### United States

Environmental Protection

Agency Air and

Radiation

(6204J) EPA # 450R13002

November 2013



# Documentation for

EPA Base Case v.5.13

Using the Integrated Planning

Model

### Cover: EPA Base Case v.5.13 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 continenta l U.S. Representation of the electric

power sector in Canada is also included for purposes of integrated projections. The map appearing on the

cover shows the 64 model regions used to characterize the operation of the U.S. electric power system in

the low er continental U.S. and 11 model regions in Canada. EPA Base Case v.5.13 was developed by

EPA’s Clean Air Markets Division with technical support from ICF International, Inc. The IPM modeling

platform is a product of ICF Resources, LLC, an operating compa ny 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.5.13

Using the Integrated Planning Model

U.S. Environmental Pr otection Agency

Clean Air Markets Division

1200 Pennsylvania Avenue, NW (6204J)

Washington, D.C. 20460

(www.epa.gov/airmarkets)

November 2013

### i 

# Acknowledgment

This document was prepared by U.S. EPA’s Clean Air Markets Division, Office of Air and Radiation. ICF

Incorporated, L.L

. C. , an operating company of ICF International, provided technical support under EPA

Contract s EP

- W

- 13

- 009 and EP

- W

- 08

- 018

.

### ii TABLE OF CONTENTS

Acknowledgment

................................ ................................ ................................ ................................ ............ i

1. Introduction

................................ ................................ ................................ ................................ ....... 1

- 1

2. Modeling Fram ework

................................ ................................ ................................ ........................ 2

- 1

2.1 IPM Overview

................................ ................................ ................................ ......................... 2

- 1

2.1.1 Purpose and Capabilities

................................ ................................ ......................... 2

- 1

2.1.2 Applications

................................ ................................ ................................ .............. 2

- 2

2.2 Model Structure and Formulation

................................ ................................ ........................... 2

- 2

2.2.1 Objective Function

................................ ................................ ................................ ... 2

- 2

2.2.2 Decision Variables

................................ ................................ ................................ ... 2

- 3

2.2.3 Constraints

................................ ................................ ................................ ............... 2

- 4

2.3 Key Methodological Features of IPM

................................ ................................ ..................... 2

- 4

2.3.1 Model Plants

................................ ................................ ................................ ............ 2

- 5

2.3.2 Model Run Years

................................ ................................ ................................ ..... 2

- 6

2.3.3 Cost Accounting

................................ ................................ ................................ ....... 2

- 6

2.3.4 Modeling Wholesale Electricity Markets

................................ ................................ .. 2

- 7

2.3.5 Load Duration Curves (LDC)

................................ ................................ ................... 2

- 7

2.3.6 Dispatch Modeling

................................ ................................ ................................ ... 2

- 9

2.3.7 Fuel Modeling

................................ ................................ ................................ ........ 2

- 10

2.3.8 Transmission Modeling

................................ ................................ .......................... 2

- 10

2. 3.9 Perfect Competition and Perfect Foresight

................................ ............................ 2

- 11

2.3.10 Air Regulatory Modeling

................................ ................................ ......................... 2

- 1 1

2.4 Hardware and Programming Features

................................ ................................ ................. 2

- 11

2.4.1 Data Parameters for Model Inputs

................................ ................................ ......... 2

- 12

2.4.2 Model Outputs

................................ ................................ ................................ ........ 2

- 13

Attachment 2

- 1 Load Duration Curves a 

### Used in EPA Base Case v.5.13

................................ ........ 2

- 14

3. Power System Operation Assumptions

................................ ................................ ........................... 3

- 1

3.1 Model Regions

................................ ................................ ................................ ........................ 3

- 1

3.2 Electric Load Modeling

................................ ................................ ................................ ........... 3

- 2

3.2.1 Demand Elasticity

................................ ................................ ................................ .... 3

- 5

3.2.2 Net Internal Demand (Peak Demand)

................................ ................................ ..... 3

- 5

3.2.3 Regional Load Shapes

................................ ................................ ............................. 3

- 6

3.3 Transmission

................................ ................................ ................................ .......................... 3

- 6

3.3.1 Inter

- regional Transmission Capability

................................ ................................ .... 3

- 6

3.3.2 Joint Transmission Capacity and Energy Limits

................................ .................... 3

- 12

3.3.3 Transmission Link Wheeling Charge

................................ ................................ ..... 3

- 14

3.3.4 Transmission Losses

................................ ................................ ............................. 3

- 14

3.4 International Imports

................................ ................................ ................................ ............. 3

- 14

3.5 Capacity, Generation, and Dispatch

................................ ................................ ..................... 3

- 14

3.5.1 Availability

................................ ................................ ................................ .............. 3

- 15

3.5.2 Capacity Factor

................................ ................................ ................................ ...... 3

- 15

3.5.3 Turndown

................................ ................................ ................................ ............... 3

- 18

3.6 Reserve Margins

................................ ................................ ................................ .................. 3

- 18

3.7 Power Plant Lifetimes

................................ ................................ ................................ ........... 3

- 20

### iii

3.8 Heat Rates

................................ ................................ ................................ ............................ 3

- 20

3.9 Existing Environmental Regulations

................................ ................................ ..................... 3

- 21

3.9.1 SO 2 

### Regulations

................................ ................................ ................................ .... 3

- 21

3.9.2 NO

x 

### Regulations

................................ ................................ ................................ .... 3

- 22

3.9.3 Multi

- Pollutant Environmental Regulations

................................ ............................ 3

- 25

3.9.4 CO 2 

### Regulations

................................ ................................ ................................ .... 3

- 26

3.9.5 State

- Specific Environmental Regulations

................................ ............................. 3

- 27

3.9.6 New Source Review (NSR) Settlements

................................ ............................... 3

- 27

3.9.7 Emission Assumptions for Potential (New) Units

................................ .................. 3

- 27

3.9.8 Energy Efficiency and Renewable Portfolio Standards

................................ ......... 3

- 27

3.10 Capacity Deployment Constraints

................................ ................................ ........................ 3

- 28

Attachment 3

- 1 NO

x 

### Rate Development in EPA Base Case v.5.13

................................ ................ 3

- 31

Attachment 3

- 2 Capacity Deployment Limits for Advanced Coal with CCS and New Nuclear in EPA

Base Case v.5.13

................................ ................................ ................................ ................. 3

- 36

Attachment 3

- 3 Nuclear Capacity Deployment Constraint in EPA Base Case v.5.13

..................... 3

- 37

4. Generating Resources

................................ ................................ ................................ ..................... 4

- 1

4.1 National Electric Energy Data System (NEEDS)

................................ ................................ .... 4

- 1

4.2 Existing Units

................................ ................................ ................................ .......................... 4

- 1

4.2.1 Population of Existing Units

................................ ................................ ..................... 4

- 1

4.2.2 Capacity

................................ ................................ ................................ ................... 4

- 4

4.2.3 Plant Loca tion

................................ ................................ ................................ .......... 4

- 5

4.2.4 Online Year

................................ ................................ ................................ .............. 4

- 5

4.2.5 Unit Configuration

................................ ................................ ................................ .... 4

