine it is replacing but additional transportation will be required to mo ve the coal to the mine loadout and partial washing would take place. To account for these additional costs $1.00/ton was added to the Cost of Production of E03 to estimate the cost for N01. In the case of N03, it was assumed that additional transportation from the mine mouth to the loadout will be required so an additional $1.00/ton was added to the model results. This adjustment was based on a separate modeling analysis that Wood Mackenzie performed to estimate the cost of production at N03. Because this modeling contained mine specific and proprietary data, it could not be included in this appendix.

(2) Because the mine costing model estimates were prepared in 2008$, the costs were converted (deflated) to constant 2007$, which is the cost basis used in EPA Base Case v.4.10. (This conversion can be seen in the 11 th 

### (“Cost of Production (2008$)”) and 12 th 

### (“Cost of Production (2007$)”) columns of the Cost Curve Worksheet.)

### Appendix 9-1.2 (3) In running the individual cost models for new mines, Wood Mackenzie makes certain assumptions regarding the start-up date for the mine. In transferring the costs generated by the cost models to the EPA Cost Curve Worksheet it is assumed that the new mines could be opened by 2012 if demand exists and EPA’s IPM model is allowed to determine when the mine will be opened.

(4) Wood Mackenzie has assumed that labor producti vity in all Producing Regions except Central Appalachia will increase at a rate of 1% per year. Productivity in Central Appalachia is expected to fall 1% per year. These productivity assumptions a ffect future Cost of Production estimates. All regions except Central Appalachia were deflated 0.4% per year while it was inflated by 0.4% per year. The 0.4% adjustment is based on the assumption that labor costs on average account for 40% of the Cost of Production. In the EPA Cost Curve Worksheet the productivity adjustments appear in the 13 th 

### (“deflator”) and 14 th 

### (“deflated cost”) columns. The 0.4% productivity growth assumption means that in the 4 years between 2008 (the net present value year used in the mine costing spreadsheets) and 2012 (the first model year in EPA’s IPM base case) productivity would increase by a factor of 1.02 (shown in the 13 th 

### column) and, consequently, the cost shown in the 12 th 

### column would have to be divided by this deflator (13 th 

### column) to obtain the deflated costs shown in the 14 th 

### column

(5) Costs were then rounded by the procedure described above in section 9.3.8 to obtain the “Final Cost” values shown in the 15 th 

### column.

Besides the adjustments described above and shown in the cost curve worksheet, other adjustments to costs were made to meet EPA’s need for cost and production data out to 2040 and beyond. Wood Mackenzie’s cost models are generally run for a maximum of 20 years even though additional, higher cost reserves are k nown to exist. When production and reserve data were pushed past reserve estimates typically used in the cost models, additional reserves were added and costs were increased. Appendix 9-2 lists the new mines that were included in the 2040 curves. As a quality assurance check, if additi onal reserves were assigned to any coal region and type, the resulting totals were tested against EIA Reserve Estimate – 2006 (Table 15_06) to insure reserves were not overstated and to establish an upper geologic limit for reserves in any

area.Appendix 9-1.3Mine: E01_SProduction DataYear Tons In-Situ Effective(000) DL Yds T/S Yds Total Yds Ratio DL % DL Yds T/S Yds Total Yds Ratio(000) (000) (000) Rehandle (000) (000) (000)2008 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82009 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82010 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82011 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82012 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82013 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82014 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82015 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82016 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82017 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82018 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82019 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82020 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82021 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82022 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82023 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82024 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82025 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82026 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82027 2,500 24,500 0 24,500 9.8 10% 26,950 0 26,950 10.82028202920302031203250,000 490,000 0 490,000 9.8 539,000 0 539,000 10.8$/tonAppendix 9-1 (Cont’d) Illustrative ExampleAppendix 9-1.4Mine: E01_SCapital Requirements ($ X 1000)Capital Contingency: 0%Miles of Rail spur: 0Coal Storage: 10000Year Acqisition Develop. Rail Facilities Truck Shovel Dragline Coal Load & Haul Miscellaneous TotalInitial Repl Initial Repl Initial Repl Initial Repl Initial Repl200800 15,878064,6809,72016,368106,646020090000002010000000201100000020120000002013000000201400000020150000 16,3680 16,368201600000002017000000020180000 4,082000 4,0822019000000000202000000000020210000000002022000000 16,3680 16,3682023000000000202400000000020250000000002026000000000202700000000020282029203020312032000 15,87800 64,6800 9,720 4,082 16,368 32,736 106,646 36,8180.00 0.00 0.00 0.32 0.00 0.00 1.29 0.00 0.19 0.08 0.33 0.65 2.13 0.74Appendix 9-1.5Mine: E01_SCapital Expenditure SummaryYear Acquisition Development Depreciable Property Tax Depreciation Working TotalAssets BaseCapital Capital200800 106,646 106,646 14,025 3,993 110,6392009 0 0 0106,646 14,025002010 0 0 0106,646 14,025002011 0 0 0106,646 14,025002012 0 0 0106,646 14,025002013 0 0 0106,646 14,025002014 0 0 0106,646 14,02500201500 16,368 106,646 3,3970 16,3682016 0 0 0106,646 3,397002017 0 0 0106,646 3,39700201800 4,082 106,646 3,9800 4,0822019 0 0 0106,646 3,980002020 0 0 0106,646 3,980002021 0 0 0106,646 3,98000202200 16,368 106,646 3,9800 16,3682023 0 0 0106,646 2,921002024 0 0 0106,646 2,921002025 0 0 0106,646 2,338002026 0 0 0106,646 2,338002027 0 0 0106,646 2,338 -3,993 -3,9932028202920302031203200 143,464141,1260 143,464Appendix 9-1.6Mine: E01_SRequired Sales PriceSales Price ($/ton): $20.716 Sev. Taxes: Rate ($/ton) $0.54Discount Rate: 10% Adjustment 0%Net Present Value: $0 Property Taxes Mill Levy (in mills): 44Cost Contingency Factors: 10% Assessment rate: 25%Initial Labor Productivity (t/mh): 9 Income Taxes: Federal 34%Average Coal Thickness (ft): 45 State 6%UMWA (Y or N): N Royalty: 12.5%Year Tons Revenue Direct Royalty Taxes (other than income) Development Depreciation Depletion Taxes Net Income Operating Capital AnnualOperating Severance Black Recl. Property Cost Cash Expenditures CashCost LungFlow Flow2008 2,500 51,789 23,958 6,474 702 1,375 875 570 0 14,025 306 122 184 14,515 110,639 -96,1242009 2,500 51,789 23,958 6,474 702 1,375 875 570 0 14,025 306 122 184 14,515 0 14,5152010 2,500 51,789 23,958 6,474 702 1,375 875 570 0 14,025 306 122 184 14,515 0 14,5152011 2,500 51,789 23,958 6,474 702 1,375 875 570 0 14,025 306 122 184 14,515 0 14,5152012 2,500 51,789 23,958 6,474 702 1,375 875 570 0 14,025 306 122 184 14,515 0 14,5152013 2,500 51,789 23,958 6,474 702 1,375 875 570 0 14,025 306 122 184 14,515 0 14,5152014 2,500 51,789 23,958 6,474 702 1,375 875 570 0 14,025 306 122 184 14,515 0 14,5152015 2,500 51,789 23,958 6,474 702 1,375 875 570 0 3,397 3,625 3,046 4,569 11,591 16,368 -4,7772016 2,500 51,789 23,958 6,474 702 1,375 875 570 0 3,397 3,625 3,046 4,569 11,591 0 11,5912017 2,500 51,789 23,958 6,474 702 1,375 875 570 0 3,397 3,625 3,046 4,569 11,591 0 11,5912018 2,500 51,789 23,958 6,474 702 1,375 875 570 0 3,980 3,625 2,813 4,219 11,825 4,082 7,7422019 2,500 51,789 23,958 6,474 702 1,375 875 570 0 3,980 3,625 2,813 4,219 11,825 0 11,8252020 2,500 51,789 23,958 6,474 702 1,375 875 570 0 3,980 3,625 2,813 4,219 11,825 0 11,8252021 2,500 51,789 23,958 6,474 702 1,375 875 570 0 3,980 3,625 2,813 4,219 11,825 0 11,8252022 2,500 51,789 23,958 6,474 702 1,375 875 570 0 3,980 3,625 2,813 4,219 11,825 16,368 -4,5432023 2,500 51,789 23,958 6,474 702 1,375 875 570 0 2,921 3,625 3,236 4,855 11,401 0 11,4012024 2,500 51,789 23,958 6,474 702 1,375 875 570 0 2,921 3,625 3,236 4,855 11,401 0 11,4012025 2,500 51,789 23,958 6,474 702 1,375 875 570 0 2,338 3,625 3,470 5,205 11,168 0 11,1682026 2,500 51,789 23,958 6,474 702 1,375 875 570 0 2,338 3,625 3,470 5,205 11,168 0 11,1682027 2,500 51,789 23,958 6,474 702 1,375 875 570 0 2,338 3,625 3,470 5,205 11,168 -3,993 15,1612028202920302031203250,000 1,035,785 479,159 129,473 14,040 27,500 17,500 11,3940 141,126 49,271 40,943 61,414 251,811 143,464 108,34720.72 9.58 2.59 0.280.55 0.35 0.230.00 2.82 0.99 0.82 1.23 5.04 2.87 2.17Appendix 9-1.7Appendix 9-1.8Mine: E02_UGProduction & Productivity Data% Washed 70%Plant Recovery: 70%Production by Equipment at Full Production% Longwall Production: 80%% CM Production: 20%% CV Production: 0%Year Clean CV ROM CM ROM LW ROM Total ROM Overall HaulTonsTonsTonsTonsTons Recovery Distance(000) (000) (000) (000) (000)% (miles)2008 2,0000506 2,026 2,532 79%02009 2,0000506 2,026 2,532 79%02010 2,0000506 2,026 2,532 79%02011 2,0000506 2,026 2,532 79%02012 2,0000506 2,026 2,532 79%02013 2,0000506 2,026 2,532 79%02014 2,0000506 2,026 2,532 79%02015 2,0000506 2,026 2,532 79%02016 2,0000506 2,026 2,532 79%02017 2,0000506 2,026 2,532 79%02018 2,0000506 2,026 2,532 79%02019 2,0000506 2,026 2,532 79%02020 2,0000506 2,026 2,532 79%02021 2,0000506 2,026 2,532 79%02022 2,0000506 2,026 2,532 79%02023 2,0000506 2,026 2,532 79%02024 2,0000506 2,026 2,532 79%02025 2,0000506 2,026 2,532 79%02026 2,0000506 2,026 2,532 79%02027 2,0000506 2,026 2,532 79%02028 2,0000506 2,026 2,532 79%0202920302031203242,00010,634 42,538 53,172 79%$/tonAppendix 9-1.9Mine: E02_UGCapital Requirements ($ X 1000)productionCapital Contingency: 0% CV MiningHt: 8.0 Capital Contingency: -75% < 500kMiles of Rail spur: 0 CM Mining Ht: 8.0 -50% 500k<= & < 750kCoal Storage: 0 LW Mining Ht: 8.0 0% >750kDepth to Shafts: 100No. of Men/Supply Shafts 1No. of Air Shafts 1Shafts: if prod < 1000k, 1 M&S + 1 Airif prod > 1000k, 2 M&S + 1 AirYear Acqisition Exploration Development Shaft Surface Prep Plant Rail Conventional SectionsContinuous MinersLongwallsFixed U/G Equip. Total& Labor Capital FacilitiesUnits Initial Repl Units Initial Repl Units Initial Repl Initial Repl Initial ReplEngineering Cost & Equip.Required $ $ Required $ $ Required $ $ $ $ $ $20081,920 59,940 13,774 11,391 18,9900 0 02 9,0001 37,50020,629173,144 020090 02 01 000 020100 02 01 000 020110 02 01 000 020120 02 01 000 020130 02 01 000 020140 02 01 0000020150 0 0 2 0 9,000 1 0 37,500 0 20,629 0 67,12920160002001000000201700020010000002018000200100000020190002001000000202000020010000002021000200100000020220 0 0 2 0 9,000 1 0 37,500 0 20,629 0 67,12920230002001000000202400020010000002025000200100000020260 02 01 000 020270 02 01 000 020280 02 01 000 020292030203120320 1,920 59,940 13,774 11,391 18,99000 09,000 18,00037,500 75,000 20,629 41,257 173,144 134,2570.00 0.05 1.43 0.33 0.270.000.00 0.000.21 0.430.89 1.79 0.49 0.98 4.12 3.20Appendix 9-1.10Mine: E02_UGCapital Expenditure SummaryYear Acquisition Exploration Depreciable AssetsProperty Tax DepreciationWorking Total& 7 Year Life 15 Year Life Total Base7 Year Life 15 Year Life Total Capital CapitalEngineering20080 1,920 67,129 104,095 171,224 171,224 9,590 6,940 16,529 5,789 178,933200900000 171,224 9,590 6,940 16,52900201000000 171,224 9,590 6,940 16,52900201100000 171,224 9,590 6,940 16,52900201200000 171,224 9,590 6,940 16,52900201300000 171,224 9,590 6,940 16,52900201400000 171,224 9,590 6,940 16,52900201500 67,1290 67,129 171,224 9,590 6,940 16,5290 67,129201600000 171,224 9,590 6,940 16,52900201700000 171,224 9,590 6,940 16,52900201800000 171,224 9,590 6,940 16,52900201900000 171,224 9,590 6,940 16,52900202000000 171,224 9,590 6,940 16,52900202100000 171,224 9,590 6,940 16,52900202200 67,1290 67,129 171,224 9,590 6,940 16,5290 67,129202300000 171,224 9,5900 9,59000202400000 171,224 9,5900 9,59000202500000 171,224 9,5900 9,59000202600000 171,224 9,5900 9,59000202700000 171,224 9,5900 9,59000202800000 171,224 9,5900 9,590 -5,789 -5,78920292030203120320 1,920 201,386 104,095 305,481201,386 104,095 305,4810 307,4010.00 0.05 4.79 2.48 7.274.79 2.48 7.27 0.00 7.32Appendix 9-1.11Mine:E02_UGRequired Sales Price ($ X 1000)Sales Price ($/ton):$36.476Sev. Taxes: Rate$0.27Discount Rate:10.00%Adjustment100.0%Net Present Value:$0Prop. Taxes Tax Rate (in mills)60Cost Contingency Factors:10%% of revenue basis for taxAverage Coal Thickness (ft):6Assessment rate:25.0%Tons/Man-hour6.0Income TaxesFederal34.00%UMWA (Y or N): YState 6.00%Product (Steam or Met) S Royalty 8.0%% of Reserve owned: 0%Year Tons Revenue Direct Royalty Taxes (other than income) Corp G&A, Development Depreciation Depletion Taxes Net Income Operating Capital AnnualOperating Severance Black Recl. Property Selling & Cost Cash Expenditures CashCost Lung Acc. Pen.Flow Flow2008 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 1,920 16,529 4,004 1,601 2,402 22,935 177,013 -154,0782009 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712010 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712011 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712012 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712013 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712014 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712015 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 67,129 -42,6572016 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712017 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712018 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712019 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712020 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712021 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 0 24,4712022 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 16,529 4,964 1,985 2,978 24,471 67,129 -42,6572023 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 9,590 5,369 4,599 6,899 21,858 0 21,8582024 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 9,590 5,369 4,599 6,899 21,858 0 21,8582025 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 9,590 5,369 4,599 6,899 21,858 0 21,8582026 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 9,590 5,369 4,599 6,899 21,858 0 21,8582027 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 9,590 5,369 4,599 6,899 21,858 0 21,8582028 2,000 72,953 34,737 5,836 216 2,200 300 1,007 2,200 0 9,590 5,369 4,599 6,899 21,858 -5,789 27,647202920302031203242,000 1,532,003 729,469 122,560 4,536 46,200 6,300 21,142 46,200 1,920 305,481 105,712 56,993 85,490 496,683 305,481 191,20236.48 17.37 2.92 0.11 1.10 0.15 0.50 1.10 0.05 7.27 2.52 1.36 2.04 11.83 7.27 4.55Appendix 9-1.12Mine: E03_UGProduction & Productivity Data% Washed 0%Plant Recovery: 100%Production by Equipment at Full Production% Longwall Production: 80%% CM Production: 20%% CV Production: 0%Year Clean CV ROM CM ROM LW ROM Total ROM Overall HaulTonsTonsTonsTonsTons Recovery Distance(000) (000) (000) (000) (000)% (miles)2008 9,0000 1,800 7,200 9,000 100%02009 9,0000 1,800 7,200 9,000 100%02010 9,0000 1,800 7,200 9,000 100%02011 9,0000 1,800 7,200 9,000 100%02012 9,0000 1,800 7,200 9,000 100%02013 9,0000 1,800 7,200 9,000 100%02014 9,0000 1,800 7,200 9,000 100%02015 9,0000 1,800 7,200 9,000 100%02016 9,0000 1,800 7,200 9,000 100%02017 9,0000 1,800 7,200 9,000 100%02018 9,0000 1,800 7,200 9,000 100%02019 9,0000 1,800 7,200 9,000 100%02020 9,0000 1,800 7,200 9,000 100%02021 9,0000 1,800 7,200 9,000 100%02022 9,0000 1,800 7,200 9,000 100%02023 9,0000 1,800 7,200 9,000 100%02024 9,0000 1,800 7,200 9,000 100%02025 9,0000 1,800 7,200 9,000 100%02026 9,0000 1,800 7,200 9,000 100%02027 9,0000 1,800 7,200 9,000 100%02028 9,0000 1,800 7,200 9,000 100%02029203020312032189,00037,800 151,200 189,000 100%$/tonAppendix 9-1.13Mine: E03_UGCapital Requirements ($ X 1000)productionCapital Contingency: 0% CV MiningHt: 10.0 Capital Contingency: -75% < 500kMiles of Rail spur: 0 CM Mining Ht: 10.0 -50% 500k<= & < 750kCoal Storage: 0 LW Mining Ht: 10.0 0% >750kDepth to Shafts: 100No. of Men/Supply Shafts 1No. of Air Shafts 1Shafts: if prod < 1000k, 1 M&S + 1 Airif prod > 1000k, 2 M&S + 1 Airlongwall initial 100 million $Year Acqisition Exploration Development Shaft Surface Prep Plant Rail Conventional Sections Continuous Miners Longwalls Fixed U/G Equip. Total& Labor Capital FacilitiesUnits Initial Repl Units Initial Repl Units Initial Repl Initial Repl Initial ReplEngineeringCost & Equip.Required $ $ Required$ $ Required$$$$$$20082,590 91,705 9,183 11,87800 0 04 12,0001 100,00031,562258,917 020090 04 01 000 020100 04 01 000 020110 04 01 000 020120 04 01 000 020130 04 01 000 020140 04 01 0000020150 0 0 4 0 12,000 1 0 100,000 0 31,562 0 143,56220160004001000000201700040010000002018000400100000020190004001000000202000040010000002021000400100000020220 0 0 4 0 12,000 1 0 100,000 0 31,562 0 143,56220230004001000000202400040010000002025000400100000020260 04 01 000 020270 04 01 000 020280 04 01 000 020292030203120320 2,590 91,705 9,183 11,878000 012,000 24,000100,000 200,000 31,562 63,124 258,917 287,1240.00 0.01 0.49 0.05 0.060.000.00 0.000.06 0.130.53 1.06 0.17 0.33 1.37 1.52Appendix 9-1.14Mine: E03_UGCapital Expenditure SummaryYear Acquisition Exploration Depreciable Assets Property Tax Depreciation Working Total Year& 7 Year Life 15 Year Life Total Base 7 Year Life 15 Year Life Total Capital CapitalEngineering20080 2,590 143,562 112,766 256,327 256,327 20,509 7,518 28,027 19,092 278,009 2008200900000 256,327 20,509 7,518 28,02700 2009201000000 256,327 20,509 7,518 28,02700 2010201100000 256,327 20,509 7,518 28,02700 2011201200000 256,327 20,509 7,518 28,02700 2012201300000 256,327 20,509 7,518 28,02700 2013201400000 256,327 20,509 7,518 28,02700 2014201500 143,5620 143,562 256,327 20,509 7,518 28,0270 143,562 2015201600000 256,327 20,509 7,518 28,02700 2016201700000 256,327 20,509 7,518 28,02700 2017201800000 256,327 20,509 7,518 28,02700 2018201900000 256,327 20,509 7,518 28,02700 2019202000000 256,327 20,509 7,518 28,02700 2020202100000 256,327 20,509 7,518 28,02700 2021202200 143,5620 143,562 256,327 20,509 7,518 28,0270 143,562 2022202300000 256,327 20,5090 20,50900 2023202400000 256,327 20,5090 20,50900 2024202500000 256,327 20,5090 20,50900 2025202600000 256,327 20,5090 20,50900 2026202700000 256,327 20,5090 20,50900 2027202800000 256,327 20,5090 20,509 -19,092 -19,092 2028202920292030203020312031203220320 2,590 430,685 112,766 543,451430,685 112,766 543,4510 546,0410.00 0.01 2.28 0.60 2.882.28 0.60 2.88 0.00 2.89Appendix 9-1.15Mine:E03_UGRequired Sales Price ($ X 1000)Sales Price ($/ton):$21.439Sev. Taxes: Rate$0.27Discount Rate:10.00%Adjustment100.0%Net Present Value:($0)Prop. Taxes Tax Rate (in mills)60Cost Contingency Factors:10%% of revenue basis for taxAverage Coal Thickness (ft): 10Assessment rate:25.0%Tons/Man-hour6.9Income TaxesFederal34.00%UMWA (Y or N):NState6.00%Product (Steam or Met)SRoyalty8.0%% of Reserve owned:0%Year Tons Revenue Direct Royalty Taxes (other than income)Corp G&A, Development Depreciation Depletion Taxes Net Income Operating Capital AnnualOperatingSeverance Black Recl. Property Selling & CostCash Expenditures CashCostLungAcc. Pen.FlowFlow2008 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,400 2,590 28,027 6,669 2,667 4,001 38,696 275,419 -236,7232009 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682010 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682011 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682012 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682013 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682014 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682015 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,768 143,562 -102,7932016 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682017 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682018 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682019 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682020 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682021 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,7680 40,7682022 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 28,027 7,964 3,185 4,778 40,768 143,562 -102,7932023 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 20,509 11,722 4,689 7,033 39,2650 39,2652024 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 20,509 11,722 4,689 7,033 39,2650 39,2652025 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 20,509 11,722 4,689 7,033 39,2650 39,2652026 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 20,509 11,722 4,689 7,033 39,2650 39,2652027 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 20,509 11,722 4,689 7,033 39,2650 39,2652028 9,000 192,950 114,552 15,436 2,106 8,490 1,350 2,663 4,4000 20,509 11,722 4,689 7,033 39,265 -19,092 58,3572029203020312032189,000 4,051,948 2,405,584 324,156 44,226 178,286 28,350 55,917 92,400 2,590 543,451 188,494 75,398 113,096 845,041 543,451 301,59021.44 12.73 1.72 0.23 0.94 0.15 0.30 0.49 0.01 2.88 1.00 0.40 0.60 4.47 2.88 1.60Appendix 9-1.16Mine: N02_UGProduction & Productivity Data% Washed 0%Plant Recovery: 100%Production by Equipment at Full Production% Longwall Production: 80%% CM Production: 20%% CV Production: 0%Year Clean CV ROM CM ROM LW ROM Total ROM Overall HaulTonsTonsTonsTonsTons Recovery Distance(000) (000) (000) (000) (000)% (miles)2011201220132014201550005000500 100%02016 3,0000600 2,400 3,000 100%02017 5,0000 1,000 4,000 5,000 100%02018 5,0000 1,000 4,000 5,000 100%02019 5,0000 1,000 4,000 5,000 100%02020 5,0000 1,000 4,000 5,000 100%02021 5,0000 1,000 4,000 5,000 100%02022 5,0000 1,000 4,000 5,000 100%02023 5,0000 1,000 4,000 5,000 100%02024 5,0000 1,000 4,000 5,000 100%02025 5,0000 1,000 4,000 5,000 100%02026 5,0000 1,000 4,000 5,000 100%02027 5,0000 1,000 4,000 5,000 100%02028 5,0000 1,000 4,000 5,000 100%02029 5,0000 1,000 4,000 5,000 100%02030 5,0000 1,000 4,000 5,000 100%02031 5,0000 1,000 4,000 5,000 100%02032 5,0000 1,000 4,000 5,000 100%02033 5,0000 1,000 4,000 5,000 100%02034 5,0000 1,000 4,000 5,000 100%02035 5,0000 1,000 4,000 5,000 100%098,50020,100 78,400 98,500 100%$/tonAppendix 9-1.17Mine: N02_UGCapital Requirements ($ X 1000)productionCapital Contingency: 0% CV MiningHt: 8.0 Capital Contingency: -75% < 500kMiles of Rail spur: 0 CM Mining Ht: 8.0 -50% 500k<= & < 750kCoal Storage: 0 LW Mining Ht: 8.0 0% >750kDepth to Shafts: 1000No. of Men/Supply Shafts 1No. of Air Shafts 1Shafts: if prod < 1000k, 1 M&S + 1 Airif prod > 1000k, 2 M&S + 1 AirYear Acqisition Exploration Development Shaft Surface Prep Plant Rail Conventional SectionsContinuous MinersLongwallsFixed U/G Equip. Total& Labor Capital FacilitiesUnits Initial Repl Units Initial Repl Units Initial Repl Initial Repl Initial ReplEngineering Cost & Equip.Required $ $ Required $ $ Required $ $ $ $ $ $20113500 000 0 00 00 01,1091,459 020123500 000 0 00 00 00350 020133500 000 0 00 00 00350 020141,030 30,085 45,174 6,77700 0 00 00 0052,981 020150 02 6,0000 01,6317,631 020160 02 01 25,0006,79831,798 020170 03 3,0001 0816 0 3,816 020180 0 0 3 0 0 1 0 0 0 1,109 0 1,10920190003001000000202000030010000002021000300100000020220 0 0 3 0 6,000 1 0 0 0 1,631 0 7,63120230 0 0 3 0 0 1 0 25,000 0 6,798 0 31,79820240 0 0 3 0 3,000 1 0 0 0 816 0 3,81620250 0 0 3 0 0 1 0 0 0 1,109 0 1,10920260003001000000202700030010000002028000300100000020290 03 01 000 020300 03 01 000 020310 03 01 000 020320 03 01 000 020330 03 01 000 020340 03 01 000 020350 03 01 000 00 2,080 30,085 45,174 6,777000 09,000 9,00025,000 25,000 10,354 11,463 98,384 45,4630.00 0.02 0.31 0.46 0.070.000.00 0.000.09 0.090.25 0.25 0.11 0.12 1.00 0.46Appendix 9-1.18Mine: N02_UGCapital Expenditure SummaryYear Acquisition Exploration Depreciable AssetsProperty Tax DepreciationWorking TotalYear& 7 Year Life 15 Year Life Total Base7 Year Life 15 Year Life Total Capital CapitalEngineering20110350 1,1090 1,109 1,10915801580 1,459 201120120350 0 0 01,109 158 0158 0350 201220130350 0 0 01,109 158 0158 0350 201320140 1,0300 82,036 82,036 53,060158 5,469 5,6270 83,066 2014201500 7,6310 7,631 60,691 1,249 5,469 6,718 1,015 8,646 2015201600 31,7980 31,798 92,489 5,791 5,469 11,260 4,840 36,638 2016201700 3,8160 3,816 96,304 6,336 5,469 11,805 3,903 7,719 2017201800 1,1090 1,109 96,304 6,336 5,469 11,8050 1,109 2018201900 0 0 096,304 6,336 5,469 11,805 0 02019202000 0 0 096,304 6,336 5,469 11,805 0 02020202100 0 0 096,304 6,336 5,469 11,805 0 02021202200 7,6310 7,631 96,304 6,336 5,469 11,8050 7,631 2022202300 31,7980 31,798 96,304 6,336 5,469 11,8050 31,798 2023202400 3,8160 3,816 96,304 6,336 5,469 11,8050 3,816 2024202500 1,1090 1,109 96,304 6,336 5,469 11,8050 1,109 2025202600 0 0 096,304 6,336 5,469 11,805 0 02026202700 0 0 096,304 6,336 5,469 11,805 0 02027202800 0 0 096,304 6,336 5,469 11,805 0 02028202900 0 0 096,304 5,246 05,246 0 02029203000 0 0 096,304 704 0704 0 02030203100 0 0 096,304 158 0158 0 02031203200 0 0 096,304 0 0 0 0 02032203300 0 0 096,304 0 0 0 0 02033203400 0 0 096,304 0 0 0 0 02034203500 0 0 096,304 0 0 0-9,759 -9,759 20350 2,080 89,816 82,036 171,85289,816 82,036 171,8520 173,9320.00 0.02 0.91 0.83 1.740.91 0.83 1.74 0.00 1.77Appendix 9-1.19Mine:N02_UGRequired Sales Price ($ X 1000)Sales Price ($/ton):$20.007Sev. Taxes: Rate$0.27Discount Rate:10.00%Adjustment100.0%Net Present Value:($0)Prop. Taxes Tax Rate (in mills)60Cost Contingency Factors: 10%% of revenue basis for taxAverage Coal Thickness (ft): 8Assessment rate:25.0%Tons/Man-hour8.0Income TaxesFederal34.00%UMWA (Y or N):NState6.00%Product (Steam or Met)SRoyalty8.0%% of Reserve owned:0%Year Tons Revenue Direct Royalty Taxes (other than income)Corp G&A, Development Depreciation Depletion Taxes Net Income Operating Capital AnnualOperatingSeverance Black Recl. Property Selling & CostCash Expenditures CashCostLungAcc. Pen.FlowFlow201100 0 00 0 0 0 0350 158 0 -203 -305 -147 1,109 -1,256201200 0 00 0 0 0 0350 158 0 -203 -305 -1470 -147201300 0 00 0 0 0 0350 158 0 -203 -305 -1470 -147201400 0 00 0 0 0 0 1,030 5,627 0 -2,663 -3,994 1,633 82,036 -80,4032015 500 10,003 6,089 8000 440 75 138 5500 6,718 0 -1,923 -2,884 3,834 8,646 -4,8122016 3,000 60,021 35,131 4,802486 2,641 450 828 3,3000 11,260 562 225 337 12,159 36,638 -24,4792017 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,579 7,719 11,8602018 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,579 1,109 18,4702019 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,5790 19,5792020 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,5790 19,5792021 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,5790 19,5792022 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,579 7,631 11,9482023 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,579 31,798 -12,2182024 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,579 3,816 15,7642025 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,579 1,109 18,4702026 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,5790 19,5792027 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,5790 19,5792028 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 11,805 4,859 1,944 2,915 19,5790 19,5792029 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 5,246 7,363 3,566 5,349 17,9570 17,9572030 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 704 7,363 5,383 8,074 16,1400 16,1402031 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,4000 158 7,363 5,601 8,401 15,9220 15,9222032 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,40000 7,363 5,664 8,496 15,8590 15,8592033 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,40000 7,363 5,664 8,496 15,8590 15,8592034 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,40000 7,363 5,664 8,496 15,8590 15,8592035 5,000 100,035 58,551 8,003 1,026 4,402 750 1,380 4,40000 7,363 5,664 8,496 15,859 -9,759 25,61798,500 1,970,686 1,153,690 157,655 19,980 86,710 14,775 27,195 87,450 2,080 171,852 110,405 55,557 83,336 365,593 171,852 193,74120.01 11.71 1.60 0.20 0.88 0.15 0.28 0.89 0.02 1.74 1.12 0.56 0.85 3.71 1.74 1.97Appendix 9-1.20Mine: N03_UGProduction & Productivity Data% Washed 0%Plant Recovery: 100%Production by Equipment at Full Production% Longwall Production: 80%% CM Production: 20%% CV Production: 0%Year Clean CV ROM CM ROM LW ROM Total ROM Overall HaulTonsTonsTonsTonsTons Recovery Distance(000) (000) (000) (000) (000)% (miles)200720082009201020110000 #DIV/0!020120000 #DIV/0!020130000 #DIV/0!02014 1,0000200800 1,000 100%02015 5,0000 1,000 4,000 5,000 100%02016 5,0000 1,000 4,000 5,000 100%02017 5,0000 1,000 4,000 5,000 100%02018 5,0000 1,000 4,000 5,000 100%02019 5,0000 1,000 4,000 5,000 100%02020 5,0000 1,000 4,000 5,000 100%02021 5,0000 1,000 4,000 5,000 100%02022 5,0000 1,000 4,000 5,000 100%02023 5,0000 1,000 4,000 5,000 100%02024 5,0000 1,000 4,000 5,000 100%02025 5,0000 1,000 4,000 5,000 100%02026 5,0000 1,000 4,000 5,000 100%02027 5,0000 1,000 4,000 5,000 100%02028 5,0000 1,000 4,000 5,000 100%02029 5,0000 1,000 4,000 5,000 100%02030 5,0000 1,000 4,000 5,000 100%02031 5,0000 1,000 4,000 5,000 100%086,00017,200 68,800 86,000 100%$/tonAppendix 9-1.21Mine: N03_UGCapital Requirements ($ X 1000)productionCapital Contingency: 0% CV MiningHt: 10.0 Capital Contingency: -75% < 500kMiles of Rail spur: 0 CM Mining Ht: 10.0 -50% 500k<= & < 750kCoal Storage: 0 LW Mining Ht: 10.0 0% >750kDepth to Shafts: 100No. of Men/Supply Shafts 1No. of Air Shafts 1Shafts: if prod < 1000k, 1 M&S + 1 Airif prod > 1000k, 2 M&S + 1 AirYear Acqisition Exploration Development Shaft Surface Prep Plant Rail Conventional SectionsContinuous MinersLongwallsFixed U/G Equip. Total& Labor Capital FacilitiesUnits Initial Repl Units Initial Repl Units Initial Repl Initial Repl Initial ReplEngineering Cost & Equip.Required $ $ Required $ $ Required $ $ $ $ $ $20073500 000 0 00 00 01,1091,459 020083500 000 0 00 00 00350 020093500 000 0 00 00 00350 020103500 000 0 00 00 00350 020113500 000 0 00 00 00350 020123500 000 0 00 00 00350 020131,530 49,835 9,183 8,41200 0 00 00 00 0 19,124 020140 0 0 1 3,000 0 1 50,000 0 14,411 1,109 67,411 1,10920150 0 0 3 6,000 0 1 0 0 1,631 0 7,631 0201600030010000002017000300100000020180003001000000201900030010000002020000300100000020210 0 0 3 0 3,000 1 0 50,000 0 15,520 0 68,52020220 0 0 3 0 6,000 1 0 0 0 1,631 0 7,631202300030010000002024000300100000020250 03 01 000 020260 03 01 000 020270 03 01 000 020280 03 01 000 020290 03 01 000 020300 03 01 000 020310 03 01 000 00 3,630 49,835 9,183 8,412000 09,000 9,00050,000 50,000 17,151 18,260 97,375 77,2600.00 0.04 0.58 0.11 0.100.000.00 0.000.10 0.100.58 0.58 0.20 0.21 1.13 0.90Appendix 9-1.22Mine: N03_UGCapital Expenditure SummaryYear Acquisition Exploration Depreciable AssetsProperty Tax DepreciationWorking Total& 7 Year Life 15 Year Life Total Base7 Year Life 15 Year Life Total Capital CapitalEngineering20070350 1,1090 1,109 1,10915801580 1,45920080350000 1,1091580158035020090350000 1,1091580158035020100350000 1,1091580158035020110350000 1,1091580158035020120350000 1,1091580158035020130 1,5300 67,429 67,429 18,703158 4,495 4,6540 68,959201400 68,5200 68,520 86,114 9,789 4,495 14,284 1,745 70,265201500 7,6310 7,631 93,745 10,879 4,495 15,374 6,982 14,613201600000 93,745 10,879 4,495 15,37400201700000 93,745 10,879 4,495 15,37400201800000 93,745 10,879 4,495 15,37400201900000 93,745 10,879 4,495 15,37400202000000 93,745 10,879 4,495 15,37400202100 68,5200 68,520 93,745 10,879 4,495 15,3740 68,520202200 7,6310 7,631 93,745 10,879 4,495 15,3740 7,631202300000 93,745 10,879 4,495 15,37400202400000 93,745 10,879 4,495 15,37400202500000 93,745 10,879 4,495 15,37400202600000 93,745 10,879 4,495 15,37400202700000 93,745 10,879 4,495 15,37400202800000 93,745 1,0900 1,090002029 0 0 0 0 093,745000002030 0 0 0 0 093,74500000203100000 93,745000 -8,727 -8,7270 3,630 153,411 67,429 220,841153,411 67,429 220,8410 224,4710.00 0.04 1.78 0.78 2.571.78 0.78 2.57 0.00 2.61Appendix 9-1.23Mine:N03_UGRequired Sales Price ($ X 1000)Sales Price ($/ton):$19.670Sev. Taxes: Rate$0.27Discount Rate:10.00%Adjustment100.0%Net Present Value:$0Prop. Taxes Tax Rate (in mills)60Cost Contingency Factors: 10%% of revenue basis for taxAverage Coal Thickness (ft):8Assessment rate:25.0%Tons/Man-hour10.0Income TaxesFederal34.00%UMWA (Y or N):NState6.00%Product (Steam orMet)SRoyalty8.0%% of Reserve owned:0%Year Tons Revenue Direct Royalty Taxes (other than income)Corp G&A, Development Depreciation Depletion Taxes Net Income Operating Capital AnnualOperatingSeverance Black Recl. Property Selling & CostCash Expenditures CashCostLungAcc. Pen.FlowFlow2007000 00 0 0 0 0350 1580 -203 -305 -147 1,109 -1,2562008000 00 0 0 0 0350 1580 -203 -305 -1470 -1472009000 00 0 0 0 0350 1580 -203 -305 -1470 -1472010000 00 0 0 0 0350 1580 -203 -305 -1470 -1472011000 00 0 0 0 0350 1580 -203 -305 -1470 -1472012000 00 0 0 0 0350 1580 -203 -305 -1470 -1472013000 00 0 0 0 0 1,530 4,6540 -2,473 -3,710 943 67,429 -66,4862014 1,000 19,670 10,473 1,5740 865 150 271 9900 14,2840 -3,575 -5,362 8,921 70,265 -61,3442015 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,080 14,613 9,4672016 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,0800 24,0802017 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,0800 24,0802018 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,0800 24,0802019 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,0800 24,0802020 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,0800 24,0802021 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,080 68,520 -44,4402022 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,080 7,631 16,4492023 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,0800 24,0802024 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,0800 24,0802025 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,0800 24,0802026 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,0800 24,0802027 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 15,374 5,441 2,177 3,265 24,0800 24,0802028 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,4000 1,090 7,238 7,171 10,757 19,0850 19,0852029 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,40000 7,238 7,607 11,411 18,6490 18,6492030 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,40000 7,238 7,607 11,411 18,6490 18,6492031 5,000 98,349 52,364 7,868 1,026 4,327 750 1,357 4,40000 7,238 7,607 11,411 18,649 -8,727 27,37786,000 1,691,596 900,654 135,328 17,442 74,430 12,900 23,344 75,790 3,630 220,841 99,690 51,019 76,528 397,059 220,841 176,21919.67 10.47 1.57 0.20 0.87 0.15 0.27 0.88 0.04 2.57 1.16 0.59 0.89 4.62 2.57 2.05Note: Mine cost ($19.67/ton) plus transportation andloadout costs ($1.00/ton) = Total FOB cost ($20.67/ton)Appendix 9-1.24deflator =- 0.004/yr for all regi2012 Prob. Prod.2012 Est. ReservesYear Abbrev CoalType_GradeCODER CODEF Mine Step Name Existingor NewMineTypeHeat Content(MMBtu/Ton)Cost ofProduction(2008$/Ton)Cost ofProduction(2007$/Ton)deflator deflatedcostFinal Cost(Rounded)CoalProduction(MillionTons/Year)CumProductionCoal Reserves(Million Tons)2012 CG BB CGH SCZ N02 N02 N U 22.01 $20.01 $19.59 1.02 $19.21 $19.25 5.000 5.000 50.002012 CG BB CGH SCZ N03 N03 N U 22.01 $20.67 $20.24 1.02 $19.84 $19.75 5.000 10.000 95.002012 CG BB CGM SCZ E01 E01 E S 22.01 $20.72 $20.29 1.02 $19.89 $20.00 2.500 12.500 20.002012 CG BB CGH SCZ E03 E03 E U 22.01 $21.44 $20.99 1.02 $20.58 $20.50 9.000 21.500 80.002012 CG BB CGH SCZ N01 N01 N U 22.01 $22.44 $21.97 1.02 $21.54 $21.50 9.000 30.500 70.002012 CG BB CGM SCZ E02 E02 E U 22.01 $36.48 $35.72 1.02 $35.01 $35.00 2.000 32.500 20.00EPA CODES WM CODES2012 Final Cost (Rounded)$0$5$10$15$20$25$30$35$405.0 10.0 12.5 21.5 30.5 32.5Cumulative Production (Million Tons)