- 6

4.2.6 Model Plant Aggregation

................................ ................................ ......................... 4

- 6

4.2.7 Cost and Performance Characteristics of Existing Units

................................ ....... 4

- 10

4.2.8 Life Extension Costs for Existing Units

................................ ................................ .. 4

- 16

4.3 Planned

- Committed Units

................................ ................................ ................................ ..... 4

- 16

4.3.1 Population and Model Plant Aggregation

................................ .............................. 4

- 16

4.3.2 Capacity

................................ ................................ ................................ ................. 4

- 22

4.3.3 State and Model Region

................................ ................................ ........................ 4

- 22

4.3.4 Online and Retirement Year

................................ ................................ .................. 4

- 22

4.3.5 Unit Configuration, Cost and Performance

................................ ............................ 4

- 22

4.4 Potential Units

................................ ................................ ................................ ....................... 4

- 22

4.4.1 Methodology Used to Derive the Cost and Performance Characteristics of

Conventional Potential Units

................................ ................................ .................. 4

- 23

4.4.2 Cost and Performance for Potential Conventional Units

................................ ....... 4

- 23

4.4.3 Short

- Term Capital Cost Adder

................................ ................................ ............. 4

- 23

4.4.4 Regional Cost Adjustment

................................ ................................ ..................... 4

- 24

4.4.5 Cost and Performance for Potential Renewable Generating and Non

- Conventional

Technol ogies

................................ ................................ ................................ .......... 4

- 31

4.5 Nuclear Units

................................ ................................ ................................ ........................ 4

- 59

4.5.1 Existing Nuclear Units

................................ ................................ ............................ 4

- 59

4. 5.2 Potential Nuclear Units

................................ ................................ .......................... 4

- 60

5. Emission Control Technologies

................................ ................................ ................................ ........ 5

- 1

5.1 Sulfur Dioxide Control Technologies

- Scrubbers

................................ ................................ .. 5

- 1

5.1.1 Methodology for Obtaining SO 2 

### Controls Costs

................................ ...................... 5

- 2

5.2 Nitrogen Oxides Control Technology

................................ ................................ ..................... 5

- 5

### iv 5.2.1 Combustion Controls

................................ ................................ ............................... 5

- 5

5.2.2 Post

- combustion NO

x 

### Controls

................................ ................................ ................ 5

- 5

5.2.3 Methodology for Obtaining SCR Costs for Coal

................................ ...................... 5

- 6

5.2.4 Methodology for Obtaining SCR Costs for Oil/Gas Steam Units

............................. 5

- 8

5.2.5 Methodology for Obtaining SNCR Costs

................................ ................................ . 5

- 8

5.2.6 SO 2 

### and NO

x 

### Controls for Units with Capacities from 25 MW to 100 MW (25 MW ≤

capacity < 100 MW)

................................ ................................ ................................ . 5

- 8

5.3 Biomass Co

- firing

................................ ................................ ................................ ................... 5

- 9

5.4 Mercury Control Technologies

................................ ................................ .............................. 5

- 10

5.4.1 Mercury Content of Fuels

................................ ................................ ...................... 5

- 10

5.4.2 Mercury Emission Modification Factors

................................ ................................ . 5

- 11

5.4.3 Mercury Control Capabilities

................................ ................................ .................. 5

- 15

5.4.4 Methodology for Obtaining ACI Control Costs

................................ ....................... 5

- 17

5.5 Hydrogen Chloride (HCl) Control Technologies

................................ ................................ ... 5

- 18

5.5.1 Chlorine Content of Fuels

................................ ................................ ...................... 5

- 18

5.5.2 HCl Removal Rate Assumptions for Existing and Potential Unit s

......................... 5

- 20

5.5.3 HCl Retrofit Emission Control Options

................................ ................................ ... 5

- 20

5.6 Fabric Filter (Baghouse) Cost Development

................................ ................................ ........ 5

- 22

5.6.1 MATS Filterable Particulate Matter (PM) Compliance

................................ ........... 5

- 24

5.7 Coal

- to

- Gas Conversions

................................ ................................ ................................ ..... 5

- 25

5.7.1 Boiler Modifications For Coal

- To

- Gas Conversions

................................ .............. 5

- 25

5.7.2 Natural Gas Pipeline Requirements For Coal

- To

- Gas Conversions

..................... 5

- 27

5.8 Natural Gas Co

- firing

................................ ................................ ................................ ............ 5

- 33

6. CO 2 

### Capture, Transport, and Storage

................................ ................................ .............................. 6

- 1

6.1 CO 2 

### Capture

................................ ................................ ................................ ........................... 6

- 1

6.2 CO 2 

### Storage

................................ ................................ ................................ ........................... 6

- 2

6.3 CO 2 

### Transport

................................ ................................ ................................ ......................... 6

- 5

7. Set

- up Parameters and Rules

................................ ................................ ................................ .......... 7

- 1

7.1 Run Year Mapping

................................ ................................ ................................ .................. 7

- 1

7.2 Retrofit Assignments

................................ ................................ ................................ .............. 7

- 1

7.3 Emissions Tradin g and Banking

................................ ................................ ............................. 7

- 4

8. Financial Assumptions

................................ ................................ ................................ ..................... 8

- 6

8.1 Introduction to Risk

................................ ................................ ................................ ................. 8

- 6

8.1.1 Market Structure Risks

................................ ................................ ............................. 8

- 6

8.1.2 Technology Risks

................................ ................................ ................................ ..... 8

- 7

8.1.3 Financing Structure Risks and Approach

................................ ................................ 8

- 7

8.2 Calculation of the Financial Discount Rate

................................ ................................ ............. 8

- 8

8.2.1 Introduction to Discount Rate Calculations

................................ .............................. 8

- 8

8.2.2 Choosing a Discount Rate

................................ ................................ ....................... 8

- 8

8.2.3 Discount Rate Components

................................ ................................ ..................... 8

- 8

8.2.4 Market Structure: Utility

- Merchant Financing Ratio

................................ ................. 8

- 9

8.2.5 Debt and Equity Shares and Technology Risk

................................ ...................... 8

- 10

8.3 Calculation of Capital Charge Rate

................................ ................................ ...................... 8

- 13

8.3.1 Introduction to Capital Charge Rate Calculations

................................ .................. 8

- 13

8.3.2 Capital Charge Rate Components

................................ ................................ ......... 8

- 14

### v 8.3.3 Capital Charge Rate Calculation Process

................................ ............................. 8

- 15

9. Coal

................................ ................................ ................................ ................................ .................. 9

- 1

9.1 Coal Market Representation in EPA Base Case v.5.13

................................ ......................... 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

- 12

9.1.4 Emission Factors

................................ ................................ ................................ ... 9

- 13

9.1.5 Coal Grade Assignments

................................ ................................ ....................... 9

- 15

9. 2 Coal Supply Curves

................................ ................................ ................................ .............. 9

- 15

9.2.1 Nature of Supply Curves Developed for EPA Base Case v.5.13

.......................... 9

- 15

9.2.2 Cost Components in the Supply Curves

................................ ................................ 9

- 17

9.2.3 Procedures Employed in Determining Mining Costs

................................ ............. 9

- 17

9.2.4 Procedure Used In Determining Mine Productivity

................................ ................ 9

- 18

9.2.5 Procedure to Determine Total Recoverable R eserves by Region and Type

......... 9

- 19

9.2.6 New Mine Assumptions

................................ ................................ ......................... 9

- 19

9.2.7 Other Notable Procedures

................................ ................................ ..................... 9

- 19

9.2.8 Supply Curve Development

................................ ................................ ................... 9

- 21

9.2.9 EPA Base Case v.5.13 Assumptions and Outlooks for Major Supply Basins

....... 9

- 23

9.3 Coal Transportation

................................ ................................ ................................ .............. 9

- 24

9.3.1 Coal Transportation Matrix Overview

................................ ................................ .... 9

- 24

9.3.2 Calculation of Coal Transportation Distances

................................ ....................... 9

- 25

9.3.3 Overview of Rail Rates

................................ ................................ .......................... 9

- 26

9.3.4 Truck Rates

................................ ................................ ................................ ............ 9

- 28

9.3.5 Barge and Lake Vessel Rates

................................ ................................ ............... 9

- 29

9.3.6 Transportation Rates for Imported Coal

................................ ................................ 9

- 29

9.3.7 Other Transportation Costs

................................ ................................ ................... 9

- 30

9.3.8 Long

- Term Escalation of Transportation Rates

................................ ..................... 9

- 30

9.3.9 Market Drivers Moving Forward

................................ ................................ ............. 9

- 32

9.3.10 Other Considerations

................................ ................................ ............................. 9

- 34

9. 4 Coal Exports, Imports, and Non

- Electric Sectors Demand

................................ .................. 9

- 35

Attachment 9

- 1 Mining Cost Estimation Methodology and Assumptions

................................ ....... 9

- 37

10. Natural Gas

................................ ................................ ................................ ................................ .... 10

- 1

10.1 Overview of IPM’s Natural Gas Module

................................ ................................ ............... 10

- 1

10.2 Key Components of the New IPM Natural Gas Module

................................ ....................... 10

- 3

10.2.1 Note on the Modeling Time Horizon and Pre

- and Post

- 2040 Input Assumptions

.. 10

-

14

10.3 Resource Characterization and Economic Evaluation

................................ ....................... 10

- 15

10.3.1 Resource and Reserves Assessment

................................ ................................ .. 10

- 16

10.3.2 Frontier Resources (Alaska and Mackenzie Delta)

................................ ............. 10

- 18

10.3.3 Use of the HSM resource and reserves data in EPA Base Case using IPM v.5.13

Natural Gas Module

................................ ................................ ............................. 10

- 18

10.3.4 Undiscovered Resource Appreciation

................................ ................................ . 10

- 21

10.4 Exploration, Development, and Production Costs and Constraints

................................ .. 10

- 22

10.4.1 Exploration and Development C ost

................................ ................................ ..... 10

- 22

10.4.2 Resource Discovery and Drilling Constraints

................................ ...................... 10

- 25

10.4.3 Reserves

- to

- Production (R/P) Ratio

................................ ................................ .... 10

- 27

10.4.4 Variable Costs, Natural Gas Liquid Share, and Crude Oil Share

........................ 10

- 27

10.4.5 Lease and Plant Gas Use

................................ ................................ .................... 10

- 27

### vi

10.5 Liquefied Natural Gas (LNG) Imports

................................ ................................ ................. 10

- 28

10.5.1 Liquefaction Facilities and LNG Supply

................................ ............................... 10

- 28

10.5.2 Regasification Facilities

................................ ................................ ....................... 10

- 29

10.5.3 LNG Regasification Capacity Expansions

................................ ........................... 10

- 30

10.6 End Use Demand

................................ ................................ ................................ ............... 10

- 31

10.6.1 Step 1: Developing Sector Specific Econometric Models of Non

- Power Sector

Demand

................................ ................................ ................................ ............... 10

- 31

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

- 33

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

- 33

10.6.4 The Use of Firm Gas Demand to Represent LNG Exports

................................ . 10

- 35

10.7 Pipeline Network

................................ ................................ ................................ ................. 10

- 35

10.7.1 Network Structure

................................ ................................ ................................ 10

- 35

10.7.2 Pipeline Transportation Costs

................................ ................................ .............. 10

- 36

10.7.3 Pipeline Capacity Expansion Logic

................................ ................................ ...... 10

- 37

10.8 Gas Storage

................................ ................................ ................................ ........................ 10

- 38

10.8.1 Stora ge Capacity and Injection/Withdrawal Constraints

................................ ...... 10

- 40

10.8.2 Variable Cost and Fuel Use

................................ ................................ ................. 10

- 41

10.8.3 Storage Capacity Expansion Logic

................................ ................................ ...... 10

- 42

10.9 Fuel Prices

................................ ................................ ................................ .......................... 10

- 43

10.9.1 Crude Oil and Natural Gas Liquids Prices

................................ ........................... 10

- 43

10.9.2 Natural Gas Prices

................................ ................................ ............................... 10

- 44

10.10 Outputs and Glossary of Terms

................................ ................................ .......................... 10

- 44

10.10.1 Outputs from the IPM Natural Gas Module

................................ .......................... 10

- 44

10.10.2 Glossary of Terms Used in this Section

................................ ............................... 10

- 44

11. Other Fuels and Fuel Emission Factor Assumptions

................................ ................................ ..... 11

- 1

11.1 Fuel Oil

................................ ................................ ................................ ................................ . 11

- 1

11.2 Biomass

................................ ................................ ................................ ................................ 11

- 2

11.3 Nuclear Fuel

................................ ................................ ................................ ......................... 11

- 5

11.4 Waste Fuels

................................ ................................ ................................ .......................... 11

- 5

11.5 Fuel Emission Factors

................................ ................................ ................................ .......... 11

- 5

### vii LIST OF TABLES

Table 1

- 1 Updates in the EPA Base Case v.5.13

................................ ................................ ...................... 1

- 2

Table 1

- 2 Plant Types in EPA Base Case v.5.13

................................ ................................ ..................... 1

- 3

Table 1

- 3 Emission Control Technologies in EPA Base Case v.5.13

................................ ....................... 1

- 3

Table 3

- 1 Mapping of NERC Regions and NEMS Regions with EPA Base Case v.5.13 Model Regions 3

-

3

Table 3

- 2 Electric Load Assumptions in EPA Base Case v.5.13

................................ .............................. 3

- 5

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.5.13

.............. 3

- 6

Table 3

- 5 Annual Joint Capacity and Energy Limits to Transmission Capabilities Between Model Regions

in E PA Base Case v.5.13

................................ ................................ ................................ ......................... 3

- 13

Table 3

- 6 International Electricity Imports in EPA Base Case v.5.13

................................ ..................... 3

- 14

Table 3

- 7 Availability Assumptions in the EPA Base Case v.5.13

................................ ......................... 3

- 15

Table 3

- 8 Seasonal Hydro Capacity Factors (%) in the EPA Base Case v.5.13

................................ .... 3

- 16

Table 3

- 9 Planning Reserve Margins in EPA Base Case v.5.13

................................ ............................ 3

- 18

Table 3

- 10 Lower and Upper Limits Applied to Heat Rate Data in NEEDS v.5.13

................................ . 3

- 21

Table 3

- 11 State

- of

- the

- Art Combustion Control Configurations by Boiler Type

................................ .... 3

- 24

Table 3

- 12 Emission and Removal Rate Assumptions for Potential (New) Units in EPA Base Case v.5.13