Cash Costs $/ton

### Appendix 9-2.1 

## Appendix 9-2 New Mines Included in 2040 Curves

Year Coal Supply Region Coal Grade Step Name Heat Content (MMBtu/Ton) Cost of Production (2007$/Ton) Coal Production

(Million Tons/Year) Coal Reserves (Million Tons)

2040 AL BB N01 24.82 $39.50 4 13 2040 AL BB N02 24.82 $42.00 0 25 2040 AL BB N03 24.82 $44.50 4 50 2040 AL BB N04 24.82 $47.00 0 50 2040 AL BD N01 24 $47.00 2 49 2040 AL BD N02 24 $58.00 2 50 2040 AL BD N03 24 $70.00 2 50 2040 AL BD N04 24 $82.00 2 50 2040 AL BE N01 23.82 $63.00 0.5 121 2040 AL BE N02 23.82 $69.00 0.5 115 2040 AL BE N03 23.82 $75.00 0.5 115 2040 AL BE N04 23.82 $82.00 0.5 98 2040 AZ BB N01 24.64 $16.75 5 450 2040 CG BA N01 21.49 $18.25 12 70 2040 CG BA N02 21.49 $29.00 1 40 2040 CG BA N03 21.49 $23.00 3 350 2040 CG BB N01 22.01 $19.25 9 70 2040 CG BB N02 22.01 $16.75 5 50 2040 CG BB N03 22.01 $17.75 5 95 2040 CR BA N02 25.5 $27.00 3 60 2040 CR BD N01 22.2 $27.00 0.3 10 2040 CU BB N01 23.22 $19.25 8 100 2040 IL BE N01 23 $32.50 1 55 2040 IL BE N02 23 $32.00 1 15 2040 IL BE N03 23 $33.50 1 15 2040 IL BE N04 23 $35.00 1 15 2040 IL BE N05 23 $37.00 1 15 2040 IL BE N06 23 $38.50 1 15 2040 IL BE N07 23 $40.50 1 15 2040 IL BE N08 23 $42.00 1 15 2040 IL BE N09 23 $44.00 1 15 2040 IL BE N10 23 $45.50 1 15 2040 IL BE N11 23 $47.00 1 15 2040 IL BE N12 23 $49.00 1 15 2040 IL BE N13 23 $51.00 1 15 2040 IL BE N14 23 $52.00 1 15 2040 IL BE N15 23 $86.00 1 15 2040 IL BG N01 23.01 $24.00 5 398 2040 IL BG N02 23.01 $27.00 2.5 9.6 2040 IL BG N03 23.01 $28.50 3 38 2040 IL BG N04 23.01 $37.50 4 130 2040 IL BG N05 23.01 $46.00 0.5 8 2040 IL BG N06 23.01 $28.50 4 200 2040 IL BG N07 23.01 $32.50 4 200

### Appendix 9-2.2 Year Coal Supply Region Coal Grade Step Name Heat Content (MMBtu/Ton) Cost of Production (2007$/Ton) Coal Production

(Million Tons/Year) Coal Reserves (Million Tons)

2040 IL BG N08 23.01 $37.00 4 200 2040 IL BG N09 23.01 $41.00 4 200 2040 IL BG N10 23.01 $45.50 4 200 2040 IL BG N11 23.01 $54.00 4 200 2040 IL BG N12 23.01 $86.00 4 200 2040 IL BH N01 22.19 $24.00 5 177.4 2040 IL BH N02 22.19 $24.00 5 253.2 2040 IL BH N03 22.19 $24.00 5 221.9 2040 IL BH N04 22.19 $24.50 3.7 107.5 2040 IL BH N05 22.19 $24.00 3 70 2040 IL BH N06 22.19 $24.00 3 60 2040 IL BH N07 22.19 $27.50 7.8 460 2040 IL BH N08 22.19 $29.00 0.5 2 2040 IL BH N09 22.19 $31.50 2.2 125 2040 IL BH N10 22.19 $35.00 0.2 3 2040 IL BH N11 22.19 $38.50 1.9 37.8 2040 IL BH N12 22.19 $48.50 0.8 20 2040 IL BH N13 22.19 $53.00 0.5 4 2040 IL BH N14 22.19 $86.00 5 250 2040 IN BD N01 22.62 $30.00 0.5 10 2040 IN BD N02 22.62 $32.00 0.5 10 2040 IN BD N03 22.62 $34.50 0.5 10 2040 IN BD N04 22.62 $86.00 0.5 10 2040 IN BE N01 23.43 $24.00 0.1 0.5 2040 IN BE N02 23.43 $34.50 1 15 2040 IN BE N03 23.43 $37.00 1 26 2040 IN BE N04 23.43 $86.00 1 15 2040 IN BG N01 23.37 $27.50 1.5 30 2040 IN BG N02 23.37 $30.50 1.5 30 2040 IN BG N03 23.37 $33.00 0.5 6 2040 IN BG N04 23.37 $35.00 1.6 9.5 2040 IN BG N05 23.37 $38.00 1 418.1 2040 IN BG N06 23.37 $84.00 1 5 2040 IN BH N01 23.41 $31.00 1.5 60 2040 IN BH N02 23.41 $34.00 0.2 3 2040 IN BH N03 23.41 $49.50 0.8 11 2040 IN BH N04 23.41 $28.50 1.5 60 2040 IN BH N05 23.41 $29.50 1.5 60 2040 IN BH N06 23.41 $30.00 1.5 60 2040 IN BH N07 23.41 $31.00 1.5 60 2040 IN BH N08 23.41 $32.00 1.5 60 2040 IN BH N09 23.41 $33.00 1.5 60 2040 IN BH N10 23.41 $34.00 1.5 60 2040 IN BH N11 23.41 $35.00 1.5 60 2040 IN BH N12 23.41 $36.00 1.5 60

### Appendix 9-2.3 Year Coal Supply Region Coal Grade Step Name Heat Content (MMBtu/Ton) Cost of Production (2007$/Ton) Coal Production

(Million Tons/Year) Coal Reserves (Million Tons)

2040 IN BH N13 23.41 $37.00 1.5 60 2040 IN BH N14 23.41 $38.00 1.5 60 2040 IN BH N15 23.41 $39.00 1.5 60 2040 IN BH N16 23.41 $40.00 1.5 60 2040 IN BH N17 23.41 $40.50 1.5 60 2040 IN BH N18 23.41 $41.50 1.5 60 2040 IN BH N19 23.41 $42.50 1.5 60 2040 IN BH N20 23.41 $43.50 1.5 60 2040 IN BH N21 23.41 $44.50 1.5 60 2040 IN BH N22 23.41 $45.50 1.5 60 2040 IN BH N23 23.41 $46.50 1.5 60 2040 IN BH N24 23.41 $47.50 1.5 60 2040 IN BH N25 23.41 $48.50 1.5 60 2040 IN BH N26 23.41 $49.50 1.5 60 2040 IN BH N27 23.41 $50.00 1.5 60 2040 IN BH N28 23.41 $51.00 1.5 60 2040 IN BH N29 23.41 $52.00 1.5 60 2040 IN BH N30 23.41 $53.00 1.5 60 2040 IN BH N31 23.41 $54.00 1.5 60 2040 IN BH N32 23.41 $86.00 1.5 60 2040 KE BA N01 25.32 $68.00 0.3 11.9 2040 KE BA N02 25.32 $88.00 0.3 15 2040 KE BA N03 25.32 $108.00 0.3 15 2040 KE BA N04 25.32 $128.00 0.3 15 2040 KE BB N01 25.79 $87.00 0.5 71.1 2040 KE BB N02 25.79 $99.00 0.5 80 2040 KE BB N03 25.79 $111.00 0.5 80 2040 KE BB N04 25.79 $123.00 0.5 80 2040 KE BD N01 25.33 $74.00 4.1 192.7 2040 KE BD N02 25.33 $87.00 3 170 2040 KE BD N03 25.33 $100.00 2 180 2040 KE BD N04 25.33 $114.00 1 152 2040 KE BE N01 25.14 $71.00 4.1 351.8 2040 KE BE N02 25.14 $91.00 3 320 2040 KE BE N03 25.14 $111.00 2 150 2040 KE BE N04 25.14 $130.00 1 145 2040 KE BG N01 24.09 $57.00 0.3 4 2040 KE BG N02 24.09 $87.00 0.3 4 2040 KE BG N03 24.09 $117.00 0.3 4 2040 KE BG N04 24.09 $148.00 0.3 4 2040 KS BG N01 25.32 $36.50 0.1 1.2 2040 KS BG N02 25.32 $44.50 0.1 1.2 2040 KS BG N03 25.32 $52.00 0.1 1.2 2040 KS BG N04 25.32 $59.00 0.1 1.2 2040 KS BG N05 25.32 $67.00 0.1 1.2

### Appendix 9-2.4 Year Coal Supply Region Coal Grade Step Name Heat Content (MMBtu/Ton) Cost of Production (2007$/Ton) Coal Production

(Million Tons/Year) Coal Reserves (Million Tons)

2040 KS BG N06 25.32 $86.00 0.1 1.2 2040 KW BD N01 24.23 $59.00 0.7 10 2040 KW BD N02 24.23 $67.00 0.5 105 2040 KW BD N03 24.23 $86.00 0.5 105 2040 KW BE N01 24.45 $30.50 2 300 2040 KW BE N02 24.45 $33.50 0.5 30 2040 KW BE N03 24.45 $34.00 0.5 30 2040 KW BE N04 24.45 $34.50 0.5 30 2040 KW BE N05 24.45 $35.00 0.5 30 2040 KW BE N06 24.45 $35.50 0.5 30 2040 KW BE N07 24.45 $36.50 0.5 30 2040 KW BE N08 24.45 $37.00 0.5 30 2040 KW BE N09 24.45 $37.50 0.5 30 2040 KW BE N10 24.45 $38.00 0.5 30 2040 KW BE N11 24.45 $38.50 0.5 30 2040 KW BE N12 24.45 $39.00 0.5 30 2040 KW BE N13 24.45 $39.50 0.5 30 2040 KW BE N14 24.45 $40.00 0.5 30 2040 KW BE N15 24.45 $40.50 0.5 30 2040 KW BE N16 24.45 $41.50 0.5 30 2040 KW BE N17 24.45 $42.00 0.5 30 2040 KW BE N18 24.45 $42.50 0.5 30 2040 KW BE N19 24.45 $43.00 0.5 30 2040 KW BE N20 24.45 $86.00 0.5 30 2040 KW BG N01 23.93 $26.00 3 24.8 2040 KW BG N02 23.93 $30.00 3 195 2040 KW BG N03 23.93 $32.00 3 156.8 2040 KW BG N04 23.93 $36.50 4 104.5 2040 KW BG N05 23.93 $37.00 2 20 2040 KW BG N06 23.93 $50.00 3 180 2040 KW BG N07 23.93 $86.00 3 180 2040 KW BH N01 22.84 $29.50 5 17 2040 KW BH N02 22.84 $32.00 1 4 2040 KW BH N03 22.84 $34.00 2 22 2040 KW BH N04 22.84 $38.00 1 7.8 2040 KW BH N05 22.84 $53.00 1 30 2040 KW BH N06 22.84 $53.00 2 240 2040 KW BH N07 22.84 $53.00 2 240 2040 KW BH N08 22.84 $53.00 2 240 2040 KW BH N09 22.84 $53.00 2 240 2040 KW BH N10 22.84 $53.00 2 240 2040 KW BH N11 22.84 $54.00 2 240 2040 KW BH N12 22.84 $86.00 2 240 2040 LA LE N01 14.09 $19.00 2 75 2040 LA LE N02 14.09 $43.00 2 75

### Appendix 9-2.5 Year Coal Supply Region Coal Grade Step Name Heat Content (MMBtu/Ton) Cost of Production (2007$/Ton) Coal Production

(Million Tons/Year) Coal Reserves (Million Tons)

2040 MD BB N01 24.64 $71.00 0.3 5 2040 MD BB N02 24.64 $76.00 0.3 5 2040 MD BB N03 24.64 $80.00 0.3 5 2040 MD BB N04 24.64 $85.00 0.3 5 2040 MD BD N01 26.32 $65.00 0.7 2.5 2040 MD BD N02 26.32 $70.00 0.7 7.5 2040 MD BD N03 26.32 $76.00 0.7 8 2040 MD BD N04 26.32 $85.00 0.7 7 2040 MD BE N01 24.85 $44.00 0.3 3 2040 MD BE N02 24.85 $58.00 0.3 3 2040 MD BE N03 24.85 $73.00 0.3 3 2040 MD BE N04 24.85 $88.00 0.3 3 2040 MD BG N01 23.26 $55.00 0.4 15 2040 MD BG N02 23.26 $62.00 0.4 15 2040 MD BG N03 23.26 $69.00 0.4 15 2040 MD BG N04 23.26 $78.00 0.4 15 2040 ME LD N01 13 $11.75 15 550 2040 ME LD N02 13.55 $12.50 15 550 2040 ME LD N03 13.85 $14.75 15 400 2040 ME LD N04 13.1 $15.00 1 50 2040 ME LD N05 14.2 $15.50 15 3500 2040 ME LD N06 13 $16.00 15 970 2040 ME LD N07 13.4 $17.00 15 1400 2040 ME LD N08 13.3 $17.00 15 455 2040 ME LD N09 13.2 $24.00 10 215 2040 MP SA N01 18.9 $13.75 15 450 2040 MP SA N02 18.9 $14.75 15 450 2040 MP SA N03 18.9 $17.00 15 450 2040 MP SD N01 17.23 $14.25 15 450 2040 MP SD N02 17.23 $13.00 15 450 2040 MP SD N03 17.23 $15.25 15 450 2040 MS LE N01 12.8 $15.75 3 96 2040 MS LE N02 12.8 $43.00 1 40 2040 MT BB N01 21 $23.50 5 450 2040 ND LD N01 13.7 $15.75 18 500 2040 ND LD N02 13.7 $15.25 9 565 2040 ND LD N03 13.7 $13.75 1 512 2040 ND LE N01 13.46 $13.75 6 589 2040 ND LE N02 13.46 $17.00 4 500 2040 ND LE N03 13.46 $26.50 2 51 2040 NS BB N01 26.4 $43.00 0.6 22 2040 NS BD N01 22 $23.00 0.4 16 2040 NS BD N02 22 $43.00 0.4 20 2040 NS BE N01 17 $14.25 6 180 2040 NS BE N02 22 $43.00 0.4 20

### Appendix 9-2.6 Year Coal Supply Region Coal Grade Step Name Heat Content (MMBtu/Ton) Cost of Production (2007$/Ton) Coal Production

(Million Tons/Year) Coal Reserves (Million Tons)

2040 OH BB N01 24.68 $76.00 1 50 2040 OH BB N02 24.68 $83.00 1 50 2040 OH BB N03 24.68 $91.00 1 50 2040 OH BB N04 24.68 $100.00 1 50 2040 OH BD N01 25.55 $63.00 0.3 94 2040 OH BD N02 25.55 $69.00 0.3 100 2040 OH BD N03 25.55 $75.00 0.3 100 2040 OH BD N04 25.55 $82.00 0.3 100 2040 OH BE N01 25.24 $57.00 0.5 208 2040 OH BE N02 25.24 $69.00 0.5 210 2040 OH BE N03 25.24 $81.00 0.5 210 2040 OH BE N04 25.24 $94.00 0.5 230 2040 OH BG N01 24.34 $49.00 4 446 2040 OH BG N02 24.34 $60.00 4 450 2040 OH BG N03 24.34 $70.00 4 450 2040 OH BG N04 24.34 $82.00 4 450 2040 OH BH N01 23.92 $50.00 4 500 2040 OH BH N02 23.92 $60.00 4 500 2040 OH BH N03 23.92 $72.00 4 500 2040 OH BH N04 23.92 $86.00 4 500 2040 OK BE N01 22.15 $45.00 0.3 3.6 2040 OK BE N02 22.15 $48.00 0.3 3.6 2040 OK BE N03 22.15 $51.00 0.3 3.6 2040 OK BE N04 22.15 $55.00 0.3 3.6 2040 OK BE N05 22.15 $86.00 0.3 3.6 2040 PC BD N01 25.06 $64.00 0.5 7.6 2040 PC BD N02 25.06 $73.00 0.5 5 2040 PC BD N03 25.06 $83.00 0.5 5 2040 PC BD N04 25.06 $94.00 0.5 5 2040 PC BE N01 25.66 $39.50 1.5 15 2040 PC BE N02 25.66 $54.00 1.5 15 2040 PC BE N03 25.66 $69.00 1.5 15 2040 PC BE N04 25.66 $84.00 1.5 15 2040 PC BG N01 25.33 $51.00 1 22 2040 PC BG N02 25.33 $60.00 1 25 2040 PC BG N03 25.33 $68.00 1 20 2040 PC BG N04 25.33 $77.00 1 20 2040 PC BH N01 23.39 $51.00 0.5 10 2040 PC BH N02 23.39 $60.00 0.5 10 2040 PC BH N03 23.39 $68.00 0.5 10 2040 PC BH N04 23.39 $77.00 0.5 10 2040 PW BD N01 24.26 $38.50 0.8 122 2040 PW BD N02 24.26 $43.00 0.8 126 2040 PW BD N03 24.26 $47.00 0.8 130 2040 PW BD N04 24.26 $51.00 0.8 134

### Appendix 9-2.7 Year Coal Supply Region Coal Grade Step Name Heat Content (MMBtu/Ton) Cost of Production (2007$/Ton) Coal Production

(Million Tons/Year) Coal Reserves (Million Tons)

2040 PW BE N01 26.22 $37.00 2 744 2040 PW BE N02 26.22 $40.50 2 784 2040 PW BE N03 26.22 $44.00 2 824 2040 PW BE N04 26.22 $46.00 2 864 2040 PW BG N01 25.86 $37.00 5 567 2040 PW BG N02 25.86 $39.50 5 667 2040 PW BG N03 25.86 $42.50 5 767 2040 PW BG N04 25.86 $64.00 1 767 2040 TN BB N01 24.18 $101.00 0.3 3.8 2040 TN BB N02 24.18 $106.00 0.3 3.8 2040 TN BB N03 24.18 $112.00 0.3 3.8 2040 TN BB N04 24.18 $118.00 0.3 3.8 2040 TN BD N01 23.91 $101.00 0.3 4.5 2040 TN BD N02 23.91 $104.00 0.3 4.5 2040 TN BD N03 23.91 $107.00 0.3 4.5 2040 TN BD N04 23.91 $110.00 0.3 4.2 2040 TN BE N01 26.75 $85.00 0.3 6.3 2040 TN BE N02 26.75 $93.00 0.3 6 2040 TN BE N03 26.75 $101.00 0.3 6 2040 TN BE N04 26.75 $110.00 0.3 6 2040 TX LE N01 13.22 $11.50 11 219 2040 TX LE N02 13.22 $14.75 4 150 2040 TX LE N03 13.22 $15.75 6 127 2040 TX LE N04 13.22 $15.25 7 114 2040 UT BA N01 23.68 $20.00 1 10 2040 UT BA N02 23.68 $24.50 4 80 2040 UT BA N03 23.68 $24.50 4 25 2040 UT BB N01 23.23 $25.50 4 50 2040 UT BB N02 23.23 $21.50 2 22 2040 UT BD N01 23.05 $24.00 4 45 2040 UT BD N02 23.05 $41.00 3 50 2040 UT BE N01 25.06 $28.50 2 40 2040 VA BA N01 22.7 $103.00 0.3 44.4 2040 VA BA N02 22.7 $108.00 0.3 40 2040 VA BA N03 22.7 $115.00 0.3 40 2040 VA BA N04 22.7 $121.00 0.3 39 2040 VA BB N01 25.97 $94.00 0.8 87.2 2040 VA BB N02 25.97 $103.00 0.8 85 2040 VA BB N03 25.97 $112.00 0.8 85 2040 VA BB N04 25.97 $120.00 0.8 84 2040 VA BD N01 25.76 $85.00 1.1 68.4 2040 VA BD N02 25.76 $94.00 0.8 50 2040 VA BD N03 25.76 $103.00 0.8 50 2040 VA BD N04 25.76 $112.00 0.8 42 2040 VA BE N01 26.03 $68.00 0.5 40.6

### Appendix 9-2.8 Year Coal Supply Region Coal Grade Step Name Heat Content (MMBtu/Ton) Cost of Production (2007$/Ton) Coal Production

(Million Tons/Year) Coal Reserves (Million Tons)

2040 VA BE N02 26.03 $78.00 0.5 26 2040 VA BE N03 26.03 $89.00 0.5 26 2040 VA BE N04 26.03 $112.00 0.5 26 2040 WG BB N01 21.67 $24.50 0.5 15 2040 WG BB N02 21.67 $23.50 6 190 2040 WG BB N03 21.67 $22.50 3 55 2040 WG SD N01 18.5 $19.75 1 13 2040 WL SB N01 17.15 $17.75 15 450 2040 WL SB N02 17.15 $10.75 15 450 2040 WL SB N03 17.15 $11.75 15 450 2040 WL SB N04 17.15 $13.00 15 450 2040 WN BD N01 25.01 $45.50 0.4 1.6 2040 WN BD N02 25.01 $65.00 0.4 2 2040 WN BD N03 25.01 $85.00 0.4 2 2040 WN BD N04 25.01 $104.00 0.4 2 2040 WN BE N01 25.67 $29.00 2 40 2040 WN BE N02 25.67 $49.00 2 40 2040 WN BE N03 25.67 $69.00 2 40 2040 WN BE N04 25.67 $89.00 2 40 2040 WN BG N01 26.03 $32.50 6.5 600 2040 WN BG N02 26.03 $48.00 5 600 2040 WN BG N03 26.03 $64.00 5 600 2040 WN BG N04 26.03 $80.00 5 600 2040 WN BH N01 25.15 $34.00 5 125 2040 WN BH N02 25.15 $53.00 5 125 2040 WN BH N03 25.15 $72.00 5 125 2040 WN BH N04 25.15 $91.00 5 75 2040 WS BA N01 26.2 $98.00 0.3 4.7 2040 WS BA N02 26.2 $107.00 0.3 4.7 2040 WS BA N03 26.2 $112.00 0.3 4.7 2040 WS BA N04 26.2 $122.00 0.3 4.6 2040 WS BB N01 24.73 $90.00 0.5 106.8 2040 WS BB N02 24.73 $107.00 0.5 100 2040 WS BB N03 24.73 $114.00 0.5 100 2040 WS BB N04 24.73 $122.00 0.5 100 2040 WS BD N01 24.64 $82.00 1.5 384.8 2040 WS BD N02 24.64 $89.00 1.5 333 2040 WS BD N03 24.64 $120.00 1.5 333 2040 WS BD N04 24.64 $147.00 1.5 333 2040 WS BE N01 24.38 $69.00 1.4 420.7 2040 WS BE N02 24.38 $84.00 1.3 420 2040 WS BE N03 24.38 $99.00 1.3 420 2040 WS BE N04 24.38 $113.00 1.3 420 2040 WS BG N01 25.64 $69.00 0.8 80 2040 WS BG N02 25.64 $80.00 0.8 79

### Appendix 9-2.9 Year Coal Supply Region Coal Grade Step Name Heat Content (MMBtu/Ton) Cost of Production (2007$/Ton) Coal Production

(Million Tons/Year) Coal Reserves (Million Tons)

2040 WS BG N03 25.64 $92.00 0.8 79 2040 WS BG N04 25.64 $104.00 0.8 80

### Appendix 9-3.1 

## Appendix 9-3 Coal Transportation Matrix in EPA Base Case v.4.10

This is a small excerpt of the data in Appendix 9-3. The complete data set in spreadsheet format can be downloaded via the link found at www.epa.gov/airmarkets/progsregs/epaipm/BaseCasev410.html

Coal Supply

Region – Description Coal Demand Region Coal Demand Region Description Total Cost (2007$/Ton) Escalation/ Year (2012- 2025) Escalation/ Year (2026- 2054)

Arizona AMM1 AMMM_High-Cost Competitive_Mine Mouth_Rail 1.13 1 1
New Mexico, San Juan AMM1 AMMM_High-Cost Competitive_Mine Mouth_Rail 1.13 1 1
West Virginia, North NAI1 NAIN_High-Cost Competitive_Mine Mouth_Rail 1.13 1 1
Wyoming, Powder River Basin (8400) NAI1 NAIN_High-Cost Competitive_Mine Mouth_Rail 36.93 1 1.0021

Louisiana TXL1 TXLG_High-Cost Competitive_Mine Mouth_Rail 1.13 1 1
Wyoming, Powder River Basin (8400) TXL1 TXLG_High-Cost Competitive_Mine Mouth_Rail 24.58 1 1.0021

Wyoming, Powder River Basin (8800) NAI1 NAIN_High-Cost Competitive_Mine Mouth_Rail 37.3 1 1.0021

Wyoming, Powder River Basin (8800) TXL1 TXLG_High-Cost Competitive_Mine Mouth_Rail 22.63 1 1.0021

Montana, East DAL1 DALG_High-Cost Competitive_Mine Mouth_Truck/Conveyor Belt 4.83 1 1.0042
Wyoming, Powder River Basin (8400) DAL1 DALG_High-Cost Competitive_Mine Mouth_Truck/Conveyor Belt 10.79 1 1.002

Wyoming, Powder River Basin (8400) PRB1 PRB_High-Cost Competitive_Mine Mouth_Truck/Conveyor Belt 2.21 1 1.0011

Wyoming, Powder River Basin (8400) WYG1 WYGR_High-Cost Competitive_Mine Mouth_Truck/Conveyor Belt 2.21 1 1.0011

Wyoming, Powder River Basin (8800) DAL1 DALG_High-Cost Competitive_Mine Mouth_Truck/Conveyor Belt 11.23 1 1.002

Wyoming, Powder River Basin (8800) PRB1 PRB_High-Cost Competitive_Mine Mouth_Truck/Conveyor Belt 1.13 1 1

Alabama ALR1 ALRL_High-Cost Competitive_Not Mine Mouth_Rail 0 1 1
Colorado, Green River ALR1 ALRL_High-Cost Competitive_Not Mine Mouth_Rail 33.86 1 1.0022

Colorado, Uinta ALR1 ALRL_High-Cost Competitive_Not Mine Mouth_Rail 39.76 1 1.0022

Illinois ALR1 ALRL_High-Cost Competitive_Not Mine Mouth_Rail 25.73 1 1.0022

Indiana ALR1 ALRL_High-Cost Competitive_Not Mine Mouth_Rail 23.32 1 1.0022

Kentucky East ALR1 ALRL_High-Cost Competitive_Not Mine Mouth_Rail 26.57 1 1.0022

### Appendix 9-4.1 

## Appendix 9-4 Coal Supply Curves in EPA Base Case v.4.10

This is a small excerpt of the data in Appendix 9-4. The complete data set in spreadsheet format and complete set of graphs can be downloaded via the link found at www.epa.gov/airmarkets/progsregs/epa-ipm/BaseCasev410.html

Year Coal Supply Region Coal Grade Step Name Heat Content (MMBtu/Ton) Cost of Production (2007$/Ton) Coal Production

(Million Tons/Year) Coal Reserves

(Million Tons) 2012 AL BB E01 24.82 43.1 1.0 111
2012 AL BB N01 24.82 44.5 4.0 13
2012 AL BB N02 24.82 47.0 0.0 25
2012 AL BB E02 24.82 48.9 0.9 51
2012 AL BB E03 24.82 49.9 5.6 73
2012 AL BB N03 24.82 49.9 4.0 50
2012 AL BB E04 24.82 50.9 1.0 5
2012 AL BB N04 24.82 52.9 0.0 50
2012 AL BB E05 24.82 53.8 0.8 22
2012 AL BD E01 24 43.1 0.4 233
2012 AL BD E02 24 44.5 0.3 9
2012 AL BD E03 24 48.9 0.4 16
2012 AL BD E04 24 52.9 0.9 10
2012 AL BD N01 24 52.9 2.0 49
2012 AL BD E05 24 53.8 0.3 1
2012 AL BD E06 24 56.8 0.7 26
2012 AL BD E07 24 64.6 0.4 24
2012 AL BD N02 24 65.6 2.0 50
2012 AL BD E08 24 73.4 0.1 6
2012 AL BD N03 24 78.3 2.0 50
2012 AL BD E09 24 85.1 0.5 26
2012 AL BD N04 24 92.0 2.0 50
2012 AL BE E01 23.82 36.2 1.8 93
2012 AL BE E02 23.82 43.1 0.4 14
2012 AL BE E03 23.82 45.5 0.1 0
2012 AL BE E04 23.82 50.9 0.4 10
2012 AL BE E05 23.82 54.8 0.9 16
2012 AL BE E06 23.82 62.6 0.2 5
2012 AL BE N01 23.82 70.5 0.5 121Coal Supply Curve - WH_SA$0$5$10$15$20$250 100 200 300 400 500 600Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WH_SB$0$2$4$6$8$10$12$14$16$18$200 10 20 30 40 50 60 70 80 90 100Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WL_SB$0$5$10$15$20$250 100 200 300 400 500 600Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - MP_SA$0$2$4$6$8$10$12$14$16$18$200 10 20 30 40 50 60 70 80 90Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - MP_SD$0$5$10$15$20$250 10 20 30 40 50 60 70 80 90 100Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - MT_BB$0$5$10$15$20$25$300 2 4 6 8 10 12 14 16 18 20Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WG_BB$0$5$10$15$20$25$300 1 2 3 4 5 6 7 8 9 10Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WG_SD$0$5$10$15$20$25$300246810 12 14 16 18Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - KS_BG$0$20$40$60$80$100$12000.20.40.60.811.21.4Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - OK_BE$0$20$40$60$80$100$12000.511.522.5Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - NS_BB$0$10$20$30$40$50$6000.20.40.60.811.21.4Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - NS_BD$0$10$20$30$40$50$600123456789Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - NS_BE$0$10$20$30$40$50$60051015202530Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - AZ_BB$0$2$4$6$8$10$12$14$16$18$200 2 4 6 8 10 12 14Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - CG_BA$0$5$10$15$20$25$30$35$400 5 10 15 20 25 30 35 40Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - CG_BB$0$5$10$15$20$25$30$35$400 10 20 30 40 50 60Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - CR_BA$0$5$10$15$20$25$30$3500.511.522.533.5Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - CR_BD$0$5$10$15$20$25$3000.050.10.150.20.250.30.35Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - CU_BA$0$5$10$15$20$25$300 2 4 6 810 12 14Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - CU_BB$0$5$10$15$20$25$300 2 4 6 8 10 12 14 16Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - CU_BD$0$2$4$6$8$10$12$14$160 0.05 0.1 0.15 0.2 0.25 0.3 0.35Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - UT_BA$0$5$10$15$20$25$30$35$40$450 2 4 6 8 10 12 14 16 18Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - UT_BB$0$5$10$15$20$25$30$35$40$450 5 10 15 20 25 30 35 40 45Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - UT_BD$0$5$10$15$20$25$30$35$40$45$500 5 10 15 20 25Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - UT_BE$0$5$10$15$20$25$30$35$400 0.5 1 1.5 2 2.5 3 3.5 4 4.5Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - PC_BD$0$20$40$60$80$100$1200123456Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - PC_BE$0$10$20$30$40$50$60$70$80$90$1000 5 10 15 20 25Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - PC_BG$0$10$20$30$40$50$60$70$80$90$1000123456789Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - PC_BH$0$10$20$30$40$50$60$70$80$90$1000 0.5 1 1.5 2 2.5Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - PW_BD$0$10$20$30$40$50$60$700 2 4 6 810 12Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - PW_BE$0$10$20$30$40$50$60$700510 15 20 25 30 35 40 45Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - PW_BG$0$10$20$30$40$50$60$70$800 2 4 6 8 10 12 14 16 18Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - OH_BB$0$20$40$60$80$100$1200 0.5 1 1.5 2 2.5 3 3.5 4 4.5Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - OH_BD$0$10$20$30$40$50$60$70$80$90$1000 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - OH_BE$0$20$40$60$80$100$120012345678Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - OH_BG$0$10$20$30$40$50$60$70$80$90$1000 5 10 15 20 25 30 35 40 45Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - OH_BH$0$20$40$60$80$100$1200 2 4 6 8 10 12 14 16 18Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - MD_BB$0$10$20$30$40$50$60$70$80$90$1000 0.2 0.4 0.6 0.8 1 1.2 1.4Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - MD_BD$0$10$20$30$40$50$60$70$80$90$1000 0.5 1 1.5 2 2.5 3Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - MD_BE$0$20$40$60$80$100$1200 0.511.522.533.5Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - MD_BG$0$10$20$30$40$50$60$70$80$90$1000 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WN_BD$0$20$40$60$80$100$120$1400 2 4 6 810 12Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WN_BE$0$20$40$60$80$100$120051015202530Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WN_BG$0$10$20$30$40$50$60$70$80$90$1000 10 20 30 40 50 60 70Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WN_BH$0$20$40$60$80$100$1200510152025Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - TX_LD$0$5$10$15$20$250 1 2 3 4 5 6 7 8 9 10Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - TX_LE$0$5$10$15$20$250 10 20 30 40 50 60 70Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - TX_LG$0$2$4$6$8$10$120 2 4 6 810 12Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - LA_LE$0$10$20$30$40$50$600123456789Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - KW_BD$0$20$40$60$80$100$1200 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - KW_BE$0$20$40$60$80$100$1200 2 4 6 810 12 14Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - KW_BG$0$20$40$60$80$100$1200 10 20 30 40 50 60Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - KW_BH$0$20$40$60$80$100$1200510 15 20 25 30 35 40 45Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - IL_BE$0$20$40$60$80$100$120051015202530Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - IL_BG$0$20$40$60$80$100$1200 10 20 30 40 50 60 70 80 90Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - IL_BH$0$20$40$60$80$100$120020406080100120Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - IN_BD$0$20$40$60$80$100$1200123456789Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - IN_BE$0$20$40$60$80$100$1200123456789Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - IN_BG$0$10$20$30$40$50$60$70$80$90$1000 5 10 15 20 25Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - IN_BH$0$20$40$60$80$100$1200 10 20 30 40 50 60 70 80Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - MS_LE$0$10$20$30$40$50$600123456789Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - ND_LD$0$2$4$6$8$10$12$14$16$180 10 20 30 40 50 60Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - ND_LE$0$5$10$15$20$25$300510152025Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - ME_LD$0$5$10$15$20$25$30020406080100120140Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WS_BA$0$20$40$60$80$100$120$1400 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WS_BB$0$20$40$60$80$100$120$1400 2 4 6 8 10 12 14 16 18 20Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WS_BD$0$20$40$60$80$100$120$140$1600 10 20 30 40 50 60Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WS_BE$0$20$40$60$80$100$1200123456Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - WS_BG$0$20$40$60$80$100$1200 0.511.522.533.5Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - VA_BA$0$20$40$60$80$100$120$14000.20.40.60.811.21.41.6Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - VA_BB$0$20$40$60$80$100$120$1400 0.511.522.533.5 4Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - VA_BD$0$20$40$60$80$100$1200 2 4 6 810 12 14 16Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - VA_BE$0$20$40$60$80$100$1200 0.511.522.5 3Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - KE_BA$0$20$40$60$80$100$120$1400 0.511.522.533.5Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - KE_BB$0$20$40$60$80$100$120$1400 2 4 6 810 12Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - KE_BD$0$20$40$60$80$100$120$1400 10 20 30 40 50 60 70 80Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - KE_BE$0$20$40$60$80$100$120$140051015202530Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - KE_BG$0$20$40$60$80$100$120$140$160$1800 0.5 1 1.5 2 2.5Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - TN_BB$0$20$40$60$80$100$12000.20.40.60.811.2Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - TN_BD$0$20$40$60$80$100$1200 0.511.522.533.5 4Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - TN_BE$0$20$40$60$80$100$12000.20.40.60.811.21.4Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - AL_BD$0$10$20$30$40$50$60$70$80$90$1000 2 4 6 8 10 12 14Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - AL_BE$0$10$20$30$40$50$60$70$80$90$10001234567Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 CurveCoal Supply Curve - AL_BB$0$10$20$30$40$50$600 2 4 6 8 10 12 14 16 18 20Cumulative Production (MMTons)

Cost (2007$/Ton)2012 Curve2015 Curve2020 Curve2030 Curve

### 10-1 

# 10 Natural Gas
This chapter describes how natural gas supply, demand, and costing are modeled in EPA Base Case v.4.10. Section 10.1 indicates that nat ural gas supply dynamics are directly (i.e., endogenously) modeled in the new base case. This contrasts to previous EPA base cases where natural gas supply curves and related assumptions were developed outside of IPM and then treated as an (exogenous) input to the base case. Section 10.2 gives an overview of the new natural gas module. Section 3.9 treats in detail the specific components of the module that are only briefly described in Section 10.2. Specifically, Sections 10.3 and 10.4 describe the very detailed process-engineering model and data sources used to characterize North American conventional, unconventional, and frontier natural gas resources and reserves and to derive all the cost components incurred in bringing natural gas from the ground to the pipeline. These sections also discuss resource constraints affecting pr oduction and the assumptions (in the form of cost indices) used to depict expected changes in co sts over the 2012-2050 modeling time horizon.