................................ ................................ ................................ ................................ ................................ . 3

- 30

Table 3

- 1.1 Cutoff and Floor NO

x 

### Rates (lb/MMBtu) in EPA Base Case v.5.13

................................ ...... 3

- 35

Table 3

- 1.2 NO

x 

### Removal Efficiencies for Different Combustion Control Configurations in EPA Base Case

v.5.13

................................ ................................ ................................ ................................ ........................ 3

- 35

Table 3

- 13 State Power Sector Regulations included in EPA Base Case v.5.13

................................ .... 3

- 39

Table 3

- 14 New Source Review (NSR) Settlements in EPA Base Case v.5.13

................................ ...... 3

- 47

Table 3

- 15 State Settlements i n EPA Base Case v.5.13

................................ ................................ ......... 3

- 66

Table 3

- 16 Citizen Settlements in EPA Base Case v.5.13

................................ ................................ ...... 3

- 69

Table 3

- 17 Renewable Portfolio Standards in EPA Base Case v.5.13

................................ ................... 3

- 71

Table 3

- 18 Complete Availability Assumptions in EPA Base Case v.5.13

................................ .............. 3

- 73

Table 3

- 19 BART Regulati ons included in EPA Base Case v.5.13

................................ ......................... 3

- 74

Table 4

- 1 Data Sources for NEEDS v.5.13 for EPA Base Case v.5.13

................................ ................... 4

- 2

Table 4

- 2 Rules Used in Populating NEEDS v.5.13 for EPA Base Case v.5.13

................................ ...... 4

- 2

Table 4

- 3 Summary Population (through 2010) of Existing Units in NEEDS v.5.13

................................ . 4

- 4

Table 4

- 4 Hierarchy of Data Sources for Capacity in NEEDS v.5.13

................................ ....................... 4

- 4

Table 4

- 5 Capacity

- Parsing Algo rithm for Steam Units in NEEDS v.5.13

................................ ................ 4

- 5

Table 4

- 6 Data Sources for Unit Configuration in NEEDS v.5.13 for EPA Base Case v.5.13

.................. 4

- 6

Table 4

- 7 Aggregation Profile of Model Plants as Provided at Set Up of EPA Base Case v.5.13

........... 4

- 7

Table 4

- 8 VOM Assumptions in EPA Base Case v.5.13

................................ ................................ ........ 4

- 10

Table 4

- 9 FOM Assumptions Used in EPA Base Case v.5.13

................................ ............................... 4

- 12

Table 4

- 10 Life Extension Cost Assumptions Used in EPA Base Case v.5.13

................................ ...... 4

- 16

Table 4

- 11 Summary of Planned

- Committed Units in NEEDS v.5.13 for EPA Base Case v.5.13

......... 4

- 16

Table 4

- 12 Planned

- Committed Units by Model Region in NEEDS v.5.13 for EPA Base Case v.5.13

. 4

- 17

Table 4

- 13 Performance and Unit Cost Assumptions for Potential (New) Capacity from Conventional

Technologies in EPA Base Case v.5.13

................................ ................................ ................................ .. 4

- 25

Table 4

- 14 Short

- Term Capital Cost Adders for New Power Plants in EPA Base Case v.5.13 (2011$) 4

- 25

Table 4

- 15 Regional Cost Adjustment Factors for Conventional and Renewable Generating Technologies

in EPA Base Case v.5.13

................................ ................................ ................................ ......................... 4

- 26

Table 4

- 16 Performance and Unit Cost Assumptions for Potential (New) Renewable and Non

Conventional Technology Capacity in EPA Base Case v.5.13

................................ ................................ 4

- 29

### viii

Table 4

- 17 Onshore Regional Potential Wind Capacity (MW) by Wind and Cost Class in EPA Base Case

v.5.13

................................ ................................ ................................ ................................ ........................ 4

- 31

Table 4

- 18 Offshore Shallow Regional Potential Wind Capacity (MW) by Wind and Cost Class in EPA

Base Case v.5.13

................................ ................................ ................................ ................................ ..... 4

- 39

Table 4

- 19 Offshore Deep Regional Potential Wind Capacity (MW) by Wind and Cost Class in EPA Base

Case v.5.13

................................ ................................ ................................ ................................ .............. 4

- 42

Excerpt of Table 4

- 20 Representative Wind Generation Profiles in EPA Base Case v.5.13

.................. 4

- 45

Table 4

- 21 Onshore Reserve Margin Contribution an Average Capacity Factor by Wind Class

........... 4

- 46

Table 4

- 22 Offshore Shallow Reserve Margin Contribution an Average Capacity Factor by Wind Class

.. 4

-

46

Table 4

- 23 Offshore Deep Reserve Margin Contribution an Average Capacity Factor by Wind Class

. 4

- 46

Tabl e 4

- 24 Capital Cost Adjustment Factors for New Wind Plants in Base Case v.5.13

....................... 4

- 47

Table 4

- 25 Example Calculations of Wind Gener ation Potential, Reserve Margin Contribution, and

Capital Cost for Onshore Wind in WECC_CO at Wind Class 3, Cost Class 2

................................ ........ 4

- 47

Table 4

- 26 Solar PV Regional Potential Capacity (MW) in EPA Base Case v.5.13

................................ 4

- 48

Table 4

- 27 Solar Thermal Regional Potential Capacity (MW) in EPA Base Case v.5.13

....................... 4

- 51

Excerpt of Table 4

- 28 Representative Solar Generation Profiles in EPA Base v.5.13

............................ 4

- 52

Table 4

- 29 Solar Photovoltaic Reserve Mar gin Contribution and Average Capacity Factor by State and

Solar Thermal Reserve Margin Contribution and Average Capacity Factor by Class

............................. 4

- 53

Table 4

- 30 Regional Assumptions on Potential Geothermal Electric Capacity

................................ ...... 4

- 54

Table 4

- 31 Potential Geothermal Capacity and Cost Characteristics by Model Region

........................ 4

- 54

Table 4

- 32 Regional Assumptions on Potential Electric Capacity fr om New Landfill Gas Units (MW)

.. 4

- 56

Table 4

- 33 Nuclear Upratings (MW) as Incorporated in EPA Base Case v.5.13

................................ ... 4

- 60

Table 4

- 34 Characteristics of Existing Nuclear Units

................................ ................................ ............... 4

- 60

Excerpt from Table 4

- 35 Capacity Not Included Based on EIA Fo r m 860

- Existing Units

..................... 4

- 62

Table 4

- 36 Capacity Not Included Due to Recent Announcements

................................ ........................ 4

- 62

Table 5

- 1 Summary of Emission Control Technology Retrofit Options in EPA Base Case v.5.13

............ 5

- 1

Table 5

- 2 Summary of Retrofit SO 2 

### Emission Control Performance Assumptions in Base Case v.5.13

.. 5

- 2

Table 5

- 3 Illustrative Scrubber Costs (2011$) for Representative Sizes and Heat Rates under the

Assumptions in EPA Base Case v.5.13

................................ ................................ ................................ ..... 5

- 4

Table 5

- 4 Cost (2011$) of NO

x 

### Comb ustion Controls for Coal Boilers (300 MW Size)

............................. 5

- 5

Table 5

- 5 Summary of Retrofit NO

x 

### Emission Control Performance Assumptions

................................ ... 5

- 6

Table 5

- 6 Illustrative Post

- combustion NO

x 

### Control Costs (2011$) for Coal Plants for Representative

Sizes and Heat Rates under the A ssumptions in EPA Base Case v.5.13

................................ ................. 5

- 7

Table 5

- 7 Post

- Combustion NO

x 

### Controls for Oil/Gas Steam Units in EPA Base Case v.5.13

................ 5

- 8

Table 5

- 8 Biomass Co

- firing for Coal Plants

................................ ................................ .............................. 5

- 9

Table 5

- 9 Assumptions on Mercury Concentration in Non

- Coal Fuel in EPA Base Case v.5.13

............ 5

- 11

Table 5

- 10 Mercury Emission Modification Factors Used in EPA Base Case v.5.13

.............................. 5

- 12

Table 5

- 11 Definition of Acronyms for Existing Controls

................................ ................................ ......... 5

- 14

Table 5

- 12 Key to Burner Type Designations in Table 5

- 10

................................ ................................ .... 5

- 14

Table 5

- 13 Assignment Scheme for Mercury Emissions Control Using Activated Carbon Injection (ACI) in

EPA Base Case v.5.13

................................ ................................ ................................ ............................. 5

- 16

Table 5

- 14 Illustrative Activated Carbon Injection (ACI) Costs (2011$) for Representative Sizes and Heat

Rates under the Assumptions in EPA Base Case v.5.13

................................ ................................ ........ 5

- 19

Table 5

- 15 HCl Removal Rate Assumptions for Potential (New) and Existing Units in EPA Base Case

v.5.13

................................ ................................ ................................ ................................ ........................ 5

- 20

Table 5

- 16 Summary of Retrofit HCl (and SO 2 

### ) Emission Control Performance Assumptions in v.5.13 5

- 21

Table 5

- 17 Illustrative Dry Sorbent Injection (DSI) Costs for Representative Sizes and Heat Rates under

Assumptions in EPA Base Case v.5.13

................................ ................................ ................................ ... 5

- 23

### ix

Table 5

- 18 Illustrative Particulate Controls for Costs (2011$) for Representative Sizes and Heat Rates

under the Assumptions in EPA Base Case v.5. 13

................................ ................................ ................... 5

- 23

Table 5

- 19 Electrostatic Precipitator (ESP) Upgrades as Implemented in EPA Base Case v.5.13

--- Characteristics, Trigger Po ints, Associated Costs, and Performance Improvements

............................. 5

- 24

Excerpt from Table 5

- 20 ESP Upgrade Provided to Existing Units without Fab ric Filters so that They Meet

Their Filterable PM Compliance Requirement

................................ ................................ ......................... 5

- 26

Table 5

- 21 Cost and Performance Assumptions for Coal

- to

- Gas Retrofits

................................ ............. 5

- 26

Excerpt from Table 5

- 22 Cost of Building Pipelines to Coal Plants

................................ ......................... 5

- 32

Table 6

- 1 Performance and Unit Cost Assumptions for Carbon Capture Retrofits on Pulverized Coal

Plants

................................ ................................ ................................ ................................ ......................... 6

- 1

Excerpt from Table 6

- 2 CO 2 

### Storage Cost Curves in EPA Base Case v.5.13

................................ .......... 6

- 4

Excerpt from Table 6

- 3 CO 2 

### Transportation Matrix in EPA Base Case v.5.13

................................ .......... 6

- 6

Table 7

- 1 Run Years and Analysis Year Mapping Used in the EPA Base Case v.5.13

........................... 7

- 1

Table 7

- 2 First Stage Retrofit Assignment Scheme in EPA Base Case v.5.1 3

................................ ........ 7

- 2

Table 7

- 3 Second and Third Stage Retrofit Assignment Scheme in EPA Base Case v.5.13

.................. 7

- 3

Table 7

- 4 Trading and Banking Rules in EPA Base Case v.5.13

................................ .............................. 7

- 5

Table 8

- 1 Capital Structure Assumptions in EPA Base Case v.5.13

................................ ....................... 8

- 10

Table 8

- 2 Debt Rates for EPA Base Case v.5.13

................................ ................................ .................... 8

- 11

Table 8

- 3 U.S. Real Capital Charge Rates a 

### for EPA Base Case v.5.13

................................ .................. 8

- 13

Table 8

- 4 Book Life, Debt Life and Depreciation Schedules for EPA Base Case v. 5.13

....................... 8

- 14

Table 9

- 1 Coal Supply Regions in EPA Base Case

................................ ................................ ................. 9

- 2

Table 9

- 2 Coal Demand Regions in EPA Base Case

................................ ................................ ............... 9

- 4

Table 9

- 3 Coal Rank Heat Cont ent Ranges

................................ ................................ ........................... 9

- 12

Table 9

- 4 Coal Grade SO 2 

### Content Ranges

................................ ................................ ........................... 9

- 12

Table 9

- 5 Coal Quality Characteristics by Supply Region and Coal Grade

................................ ........... 9

- 13

Table 9

- 6 Example of Coal Assignments Made in EPA Base Case

................................ ........................ 9

- 15

Table 9

- 7 Basin

- Level Gr oupings Used in Preparing v.5.13 Coal Supply Curves

................................ ... 9

- 16

Table 9

- 8 Rail Competition Definitions

................................ ................................ ................................ ..... 9

- 26

Table 9

- 9 Assumed Eastern Rail Rates for 2012 (2011 mills/ton

- mile)

................................ ................... 9

- 27

Table 9

- 10 Assumed Midwestern Rail Rates for 2012 (2011 mills/ton

- mile)

................................ ........... 9

- 27

Table 9

- 11 Assumed Non

- PRB Western Rail Rates for 2012 (2011 mills/ton

- mile)

................................ 9

- 28

Table 9

- 12 Assumed PRB Western Rail Rates for 2012 (2011 mills/ton

- mile)

................................ ....... 9

- 28

Table 9

- 13 Assumed Truck Rates for 2012 (2011 Real Dollars)

................................ ............................. 9

- 28

Table 9

- 14 Assumed Barge Rates for 2012 (2011 Real Dollars)

................................ ............................ 9

- 29

Table 9

- 15 Assumed Other Transportation Rates for 2012 (2011 Real Dollars)

................................ .... 9

- 30

Table 9

- 16 EIA AEO Diesel Fuel Forecast, 2012

- 2030 (2011 Real Dollars)

................................ ........... 9

- 32

Table 9

- 17 ABARES Forecast of Iron Ore Prices

................................ ................................ .................... 9

- 33

Table 9

- 18 Summary of Expected Escalation for Coal Transportation Rates, 2013

- 2050

..................... 9

- 34

Table 9

- 19 Coal Exports

................................ ................................ ................................ .......................... 9