Section 10.5 describes how liquefied natural gas (L NG) imports are represented in the natural gas module. The section covers the assumptions regarding liquefaction facilities, LNG supply, regasification capacity, and related costs. Section 10.6 turns to demand side issues, in particular, how non-power sector residential, commercial, and industrial demand is represented. Section 10.7 describes the detailed characterization of the natural gas pipeline network, the pipeline capacity expansion logic, and the assumptions and procedures used to capture pipeline transportation costs. Section 10.8 treats issues related to natural gas storage: capacity characterization and expansion logic, injection/wit hdrawal rates, and associated costs. Section 10.9 describes the crude oil and natural gas liquids (NGL) price projections that are exogenous inputs in the natural gas module. They figure in the modeling of natural gas because they are a source of revenue which influence the explorat ion and development of hydrocarbon resources. The chapter concludes in Section 10.10 with a discussion of key gas market parameters in the report and proxy natural gas supply curves from the new integrated model based on results in EPA Base Case v.4.10. The proxy curves are meant to provide a point of comparison with the natural gas supply curves used in previous EPA IPM base case.



## 10.1 Overview of IPM’s Natural Gas Module In previous EPA base cases natural gas supply curves and related assumptions were developed outside of IPM and provided in an iterative fashion as static inputs to the base case. Regional gas price forecast, in the form of basis differentials 1 

### , was also provided as static inputs for delivered gas price calculations. The iteration proc ess began with a set of supply curves and basis differentials generated from a series of runs using external stand-alone gas model. Results from the IPM run such as power sector gas demand and heat rates were fed back to the gas model as inputs for the next iteration. It was a time consuming process as it took several iterations to converge and involving human interaction between iterations.

In EPA Base Case v.4.10 natural gas supply, demand, transportation, storage, and related costs are modeled directly in IPM through the incorporat ion of a new natural gas module. In the new system, natural gas supply curves are gener ated endogenously for each region, and the balance between the natural gas supply and demand is solved in all regions simultaneously. Integrating natural gas modeling into IPM as illustrated in Figure 10-1 and Figure 10-2 has some advantages over the previous modeling approach. The dire ct interaction between the electric and the gas modules captures the overall gas supply and demand dynamic and requires no iteration. The

1 

### In natural gas discussions “basis” refers to differences in the price of natural gas in two different geographical locations. In the marketplace “bas is” typically means the difference between the NYMEX futures price at the Henry Hub and the cash price at other market points. In the modeling context “basis” means the difference in natural gas prices between any two nodes at the same instance in time.

### 10-2 model solves for gas price in each region, therefore, it does not need to import gas price basis differentials as in the previous approach.

The result from a modeling standpoint is a new integrated natural gas module with a linear programming (LP) structure that is fully consistent and compatible with that used in IPM. Natural gas LP components, which include objective func tion parameters consistent in form with IPM’s cost minimization, a series of decision variabl es, and a set of constraints, have been added to the original LP structure of the IPM electricity m odule. This integration makes the gas module a working component of the IPM modeling framework.

To a certain extent, the design and assumptions of the new natural gas module are similar to those in ICF International’s private practice Gas Market Model (GMM) which has been used extensively for forecasting and market analyses in the North American natural gas market. To provide these new natural gas modeling capabilities within IPM and still maintain an acceptable model size and solution time, however, simplif ications of some of the GMM design and assumptions were made.

Seasonality in the gas module is made consistent with that in IPM and is currently modeled with two seasons (summer and winter), each with up to six IPM load periods that correspond to the IPM electric sector load duration curve (LDC) s egments. The gas module also employs a similar run year concept as in IPM where, in order to manage model size, individual calendar years over the entire modeling period are mapped to a lesser number of run years. In the current version, both modules use the same run year mapping.

Figure 10-1 Modeling and Data Structure in EPA Base Case v.4.10.

### 10-3 Figure 10-2 Natural Gas Module in EPA Base Case v.4.10.

### 10-4 

## 10.2 Key Components of the New IPM Natural Gas Module The new gas module is a full supply/demand equilibrium model of the North American gas market. Most of the structure and data for the gas module are derived from ICF’s Gas Market Model (GMM). It consists of 114 supply/demand/storage nodes and 14 LNG regasification facility locations that are tied together by a series of links that represent the North American natural gas transmission network as shown in Figure 10-3. The list of the 114 nodes is tabulated in Table 10-1.

Key elements of the natural gas module (which are described in detail in Sections 10.3-10.9) include:

Natural Gas Resources are modeled by a set of base year resource cost curves, which represent undiscovered resource availability or reco verable resource as a function of exploration & development (E&D) cost for 77 supply regions. “Resource Appreciation” 2 

### is added to the resource base to account for additional resources from plays that are not included in the resource base estimates due to lack of knowledge and technology to economically recover the resources. The construction of the resource cost curv es are based on resource characterizations and economic evaluations from the Hydrocarbon Supply Model (HSM) of the GMM. (The HSM is discussed in greater detail in Sections 10.3 and 10.4 below.) Figure 10-4 depicts the geographic locations of the supply regions and Table 10-2 pr ovides a list of the supply regions and a mapping of the regions to the modeling nodes.

Natural Gas production from the 77 supply regions is calculated from the resource cost curves based on exploration and development activities that are a function of drilling success rate, rigs availability, reserves-to-production (R/P) ratio, and the costs of exploration, reserves development, and production that are applicable in the specific regions.

LNG import level for each of the LNG regasification facilities is calculated from LNG supply availability curves (derived from the LNG suppl y curve module of GMM) based on the solution gas price and the regasification capacity at the corresponding LNG node. Availability and regasification capacity of the facilities are specif ied as inputs. The model has the capability to expand regasification capacity. However, due to excess of LNG regasification capacity already in the system and a relatively low electricity demand growth assumption in the EPA Base Case v.4.10, the regasification expansion feature is turned off. If future economic demands more LNG capacity, it can be turned back on.

End use natural gas demand for the non-power sectors (i.e. the residential, commercial, and industrial sectors) is incorporated in the IPM LP structure through node-level interruptible and firm demand curves derived from the GMM natural gas demand module. (These are discussed in greater detail in Section 10.6 below.) The gas consumption in the non-power sectors is calculated within the gas module and the power sector consum ption is calculated within the IPM electricity dispatch module. Figure 10-5 shows t he geographic locations of the demand regions.

2 

### Resource appreciation represents growth in ultimate resource estimates attributed to success in extracting resource from known plays such as natural gas from shales, coal seams, offshore deepwater, and gas hydrates that are not included in the resource base estimates.

### 10-5 Figure 10-3 Gas Transmission Network Map

Not all LNG nodes are shown



### Table 10-1 List of Nodes

Node Name Supply Demand Transit, Import/ Export Underground Storage Peakshaving Storage

1 New England X X
2 Everett TRANS X
3 Quebec X X
4 New York City X X
5 Niagara X X X X
6 Leidy X X X X
7 Cove Point TRANS X
8 Georgia X X
9 Elba Is TRANS X
10 South Florida X X
11 East Ohio X X X X
12 Maumee/Defiance X X X
13 Lebanon X X X
14 Indiana X X X X
15 South Illinois X X X X
16 North Illinois X X X X
17 Southeast Michigan X X X X
18 Tennessee/Kentucky X X X X
19 MD/DC/Northern VA X X
20 Wisconsin X X X

### 10-6 Node Name Supply Demand Transit, Import/ Export Underground Storage Peakshaving Storage

21 Northern Missouri X X X
22 Minnesota X X X X
23 Crystal Falls X X X
24 Ventura X X X X
25 Emerson Imports X
26 Nebraska X X X X
27 Great Plains X
28 Kansas X X X X
29 East Colorado X X X X
30 Opal X X X X
31 Cheyenne X X X
32 San Juan Basin X X X
33 EPNG/TW X X X
34 North Wyoming X X X
35 South Nevada X X X
36 SOCAL Area X X X X
37 Enhanced Oil Recovery Region X X
38 PGE Area X X X X
39 Pacific Offshore X
40 Monchy Imports X
41 Montana/North Dakota X X X X
42 Wild Horse Imports X
43 Kingsgate Imports X
44 Huntingdon Imports X
45 Pacific Northwest X X X X
46 NPC/PGT Hub X X
47 North Nevada X X X
48 Idaho X X X
49 Eastern Canada Offshore X
50 Atlantic Offshore X
51 Reynosa Imp/Exp X
52 Juarez Imp/Exp X
53 Naco Imp/Exp X
54 North Alabama X X X X
55 Alabama Offshore X
56 North Mississippi X X X X
57 East Louisiana Shelf X
58 Eastern Louisiana Hub X X X X

59 Viosca Knoll/Desoto/Miss Canyon X

60 Henry Hub X X X X
61 North Louisiana Hub X X X X

62 Central and West Louisiana Shelf X

63 Southwest Texas X X X
64 Dallas/Ft Worth X X X X
65 E. TX (Katy) X X X X

### 10-7 Node Name Supply Demand Transit, Import/ Export Underground Storage Peakshaving Storage

66 S. TX X X X
67 Offshore Texas X
68 NW TX X X X
69 Garden Banks X
70 Green Canyon X
71 Eastern Gulf X
72 North British Columbia X X X
73 South British Columbia X X
74 Caroline X X X
75 Empress X
76 Saskatchewan X X X
77 Manitoba X X X
78 Dawn X X X
79 Philadelphia X X
80 West Virginia X X X X
81 Eastern Canada Demand X X
82 Alliance Border Crossing X
83 Wind River Basin X X X
84 California Mexican Exports X
85 Whitehorse X
86 MacKenzie Delta X
87 South Alaska X X
88 Central Alaska X
89 North Alaska X
90 Arctic X
91 Norman Wells X
92 Southwest VA X X X X
93 Southeast VA X X
94 North Carolina X X
95 South Carolina X X
96 North Florida X X X
97 Arizona X X X
98 Southwest Michigan X X X X
99 Northern Michigan X X X X
100 Malin Interchange X
101 Topock Interchange X
102 Ehrenberg Interchange X
103 SDG&E Demand X X
104 Eastern New York X X
105 New Jersey X X
106 Toronto X X
107 Carthage X X X X
108 Southwest Oklahoma X X X X
109 Northeast Oklahoma X X X X
110 Southeastern Oklahoma X X X X
111 Northern Arkansas X X X X

### 10-8 Node Name Supply Demand Transit, Import/ Export Underground Storage Peakshaving Storage

112 Southeast Missouri X X X X
113 Uinta/Piceance X X X X
114 South MS/AL X X X X



### Figure 10-4 Gas Supply Regions Map

Canadian Arctic and Alaskan Supply Regions not shown

### 10-1 Table 10-2 List of Gas Supply Regions Supply Region Number Node Number Region Name

1 5 Niagara
2 6 Leidy 3 96 Florida
4 11 East Ohio
5 12 Maumee/ Defiance 6 13 Lebanon
7 14 Indiana 8 15 South Illinois 9 16 North Illinois 10 17 Southeast Michigan
11 18 Tennessee/Kentucky
12 92 SW Virginia 13 20 Wisconsin
14 21 Northern Missouri 15 22 Minnesota 16 23 Crystal Falls
17 24 Ventura 18 26 Nebraska 19 28 Kansas 20 29 East Colorado
21 30 Opal
22 31 Cheyenne 23 32 San Juan Basin
24 33 EPNG/TW 25 34 North Wyoming 26 97 Arizona
27 36 SOCAL Area
28 38 PGE Area 29 39 California Offshore 30 41 Montana/ North Dakota 31 45 Pacific Northwest 32 47 North Nevada 33 48 Idaho 34 49 Eastern Canada Offshore 35 50 Atlantic Offshore 36 54 North Alabama 37 55 Alabama Offshore 38 56 North Mississippi 39 57 East Louisiana Shelf 40 58 Eastern Louisiana Hub

41 59 Viosca Knoll S./ Desoto Canyon/Mississippi Canyon 42 60 Henry Hub 43 61 North Louisiana Hub
44 62 Central and West Louisiana Shelf

### 10-2 Supply Region Number Node Number Region Name

45 63 Southwest Texas 46 64 Dallas/Fort Worth
47 65 E. TX (Katy) 48 66 S. TX 49 67 Offshore Texas
50 68 NW TX 51 69 Garden Banks 52 70 Green Canyon 53 71 Florida off-shore moratorium area
54 72 North British Columbia
55 74 Caroline 56 76 Saskatchewan
57 77 Manitoba 58 78 Dawn 59 80 West Virginia 60 83 Wind River Basin 61 86 McKenzie Delta 62 87 Southern Alaska 63 88 Central Alaska 64 89 Northern Alaska 65 90 Arctic 66 91 Norman Wells 67 37 Enhanced Oil Recovery Region
68 98 Southwest Michigan 69 99 Central Michigan 70 107 Carthage
71 108 Southwest Oklahoma
72 109 Northeast Oklahoma 73 110 Southeastern Oklahoma
74 111 Northern Arkansas
75 112 Southeast Missouri 76 113 Uinta/Piceance
77 114 South MS/AL

### 10-3 Figure 10-5 Gas Demand Regions Map

Natural gas pipeline network is modeled by 343 transmission links or segments (excluding pipeline connections with LNG nodes) that represent major interstate transmission corridors throughout North America (Figure 10-3). The pipeline corridors represent a group of interstate pipelines along the corridor. The list of key intersta te pipelines by links is tabulated in Table 10-3. Each of the links has an associated discount curve (derived from GMM natural gas transportation module), which represents the marginal value of gas transmission on that pipeline segment as a function of the pipeline’s load factor. 1 

### Starting year of operation and transmission capacity (in units of BBtu/day) are specified as inputs and the model allows for capacity expansions.

Table 10-3 List of Key Pipelines

Link Pipeline

6 - 5 DOMINION TRANS (CNG) 6 - 5 COLUMBIA GAS TRANS CORP 5 - 6 NATIONAL FUEL GAS SUPPLY CO
11 - 6 DOMINION TRANS (CNG) 11 - 6 COLUMBIA GAS TRANS CORP
6 - 19 DOMINION TRANS (CNG)
6 - 79 TEXAS EASTERN TRANS CORP 6 - 79 TRANSCONTINENTAL GAS P L CO

1 

### In this context “load factor” refers to the percentage of the pipeline capacity that is utilized at a given time.

### 10-4 Link Pipeline

6 - 79 TENNESSEE GAS PIPELINE CO
80 - 6 COLUMBIA GAS TRANS CORP 80 - 6 EQUITRANS INC
96 - 8 SOUTHERN NATURAL GAS CO
96 - 10 FLORIDA GAS TRANS CO
80 - 11 COLUMBIA GAS TRANS CORP
17 - 12 ANR PIPELINE CO
98 - 12 ANR PIPELINE CO 14 - 13 PANHANDLE EASTERN P L CO
98 - 14 TRUNKLINE GAS CO
16 - 15 NAT GAS P L CO OF AMERICA
16 - 20 ANR PIPELINE CO
16 - 98 ANR PIPELINE CO
78 - 17 BLUEWATER PIPELINE CO
99 - 17 MICHCON
18 - 80 COLUMBIA GAS TRANS CORP
18 - 92 EAST TENNESSEE NAT GAS CO
79 - 19 EASTERN SHORE NAT GAS CO 19 - 79 TRANSCONTINENTAL GAS P L CO 19 - 79 COLUMBIA GAS TRANS CORP
92 - 19 COLUMBIA GAS TRANS CORP 23 - 99 GREAT LAKES GAS TRANS LTD 106 - 23 GREAT LAKES GAS TRANS LTD
41 - 27 WILLISTON BASIN P L CO
28 - 29 COLORADO INTERSTATE GAS
68 - 28 COLORADO INTERSTATE GAS 31 - 30 COLORADO INTERSTATE GAS 31 - 30 SOUTHERN STAR CENTRAL (WILLIAMS) 30 - 31 WYOMING INTERSTATE CO 30 - 48 NORTHWEST PIPELINE CORP 113 - 30 NORTHWEST PIPELINE CORP 113 - 32 NORTHWEST PIPELINE CORP 63 - 33 EL PASO NAT GAS CO 63 - 33 TRANSWESTERN PIPELINE CO 68 - 33 TRANSWESTERN PIPELINE CO 37 - 36 SOCAL GAS 37 - 38 PACIFIC GAS & ELECTRIC 40 - 41 NORTHWEST ENERGY 41 - 83 WILLISTON BASIN P L CO 73 - 43 TERASEN (BC GAS) 46 - 45 NORTHWEST PIPELINE CORP
46 - 48 NORTHWEST PIPELINE CORP
66 - 51 TENNESSEE GAS PIPELINE CO
51 - 66 TEXAS EASTERN TRANS CORP
54 - 114 TRANSCONTINENTAL GAS P L CO
58 - 56 GULF SOUTH (KOCH)
60 - 58 TRANSCONTINENTAL GAS P L CO

### 10-5 Link Pipeline

60 - 58 SOUTHERN NATURAL GAS CO 60 - 58 FLORIDA GAS TRANS CO 60 - 58 TENNESSEE GAS PIPELINE CO 60 - 58 TEXAS EASTERN TRANS CORP 60 - 58 GULF SOUTH (KOCH)
60 - 65 NAT GAS P L CO OF AMERICA
61 - 107 CENTERPOINT ENERGY (RELIANT) 64 - 63 TXU LONESTAR GAS PIPELINE 64 - 63 EPGT TEXAS PIPELINE (VALERO) 65 - 63 OASIS 66 - 63 EPGT TEXAS PIPELINE (VALERO) 68 - 63 TRANSWESTERN PIPELINE CO 63 - 68 EL PASO NAT GAS CO 63 - 97 EL PASO NAT GAS CO
65 - 64 TXU FUEL CO 65 - 64 TXU LONESTAR GAS PIPELINE
108 - 68 NAT GAS P L CO OF AMERICA 108 - 68 EL PASO NAT GAS CO 68 - 108 ANR PIPELINE CO
106 - 78 UNION GAS
102 - 84 BAJA NORTE
99 - 98 ANR PIPELINE CO 99 - 98 MICHIGAN GAS STORAGE
101 - 102 EL PASO NAT GAS CO

Natural gas storage is modeled by 180 underground and LNG peak shaving 2 

### storage facilities that are linked to individual nodes. The underground storage is grouped into three categories based on storage “Days Service” 3 

### : (1) 20-day for high deliverability 4 

### storage such as salt caverns, (2) 80-day for depleted 5 

### and aquifer 6 

### reservoirs, and (3) over 80 days mainly for depleted reservoirs. Figure 10-6 shows natural gas storage facility node map. The level of gas storage withdrawals and injections are calculated wit hin the supply and demand balance algorithm based on working gas 7 

### levels, gas prices, and extraction/injection rates and costs. Starting year of

2 

### LNG peak shaving facilities supplement deliveries of natural gas during times of peak periods. LNG peak shaving facilities have a regasification unit attached, but may or may not have a liquefaction unit. Facilities without a liquefaction unit depend upon tank trucks to bring LNG from nearby sources.

3 

### “Days Service” refers to the number of day s required to completely withdraw the maximum working gas inventory associated with an underground storage facility.

4 

### High deliverability storage is depleted reservoir storage facility or Salt Cavern storage whose design allows a relatively quick turnover of the working gas capacity.

5 

### A gas or oil reservoir that is converted for gas storage operations. Its economically recoverable reserves have usually been nearly or completely produced prior to the conversion.

6 

### The underground storage of natural gas in a porous and permeable rock formation topped by an impermeable cap rock, the pore space of which was originally filled with water.

7 

### The term “working gas” refers to natural gas that has been injected into an underground storage facility and stored therein temporarily with the intention of withdrawing it. It is distinguished from “base (or cushion) gas” which refers to the volume of gas that remains permanently in the storage

### 10-6 operation and working gas capacity (in units of BBtu) are specified as inputs and the model allows for capacity expansions. The location of the storage facilities is shown in Figure 10-6.

Natural gas prices are market clearing prices derived from the supply and demand balance at each of the model’s nodes for each segment of IP M’s electricity sector’s seasonal load duration curve (LDC). On the supply-side, prices ar e determined by production and storage price curves that reflect prices as a function of production and storage utilization. Prices are also affected by the “pipeline discount” curves discussed earlier, which represent the marginal value of gas transmission as a function of a pipeline’s load factor and result in changes in basis differential. On the demand-side, the price/quantity relationship is represented by demand curves that capture the fuel-switching behavior of end-users at different price levels. The model balances supply and demand at all nodes and yields market clearing prices determined by the specific shape of the supply and demand curves at each node.

10.2.1 Note on the Modeling Time Horizon and Pre- and Post-2030 Input Assumptions The time horizon of previous EPA IPM base cases extended no further than 2030. This made possible a detailed bottom-up development of nat ural gas assumptions from available data sources. To support analysis of climate change policies, which have a longer time horizon than previously analyzed policies for conventional air pollutants, the time horizon of EPA’s new Base Case v.4.10 extends to 2050. The same detailed bottom-up approach that was employed previously is employed out to 2030. Beyond 2030, where detailed data is not readily available, various technically plausible simplifying assump tions were made. For example, natural gas demand growth from 2030 to 2050 for the non-power sectors (i.e. residential, commercial, and industrial) is assumed to be the same as the level of growth from 2020 to 2030. Resource growth assumptions (for resource appreciation) that were applied for pre-2030 are extended beyond 2030. Post 2030 price projections for crude oil and natural gas liquid 8 

### (NGL) are assumed to be flat at 2030 price levels. The pre-2030 price projections were adapted from AEO 2010.

reservoir in order to maintain adequate pressure and deliverability rates throughout the withdrawal

season.

8 

### Those hydrocarbons in natural gas that are separ ated from the gas as liquids in gas processing or cycling plants. Generally such liquids consist of propane and heavier hydrocarbons and are commonly referred to as lease condensate, natur al gasoline, and liquefied petroleum gases.