- 35

Table 9

- 20 Residential, Commercial, and Industrial Demand

................................ ................................ . 9

- 35

Table 9

- 21 Coal to Liquids Demand

................................ ................................ ................................ ........ 9

- 36

Table 9

- 22 Coal Import Limits

................................ ................................ ................................ .................. 9

- 36

Table 9

- 24 Coal Supply Curves in EPA Base Case v.5.13

................................ ................................ ..... 9

- 38

Table 10

- 1 List of Nodes

................................ ................................ ................................ ......................... 10

- 4

Table 10

- 2 List of Gas Supply Regions

................................ ................................ ................................ .. 10

- 7

Table 10

- 3 List of Key Pipelines

................................ ................................ ................................ ............. 10

- 9

Table 10

- 4 U.S. and Canada Natural Gas Resources and Reserves

................................ .................. 10

- 19

Table 10

- 5 Exploration and Development Assumptions for EP A Base Case v.5.13

............................ 10

- 25

### x

Table 10

- 6 North American LNG Regasification Facilities

................................ ................................ .... 10

- 29

Table 10

- 7 Summer and Winter Load Segments in EPA Base Case v.5.13

................................ ........ 10

- 33

Table 10

- 8 List of Storage Nodes

................................ ................................ ................................ ......... 10

- 39

Table 10

- 9 Storage Capacity and Injection/Withdrawal Rates (BOY 2015)

................................ ......... 10

- 41

Table 10

- 10 Base Year 2015 Average Levelized Storage Capital Cost

................................ ............... 10

- 43

Table 10

- 11 Glossary of Natural Gas Terms Used in Documentation

................................ ................. 10

- 44

Table 11

- 1 Fuel Oil Prices by NEMS Region in EPA Base Case v.5.13

................................ ................ 11

- 1

Excerpt from Table 11

- 2 Biomass Supply Curves in EPA Base Case v.5.13

................................ ......... 11

- 3

Table 11

- 3 Non

- Elec tric Biomass Demand by Census Division in EPA Base Case v.5.13

................... 11

- 4

Table 11

- 4 Waste Fuels in NEEDS v.5.13 and EPA Base Case v.5.13

................................ ................. 11

- 5

Table 11

- 5 Fuel Emission Factor Assumptions in EPA Base Case v.5.13

................................ ............. 11

- 6

### xi LIST OF FIGURES

Figure 1

- 1 Modeling and Data Structures in EPA Base Case v.5.13

................................ ....................... 1

- 4

Figure 2

- 1 Hypothetical Chronological Hourly Load Curve and Seasonal Load Duration Curve in EPA

Base Case v.5.13

................................ ................................ ................................ ................................ ....... 2

- 8

Figure 2

- 2 Stylized Depiction of Load Duration Curve Used in EPA Base Case v.5.13

.......................... 2

- 9

Figure 2

- 3 Stylized Dispatch Order in EPA Base Case v.5.13

................................ ............................... 2

- 10

Figure 3

- 1 EPA Base Case v.5.13 Model Regions

................................ ................................ ................... 3

- 2

Figure 3

- 2 Scheduled Retirements of Existing Nuclear Capacity Under 60

- Year Life Assumption

....... 3

- 20

Figure 3

- 3 Modeling Process for Obtaining Projected NO

x 

### Emission Rates

................................ ........... 3

- 23

Figure 3

- 4 How One of the Four NO

x 

### Modes Is Ultimately Selected for a Unit

................................ ....... 3

- 24

Figure 5

- 1 Calculations Performed in Costing Lateral Pipeline Requirement

................................ ........ 5

- 28

Figure 5

- 2 Number of Laterals Required per Boiler

................................ ................................ ................. 5

- 29

Figure 5

- 3 Miles of Pipeline Required per Boiler

................................ ................................ ..................... 5

- 29

Figure 5

- 4 Diameter of Laterals

................................ ................................ ................................ ............... 5

- 30

Figure 5

- 5 Total Inch

- Miles of Laterals Required per Boiler

................................ ................................ .... 5

- 30

Figure 5

- 6 Total Cost to Each Boiler

................................ ................................ ................................ ........ 5

- 31

Figure 5

- 7 Cost per kW of Boiler Capacity

................................ ................................ ............................... 5

- 31

Figure 9

- 1 Map of the Coal Supply Regions in EPA Base Case v.5.13

................................ ................... 9

- 3

Figure 9

- 2 Coal Mine Productivity (2000

- 2011)

................................ ................................ ....................... 9

- 20

Figure 9

- 3 Average Annual Cost Growth Assumptions by Region (2012

- 2050)

................................ ..... 9

- 20

Figure 9

- 4 Maximum Annual Coal Product ion Capacity

................................ ................................ .......... 9

- 21

Figure 9

- 5 Illustration of Preliminary Step in Developing a Cumulative Coal Supply Curve

.................. 9

- 21

Figure 9

- 6 Illustration of Final Step in Developing a Cumulative Coal Supply Curve

............................. 9

- 22

Figure 9

- 7 Example Coal Supply Curve in Stepped Format

................................ ................................ ... 9

- 22

Figure 9

- 8 Calculation of Multi

- Mode Transportation Costs (Example)

................................ .................. 9

- 25

Figure 9

- 9 Rail Cost Indices Pe rformance (2Q2008

- 4Q2011)

................................ ............................... 9

- 31

Figure 9

- 10 Long

- Run Marginal Cost Breakdown by Transportation Mode

................................ ........... 9

- 32

Figure 10

- 1 Modeling and Data Structure in EPA Base Case v.5.13

................................ ..................... 10

- 2

Figure 10

- 2 Natural Gas Module in EPA Base Case v.5.13

................................ ................................ ... 10

- 2

Figure 10

- 3 Gas Transmission Network Map

................................ ................................ ......................... 10

- 4

Figure 10

- 4 Gas Supply Regions Map

................................ ................................ ................................ .... 10

- 7

Figure 10

- 5 Gas Demand Regions Map

................................ ................................ ................................ . 10

- 9

Figure 10

- 6 Natural Gas Storage Facility Node Map

................................ ................................ ............ 10

- 15

Figure 10

- 7 Resource Cost Curves at the Beginning of Year 2015

................................ ..................... 10

- 21

Figure 10

- 8 Exploration & Development and Production Processes and Costs to Bring Undiscovered

Resource into Reserves and Production

................................ ................................ ............................... 10

- 22

Figure 10

- 9 E&D and Production Technology Improvement Factor

................................ ..................... 10

- 23

Figure 10

- 10 Increm ental E&D Cost (BOY 2015) by Percentage of Dry Gas Resource Found

.......... 10

- 24

Figure 10

- 11 Drilling Rig Speed Constraint

................................ ................................ .......................... 10

- 27

Figure 10

- 12 North American LNG Supply Curves

................................ ................................ ............... 10

- 29

Figure 10

- 13 North American LNG Regasification Facilities Map

................................ ........................ 10

- 30

Figure 10

- 14 Examples of Firm Demand Curves by Electric Load Segment

................................ ....... 10

- 34

Figure 10

- 15 Examples of Interruptible Demand Curves by Electric Load Segment

........................... 10

- 34

Figure 10

- 16 LNG Export Assumptions in EPA Base Case v.5.13.