### 10-7 Figure 10-6 Natural Gas Storage Facility Node Map

7 7

7

7

7 3 8

10 7

20-Day 80-Day Over 80 Days LNG Peak Shaving



## 10.3 Resource Characterizati on and Economic Evaluation The GMM Hydrocarbon Supply Model (HSM) provides data related to resource characterization and economic evaluation for use in the IPM natural gas module. The current section describes data sources and methods used in the HSM to characterize the North American natural gas resource base. This section concludes with a description of how the HSM resource characterization is used in the EPA Base Case v.4.10 gas module. The next section (i.e., Section 10.4) describes the economic evaluation procedures applied to Exploration and Development (E&D) activities in the HSM and various c onstraints affecting E&D activities.

The HSM was designed for the simulation, foreca sting and analysis of natural gas, crude oil and natural gas liquids supply and cost trends in t he United States and Canada. The HSM includes a highly detailed description of both the undiscovered and discovered resources in the US and Canada. The resource base is described on a fiel d-by-field basis. The individual fields are characterized by type (i.e., oil or gas), size, and location. Location is defined both geographically and by depth. The HSM is a process-engineering model with a very detailed representation of potential gas resources and the technologies with which those resources can be proven 9 

### and produced. The degree and timing by which resources are proven and produced are determined in the model through discounted cashflow analyses of alternative investment options and behavioral assumptions in the form of inertial and cashflow constraints, and the logic underlying producers' market expectations (e.g., their response to future gas prices).

Supply results from the HSM model include undev eloped resource accounting and detailed well, reserve addition, decline rate, and financial results. These results are utilized to provide estimates

9 

### The term “proven” refers to the estimation of t he quantities of natural gas resources that analysis of geological and engineering data demonstrate with reasonable certainty to be recoverable in future years from known reservoirs under ex isting economic and operating conditions. Among the factors considered are drilling results, production, and historical trends. Proven reserves are the most certain portion of the resource base.

### 10-8 of base year economically recoverable natural gas resources and remaining reserves as a function of E&D cost for the 77 supply regions in the IPM natural gas module. The HSM also provides other data such as the level of remaining resource that could be discovered and developed in a year, exploration and development drilling requirements, production operation and maintenance (O&M) cost, resource share of crude o il and natural gas liquids, natural gas reserves to production ratio, and natural gas requirement for lease and plant use. 10



### 10.3.1 Resource and Reserves 11 

### Assessment
Data sources : The HSM uses the U.S. Geological Survey (USGS), Minerals Management Service (MMS), and Canadian Gas Potential Committee (CGPC) play-level 12 

### resource assessments as the starting point for the new field/new pool 13 

### assessments. Beyond the resource assessment data, ICF has access to numerous databases that were used for the HSM model development and other analysis. Completion-leve l production is based on IHS Energy completion level oil and gas production databases for the U.S. and Canada. The U.S. database contains information on approximately 300,000 U.S. completions. A structured system is employed to process this information and add certain ICF data (region, play, ultimate recovery, and gas composition) to each record. ICF also performs extensive quality control checks using other data sources such as the MMS completion and produc tion data for Outer Continental Shelf (OCS) areas and state production reports.

In the area of unconventional gas 14 

### , ICF has worked for many years with the Gas Research Institute (GRI)/Gas Technology Institute (G TI) to develop a database of tight gas, coalbed methane, and Devonian Shale reservoirs in the U.S. and Canada. Along with USGS assessments of continuous plays, the database was used to help develop the HSM’s “cells”, which represent resources in a specific geographic area, characterizing the unconventional resource in each basin, historical unconventional reserves estimates and typical decline curves. 15

10 

### As discussed more fully in Section 10.4, natural gas for “lease and plant use” refers to the gas used in well, field, and lease operations (such as gas used in drilling operations, heaters, dehydrators, and field compressors) and as fuel in gas processing plants.

11 

### When referring to natural gas a distinction is made between “resources” and “reserves.” “Resources” are concentrations of natural gas that are or may become of potential economic interest. “Reserves” are that part of the natural gas resource that has been fully evaluated and determined to be commercially viable to produce.

12 

### A “play” refers to a set of known or postulated natural gas (or oil) accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathway, timing, trapping mechanism, and hydrocarbon type.

13 

### A “pool” is a subsurface accumulation of oil and other hydrocarbons. Pools are not necessarily big caverns. They can be small oil-filled pores. A “field” is an accumulation of hydrocarbons in the subsurface of sufficient size to be of economic interest. A field can consist of one or more pools.

14 

### Unconventional gas refers to natural gas found in geological environments that differ from conventional hydrocarbon traps. It includes: (a) “tight gas,” i.e., natural gas found in relatively impermeable (very low porosity and permeability) sandstone and carbonate rocks; (b) “shale gas,” i.e., natural gas in the joints, fractures or the matrix of shales, the most prevalent low permeability low porosity sedimentary rock on earth; and (c) “coalbed methane,” which refers to methane (the key component of natural gas) found in coal seam s, where it was generated during coal formation and contained in the microstructure of coal. Unconventional natural gas is distinguished from conventional gas which is extracted using traditi onal methods, typically from a well drilled into a geological formation exploiting natural subsurface pressure or artificial lifting to bring the gas and associated hydrocarbons to the wellhead at the surface.

15 

### A decline curve is a plot of the rate of gas production against time. Since the production rate decline is associated with pressure decreases from oil and gas production, the curve tends to smoothly decline from a high early production rate to lower later production rate. Exponential,

### 10-9 ICF has recently revised the unconventional gas resource assessments based on new gas industry information on the geology, well producti on characteristics, and costs. The new assessments include major shale units such as the Fort Worth Barnett Shale, the Marcellus Shale, the Haynessville Shale, and Western Canada shale plays. ICF has built up a database on gas compositions in the United States and has merged that data with production data to allow the analysis of net versus raw gas production. 16



### In Canada, gas composition data are obtained from provincial agencies. These data were used to develop dry gas 17 

### production/reserves by region and processing costs in the HSM and to characterize ethane rejection 18 

### by regions. Information on oil and gas fields and pools in the U.S. come originally from Dwight’s Energydata (now IHS Energy) TOTL reservoir database. ICF has made extensive modifications to the database duri ng the creation of the Gas Information System (GASIS) database for the U.S. Department of Energy (DOE) and other projects. Field and reservoir data for Canada comes from the provin cial agency databases. These data are used to estimate the number and size of undiscovered fields or pools and their rate of discovery per increment of exploratory drilling. Additional data were obtained from the Significant Field Data Base of NRG Associates.

Methodology and assumptions : Resources in the HSM model are divided into three general categories: new fields/new pools, field apprec iation, and unconventional gas. The methodology for resource characterization and ec onomic evaluation differs for each.

Conventional resource – new fields/new pools : The modeling of conventional resource is based on a modified “Arps Roberts” equation 19 

### to estimate the rate at which new fields are discovered. The fundamental theory behind the find-rate methodology is that the probability of finding a field is proportional to the field's size as measured by it s area extent, which is highly correlated to the field's level of reserves. For this reason, larger fields tend to be found earlier in the discovery process than smaller fields. Finding that the original Arps-Roberts equation did not replicate historical discovery patterns for many of the smaller field sizes, ICF modified the equation to improve its ability to accurately track discovery rate s for mid- to small-size fields. Since these are the only fields left to be discovered in many mature areas of the U.S. and Western Canada Sedimentary Basin (WCSB), the more accurate find-rate representation is an important component in analyzing the economics of explorat ion activity in these areas. An economic evaluation is made in the model each year for potential new field exploration programs using a standard discounted after-tax cash flow (DCF) analysis. This DCF analysis takes into account how many fields of each type are expected to be found and the economics of developing each.

harmonic, and hyperbolic equations are typically used to represent the decline curve.

16 

### Raw gas production refers to the volumes of natural gas extracted from underground sources, whereas net gas production refers to the volume of purified, marketable natural gas leaving the natural gas processing plant.

17 

### Natural gas is a combustible mixture of hy drocarbon gases. Although consisting primarily of methane, the composition of natural gas can vary widely to include propane, butane, ethane, and pentane. Natural gas is referred to as 'dry' when it is almost pure methane, having had most of the other commonly associated hydrocarbons remov ed. When other hydrocarbons are present, the natural gas is called 'wet'.

18 

### Ethane rejection occurs when the ethane component in the natural gas stream is not recovered in a gas processing plant but left in the marketable natural gas stream. Ethane rejection is deployed when the value of ethane is worth more in the gas stream than as an a separate commodity or as a component of natural gas liqui ds (NGL), which collectively refers to ethane, propane, normal butane, isobutane, and pentanes in processed and purified finished form. Information that characterizes ethane rejection by region can play a role in determining the production level and cost of natural gas by region.

19 

### “Arps-Roberts equation” refers to the statistica l model of petroleum discovery developed by J. J. Arps, and T. G. Roberts, T. G.,in the 1950’s.

### 10-10 Conventional resource – field appreciation : The model maintains inventories of potential resources that can be proved from already discovered fields. These inventories are referred to as appreciation, growth-to-known or “probables.” As the model simulation proceeds, these probables inventories are drawn down as the resources are proved. At the same time, the inventories of probables are increased due to future year appreciation of new fields that are added to the discovered fields’ data set during the model simulation.

Unconventional resource : The Enhanced Recovery Module (or ERM) within the HSM, covers that portion of the resource base which falls outside the scope of the "conventional" oil and gas field discovery process dealt with elsewhere in the model. The ERM includes coalbed methane, shale gas, and tight gas. These resources generally co rrespond to the “continuous plays” designated by the USGS in its resource assessments. The ERM is organized by "cells", which represent resources in a specific geographic area. A cell can represent any size of area ranging from the entire region/depth interval to a single formation in a few townships of a basin. Each cell is evaluated in the model using the same discounted cashflow analysis used for new and old field investments. The ERM cells also are subject to the inertial and cashflow constraints affecting the other types of investment options in the model. The model reports total wells drilled, reserve additions, production, and dollars invested for each type of ERM cell (e.g., coalbed methane) within a region.

10.3.2 Frontier Resources (Alaska and Mackenzie Delta) Besides the three general categories of resources described above, the handling of frontier resources in the HSM is worth noting. Frontier resources such as Alaska North Slope and Mackenzie Delta are subject to similar resource assessment and economic evaluation procedures as applied to other regions. Results from HSM simulation for the two frontier regions can be seen in Table 10-4. It shows an undiscovered resource potential is 126.8 Tcf for Alaska North Slope and 32.9 Tcf for Mackenzie Delta and remaining gas reserves of 25.5 Tcf and 0.4 Tcf for Alaska North Slope and Mackenzie Delta, respectively. However, unlike other regions, the resources from these regions are stranded to date due to lack of effective commercial access to markets. In fact, 6-8 Bcf/d of gas that is currently produced as part of the oil activities in the Alaska North Slope is re-injected back into the Slope’s oil reservoirs as part of the pressure maintenance programs. Several development proposals have been put forward for bringing this gas to market in order to realize the long-held goal of monetizing the Alaska North Slope and Mackenzie Delta gas.

In developing the gas resource assumptions for EPA Base Case v.4.10, two gas pipeline projects were identified for bringing the two frontier gas supply resources to the markets in the U.S. and Canada. A diagnostic run was made with both potential pipeline projects turned on, letting the model decide the starting year of the projects and subsequent pipeline capacity expansions. Analysis of pipeline capacities and flows indicat ed that the Alaska gas pipeline project would be feasible starting from 2035, but the Mackenzie Delta pipeline project would not be feasible at all due to relatively low pipeline flows. These were the assumptions used for EPA Base Case v.4.10.

Table 10-4 U.S. and Canada Natural Gas Resources and Reserves End of Year 2008 End of Year 2010

Region Undiscovered Dry Gas Resource (Tcf) Dry Gas Reserves (Tcf) Undiscovered Dry Gas Resource (Tcf) Dry Gas Reserves (Tcf)

Lower 48 Onshore Non Associated 1,629.0 208.5 1,616.2 208.0

Conventional 313.3 50.1 306.6 50.0 Northeast 15.4 3.8 15.0 3.8 Gulf Coast 133.9 16.4 131.5 16.4 Midcontinent 54.4 11.2 52.7 11.2

### 10-11 End of Year 2008 End of Year 2010

Region Undiscovered Dry Gas Resource (Tcf) Dry Gas Reserves (Tcf) Undiscovered Dry Gas Resource (Tcf) Dry Gas Reserves (Tcf)

Southwest 19.2 5.6 18.6 5.6 Rocky Mountain 84.0 10.7 82.6 10.7 West Coast 6.4 2.4 6.2 2.3 Shale Gas 921.2 38.5 917.2 38.4 Northeast 254.8 3.5 254.4 3.5 Gulf Coast 433.1 13.5 431.6 13.4 Midcontinent 133.9 10.9 132.9 10.8 Southwest 61.3 10.7 60.3 10.6 Rocky Mountain 37.9 - 37.7 - West Coast 0.3 - 0.3 - Coalbed Methane 73.9 20.5 73.1 20.4 Northeast 9.5 2.0 9.5 2.0 Gulf Coast 4.3 1.2 4.2 1.2 Midcontinent 9.6 2.2 9.5 2.2 Southwest - - - - Rocky Mountain 49.8 15.1 49.3 15.0 West Coast 0.7 - 0.6 - Tight Gas 320.7 99.5 319.2 99.2 Northeast 33.7 6.6 34.0 6.6 Gulf Coast 59.6 28.9 58.2 28.9 Midcontinent 4.6 7.5 4.6 7.5 Southwest 6.1 15.2 5.9 15.1 Rocky Mountain 205.1 41.2 204.9 41.1 West Coast 11.5 - 11.6 - Lower 48 Offshore Non Associated 137.1 10.5 136.3 10.5

Lower 48 AssociatedDissolved Gas 145.1 17.9 143.5 17.9

Total Lower 48 1,911.2 237.0 1,895.9 236.3 Alaska 153.6 35.2 153.3 35.2 Alaska North Slope 126.8 25.2 126.8 25.2 Alaska - Other 26.8 9.9 26.5 9.9 Total U.S. 2,064.8 272.1 2,049.2 271.5

Canada Non Associated 667.9 56.7 667.3 56.5 Conventional 121.4 43.6 119.6 43.5 Shale Gas 508.8 0.5 511.1 0.5 Coalbed Methane 27.4 12.5 26.7 12.5 Tight Gas 10.3 - 9.9 -Canada AssociatedDissolved Gas 8.1 2.7 8.0 2.7

Eastern Canada Offshore 71.8 2.5 71.6 2.5 MacKenzie Delta 32.9 0.4 32.9 0.4 Total Canada 780.8 62.3 779.8 62.1

### 10-12 10.3.3 Use of the HSM resource and reserves data in EPA Base Case using IPM v.4.10 Natural Gas Module The base year for the integrated gas-electricity module in EPA Base Case using IPM v.4.10 is 2012. Having a base year in the future has imp lications on how the model is run and how the gas reserves and resources data are set up. The IPM run begins with a gas module only run for year 2011 to provide end of year (EOY) 2011 reserves and resources as the starting point for the integrated run from 2012 onward. This in turn requires the reserves and resources data to be provided for the EOY 2010. Since the data from the HSM are as of EOY 2008, a two-year production forecast needs to be conducted to estimate the EOY 2010 gas resources and reserves. This production forecast is done using the GMM with the EPA Base Case assumptions. In the future if an IPM sensitivity analysis case is performed whose assumptions are likely to have a significant impact on gas reserves and resources in the 2009-2010 timeframe, the HSM projection of EOY 2010 gas resources and reserves may have to be re-run.

The last two columns in Table 10-4 give a snapshot of the starting natural gas resource and reserve assumptions that were provided by HSM to EPA Base Case v.4.10 for EOY 2010. In this table, undiscovered resources represent the economic volume of dry gas that could be discovered and developed with current technology through ex ploration and development at a specified maximum wellhead gas price. The reserves are remaining dry gas volumes to be produced from existing developed fields. For EPA Base Case the maximum wellhead price for the resource cost curves is capped at around $14/MMBtu (in real 2007 dollars). The ultimate potential undiscovered resources available are actually higher than those presented in Table 10-4 but it would cost more than $14/MMBtu to recover them. (It is important to note that this price is for wet 20 

### gas at the wellhead in the production nodes and is found to be high enough to cover the range of wellhead prices for EPA scenarios. The dry gas price at the receiving nodes can be higher than $14/MMBtu which depends on the share of dry gas, lease and plant use, gas processing cost, production O&M cost, and pipeline transportation co sts.) The approach used in the HSM to derive these costs is described more fully in section 10.4 below.

Since the new IPM natural gas module differentiates conventional gas from unconventional gas, these are shown separately in Table 10-4. The conventional gas is subcategorized into nonassociated gas from gas fields and associated gas 21 

### from oil fields. The unconventional gas is subdivided into coalbed methane, shale gas, and tight gas. The U.S. and Canada natural gas undiscovered resources and remaining reserves as of EOY 2008 and their estimates for the EOY 2010 are also shown in Table 10-4.

Figure 10-7 presents resource cost curves for the EOY 2010 initializing gas assumptions that the HSM provides for EPA Base Case v.4.10. The cost curves show the recoverable resources at different price levels. Separate resource cost curves are shown for key regions as well as for conventional and unconventional gas. The recoverable resources shown at maximum wellhead prices in these graphs are those tabulated in Table 10-4 under EOY 2010 column. The y-axis of the resource cost curves shows the cost at the wellhead of bringing the volume of undiscovered resource indicated on the x-axis into the reserv es category. Figure 10-8 diagrams the exploration & development and production processes and the associated costs required to bring undiscovered resource into reserves and production.

20 

### A mixture of hydrocarbon compounds and small quantities of various nonhydrocarbons existing in the gaseous phase or in solution with crude oil in porous rock formations at reservoir conditions. The principal hydrocarbons normally contained in the mixture are methane, ethane, propane, butane, and pentane. Typical nonhydrocarbon gases that may be present in reservoir natural gas are water vapor, carbon dioxide, hydrogen sulfide, nitrogen and trace amounts of helium.

21 

### Associated gas refers to natural gas that is produced in association with crude oil production, whereas non-associated gas is natural gas that is not in contact with significant quantities of crude oil in the reservoir.

### 10-13 Figure 10-7 Resource Cost Curves at the End of Year 2010

10.3.4 Undiscovered Resource Appreciation Undiscovered resource appreciation is additional re sources from hydrocarbon plays that were not included in the resource base estimates. It differs from field appreciation or reserves appreciation category discussed above which comes from already discovered fields. Natural gas from shales, coal seams, offshore deepwater, and gas hydrat es may not be included in the resource base assessments due to lack of knowledge and technology to economically recover the resource. As new technology becomes available, these unt apped resources can be produced economically in the future. One example is the advancements in horizontal drilling and hydraulic fracture technologies to produce gas from shale formations. For EPA Base Case, the undiscovered gas resource is assumed to grow at 0.2% per y ear for conventional gas and 0.75% per year for unconventional gas. The EOY 2010 undiscovered resources in Table 10-4 and Figure 10-7 include resource appreciation in 2009 and 2010.

### 10-14 Figure 10-8 Exploration & Development and Production Processes and Costs to Bring Undiscovered Resource into Reserves and Production

Production (Wet)

- Reserves-to-production ratio - Production cost Production



### Reserves Undiscovered Resource

- Resource potential - Resource discovery factor - Exploration & Development drilling requirement - Exploration & Development costs Exploration & Development 

### Production (Dry)

- Dry gas share - Lease and plant use - Gas processing cost Processing



## 10.4 Exploration, Development, a nd Production Costs and Constraints 10.4.1 Exploration and Development Cost Exploration and development (E&D) cost or resource cost is the expenditure for activities related to discovering and developing hydrocarbon resources. The E&D cost for natural gas resources is a function of many factors such as geographic location, field type, size, depth, exploratory success rates, and platform, drilling and other costs. The HSM contains base year cost for wells, platforms, operating costs and all other relevant cost items. In addition to the base year costs, the HSM contains cost indices that adjust costs over time. These indices are partly a function of technology drivers such as improved exploratory success rates, cost reductions in platform, drilling and other costs, improved recovery per well, and partly a function of regression-based algorithms that relate cost to oil and gas prices and industry activity. As oil and gas prices and industry activity increase, the cost for seismic, drilling & completion services, casing and tubing and lease equipment goes up.

Other technology drivers affect exploratory success rates and reduce the need to drill exploratory wells. A similar adjustment is made to take into account changes over time in development success rates, but the relative effect is much smaller because development success rates are already rather high. The technology drivers that increase recovery per well are differentiated in the HSM by region and by type of gas. Genera lly, the improvements are specified as being greater for unconventional gas because their recovery factors are much lower than those of conventional gas.

The HSM model provides estimates of E&D cost and the level of economically viable gas resource by region as a function of E&D cost. The HSM increased recovery as a function of technology improvement by region is converted to E&D and production technology improvement over time in the form of cost reduction factors by onshore, offshore shelf, and offshore deepwater as shown in

### 10-15 0.5 0.6 0.7 0.8 0.9 1.0

2010 2020 2030 2040 2050 2060E&D Cost Multiplier

Onshore Offshore Shelf Offshore Deepw ater 

### Figure 10-9. The average cost reduction factors for onshore, offshore shelf, and offshore deepwater E&D activities are -0.9% per year, -0.7 % per year, and -0.4% per year, respectively. These factors are predominantly affected by the le vel of E&D investments in the regions. The expected aggressive onshore E&D activities to find and produce unconventional gas resources, such as shale gas, will lead to more research in horizontal drilling and hydraulic fracturing technologies to improve productions and lower the cost s. This is reflected in higher cost reduction factors for the onshore regions.

Figure 10-10 shows E&D cost needed to discove r and develop 2.5%, 5%, and 7.5% of the remaining undiscovered resource in 2011 by natural gas supply region.

Figure 10-9 E&D and Production Technology Improvement Factor

Figure 10-10

### 10-16 Incremental E&D Cost (EOY 2010) by Percentage of Resource Found

0 2 4 6 81 0 (5) Niagara

(6) Leidy (11) East Ohio

(14) Indiana (15) South Illinois (16) North Illinois (18) Tennessee/Kentucky (21) Northern Missouri (28) Kansas (29) East Colorado (30) Opal (32) San Juan Basin (34) North Wyoming

(36) SOCAL Area (37) Enhanced Oil Recovery Region (38) PGE Area (41) Montana/ North Dakota (45) Pacific Northw est (49) Eastern Canada Offshore (54) North Alabama (55) Alabama Offshore (57) East Louisiana Shelf (58) Eastern Louisiana Hub (59) Viosca Knoll S./ Desoto (60) Henry Hub (61) North Louisiana Hub (62) Central and West Louisiana Shelf (63) Southw est Texas (64) Dallas/Fort Worth (65) E. TX (Katy) (66) S. TX (67) Offshore Texas (68) NW TX (69) Garden Banks (70) Green Canyon (71) Florida off-shore moratorium (72) North British Columbia (74) Caroline (76) Saskatchew an

(80) West Virginia (83) Wind River Basin

(86) McKenzie Delta (87) Southern Alaska (89) Northern Alaska (90) Arctic (92) SW Virginia (96) Florida (98) Southw est Michigan (99) Central Michigan (107) Carthage (108) Southw est Oklahoma (109) Northeast Oklahoma (110) Southeastern Oklahoma (111) Northern Arkansas (113) Uinta/Piceance

(114) South MS/AL

Resource Cost at Wellhead (2007$/MMBtu)

2.5% 5.0% 7.5%

### 10-17 10.4.2 Resource Discovery and Drilling Constraints As mentioned above the simulation in HSM also prov ides other data such as resource discovery factors which describe the maximum share of remaining undiscovered resource that could be discovered and developed in a year and drilling requirements which describe the drilling required for successful exploration and development. These two parameters are constraints to the development of the resource and their values are not time dependent. The resource discovery constraint is the same for all regions and is assumed to be 4% of the remaining undiscovered resource (column 4 in Table 10-5). The drilling requi rement constraint (column 5 in Table 10-5) is 10,000 feet for every billion cubic feet of incremental resource discovered for onshore and 2,500 feet/Bcf for offshore.