................................ ...................... 10

- 35

Figure 10

- 17 New England Pipeline Corridors in 2020

................................ ................................ ......... 10

- 36

Figure 10

- 18 Example Pipeline Discount Curve

................................ ................................ ................... 10

- 37

Figure 10

- 19 Pipeline Cost Growth Factor

................................ ................................ ............................ 10

- 37

Figure 10

- 20 Storage Cost Growth Factor

................................ ................................ ............................ 10

- 41

### xii

Figure 10

- 21 Example Maximum Storage Capacity Expansion

................................ ........................... 10

- 42

Figure 10

- 22 Crude Oil and NGL Pric es

................................ ................................ ............................... 10

- 43

### 1

- 1



# 1. Introduction

This document describes the nature, structure, and capabilities of the Integrated Planning Model (IPM)

and th e assumptions underlying the base case (designated EPA Base Case v.5.13) that was developed

by the U.S. Environmental Protection Agency (EPA) with technical support from ICF, Inc . (ICF) . IPM is a

multi

- regional, dynamic, deterministic linear programming model of the U.S. electric power sector. It

provides forecasts 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 

### ), mercury (Hg), and HCl from the electric

power sector.

This new base case (v.5.13) incorporates important structural improvements and data updates with

respect to the previous version (v.4.10). A new v ersion number ( moving from v

. 4 to v

. 5) indicates a

substantial change to Base Case architecture (such as this version’s significant increase in the number of

model regions). Changing t he portion of the version name after the ‘dot’ ( moving from

. 10 to

. 13) indicates

the calibration of the model to more recent information (most importantly electricity demand project ions)

from a particular iteration of the Energy Information Agency’s (EIA) Annual Energy Outlook (AEO) , in this

case AEO 2013.

Base cases, like EPA Base Case v.5.13, serve as the starting point against which policy scenarios are

compared. Base Case v

. 5. 13 is a projection of electricity sector 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 base case was finalized in Aug ust 2013 (prior to publication of this

documentation). Section 3.9 contains a detailed discussion of the environmental regulations included in

EPA Base Case v.5.13 , which are summarized below

.

EPA Base Case v.5.13 includes the Clean Air Interstate Rul e (CAIR), a Federal regulatory measure

for achieving the 1997 National Ambient Air Quality Standards (NAAQS) for ozone (8

- hour average of

0.08 ppm) and fine particles (24

- hour average of 65 μg/m3 or less and annual average of 15 μg/m3

for particles of diam eter 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 regulatio ns that had been cited in

a ruling by the Court in July 2008. CAIR remains in effect until replaced by EPA pursuant to the

Court’s ruling . CAIR’s provisions were still in effect when EPA Base Case v.5.13 was released.

EPA Base Case v.5.13 includes NAAQS to the extent that state regulations included in EPA Base

Case v.5.13 contain measures to bring non

- attainment areas into attainment. A summary of these

state regulations can be found in Appendix 3

- 2. Apart from these state regulations, individual permit s

issued by states in response to NAAQS are captured (a) to the extent that they are reflected in the

NO

x 

### rates reported to EPA under CAIR, 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.

EPA Base Case v.5.13 includes the Mercury and Air Toxics Rule (MATS), which was finalized in

2011. MATS establishes National Emissions Standards for Hazardous Air Pollutants (NESHAPS) for

the “electric utility steam generating unit” source category.

EPA Base Case v.5.13 also reflects the final actions EPA has taken to implement the Regional Haze

Rule. This regulation requires s tates 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 Ret rofit

Technology (BART) emission targets for sources placed in operation between 1962 and 1977. Since

2010, EPA has approved SIPs or, in a very few cases, put in place regional haze Federal

Implementation Plans for several states. The BART limits approved in these plans (as of August 29,

2013) that will be in place for EGUs are represented in the EPA Base Case v.5.13.

### 1

- 2

Table 1

- 1 lists updates included in EPA Base Case v.5.13 listed in the order they appear in this

documentation report. Updates that are highlighted in gray were “non

- routine” in the sense that they

constituted new modeling capabilities, notable extensions beyond the capabilities provided in previous

EPA base cases, or significant reviews of important assumptions.

Table 1

- 1 Updates in the EPA Base Case v.5.13

Description For More

Information

Modeling Framework

Expansion of US model regions from 32 to 64 Section 3.1

Incorporation of three stages of environmental retrofits Section 7.2

Power System Operation

Updated capacity deployment constraints (for new advanced coal with carbon capture,

carbon capture retrofits, and new nuclear) Section 3.10 and

Attachment 3

- 1

Updated inventory of state emission regulations, including RGGI and AB32 (as of August

2013) Table 3

- 12

Updated inventories of NSR, state, and citizen settlements (as of August 2013) Table 3

- 13

Updated transmission TTC's (2012

- 2013 ISO/RTO and NERC reports) Table 3

- 4 and

Table 3

- 5

Updated regional reserve margins (NERC 2012) Table 3

- 9

AEO NEMS region level electricity demand is disaggregated to IPM model region level Table 3

- 2

Generating Resources

Updates to NEEDS, the database of existing and planned

- committed units and their

emission control configurations (Primary Sources: 2010, 2011 EIA Form 860, ETS 2011,

NERC ES&D 2011, AEO 2013) Table 4

- 1

Updated cost and performance characteristics for potential (new) conventional

, nuclear and

renewable generating units (AEO 2013 and NETL) Table 4

- 13 and

Table 4

- 16

New renewable units including biomass, wind, solar, geothermal and landfill gas are

modeled at a state level within each IPM region (Resource assumptions from NREL) Section 4.4.5

Emission Control Technologies

Complete update of cost and performance assumptions for SO 2



### , NO

x 

### , Hg and HCl emission

controls based on engineering studies by Sargent and Lundy

Updated cost and performance assumptions for coal

- to

- gas and retrofit options Section 5.7

Set

- Up Parameters and Rules

Modeling time horizon with seven model run years (2016, 2018, 2020, 2025, 2030, 2040,

2050) Section 7.1

CAIR, MATS, and BART are part of Base Case Section 7.3

All costs and prices are in 2011 dollars

Financial assumptions

Update of discount and capital charge rate assumptions based on a hybrid capital cost

model of utility and merchant finance structures for new units Section 8.2.2

Use of separate capital charge rates for retrofits based on utility and merchant finance

structures Section 8.3.2

Coal

Complete update of coal supply curves and transportation matrix (Wood Mackenzie 2012

-

2013 and Hellerworx 2012

- 2013) Table 9

- 23 and

Table 9

- 24

Coal demand regions are now disaggregated to the coal facility (ORIS) level Table 9

- 2

Natural Gas

Update of unconventional gas resource base (ICF 2013) Section 10.4

Other Fuels

Update of price assumptions for fuel oil, nuclear fuel and waste fuel (AEO 2013) Section 11.1

Incorporation of biomass supply curves at a state level (AEO 2013) Section 11.2

Biomass storage costs are added to the agricultural residues component of the biomass

supply curves Section 11.2

### 1

- 3

Table 1

- 2 lists the types of plants included in the EPA Base Case v.5.13.