Table 10-5 Exploration and Development Assumptions for EPA Base Case v.4.10
Fraction of Hydrocarbons that are Natural Gas

Liquids (NGLs) Fraction of Hydrocarbons that are Crude Oil Max Share of Resources that can be Developed

per Year Exploration, Development Drilling Required Lease and Plant Use Region

(Fraction) (Fraction) (Fraction) (Ft/Bcf) (Fraction) (5) Niagara 0.01 0.04 0.04 10,000 0.05 (6) Leidy 0.00 0.05 0.04 10,000 0.03 (11) East Ohio 0.00 0.23 0.04 10,000 0.01 (14) Indiana 0.05 0.81 0.04 10,000 0.02 (15) South Illinois 0.10 0.10 0.04 10,000 0.30 (16) North Illinois 0.10 0.10 0.04 10,000 0.30 (18) Tennessee/Kentucky 0.12 0.10 0.04 10,000 0.04
(21) Northern Missouri 0.15 0.46 0.04 10,000 0.04 (28) Kansas 0.13 0.17 0.04 10,000 0.04 (29) East Colorado 0.09 0.10 0.04 10,000 0.05 (30) Opal 0.09 0.10 0.04 10,000 0.05 (32) San Juan Basin 0.09 0.10 0.04 10,000 0.13 (34) North Wyoming 0.09 0.10 0.04 10,000 0.05 (36) SOCAL Area 0.01 0.91 0.04 10,000 0.13 (37) Enhanced Oil Recovery Region 0.02 0.81 0.04 10,000 0.13
(38) PGE Area 0.03 0.88 0.04 10,000 0.13 (41) Montana/ North Dakota 0.04 0.72 0.04 10,000 0.13
(45) Pacific Northwest 0.26 0.00 0.04 10,000 0.02 (49) Eastern Canada Offshore 0.01 0.88 0.04 2,500 0.06
(54) North Alabama 0.04 0.22 0.04 10,000 0.03 (55) Alabama Offshore 0.04 0.22 0.04 2,500 0.03
(57) East Louisiana Shelf 0.07 0.52 0.04 2,500 0.04
(58) Eastern Louisiana Hub 0.11 0.27 0.04 10,000 0.04
(59) Viosca Knoll S./ Desoto Canyon/Mississippi Canyon 0.07 0.52 0.04 2,500 0.04

(60) Henry Hub 0.11 0.27 0.04 10,000 0.04

### 10-18 Fraction of Hydrocarbons that are Natural Gas

Liquids (NGLs) Fraction of Hydrocarbons that are Crude Oil Max Share of Resources that can be Developed

per Year Exploration, Development Drilling Required Lease and Plant Use Region

(Fraction) (Fraction) (Fraction) (Ft/Bcf) (Fraction) (61) North Louisiana Hub 0.08 0.08 0.04 10,000 0.04
(62) Central and West Louisiana Shelf 0.07 0.52 0.04 2,500 0.04
(63) Southwest Texas 0.12 0.52 0.04 10,000 0.05 (64) Dallas/Fort Worth 0.07 0.06 0.04 10,000 0.05 (65) E. TX (Katy) 0.17 0.19 0.04 10,000 0.05 (66) S. TX 0.12 0.07 0.04 10,000 0.05 (67) Offshore Texas 0.06 0.25 0.04 2,500 0.05 (68) NW TX 0.22 0.05 0.04 10,000 0.05 (69) Garden Banks 0.06 0.25 0.04 2,500 0.04 (70) Green Canyon 0.07 0.52 0.04 2,500 0.04 (71) Florida off-shore moratorium area 0.07 0.52 0.04 2,500 0.04
(72) North British Columbia 0.01 0.05 0.04 10,000 0.08
(74) Caroline 0.03 0.19 0.04 10,000 0.10 (76) Saskatchewan 0.00 0.73 0.04 10,000 0.07 (80) West Virginia 0.07 0.04 0.04 10,000 0.04 (83) Wind River Basin 0.09 0.10 0.04 10,000 0.05 (86) McKenzie Delta 0.00 0.00 0.04 10,000 0.08 (87) Southern Alaska 0.00 0.00 0.04 10,000 0.08 (89) Northern Alaska 0.00 0.00 0.04 10,000 0.08 (90) Arctic 0.05 0.55 0.04 10,000 0.08 (92) SW Virginia 0.00 0.01 0.04 10,000 0.02 (96) Florida 0.02 0.82 0.04 10,000 0.21 (98) Southwest Michigan 0.04 0.11 0.04 10,000 0.04
(99) Central Michigan 0.04 0.11 0.04 10,000 0.04 (107) Carthage 0.08 0.08 0.04 10,000 0.05 (108) Southwest Oklahoma 0.13 0.17 0.04 10,000 0.04
(109) Northeast Oklahoma 0.13 0.17 0.04 10,000 0.04
(110) Southeastern Oklahoma 0.13 0.17 0.04 10,000 0.04
(111) Northern Arkansas 0.00 0.13 0.04 10,000 0.04
(113) Uinta/Piceance 0.09 0.10 0.04 10,000 0.05 (114) South MS/AL 0.04 0.22 0.04 10,000 0.03



### Other drilling constraints include rig capacity, rig retirement, rig growth, and drilling speed. Values for the constraints are specified for each of the three drilling category: (1) onshore, (2) offshore shelf, and (3) offshore deepwater. The drilling rig c apacity constraint shows the number of drilling rigs initially available in the base year 2011. The initial rig counts are 3,798 rigs for onshore, 115 rigs for offshore shelf, and 115 rigs for offshor e deepwater and the numbers can change over time controlled by rig retirement and rig growth constraint s. The drilling rig retirement constraint is the

### 10-19 share of rig capacity that can retire in a year. The drilling rig growth constraint is the maximum increase of total rig count in a year. The drilling retirement and growth are assumed to be the same for all drilling category and the constraints are set to 0.5% per year and 3.5% per year, respectively.

Another growth constraint, minimum drilling capacity increase, is implemented to force the rig count to grow by at least one rig in each drilli ng category. The drilling speed constraint is the required speed in feet/day/rig for successful exploration and development. The drilling speed required for successful E&D grows over time, as shown in Figure 10-11 and differs for onshore and offshore (which in this case includes both shelf and deep shelf).

Figure 10-11 Drilling Rig Speed Constraint

0 100 200 300 400 500 600 700

2000 2010 2020 2030 2040 2050 2060Drilling Speed (Feet/Day/Rig)

Onshore Offshore



### 10.4.3 Reserves-to-Production (R/P) Ratio The reserves-to-production ratio is the remaining amount of reserves, expressed in years, to be produced with a current annual production rate. In the IPM gas module, the R/P data obtained from the HSM is provided in the form of production-to -reserves (P/R) ratio (or reciprocal of the R/P ratio). The P/R ratio is used to calculate annual wet gas production from the reserves and the value varies by resource type and production node. For conventional gas the P/R ratio ranges from 0.04 (or 25 years of R/P) to 0.25 (or 4 years of R/P) with average of 0.13 (or 8 years of R/P). The P/R ratio of shale and tight gas is half of that of the conventional gas with average P/R ratio of 0.06 (or 16 years of R/P). Coalbed methane gas has the lowest P/R ratio with average of 0.03 (or 32 years of R/P) or half of that of the tight and shale gas.

10.4.4 Variable Costs, Natural Gas Liquid Share, and Crude Oil Share In the IPM natural gas module, the variable costs include production operations and maintenance (O&M) cost and gas processing cost. The production O&M cost for 2011 is estimated to be $0.51/MMBtu (in real 2007 dollars) and is assumed to be the same for all supply regions. The production O&M cost is expected to decline over time due to improvements in production technology. In the model the same technology improvement factor shown in Figure 10-9 is applied to the production O&M cost.

The resource data from the HSM is provided in the form of total hydrocarbon (oil, gas, and NGL) resource. The HSM also provides the allocations of the hydrocarbon for dry gas, oil, and NGL.

### 10-20 Table 10-5 shows the shares of NGL (column 2) and crude oil (column 3) by supply region. Associated gas from crude oil and NGL from wet gas are processed in gas processing plants to produce pipeline quality dry gas. Node level gas pr ocessing cost for IPM natural gas module is obtained from the GMM. The processing cost varies from $0.07/MMBtu (of wet gas in real 2007 dollars) to $0.56/MMBtu with average of $0.22/MMBtu.

10.4.5 Lease and Plant Gas Use The term “lease and plant gas” refers to the gas us ed in well, field, and lease operations (such as gas used in drilling operations, heaters, dehydrators, and field compressors) and as fuel in gas processing plants. The data for lease and plant gas use is derived for the HSM as a fraction of wet gas production and varies by region. The value ranges from 0.01 to as high as 0.3 with an average of around 0.06 (column 6 in Table 10-5).



## 10.5 Liquefied Natural Gas (LNG) Imports As described earlier, most of the data related to North American LNG imports is derived from the GMM LNG model. Based on a comprehensive dat abase of existing and potential liquefaction and regasification facilities and worldwide LNG import/e xport activities, the model uses a simulation procedure to create the base year 2011 North Am erican LNG supply curves and projections of regasification capacity and costs.

Key elements of the LNG model are described below.

10.5.1 Liquefaction Facilities and LNG Supply The supply side of the GMM LNG model takes into account capacities from existing as well as potential liquefaction facilities. The lower and upper boundaries of supply capacity allocated for each North American regasification facility are set by available firm contracts and swing supplies. Three point LNG supply curves are generated within this envelope where: (1) the lower point is the amount of firm LNG supply, (2) the upper bound is the firm imports plus the maximum swing imports available for that facility, and (3) the midpoint is the average of the minimum and maximum values. Prices for the minimum and maximum points are tied to Refiner Acquisition Cost of Crude (RACC) price. 22 

### The minimum price represents minimum production cost for liquefaction facilities and is set at 0.5 of RACC price and the maximum price is set at 1.5 of RACC price. The prices are then shifted up for winter months and shifted down in the summer months to represent the seasonal variation in competition from European LNG consumers.

The individual LNG supply curves from the GMM LNG model are aggregated to create total North American LNG supply curves describing LNG availability serving the North American regasification facilities. The three point curves are converted to six points by linear interpolation to provide more supply steps in the IPM natural gas module. Two LNG supply curves, one for winter and one for summer, are specified for each year starting from 2011 until 2054 to capture growth as well as seasonal variation of the LNG supp lies. Figure 10-12 shows the North American LNG supply curves for the winters and summers of 2011 and 2050.

22 

### Refiner Acquisition Cost of Crude Oil (RACC) is a term commonly use in discussing crude oil. It is the cost of crude oil to the refiner, including transportation and fees. The composite cost is the weighted average of domestic and imported crude oil costs.

### 10-21 Figure 10-12 North American LNG Supply Curves

Winter 2011

0 10 20 30 40 50

0 5 10 15 20 25 30
Max quantity LNG (Bcf/day)LNG Price (2007 $/MMBtu)

Winter 2050

0 10 20 30 40 50

0 5 10 15 20 25 30
Max quantity LNG (Bcf/day)LNG Price (2007 $/MMBtu) Summer 2011

0 10 20 30 40 50

0 5 10 15 20 25 30
Max quantity LNG (Bcf/day)LNG Price (2007 $/MMBtu)

Summer 2050

0 10 20 30 40 50

0 5 10 15 20 25 30
Max quantity LNG (Bcf/day)LNG Price (2007 $/MMBtu)



### 10.5.2 Regasification Facilities For the EPA Base Case, ten existing and four potentials North American LNG regasification facilities are considered in the IPM natural gas module. Table 10-6 lists the 14 facilities (current existing facilities are highlighted), the destinat ion nodes where the LNG are delivered, and the base year 2011 capacity for each of the regasificat ion facility. Figure 10-13 provides a map of these facilities. Existing Penuelas LNG facilitie s in Puerto Rico are not included because they are not part of the natural gas network in the IPM gas module. However, the electric generating units that consume gas from the Penuelas LNG facilitie s are included in the IPM electric module. In EPA Base Case v.4.10. the Penuelas LNG facilities are modeled with a fixed 150 MMcfd gas supply into Florida node and a link to connect the gas supply to the electric generating units in Puerto Rico.

Table 10-6 North American LNG Regasification Facilities

No LNG Regasification Facility Node Location Base Year (2011) Regasification Capacity (Bcf/day) 1 Cove Point (7) Cove Point TRANS 1.50
2 Elba Island (9) Elba Is TRANS 2.10 3 Everett (2) Everett TRANS 0.70

### 10-22 No LNG Regasification Facility Node Location Base Year (2011) Regasification Capacity (Bcf/day) 4 Gulf Gateway (69) Garden Banks 0.50
5 Lake Charles (60) Henry Hub 2.10 6 Altamira (51) Reynosa Imp/Exp 1.00
7 Costa Azul (84) California Mexican Exports 2.00
8 Cameron LNG (60) Henry Hub 1.50 9 Freeport LNG (65) E. TX (Katy) 1.50
10 Golden Pass (65) E. TX (Katy) 2.00
11 Canaport (49) Eastern Canada Offshore 1.00
12 Sabine Pass (60) Henry Hub 2.60

13 Gulf LNG Energy LLC (56) North Mississippi 1.00

14 Northeast Gateway (1) New England 0.80

Figure 10-13 North American LNG Regasification Facilities Map

(1) Cove Point

(2) Elba Island (3) Everett

(14) NE Gateway

(4) Gulf Gateway

(5) Lake Charles

(6) Altamira (7) Costa Azul (8) Cameron LNG

(10) Golden Pass

(9) Freeport LNG (11) Canaport

(12) Sabin Pass

(13) Gulf LNG Energy

Existing Potential



### 10.5.3 LNG Regasification Capacity Expansions The IPM natural gas module has two constraints for the regasification capacity expansion: (1) minimum LNG regasification facility capacity expansion and (2) maximum LNG regasification facility capacity expansion. The values are spec ified for each facility and year where the minimum constraint is used to force the model to add regas ification capacity and the maximum constraint is the upper bound for the capacity expansion.

### 10-23 The decision of whether to expand regasification c apacity is controlled by the two constraints and by a levelized capital cost for regasification capacity expansion. The base year 2011 levelized capital cost for capacity expansion (in real 2007 dollars per MMBtu of capacity expansion) is specified for each facility. A cost multiplier can be applied to represent the increase in levelized capital cost over time. The constraints for the capacity expansion can be used to turn on or off the regasification capacity expansion feature in the model. Setting both constraints to zero will deactivate this feature.

If the regasification capacity is allowed to expand, the model can add capacity to a facility within the minimum and maximum constraints if the cost of the regasification expansion contributes to the optimal solution, i.e., minimizes the overall costs to the power sector, including the capital cost for adding new regasification capacity less their revenues. The model takes into account all possible options/projects (including regasificati on capacity expansions) in any year that do not violate the constraints and selects the combinati on of options/projects that provide the minimum objective function value. In this way, regasification capacity expansion projects will compete with each other and even with other projects such as pipeline expansions, storage expansions, etc.

Due to excess LNG regasification capacity already in the system the regasification capacity expansion feature is not deployed in EPA Base Case v.4.10. EPA scenario results show very low total LNG utilizations, less than 15%, throughout the projection period which suggest the base year LNG regasification capacity is already high and requires no expansion.



## 10.6 End Use Demand Non-power sector demand (i.e. the residential, commercial, and industrial) is modeled in the new gas module in the form of node-level firm and interruptible demand curves23. The firm demand curves are developed and used for residential, commercial, and some industrial sources, while the interruptible demand curves are developed and used exclusively for industrial sources.

A three step process is used to prepare these curves for use in the IPM gas module. First, GMM is used to develop sector specific economet ric models representing the non-power sector demand. Since the GMM econometric models are f unctions of weather, economic growth, price elasticity, efficiency and technology improvements, and other factors, these drivers, in effect, are embedded in the resulting IPM natural gas modul e demand curves. Second, projections are made using the GMM econometric models and asse mbled into monthly gas demand curves by sector and demand node. Third, using a sec ond model, seasonal and load segment specific demand curves are derived from the monthly gas demand curves. The sections below describe each of these steps in further detail.

10.6.1 Step 1: Developing Sector Specifi c Econometric Models of Non-Power Sector Demand
Residential/Commercial Sector The GMM econometric models of residential and commercial demand are based on regression analysis of historical data for 41 demand regions and are adjusted to reflect conservation, efficiency, and technology changes over time. T he regional data is allocated to the node level based on population data and information from the Energy Information Administration’s “Annual Report of Natural and Supplemental Gas Supply & Disposition” (EIA Form-176). Specifically, the econometric models used monthly Department of Energy/Energy Information Administration (DOE/EIA) data from January 1984 through Decem ber 2002 for the U.S. and monthly Statistics Canada data from January 1988 through December 2000 for Canada.

23 “Firm” refers to natural gas demand that is not subject to interruptions from the supplier, whereas “interruptible” refers to natural gas demand that is subject to curtailment or cessation by the supplier.

### 10-24 The GMM econometric models showed node-level residential and commercial gas demand to be a function of heating degree days, elasticity of gas demand relative to GDP, and elasticity of gas demand relative to gas price. The GDP elasticity was generally about 0.4 for the residential sector and 0.6 for the commercial sector. The gas price elasticity was generally less than 0.1 for both sectors. Since gas demand in these sectors is relatively inelastic, GDP and price changes have small effects on demand.

U.S. Industrial Sector The GMM econometric model of U.S. industrial gas demand employed historical data for 11 census-based regions and ten industry sectors, focusing on gas-intensive industries such as:

• Food

• Pulp and Paper

• Petroleum Refining

• Chemicals

• Stone, Clay and Glass

• Iron and Steel

• Primary Aluminum

• Other Primary Metals

• Other Manufacturing

• Non-Manufacturing

For each of these sectors three end-use categories (process heat, boilers, and other end uses) are modeled separately:

• Process heat: This includes all uses of gas for direct heating as opposed to indirect heating (e.g., steam production). The GMM econometric modeling indicated that forecasts for process heat for each industrial sector are a function of growth in output, the energy intensity trend, and the price elasticity. Growth in output over time for most industries is controlled by industrial production indices. Energy intensit y is a measure of the amount of gas consumed per unit of output. Energy intensity tends to decrease over time as industries become more efficient.

• Boilers: This category includes natural gas-fired boilers whose purpose is to meet industrial steam demand. GMM econometric models indicated that gas demand for boilers is a function of the growth in industrial output and the amount of gas-to-oil switching. Industry steam requirements grow based on industrial production growth. A large percentage of the nominally “dual-fired” boilers cannot switch due to environmental and technical constraints.

• Other end uses: This category includes all other uses for gas, including non-boiler cogeneration, on-site electricity generation, and s pace heating. Like the forecasts for process heat, the GMM econometric modeling showed “other end uses” for each industrial sector to be a function of growth in output, the energy intensity trend, and the price elasticity.

In addition to these demand models, a separate r egression model was use to characterize thechemicals sector’s demand for natural gas as a feedstock for ammonia, methanol, and nonrefinery hydrogen. Growth in the chemicals i ndustry is represented by a log-linear regression model that relates the growth to GDP and natural gas prices. As GDP growth increases, chemical industry production increases; and as gas prices increase, chemical industry production decreases.

The GMM econometric models for the U.S. industrial sector used DOE/EIA monthly data from January 1991 through December 2000.

Canada Industrial Sector The industrial sector in Canada is modeled in less detail. Canada is divided into 6 regions based on provincial boundaries. The approach employs a regression fit of historic data similar to that

### 10-25 used in the residential/commercial sectors. Sub-sectors of Canadian industrial demand are not modeled separately. The Canadian industrial sector also includes power generation gas demand. The model used Statistics Canada monthly data from January 1991 through December 2000.

10.6.2 Step 2: Use projections based on the GMM econometric models to produce monthly gas demand curves by sector and demand node The regression functions resulting from the econometric exercises described in Step 1 are used to create monthly sector- and nodal-specific gas demand cu rves. To do this the functions are first populated with the macroeconomic assumptions that are consistent with those used in EPA Base Case 4.10. For these purposes a U.S. annual GDP growth rate of 3.0%, a U.S. annual industrial production growth rate of 2.3%, and a Canadian annual GDP growth rate of 2.5 % are assumed. Then, a range of natural gas prices are fed into the regression functions. At each gas price the regression functions report out projected mont hly demand by sector and node. These are the GMM’s nodal demand curves.

10.6.3 Step 3: Develop non-electric sector natural gas demand curves that correspond to the seasons and segments in the load duration curves used in IPM A second model, the Daily Gas Load Model (DGLM), is used to create daily gas load curves based on the GMM monthly gas demand curves obtained in Step 2. The DGLM uses the same gas demand algorithms as the GMM, but uses a daily temperature series to generate daily variations in demand, in contrast to the seasonal variations in gas demand that are obtained from the GMM.

The resulting daily nodal demand data for each non-power demand sector are then re-aggregated into the two gas demand categories used in the IPM gas module: all of the residential and commercial demand plus 10% of the industrial demand is allocated to the firm gas demand curves, and the remaining 90% of the industrial demand is allocated to the interruptible gas demand curves.

IPM, the power sector model, has to take into account natural gas demand faced by electric generating units that dispatch in different segm ents of the load duration curves, since demand for natural gas and its resulting price may be very di fferent for units dispatching in the peak load segment than it is for units dispatching in t he base, high shoulder, mid shoulder, or low shoulder load segments. In addition, since seasonal differences in demand can be significant, IPM requires separate load segment demand data for each season that is modeled. In EPA Base Case v.4.10, there are two seasons: Summer (May 1 – September 30) and winter (October 1 – April 30). Therefore, the firm and interruptible daily gas demand and associated prices are allocated to the summer and winter load segment based on the app licable season and prevailing load conditions to produce the final non-electric sector gas demand curves that are used in IPM.

In EPA Base Case v.4.10, each of the summer and winter periods uses 6 load segments for pre2030 and 4 load segments for post-2030 as shown in Table 10-7. The “Peak” load segment in post-2030 is an aggregate of “Needle Peak“ and “Near Peak” load segments in the pre-2030. The “High Shoulder” load segment in post-2030 is an aggregate of “High Shoulder“ and “Middle Shoulder” load segments in the pre-2030. The sa me definitions of “Low Shoulder” and “Base” load segments are applied to both pre-2030 and post-2030. Input data for firm and interruptible demand curves are specified for all six load segments listed in the pre-2030 column of Table 10-7.

### 10-26 Table 10-7 Summer and Winter Load Segments in EPA Base Case v.4.10
Pre 2030 Post 2030

1 Needle Peak
2 Near Peak 1 Peak

3 High Shoulder
4 Middle Shoulder 2 High Shoulder

5 Low Shoulder 3 Low Shoulder
6 Base 4 Base Aggregation of summer and winter load segments from six in the pre-2030 to four in the post-2030 is performed endogenously in the model.

The non-electric sector demand curves (firm and interruptible) are generated based on GMM regressions described above with macroeconomic assumptions consistent with those of EPA Base Case v.4.10. A set of firm and interruptible gas demand curves is generated for each node and year. Examples of node-specific firm and interruptible demand curves for summer and winter load segments are shown in Figure 10-14 and Fi gure 10-15. It should be noted that firm gas demand (Figure 10-14) is very inelastic; only a small fraction of demand is shed as prices increase. The interruptible gas demand in the peak segments is also very inelastic as expected with higher elasticities in the shoulder and base load segments.

It is important to note that the non-electric gas demand curves provided to the IPM/Gas model are static inputs. The implied elasticities in the curves represent short-term elasticities based on EPA Base Case v.4.10 macroeconomic assumptions. Long-term elasticity is not factored into the gas demand curves. In other words, changes in the assumptions that affect the price/volume solutions have no effect to the long-term gas demand elasticity. Sensitivity runs with slight modifications to the macroeconomic assumptions can still use the same curves if the gas demand forecast is expected not to be much different than that from the base case. In this case, changes in the gas demand solutions have minor effect to the long-term elasticity. However, a sensitivity run (e.g. carbon policy run) with major changes in CO 2 

### allowance prices should not use the same gas demand curves because it will have higher impact to the long-term gas demand elasticity. A new set of non-electric gas demand curves needs to be generated for this type of run.

Figure 10-14 Examples of Firm Demand Curves by Electric Load Segment

New England (Node 1), Winter 2011

0 5 10 15 20 25 30 35

0.00 0.05 0.10 0.15 0.20 0.25 0.30
Firm Demand (Bcf)Gas Price(2007 $/MMBtu)

Needle Peak Near Peak High Shoulder

Middle Shoulder Low Shoulder Base New England (Node 1), Summer 2011

0 5 10 15 20 25 30 35

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Firm Demand (Bcf)Gas Price(2007 $/MMBtu)

Needle Peak Near Peak High Shoulder

Middle Shoulder Low Shoulder Base

### 10-27 Figure 10-15 Examples of Interruptible Demand Curves by Electric Load Segment

New England (Node 1), Winter 2011

0 5 10 15 20 25 30 35

0.00 5.00 10.00 15.00 20.00 25.00 30.00
Interruptible Demand (Bcf)Gas Price(2007 $/MMBtu)

Needle Peak Near Peak High Shoulder

Middle Shoulder Low Shoulder Base New England (Node 1), Summer 2011

0 5 10 15 20 25 30 35

0.00 5.00 10.00 15.00 20.00 25.00 30.00
Interruptible Demand (Bcf)Gas Price(2007 $/MMBtu)

Needle Peak Near Peak High Shoulder

Middle Shoulder Low Shoulder Base



## 10.7 Pipeline Network

10.7.1 Network Structure The pipeline network in the IPM natural gas module represents major transmission corridors (not individual pipelines) throughout North America. It contains 343 1 

### gas pipeline corridors (including bi-directional links) between the 114 nodes (Figure 10-3). Each corridor is characterized by maximum capacity and a “value of service” (disc ount curve) relationship that determines the market value of capacity as a function of load factor. 2 

### The node structure is developed to reflect points of change or influence on the pipeline system such as:

• Major demand and supply centers

• Pipeline Hubs and market centers

• Points of divergence in pipeline corridors

To illustrate the relationship of corridors and pipelines, Figure 10-16 shows the flow and capacity of five pipeline corridors in New England in 2020. Gas flows into New England along three pipeline corridors (indicated in Figure 10-16 by 3 of the 4 arrows that point into the region) representing a total of seven pipeline systems (indicated by name labels in Figure 10-16). New England also receives gas via the Everett LNG terminal (indicated in Figure 10-16 by the 4th arrow that points into the region). Also, some of the gas that flows into New England on the Iroquois system flows through the region and back to downstate New York; this is represented on the map as an export from New England (indicated in Figure 10-16 by the arrow that points away from the region).

1 

### Excluding LNG import Terminal nodes and their pipeline connections.

2 

### See footnote #3 above for a definition of “load factor.”

### 10-28 Figure 10-16 New England Pipeline Corridors in 2020

Maritimes & Northeast

Everett LNG • Portland Natural Gas • Granite State • Vermont Gas

• Algonquin • Iroquois • Tennessee

Iro q uois 59

858

MMCFD

Flow
Capacity 

### 700

950 143

249

1,228
3,524 587

953



### 10.7.2 Pipeline Transportation Costs In the IPM natural gas module, the natural gas mo ves over the pipeline network at variable cost. The variable cost as a function pipeline throughput (or pipeline discount curve) is used to determine transportation basis 3 

### (i.e., the market value of capacity) for each period in the forecast for each pipeline link. The 4-point pipeline disc ount curves in the IPM natural gas module are simplified forms of the more robust continuous di scount curves from the GMM pipeline module. The GMM pipeline discount curves have been derived in the course of extensive work to calibrate the model to actual history. The curves have been fit to basis differentials observed from actual gas prices and to annual load factors from pi peline electronic bulletin boards via Lippman Consulting, Inc.

The GMM continuous discount curves are converted to 4-point linear curves for the IPM natural gas module capturing deflection points in the GMM discount curves. Figure 10-17 depicts the base year 2011 discount curve for the pipeline corridor connecting nodes (61) North Louisiana Hub and (18) Tennessee/Kentucky. Cost growth factors shown in Figure 10-18 are applied to the pipeline discount curves to reflect cost increase over time. The cost is assumed to grow at an average rate of 0.5 percent per year.

3 

### See footnote #1 above for a definition of “basis.”

### 10-29 Figure 10-17 Example Pipeline Discount Curve

N LA Hub (61) to TN/KY (18)

0.00 0.20 0.40 0.60 0.80 1.00

0 20 40 60 80 100
Pipeline Utilization (%)Variable Cost (2007 $/MMBtu)



### Figure 10-18 Pipeline Cost Growth Factor

1.00 1.05 1.10 1.15 1.20 1.25 1.30

2010 2020 2030 2040 2050 2060



### 10.7.3 Pipeline Capacity Expansion Logic Base year pipeline capacity, derived from GMM, includes existing capacities and planned capacities that are expected to be operational fr om the beginning of 2011. The IPM natural gas module has the capability to endogenously expand the pi peline capacity. The decision of whether to expand pipeline capacity is controlled by tw o constraints, which stipulate minimum and maximum capacity additions and by the levelized capi tal cost of expanding pipeline capacity in the specific corridor and year. The minimum capacit y addition constraint forces the model to add capacity in a specified corridor and year. The maximum capacity constraint is the upper bound on capacity additions in a specified corridor and y ear. For most pipeline corridors there is no minimum or maximum capacity requirement, and so they are assigned a value of zero as their minimum capacity addition requirement and infinity 4 

### as their maximum capacity addition requirement. Where this occurs, the pipeline ex pansion is only controlled by the pipeline capital cost.

4 

### In the model this is achieved by assigning a la rge number, e.g., 100 Bcfd, for every year where there is no constraint on maximum capacity.

### 10-30 The model is allowed to add capacity to a pi peline corridor within the minimum and maximum capacity addition constraints if the cost of t he pipeline expansion contributes to the optimal solution, i.e., minimizes the overall costs to the power sector, including the capital cost for pipeline capacity expansion, less their revenues. The model takes into account all possible options/projects including capacity additions for pipe line corridors in any year that do not violate the constraints and selects the combination of options/projects that provide the minimum objective function. In this way, pipeline corridor expansion projects will compete with each other and even with other projects such as LNG regasificat ion capacity expansions, storage expansions, etc.

For EPA Base Case v.4.10, pipeline corridors connecting North Alaska (node 89) and Mackenzie Delta (node 86) to North British Columbia (node 72) have the minimum and maximum capacity addition constraints. Based on diagnostic run analysis discussed in Section 3, the Mackenzie Delta pipeline project is not made available th roughout the projection. Both capacity addition constraints for Mackenzie delta are set to zero. Based on the same analysis, the Alaska pipeline corridors (connecting nodes 89, 88, 87, and 72) are set to come online from 2035. The minimum capacity constraint is set to zero throughout the projection. The maximum capacity constraint is initially set to zero to restrict pipeli ne builds and then set to infinity from 2035.

Expansions in other pipeline corridors are not restrict ed. The model is allowed to build capacity to any pipeline corridors at any time as long as it cont ributes to minimization of the objective function. There is no reason for restricting the upper bound for capacity expansion since the IPM/Gas model was designed to be used as a long-term policy tool rather than a pipeline analysis tool. Having no restriction to the minimum capacity expans ion, however, is a limitation of the model as it may lead to unrealistic capacity expansions especia lly for large pipeline projects such as Alaska and Mackenzie Delta. Without restricting the starting dates and the capacities, the model may build unrealistically low incremental capacities throughout the projection. This was the reason for conducting the diagnostic run for Alaska and Mackenzie Delta projects. Theoretically, it is possible to add capability in the model to make decisions on minimum incremental pipeline capacity expansions. However, it requires adding a lot more constraints to the LP which may result in prohibitively large model. The workaround for ensuring reasonable capacity expansion results is to perform diagnostic runs such as that for Alaska and Mackenzie Delta.

The base year 2011 levelized pipeline capital cost (in real 2007 dollars per MMBtu/Day of pipeline capacity addition) is specified for each of the 343 pi peline links. The cost growth factors shown in Figure 10-18 are applied to derive the cost increase over time. The average levelized capital cost for pipeline capacity expansion for 2011 is $154 per MMBtu/Day. The expected levelized capital cost for North Alaska pipeline for 2035 is $305 per MMBtu/Day.



## 10.8 Gas Storage The IPM natural gas module has 108 underground storage facilities that are linked to 48 nodes. The underground storage is grouped into three categories based on storage “Days Service.” 5



### • “20-Day” high deliverability storage – 35 storage facilities

• “80-Day” depleted/aquifer reservoirs – 38 storage facilities

• “Over 80 Days” depleted/aquifer reservoirs – 35 storage facilities

The model also includes existing and potential LNG peak shaving storage facilities. The existing facilities are linked to 24 nodes with allocations based on historical capacity data. There are 48 other nodes that are linked to LNG peakshaving storage. These facilities do not currently have capacity but are included in the storage database for the purpose of future expansion. The map of storage facility locations is shown in Figure 10-6 and the list of storage facility nodes is shown in Table 10-8.

5 

### See footnote #5 above for a definition of “Days Service.”

### 10-31 In Table 10-8 an X in columns 2 (“20-Day”), 3 (“80-Day”), or 4 (“Over 80-Days”) represents an underground storage facility. There are 108 such X’s which correspond to the 108 underground storage facilities noted in the previous paragraph. These 108 X’s appear in 48 rows, which represent the linked nodes noted in the previous paragraph. The identities of these nodes are found in column 1 (“Node”). Similarly, 24 X’s in columns 5 (“Existing”) represent the 24 existing LNG peakshaving facilities and 48 X’s in column 6 (“Potential”) represent the 48 prospective LNG storage facilities.

Table 10-8 List of Storage Nodes
Underground Storage Facility LNG Peakshaving Facility Node 20- Day 80- Day Over 80 Days Existing Potential

(1) New England X (3) Quebec X X X (4) New York City X (5) Niagara X X X X (6) Leidy X X X X (8) Georgia X (10) South Florida X (11) East Ohio X X X X (12) Maumee/Defiance X (13) Lebanon X (14) Indiana X X X (15) South Illinois X X X X (16) North Illinois X X X X (17) Southeast Michigan X X X (18) Tennessee/Kentucky X X X X (19) MD/DC/Northern VA X (20) Wisconsin X (21) Northern Missouri X (22) Minnesota X X (23) Crystal Falls X (24) Ventura X X X (26) Nebraska X X (28) Kansas X X X X (29) East Colorado X X X X (30) Opal X X X (31) Cheyenne X X X (32) San Juan Basin X (33) EPNG/TW X (34) North Wyoming X (35) South Nevada X (36) SOCAL Area X X X (38) PGE Area X X X (41) Montana/North Dakota X X X (45) Pacific Northwest X X X

### 10-32 Underground Storage Facility LNG Peakshaving Facility Node 20- Day 80- Day Over 80 Days Existing Potential

(46) NPC/PGT Hub X (47) North Nevada X (48) Idaho X (54) North Alabama X X X X (56) North Mississippi X X X (58) Eastern Louisiana Hub X X X (60) Henry Hub X X X X (61) North Louisiana Hub X X X (63) Southwest Texas X X X (64) Dallas/Ft Worth X X X X (65) E. TX (Katy) X X X X (66) S. TX X (68) NW TX X (72) North British Columbia X (73) South British Columbia X X (74) Caroline X X X X (76) Saskatchewan X X X X (77) Manitoba X (78) Dawn X X X X (79) Philadelphia X (80) West Virginia X X X X (81) Eastern Canada Demand X (83) Wind River Basin X (92) Southwest VA X X X (93) Southeast VA X (94) North Carolina X (95) South Carolina X (96) North Florida X (97) Arizona X X X (98) Southwest Michigan X X X X (99) Northern Michigan X X X X (103) SDG&E Demand X (104) Eastern New York X (105) New Jersey X (106) Toronto X (107) Carthage X X X (108) Southwest Oklahoma X X (109) Northeast Oklahoma X X X (110) Southeastern Oklahoma X X X (111) Northern Arkansas X X X (112) Southeast Missouri X X (113) Uinta/Piceance X X X

### 10-33 Underground Storage Facility LNG Peakshaving Facility Node 20- Day 80- Day Over 80 Days Existing Potential

(114) South MS/AL X X X 10.8.1 Storage Capacity and Injection/Withdrawal Constraints Working gas capacity is initially allocated in the GMM to individual nodes based on historical data. Since the base year in EPA Base Case using IPM v.4.10 gas module is 2011, a projection of natural gas storage capacity at the end of 2010 is needed as a starting point. The expected working gas capacity as of EOY 2010 by locati on and storage type is obtained from the GMM as are injection and withdrawals rates. These serve as inputs to the IPM gas module, which uses them to endogenously derive gas storage withdrawals, injections, storage expansions, and associated costs. To give a sense of the EOY 2010 GMM storage input assumption in the IPM gas module, Table 10-9 shows the total worki ng gas capacity and the average daily injection and withdrawal rates as percentage of working gas capacit y for the four types of storage. Note that these are aggregated values (i.e., totals and averages); the actual GMM EOY 2010 inputs to the IPM gas module vary by location and storage type.

Table 10-9 Storage Capacity and Injection/Withdrawal Rates (EOY 2010)

Working Gas Capacity (Bcf) Average Daily Injection Rate (Percent of WG Capacity) Average Daily Withdrawal Rate (Percent of WG Capacity)
Underground Storage
20 Day 458 6.7 9.3
80 Day 3,353 1.3 2.2
Over 80 Days 944 0.5 0.9

Total 4,755
LNG Peakshaving Storage 84 0.5 12.5

10.8.2 Variable Cost and Fuel Use In the IPM natural gas module, the natural gas is injected to storage or withdrawn from storage at variable cost. The base year 2011 variable cost or commodity 6 

### charge for underground storage facilities is assumed to be 1.54 cents/MMBtu and is the same for all underground storage nodes and types. The variable cost for LNG peakshaving facility is much higher at 36 cents/MMBtu as it includes variable costs for gas liquefaction (in gas injection process) and LNG regasification (in gas withdrawal process). The variable cost is assumed to be the same for all LNG peakshaving nodes. A storage cost growth factor shown in Figure 10-19 is applied to the injection/withdrawal cost to reflect cost increase over time. The cost is assumed to grow at an average rate of 0.5 percent per year.

6 

### Storage commodity (variable) charge is generally a charge per unit of gas injected and/or withdrawn from storage as per the rights and obligations pertaining to a gas storage lease. Analogous to commodity charges for gas pipeline service

### 10-34 Figure 10-19 Storage Cost Growth Factor

1.00 1.05 1.10 1.15 1.20 1.25 1.30

2010 2020 2030 2040 2050 2060


### Fuel use for injection and withdrawal for underground storage is 1% of the gas throughput. The withdrawal fuel use for the LNG peakshaving storage is also 1% but the injection fuel use is much higher at 11% of the injection gas as it includes fuel use for gas liquefaction.

10.8.3 Storage Capacity Expansion Logic The endogenous modeling decision of whether to expand working gas storage capacity is controlled by two constraints, which stipulat e minimum and maximum capacity additions for each storage facility and year, and by the levelized capital cost of the storage expansion. The two constraints are specified as input data for each storage facility and year. The minimum constraint forces the model to add working gas capacity to the specified facility and year and the maximum constraint is the cap for the expansion. Figure 10-20 shows projected maximum storage expansion constraints for the “80-day” category storage facility in supply area Katy, Texas.

Figure 10-20 Example Maximum Storage Capacity Expansion



### Storage category: “80-day”

Supply area: Katy, Texas

0 10 20 30 40 50 60

2010 2020 2030 2040 2050 2060Bcf



### The model is allowed to add working gas capacity to a storage facility within the two constraints if the cost of storage expansion contributes to the opt imal solution, i.e., minimizes the overall costs to the power sector, including the capital cost for working gas capacity expansion less their revenues. The model takes into account all possible options/projects including working gas capacity additions for storage facilities in any year that do not violate the constraints and selects the combination of options/projects that provide the minimum objective function value. In this way,

### 10-35 storage capacity expansion projects will compete with each other and even with other projects such as LNG regasification capacity expansions, pipeline expansions, etc.

The base year 2011 levelized storage capital cost (in real 2007 dollars per MMBtu of storage capacity addition) is specified for each of the 180 storage facilities. Table 10-10 lists the average base year 2011 levelized storage capital cost for the four types of storage facility. Amongst the underground storage facilities the higher capital costs represent more storage cycles 7 

### that could be achieved in a year. On average, the capital costs for the “80-Day” and “20-Day” storage facilities are assumed to be 20 percent and 50 percent, respectively, higher than that of the “Over 80 Days” storage facility. The levelized capital cost for LNG peakshaving storage is much higher due to higher capital cost for the liquefaction unit. The cost growth factors shown in Figure 10-19 are applied to the capital cost to derive the cost increase over time. The capital cost is assumed to grow at an average rate of 0.5 percent per year.

Table 10-10 Base Year 2011 Average Levelized Storage Capital Cost

Storage Type Average Levelized Storage Capital Cost (2007$/MMBtu)
Underground Storage
20-Day 1.09
80-Day 0.86
Over 80 Days 0.72
LNG Peakshaving Storage 5.13



## 10.9 Fuel Prices 10.9.1 Crude Oil and Natural Gas Liquids Prices Since a fraction of the hydrocarbons produced in the natural gas exploration and development process are crude oil and NGLs (see columns 2 and 3 in Table 10-5), revenues from crude oil and NGL production play a key role in determining the extent of exploration and development for natural gas. To take into account these revenues, crude oil and NGL price projections are provided as inputs to the IPM natural gas module and factored into the calculation of costs in the IPM objective function.

The crude oil and NGL price projections used in t he IPM natural gas module are shown in Figure 10-21. These price projections were adapted from AEO 2009. No attempt was made to project prices beyond 2030 other than to assume that prices remain at their 2030 levels. The projected prices shown in Figure 10-21 are expressed in units of 2007$ per MMBtu. Using a crude oil Btu content of 5.8 MMBtu/Bbl, the projected crude oil prices in Figure 10-21 can be translated into the more familiar units of dollars per barrel (Bbl), in which case, prices in this figure are equivalent to $62/Bbl in 2011, $97/Bbl in 2015, $114/Bbl in 2020, and constant at $124/Bbl from 2030 (in real 2007 dollars) onward.

7 

### One storage cycle is the theoretical time required to completely inject and withdraw the working gas quantity for any given underground gas storage facility or the turnover time for the working gas capacity rating of the facility. The cycle rate of any storage facility is usually expressed in cycles per year and is the number of times the working gas volumes can theoretically be turned over each storage year. The cycle rating for Porous Storage varies from 1 to 6 per year while that for Salt Cavern Storage are as high as 12 per year.

### 10-36 Figure 10-21 Crude Oil and NGL Prices

0.00 5.00 10.00 15.00 20.00 25.00

2000 2010 2020 2030 2040 2050 2060Prices (2007 $/MMBtu)

Crude Oil Natural Gas Liquids



### 10.9.2 Natural Gas Prices Node-level natural gas prices are outputs of the model and are obtained from the optimal solution of the combined IPM electric power sector and nat ural gas linear programming (LP) model. From a technical modeling standpoint, the node gas prices are what are called “shadow prices” or “dual variable values” associated with the node mass balanc e constraints at the optimal LP solution.



## 10.10 Outputs and Proxy Natural Gas Supply Curves 10.10.1 Outputs from the New IPM Natural Gas Module Previous EPA IPM base cases reported natural gas consumption (in TBtu), Henry Hub and delivered natural gas prices (in $/MMBtu). In addition to these reports, the new natural gas module in EPA Base Case v.4.10 is capable of repor ting natural gas supply (in Tcf), disposition (in Tcf), prices (in $/MMBtu), production (in Tcf) by supply region, end-of-year reserves and annual reserve additions (in Tcf), imports and exports (in Tcf), consumption by end-use sector and census division (in Tcf), prices by census division (in $/MMBtu), and inter-regional pipeline flows and LNG imports (in Bcf).

10.10.2 Proxy Natural Gas Supply Curves In previous EPA IPM base cases a set of gas supply curves was generated outside of IPM (most recently by ICF’s NANGAS (North American Na tural Gas Analysis System) model) and then used in IPM as part of the base case input assumptions. (For a description of this approach see Appendix 8-2.9 “Technical Background Paper on the Development of Natural Gas Supply Curves for EPA Base Case 2004, v.2.1.9” in Standalone Documentation for EPA Base Case 2004 (V.2.1.9) Using the Integrated Planning Model EPA 430-R-05-011, September 2005. It is available for viewing and downloading at www.epa.gov/airmarkets/progsregs/epaipm/docs/bc8appendix.pdf .) The incorporation of the new fully integrated natural gas module into IPM eliminates the use of explicit gas suppl y curves, replacing them with more dynamic and responsive representation of an integrated natural gas supply chain and the U.S. power sector. However, it is recognized that it would be useful to have a set of proxy natural gas supply curves from the new integrated approach that could be co mpared to the natural gas supply curves used in previous EPA Base Cases. The proxy curves would only represent a one-time snapshot of supply/price relations resulting from the new int egrated approach, but at least it would provide a point of comparison with the natural gas supply curves used in previous EPA IPM base cases.

### 10-37 Table 10-11 contains the proxy supply curves for the electric power sector for 2015 and 2020. (These are years that would be directly comparabl e with supply curves from previous base cases.) The curves were generated based on GMM supply elasticities and gas demand and price solutions from the IPM/Gas model. The supply elas ticity is calculated from GMM for each of the IPM run years. The gas supply curve, for eac h IPM run year, is constructed by applying the supply elasticity around the price/quantity solution of gas consumption in the power sector from the IPM/Gas model (highlighted rows in

Table 10-11) varying the price from $3/MMBtu to $15/MMBtu (in real 2007 dollar). The supply elasticity, in the same run year, is assumed to be constant within the price range.

The proxy supply curves below s pecify annual price and volume relationships at the Henry Hub. For each listed step the price applies for all increments of supply greater than the value shown in the preceding step up to and including the supply level indicated in the current step.

Table 10-11 Proxy Natural Gas Supply Curves for EPA Base Case v.4.10

2015 2020

Gas Price (2007$/MMBtu) Gas Supply to Electric Sector (TBtu) Gas Price (2007$/MMBtu) Gas Supply to Electric Sector (TBtu)
3.00 4,385 3.00 5,688 3.11 4,469 3.08 5,762 3.23 4,552 3.16 5,836 3.34 4,633 3.23 5,909 3.45 4,713 3.31 5,981 3.56 4,792 3.39 6,052 3.68 4,870 3.47 6,123 3.79 4,946 3.55 6,192 3.90 5,022 3.63 6,261 4.02 5,096 3.70 6,329 4.13 5,169 3.78 6,397 4.24 5,242 3.86 6,464 4.36 5,313 3.94 6,530 4.47 5,384 4.02 6,595 4.58 5,453 4.09 6,660 4.69 5,522 4.17 6,724 4.81 5,590 4.25 6,788 5.08 5,741 4.53 7,000 5.34 5,887 4.82 7,205 5.61 6,030 5.10 7,405 5.88 6,171 5.38 7,601 6.15 6,308 5.66 7,791 6.42 6,442 5.95 7,977 6.69 6,574 6.23 8,159 6.95 6,703 6.51 8,337 7.22 6,831 6.80 8,511 7.49 6,956 7.08 8,682 7.76 7,078 7.36 8,850 8.03 7,199 7.64 9,014 8.29 7,318 7.93 9,176

### 10-38 2015 2020

Gas Price (2007$/MMBtu) Gas Supply to Electric Sector (TBtu) Gas Price (2007$/MMBtu) Gas Supply to Electric Sector (TBtu)
8.56 7,435 8.21 9,335 8.83 7,551 8.49 9,492 9.10 7,665 8.78 9,646 9.37 7,777 9.06 9,798 9.64 7,888 9.34 9,947 9.90 7,997 9.63 10,094 10.17 8,105 9.91 10,240 10.44 8,212 10.19 10,383 10.71 8,317 10.47 10,524 10.98 8,421 10.76 10,664 11.25 8,524 11.04 10,802 11.51 8,626 11.32 10,938 11.78 8,726 11.61 11,073 12.05 8,826 11.89 11,206 12.32 8,924 12.17 11,337 12.59 9,022 12.45 11,467 12.85 9,118 12.74 11,596 13.12 9,214 13.02 11,723 13.39 9,308 13.30 11,849 13.66 9,402 13.59 11,973 13.93 9,495 13.87 12,097 14.20 9,587 14.15 12,219 14.46 9,678 14.43 12,340 14.73 9,768 14.72 12,460 15.00 9,859 15.00 12,581

Glossary of Terms Used in this Section For ease of reference Table 10-12 assembles in one table terms that have been defined in footnotes throughout this chapter.

Table 10-12 Glossary of Natural Gas Terms Used in Documentation

Term Definition

Arps-Roberts equation “Arps-Roberts equation” refers to the statistical model of petroleum discovery developed by J. J. Arps, and T. G. Roberts, T. G., in the 1950’s.

Associated gas Associated gas refers to natural gas that is produced in association with crude oil production, whereas non-associated gas is natural gas that is not in contact with significant quantities of crude oil in the reservoir.

Basis In natural gas discussions “basis” refers to differences in the price of natural gas in two different geographical locations. In the marketplace “basis” typically means the difference between the NYMEX futures price at the Henry Hub and the cash price at other market points. In the modeling context “basis” means the difference in natural gas prices between any two nodes at the same instance in time.

### 10-39 Term Definition

Decline curve A decline curve is a plot of the rate of gas production against time. Since the production rate decline is associated with pressure decreases from oil and gas production, the curve tends to smoothly decline from a high early production rate to lower later production rate. Exponential, harmonic, and hyperbolic equations are typically used to represent the decline curve.

Depleted reservoir storage A gas or oil reservoir that is converted for gas storage operations. Its economically recoverable reserves have usually been nearly or completely produced prior to the conversion.

Dry gas Natural gas is a combustible mixture of hydrocarbon gases. Although consisting primarily of methane, the composition of natural gas can vary widely to include propane, butane, ethane, and pentane. Natural gas is referred to as 'dry' when it is almost pure methane, having had most of the other commonly associated hydrocarbons removed. When other hydrocarbons are present, the natural gas is called 'wet'.

Ethane rejection Ethane rejection occurs when the ethane component in the natural gas stream is not recovered in a gas processing plant but left in the marketable natural gas stream. Ethane rejection is deployed when the value of ethane is worth more in the gas stream than as an a separate commodity or as a component of natural gas liquids (NGL), which collectively refers to ethane, propane, normal butane, isobutane, and pentanes in processed and purified finished form. Information that characterizes ethane rejection by region can play a role in determining the production level and cost of natural gas by region.

Firm and interruptible demand “Firm” refers to natural gas demand that is not subject to interruptions from the supplier, whereas “interruptible” refers to natural gas demand that is subject to curtailment or cessation by the supplier.

High deliverability storage High deliverability storage is depleted reservoir storage facility or Salt Cavern storage whose design allows a relatively quick turnover of the working gas capacity.

Lease and plant use Natural gas for “lease and plant use” refers to the gas used in well, field, and lease operations (such as gas used in drilling operations, heaters, dehydrators, and field compressors) and as fuel in gas processing plants.

Liquefied Natural Gas (LNG) LNG is natural gas converted to liquid form by cooling it down to about -260° F. Known as liquefaction, the cooling process is performed in an “LNG train” (the liquefaction and purification facilities in LNG plants), which reduces the gas to 1/600th of its original volume. The volume reduction resulting from liquefaction makes it cost effective to transport the LNG over long distances, typically by specially designed, double-hulled ships known as LNG carriers. Once the carriers reach their import terminal destination, the LNG is transferred in liquid form to specially designed storage tanks. When needed for customers, the LNG is warmed back to a gaseous state in a regasification facility and transported to its final destination by pipelines.

LNG peakshaving facility LNG peakshaving facilities supplement deliveries of natural gas during times of peak periods. LNG peak shaving facilities have a regasification unit attached, but may or may not have a liquefaction unit. Facilities without a liquefaction unit depend upon tank trucks to bring LNG from nearby sources.

### 10-40 Term Definition

Load factor In the natural gas context “load factor” refers to the percentage of the pipeline capacity that is utilized at a given time.

Natural gas liquid (NGL) Those hydrocarbons in natural gas that are separated from the gas as liquids in gas processing or cycling plants. Generally such liquids consist of propane and heavier hydrocarbons and are commonly referred to as lease condensate, natural gasoline, and liquefied petroleum gases.

Play A “play” refers to a set of known or postulated natural gas (or oil) accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathway, timing, trapping mechanism, and hydrocarbon type.

Pool A “pool” is a subsurface accumulation of oil and other hydrocarbons. Pools are not necessarily big caverns. They can be small oil-filled pores. A “field” is an accumulation of hydrocarbons in the subsurface of sufficient size to be of economic interest. A field can consist of one or more pools.

Proven (or proved) The term “proven” refers to the estimation of the quantities of natural gas resources that analysis of geological and engineering data demonstrate with reasonable certainty to be recoverable in future years from known reservoirs under existing economic and operating conditions. Among the factors considered are drilling results, production, and historical trends. Proven reserves are the most certain portion of the resource base.

RACC price Refiner Acquisition Cost of Crude Oil (RACC) is a term commonly use in discussing crude oil. It is the cost of crude oil to the refiner, including transportation and fees. The composite cost is the weighted average of domestic and imported crude oil costs.

Raw gas Raw gas production refers to the volumes of natural gas extracted from underground sources, whereas net gas production refers to the volume of purified, marketable natural gas leaving the natural gas processing plant.

Reserves-toproduction (R/P) ratio Reserves-to-production ratio is the remaining amount of reserves, expressed in years, to be produced with a current annual production rate.

Resource and

reserves When referring to natural gas a distinction is made between “resources” and “reserves.” “Resources” are concentrations of natural gas that are or may become of potential economic interest. “Reserves” are that part of the natural gas resource that has been fully evaluated and determined to be commercially viable to produce.

Resource appreciation Resource appreciation represents growth in ultimate resource estimates attributed to success in extracting resource from known plays such as natural gas from shales, coal seams, offshore deepwater, and gas hydrates that are not included in the resource base estimates.
Storage "Days Service" Storage “Days Service” refers to the number of days required to completely withdraw the maximum working gas inventory associated with an underground storage facility.

Storage commodity charge Storage commodity (variable) charge is generally a charge per unit of gas injected and/or withdrawn from storage as per the rights and obligations pertaining to a gas storage lease. Analogous to commodity charges for gas pipeline service

### 10-41 Term Definition

Storage cycle One storage cycle is the theoretical time required to completely inject and withdraw the working gas quantity for any given underground gas storage facility or the turnover time for the working gas capacity rating of the facility. The cycle rate of any storage facility is usually expressed in cycles per year and is the number of times the working gas volumes can theoretically be turned over each storage year. The cycle rating for Porous Storage varies from 1 to 6 per year while that for Salt Cavern Storage are as high as 12 per year.

Unconventional gas Unconventional gas refers to natural gas found in geological environments that differ from conventional hydrocarbon traps. It includes: (a) “tight gas,” i.e., natural gas found in relatively impermeable (very low porosity and permeability) sandstone and carbonate rocks; (b) “shale gas,” i.e., natural gas in the joints, fractures or the matrix of shales, the most prevalent low permeability low porosity sedimentary rock on earth; and (c) “coal bed methane,” which refers to methane (the key component of natural gas) found in coal seams, where it was generated during coal formation and contained in the microstructure of coal. Unconventional natural gas is distinguished from conventional gas which is extracted using traditional methods, typically from a well drilled into a geological formation exploiting natural subsurface pressure or artificial lifting to bring the gas and associated hydrocarbons to the wellhead at the surface.

Underground storage The underground storage of natural gas in a porous and permeable rock formation topped by an impermeable cap rock, the pore space of which was originally filled with water.

Wet gas A mixture of hydrocarbon compounds and small quantities of various nonhydrocarbons existing in the gaseous phase or in solution with crude oil in porous rock formations at reservoir conditions. The principal hydrocarbons normally contained in the mixture are methane, ethane, propane, butane, and pentane. Typical nonhydrocarbon gases that may be present in reservoir natural gas are water vapor, carbon dioxide, hydrogen sulfide, nitrogen and trace amounts of helium.

Working gas The term “working gas” refers to natural gas that has been injected into an underground storage facility and stored therein temporarily with the intention of withdrawing it. It is distinguished from “base (or cushion) gas” which refers to the volume of gas that remains permanently in the storage reservoir in order to maintain adequate pressure and deliverability rates throughout the withdrawal season.

### Appendix 10-1.1 

## Appendix 10-1 EPA Base Case v.4.10 wit h AEO Gas Resource Assumptions

For purposes of comparison a variant of EPA Base Case v.4.10 was prepared with natural gas resource assumptions that were set to approximate those in the Energy Information Administration’s Annual Energy Outlook 2010.

To set up this case, EPA asked ICF International, who had developed the natural gas module for EPA Base Case v.4.10, to determine how best to represent the AEO 2010 resource assumptions in IPM in view of structural differences betw een the two models. It should be noted that the only change between the original EPA base case and this variant are the natural gas resource assumptions. The base year proved reserves assumptions were not changed. 1 

### The following is a summary of the findings of ICF’s comparison and the approach that was implemented in setting up the AEO 2010 gas resource base case variant.

ICF’s analysis of the AEO 2010 natural gas resource assumptions was based on the AEO 2010 assumptions document. 2 

### ICF found that the gas resource base in AEO 2010 is defined as technically recoverable resources (as of beginning of year 2008) without reference to economic profitability, whereas the gas resource base in IPM is defined as the economically recoverable resource (as of end of year 2010) which represents t hat portion of the original-gas-in-place that is economic to develop at wellhead prices 3 

### below $14/MMBtu (in 2007 dollars) given current technologies and industry costs.

ICF made adjustments to convert the AEO technically recoverable gas resources so that it could be used in the IPM Natural Gas Module. Their analysis found that the economically recoverable gas resource as defined in the IPM natural gas module was about 15% to 30% lower than the technically recoverable gas resource without economic consideration as used in AEO 2010. However, since the resource base for conventional, tight gas, and coalbed methane gas in the EPA base case was already 15% to 30% lower than the gas resources in the AEO 2010, ICF did not change the gas resource base for these resource types.

On the other hand, ICF found that the shale gas resource in the EPA base case was much higher than in AEO 2010. To quantify the difference in s hale gas resource, ICF first calculated that a 20% reduction was needed in order both to translate the AEO technically recoverable shale gas resource into the equivalent economically recoverable resource used in IPM and to account for resource development between 2008 (base year for the AEO gas resources) and 2011 (base year for the IPM gas resource). Once the two resource bases were expressed so they could be compared, ICF found that the AEO shale gas resource assumption was about 31% of that used in the EPA base case.

To implement this in the alternative base case, the total shale gas resource was set so it would be 31% of the shale gas resource base assumed in EPA Base Case v.4.10. No attempt was made

1 

### The base year proved reserves assumptions are described in section 10.3 of this chapter under the header “Use of the HSM resource and reserves data in EPA Base Case using IPM v.4.10 Natural Gas Module.”

2 

### Energy Information Administration, Assumptions to the Annual Energy Outlook 2010: Natural Gas Transmission and Distribution Module , DOE/EIA-0554(2010), April 9, 2010. www.eia.doe.gov/oiaf/aeo/assumption/nat_gas.html

3 

### The wellhead price is the price required to cover total wellhead resource costs including capital expenditures, cost of capital, operating costs, royalties, severance taxes and income taxes. Wellhead economics are based upon standard discounted cash flow analysis. Costs include drilling and completion, operating, geological and geophysical (G&G), and lease costs. Completion costs include hydraulic fracturing, and such costs are based upon cost per fracture stage and number of fracture stages. Drilling costs, well lateral length, number of fracture stages, and cost per fracture stage are based on analysis and data from industry.

### Appendix 10-1.2 to adjust shale gas resources by supply region so that the regional shale gas resources in AEO 2010 and EPA’s AEO Supply Case would match.

Table 10-1.1 shows the resulting shale gas resource base assumptions for AEO 2010, EPA Base Case v.4.10, and the AEO Supply Case of the EPA base case. The location of the U.S. natural gas supply regions listed in Table 10-1.1 are shown in the map in Figure 10-1.1.

Table 10-1.1 Shale Gas Resource Base in Tcf (does not include proved reserves)

AEO 2010 (a) (as of BOY 2008) EPA Base Case (b) (as of EOY 2010) AEO Supply Case (b) (as of EOY 2010)

U.S. 346.5 917.2 284.9

Northeast 73.2 254.4 79.0 Gulf Coast 90.3 431.6 134.1 Midcontinent 51.0 132.9 41.3 Southwest 59.5 60.3 18.7 Rocky Mountain 21.6 37.7 11.7 West Coast 50.9 0.3 0.1

Canada NA 511.1 158.7

(a) Technically recoverable resources without reference to economic profitability.
(b) Economically recoverable resources under we llhead gas price of $14/MMBtu (2007 dollars).



### Figure 10-1.1 U.S. Supply Region

### 11-1 

# 11 Other Fuels and Fuel Emission Factor Assumptions Besides coal (chapter 9) and natural gas (chapter 10) EPA Base Case v.4.10 also includes assumptions for residual fuel oil, biomass, nuclear fuels, and various waste fuels. The assumptions described in this chapter pertain to fuel characteristics, fuel market structures, and fuel prices for these fuels. As seen in t he previous chapter, there is an endogenous resource costing model for natural gas built into EPA Base Case v.4.10. Coal is represented via an elaborate set of supply curves and a detailed r epresentation of the associated coal transport network. Together they are designed to capture the intricacies of the resource base and market for this fuel which currently powers about 45% of U.S. electric generating capacity. As with coal, the price and quantity of biomass combust ed is determined by balancing supply and demand using a set of geographically differentiated supply cu rves. In contrast, fuel oil and nuclear fuel prices are exogenously determined and entered into IPM during model set-up as constant price points which apply to all levels of supply. Generally, the waste fuels are also modeled using price points. In this chapter each of the remaining fuels is treated in turn. The chapter concludes with a discussion of the emission factors for all the fuels represented in EPA Base Case v.4.10.



## 11.1 Fuel Oil Two petroleum derived fuels are included in EPA Base Case v.4.10: As its name implies distillate fuel oil is distilled from crude oil, whereas residual fuel oil is left as a residue of the distillation process. The fuel oil prices in EPA Base Case v.4.10 are from AEO 2010 and are shown in Table 11-1. They are regionally differentiated accord ing to the NEMS (National Energy Modeling System) regions used in AEO 2010 and are mapped to their corresponding IPM regions for use in EPA Base Case v.4.10.

Table 11-1 Fuel Oil Prices by NEMS Region in EPA Base Case v.4.10 Residual Fuel Oil Prices (2007$/MMBtu) NEMS Region 2012 2015 2020 2030 2040 - 2050 ECAR 16.40 30.04 41.07 54.20 63.77 ERCOT 20.00 21.96 23.40 25.42 26.91 MAAC 9.85 11.84 13.63 15.65 17.05 MAIN 91.20 104.84 115.87 129.00 138.57 MAPP 17.14 8.61 8.61 8.61 16.19

NY 9.63 11.73 13.59 15.61 16.99 NE 9.80 11.91 13.77 15.80 17.19 FL 10.50 12.48 13.92 15.95 17.44 SERC 10.96 12.83 14.28 16.32 17.81 SPP 17.36 21.73 23.15 25.14 26.60 NWP 19.56 21.58 23.06 25.00 26.50

RA 19.68 21.67 23.14 25.09 26.59 CA 21.63 23.83 25.10 27.24 28.56

Distillate Fuel Oil Prices (2007$/MMBtu) NEMS Region 2012 2015 2020 2030 2040 - 2050
ECAR 14.8 16.96 19.87 22.25 24.19 ERCOT 14.75 16.86 19.71 22.09 24.03 MAAC 14.82 16.99 19.9 22.3 24.25 MAIN 14.86 17.04 19.94 22.31 24.24 MAPP 14.88 17.05 19.95 22.32 24.25

NY 14.87 17.05 19.98 22.38 24.33 NE 15.07 17.23 20.13 22.53 24.49

### 11-2 Distillate Fuel Oil Prices (2007$/MMBtu) NEMS Region 2012 2015 2020 2030 2040 - 2050
FL 14.67 16.78 19.65 22.04 23.99 SERC 14.65 16.8 19.69 22.08 24.03 SPP 14.8 16.93 19.8 22.18 24.12 NWP 15.84 17.85 20.29 22.84 24.74

RA 15.59 17.73 20.6 23.02 24.97 CA 16.21 17.95 19.99 22.66 24.53



## 11.2 Biomass Biomass is offered as a fuel for existing dedicated biomass power plants and potential (new) biomass direct fired boilers (built by the model prior to 2020) and to potential (new) biomass gasification combined cycle units built by the model from 2020 forward. (See chapter 4 for a presentation of the cost and performance characteri stics for these two technologies.) In addition to its use as the prime mover fuel for these plants, it is also offered for co-firing to all coal fired power plants. (See section 5.3 for a discussion of the representation of biomass co-firing in EPA Base Case v.4.10.) As noted in the discussion in chapter 5, the combustion of biomass fuel is considered to have a net zero impact on atmospher ic carbon dioxide levels since the emissions released are equivalent in carbon content to the carbon absorbed during fuel crop growth 1



### .
EPA Base Case v.4.10 uses biomass supply curves based on those in AEO 2010. There are fourteen regional biomass fuel supply curves, one for each of the 14 NEMS coal demand regions represented in AEO 2010. Plants demand biomass from the supply curve corresponding to the NEMS coal demand region in which they are located. No inter-regional trading of biomass is allowed. Each biomass supply curve depicts the price-quantity relationship for biomass and varies over time. There is a separate curve for each m odel run year. The supply component of the curve represents the aggregate supply in a region of four types of biomass fuels: forestry residue, agricultural residue, urban wood waste, and mill residue and energy crops. The price component of the curve includes transportation cost and represents the delivered fuel cost at the plant gate. The original AEO 2010 supply curves contained 48 price steps, and are modeled as-is in EPA Base Case v.4.10. Appendix 11-1 contains the 2012-2035 biomass supply curves.

The supply curves in Appendix 11-1 represent the biomass available to both the electric and nonelectric sectors. In any given region at any poi nt in time the power sector demand from IPM has to be combined with the non-electric sector demand for biomass to obtain the price faced by the power sector. The non-electric sector demand di stribution is by NEMS coal demand region based on AEO 2010. The total national projection from AEO 2010 was scaled up to the projections obtained from the Forest and Agricultural Sector Optimization Model (FASOM), a dynamic, nonlinear programming model of the U.S. forest and agricultural sectors developed for EPA by Bruce A. McCarl, Professor of Agricultural Ec onomics at Texas A&M University. Table 11-2 shows the non-electric sector demand by run year and NEMS region.

Table 11-2 Non-Electric Biomass Demand by NEMS Region in EPA Base Case v.4.10 Non-Electric Biomass Demand (TBtu) NEMS Coal Demand Region 2012 2015 2020 2030 2040 - 2050 1 CT, MA, ME, NH, RI, and VT -- -- -- 17.1 15.7 2 NY, PA, and NJ -- -- -- -- 0.0 3 WV, MD, DC, DE, VA, NC, and SC 19.4 126.7 336.1 354.1 324.2 4 GA and FL 12.7 80.3 201.7 202.4 185.3

1 

### Hughes, E., Role of Renewables in Greenhouse Gas Reduction, Electric Power Research Institute (EPRI): November, 1998. Report TR-111883, p. 28.

### 11-3 Non-Electric Biomass Demand (TBtu) NEMS Coal Demand Region 2012 2015 2020 2030 2040 - 2050
5 OH -- -- -- -- 0.0 6 IN, IL, MI, and WI -- -- -- -- 0.0 7 KY and TN -- -- -- -- 0.0 8 AL and MS -- -- -- -- 0.0 9 MN, IA, ND, SD, NE, MO, and KS 131.8 184.3 63.8 48.0 43.9 10 TX, LA, OK, and AR 0.9 0.3 0.1 0.1 0.1 11 MT, WY, and ID 0.4 0.1 0.0 13.2 12.1 12 CO, UT, and NV 0.3 0.1 0.0 11.8 10.8 13 AZ and NM 0.3 0.1 0.0 9.0 8.2
14 WA, OR, and CA 0.2 22.7 506.7 467.6 428.1

Once the non-electric demand for biomass is factored in, biomass prices in EPA Base Case v.4.10 are derived endogenously based on the aggregate power sector demand for biomass in each region. The results are unique market-cleari ng prices for each supply region. All plants using biomass from that supply region fa ce the same market-clearing price.



## 11.3 Nuclear Fuel The AEO 2009 price assumption for nuclear fuel is used as the nuclear fuel price assumption for 2012-2050 in EPA Base Case v.4.10. The 2012, 2015, 2020, and 2030 prices are 0.71, 0.75, 0.76, and 0.84 lbs/MMBtu, respectively.



## 11.4 Waste Fuels Among the “modeled fuels” shown for existing generating units in the NEEDS, v.4.10 (the database which serves as the source of data on existing units for EPA Base Case v.4.10) are a number of waste fuels, including waste coal, petroleum coke, fossil waste, non-fossil waste, tires, and municipal solid waste (MSW). Table 11-3 describes these fuels, shows their extent of their representation in NEEDS, and then indicates the assumptions adopted in EPA Base Case v.4.10 to represent their use and pricing. It should be noted that these fuels are only provided to existing and planned committed units in EPA Base Case v.4.10. Potential new generating units that the model “builds” are not given the option to burn t hese fuels. In IPM reported output, tires, MSW, and non-fossil waste are all included under existing non-fossil other, while waste coal and petroleum coke is included under coal.

### 11-4 Table 11-3 Waste Fuels in NEEDS, v.4.10 and EPA Base Case v.4.10 Supply and Cost Modeled

Fuel in NEEDS Number of Units in NEEDS Total Capacity in NEEDS Description Modeled By Assumed Price

Waste

Coal 3 2,205 MW “Usable material that is a byproduct of previous coal processing operations. Waste coal is usually composed of mixed coal, soil, and rock (mine waste). Most waste coal is burned as-is in unconventional fluidized-bed combustors. For some uses, waste coal may be partially cleaned by removing some extraneous noncombustible constituents. Examples of waste coal include fine coal, coal obtained from a refuse bank or slurry dam, anthracite culm, bituminous gob, and lignite waste.” http://www.eia.doe.gov/glossary/in dex.cfm?id=W Supply Curve Based on AEO

2010 AEO 2010

Petroleum Coke 29 3,442 MW A residual product, high in carbon content and low in hydrogen, from the cracking process used in crude oil refining Price Point $42.24/ MMBtu

Fossil Waste 28 982 MW Waste products of petroleum or natural gas including blast furnace and coke oven gas. They do not include petroleum coke or waste coal which are specified separately among the “Modeled Fuels” Price Point 0

Non-Fossil

Waste 66 874 MW Non-fossil waste products that do not themselves qualify as biomass. These include waste products of liquid and gaseous renewable fuels (e.g., red and black liquor from pulping processes, digester gases from waste water treatment). They do not include urban wood waste which is included in biomass. Price Point 0

Tires 3 44 MW Discarded vehicle tires. Price Point 0

Municipal

Solid Waste 183 2,197 MW “Residential solid waste and some nonhazardous commercial, institutional, and industrial wastes.” http://www.eia.doe.gov/glossary/in dex.cfm Price Point 0

### 11-5 

## 11.5 Fuel Emission Factors Table 11-4 brings together all the fuel emission factor assumptions as implemented in EPA Base Case v.4.10. For sulfur dioxide and mercury in coal, where emission factors vary widely based on the rank, grade, and supply seam source of the coal , cross references are given to tables that provide more detailed treatment of the topic. Nitrogen oxides (NO X 

### ) are not included in Table 11-4 because NO X 

### levels are not primarily fuel based but are a factor of the combustion process.

Table 11-4 Fuel Emission Factor Assumptions in EPA Base Case v.4.10

Fuel Type Heat Content (Btu/lb) 1 

### Carbon Dioxide (lbs/MMBtu) 2 

### Sulfur Dioxide (lbs/MMBtu) 3 

### Mercury (lbs/TBtu) 3



### Coal Bituminous >10,260 - 13,000 205.2 - 206.6 0.67 - 6.43 1.82 - 34.71 Subbituminous > 7,500 - 10,260 212.7 - 213.1 0.58 - 1.41 4.24 - 6.44 Lignite < 7,500 213.5 - 217.0 1.46 - 3.91 7.51 - 14.88 Natural Gas -- 117.08 0 0.00014 Fuel Oil Distillate -- 161.4 0 0.48 Residual -- 161.4 - 173.9 0.3 - 2.65 0.48
Biomass -- 0 0.08 0.57
Waste Fuels Waste Coal 6,175 205.7 5.36 63.9 Petroleum Coke 14,150 225.1 7.27 23.18 Fossil Waste -- 321.1 0.08 0 Non-Fossil Waste -- 0 0 0 Tires -- 189.5 1.65 3.58 Municipal Solid Waste -- 91.9 0.35 71.85
Notes:
1 

### Distillate and Residual Oils, Biomass, Fossil Waste, Non-Fossil Waste, Tires, and Municipal Solid Waste (MSW) are priced at a $/MMBtu basis and henc e heat content is not required for modeling.
2 

### Also see Table 9-9
3 

### Also see Table 9-6, and Table 9-7 Biomass fuel is considered to have a net zero impact on atmospheric carbon dioxide levels since the emissions released are equivalent in carbon content to the carbon absorbed during fuel crop growth. (See, for example, Hughes, E., Role of Renewables in Greenhouse Gas Reduction, Electric Power Research Institute (EPRI): November, 1998. Report TR-111883, p. 28.)

" 

### Biomass Co-firing," Chapter 2 in Renewable Energy Technology Characterizations , U.S. Department of Energy and Electric Power Research Institute (EPRI), 1997. Analysis of Emissions Reduction Option for the Electric Power Industry , Office of Air and Radiation, U.S. Environmental Protection Agency, March 1999.

### Appendix 11-1 

## Appendix 11-1 Biomass Supply Curve **s** in EPA Base Case v.4. **1** 0

This is a small excerpt of the data in Appendix 11 -1. The complete data set in spreadsheet format can be downloaded via the link found at www.epa.gov/airmarkets/progsregs/epaipm/BaseCasev410.html

Year Biomass Supply Region Step Name Cost of Production (2007$/MMBtu) Biomass Production (TBtu/Year) 2012 AL, MS BM01 0.00 0 2012 AL, MS BM02 1.83 21.28 2012 AL, MS BM03 2.12 21.04 2012 AL, MS BM04 2.39 19.44 2012 AL, MS BM05 2.68 13.27 2012 AL, MS BM06 2.97 10.35 2012 AL, MS BM07 3.26 7.33 2012 AL, MS BM08 3.54 78 2012 AL, MS BM09 3.83 2.95 2012 AL, MS BM010 4.11 1.22 2012 AL, MS BM011 4.40 0.2 2012 AL, MS BM012 4.68 0 2012 AL, MS BM013 4.97 0 2012 AL, MS BM014 5.26 0 2012 AL, MS BM015 5.54 0 2012 AL, MS BM016 5.82 0 2012 AL, MS BM017 6.11 0 2012 AL, MS BM018 6.40 0 2012 AL, MS BM019 6.82 5 2012 AL, MS BM020 7.24 5 2012 AL, MS BM021 7.66 5 2012 AL, MS BM022 8.07 5 2012 AL, MS BM023 8.49 5 2012 AL, MS BM024 8.91 5 2012 AL, MS BM025 9.33 5 2012 AL, MS BM026 9.76 5 2012 AL, MS BM027 10.18 5 2012 AL, MS BM028 10.60 5 2012 AL, MS BM029 11.02 5 2012 AL, MS BM030 11.44 5 2012 AL, MS BM031 11.86 5 2012 AL, MS BM032 12.28 5 2012 AL, MS BM033 12.70 5 2012 AL, MS BM034 13.12 5 2012 AL, MS BM035 13.54 5 2012 AL, MS BM036 13.97 5 2012 AL, MS BM037 14.38 5 2012 AL, MS BM038 14.80 5 2012 AL, MS BM039 15.22 5 2012 AL, MS BM040 15.64 5