Table 1

- 2 Plant Types in EPA Base Case v.5.13

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

Onshore Wind

Offshore Shallow Wind

Offshore Deep Wind

Fuel Cells

Solar Photovoltaics

Solar Thermal

Geothermal

Landfill Gas

Other a

Note:

a Includes fossil and non

- fossil waste plants.



### Table 1

- 3 lists the emission control technologies available for meeting emission limits in EPA Base Case

v.5.13.

Table 1

- 3 Emission Control Technologies in EPA Base Case v.5.13

Sulfur Dioxide ( SO 2 

### ) and Hydrochloric Acid (HCl)

Limestone Forced Oxidation (LSFO)

Lime Spray Dryer (LSD)

Dry Sorbent Injection (with milled Trona)

FGD Upgrade Adjustment

Nitrogen Oxides (NO

x 

### )

Combustion controls

Selective catalytic reduction (SCR)

Selective non

- catalytic reduction (SNCR)

Mercury (Hg)

Combinations of SO 2 

### , NO

x 

### , and particulate control technologies

Activated Carbon Injection

Particulate Matter (PM)

Pulse

- Jet Fabric Filter (FF)

Electrostatic Precipitator (ESP) Upgrade Adjustment

Carbon Dioxide ( CO 2 

### )

Heat rate improvement

Coal

- to

- gas

Carbon Capture and Sequestration

Notes:

a Units may also select among different coal types to manage

emissions in EPA Base Case v.5.13.

### 1

- 4

Figure 1

- 1 provides a schematic of the components of the modeling and data structure used for EPA

Base Case v.5.13. This report devotes a separate ch apter 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

Eng ine”), 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.5.13. Chapter 3 covers the power system operating characteristics captured in EPA Base Case

v.5.13. Chapter 4 explores the characterization of electric generation resources. Emission control

technologies (chapter 5 ) and carbon ca pture, transport and storage (chapter 6 ) are then presented.

Chapter 7 describes certain set

- up rules and parameters em ployed in EPA Base Case v.5.13. 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 waste fuels) in chapter 11 (along with fuel emission factors).

Figure 1

- 1 Modeling an d Data Structures in EPA Base Case v.5.13

### 2

- 1



# 2. Modeling Framework

ICF developed the Integrated Planning Model (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 p urpose, 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 some overlap between sections. For example, transmission decision

variables and constraints are covered in the discussion of model structure and formulation in section 2.2’

, and transmission modeling is covered as a key methodological feature in section 2.3.8 . The different

perspectives of each section are designed to provide readers with information that is complementary

rather than repe titive.



## 2.1 IPM Overview

IPM is a well

- established model of the electric 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 pas t, applications of IPM have

included capacity planning, environmental policy analysis and compliance planning, wholesale price

forecasting, and asset valuation.

2.1.1 Purpose and Capabilities

IPM is a dynamic linear programming model that generates optimal decis ions 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. emi ssion limits, transmission capabilities, renewable generation requirements, fuel

market constraints) that are placed on the power, emissions, and fuel markets. In particular, the model is

well

- suited to consider complex treatment of emission regulations i nvolving 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 respecting smaller

- scale transmission

limitations where adequate information was available ), the model regions representing the U.S. power

market in EPA Bas e Case v

. 5.13 are largely consistent with the North American Electric Reliability

Council (NERC) assessment regions and with the organizational structures of the Regional Transmission

Organizations (RTOs) and Independent System Operators (ISOs) , which hand le dispatch on most of the

U.S. grid. IPM represents the least

- cost arrangement of electricity supply (capacity and generation) within

each model region to meet assumed future load (electricity demand) while constrained by a transmission

network of bulk t ransfer limitations on interregional power flows . 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.

### 2

- 2

IPM can incorporate a detailed representation of fuel markets and can endogenously forecast fuel prices

for c oal, natural gas, and biomass by balancing fuel demand and supply for electric generation. The

model also includes detailed fuel quality parameters to estimate emissions from electric generation.

IPM provides estimates of air emission changes, regional w holesale 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 analyzed. This adaptability has made IPM suitable for a variety of

applications. These include:

Air Regulatory Assessm ent : Since IPM contains extensive air regulatory modeling features, state and

federal air regulatory agencies have used the model extensively in support of air regulatory assessment.

Integrated Resource Planning : IPM can be used to perform least

- cost plan ning studies that

simultaneously optimize demand

- side options (load management and efficiency ), renewable options and

traditional supply

- side options.

Strategic Planning : IPM can be used to assess the costs and risks associated with alternative utility an d

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 up on 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 Structur e and Formulation

IPM employs a linear programming structure that is particularly well

- suited for analysis of the electric

sector to help decision makers plan system 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 function, key decision variables, and constraints included

in IPM for EPA Base Case v.5.13.

2.2.1 Objective Function

IPM’s objective function is to minimize the total, 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 on a net

present value basis . These costs, which the linear programming formulation attempts to minimize,

include the cost of new plant and pollution control construction, fixed and variable operating and

maintenance costs, and fuel costs. Many of these cost components are captured in the objective function

### 2

- 3

by multiplying the decision variables by a cost coefficient. Cost escalation factors are used in the

ob jective function to reflect changes in cost over time. The applicable discount rates 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 Variables

Decision var iables represent the values for which the IPM model is solving, given the cost

- minimizing

objective function described in section 2.2.1 and the set of electric system constraints detailed in section

2.2.3 . The model d etermines values for these decision variables that 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 objective 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 cost. 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 associated with potential (new) units in each model run year.

In the objective function, the decision 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 v ariables 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 model 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 representing 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, the se year

differentiated allowance decision variables are multiplied by the market price for allowances prevailing in

each run year. This formulation allows IPM to capture the inter

- temporal trading and banking of

allowances.

Fuel Decision Variables : For ea ch type of fuel and each model run year, IPM defines decision variables

representing the quantity of fuel delivered from each fuel supply region to model plants in each demand

region. Coal decision variables are further differentiated according to coal ran k (bituminous, sub

bituminous, and lignite), sulfur grade, chlorine content and mercury content (see Table 9

- 5 ). These fuel

quality decision variables do not appear in the IPM objective function, but in constraints which define the

types of fuel that each model plant is eligible to use and the supply regions 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. 5 .1 3 , see section 4.2.6

.

### 2

- 4

2.2.3 Constraints

Model constraints are implemented in IPM to accurately reflect the characteristics of and the conditions

faced by the electric sector. Among the key constraints included in EPA Base Case v.5.13 are:

Reserve Margin Constraints : Regional reserve margin constraints capture system reliabili ty

requirements by defining a minimum margin of reserve capacity (in megawatts) per year beyond the total

capacity needed to meet future peak demand that must remain in service to that region

. These reserve

capacity constraints are derived from reserve margin targets that are assumed for each region based on

information from reliability planning officials at NERC, RTOs or ISOs. If existing plus planned capacity is

not sufficient t o satisfy the annual regional reserve margin requirement, the model will “build” the required

level of new capacity. Please see Section 3.6 for more information on reserve ma rgin constraints.

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 seasonal loa d 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 reg ion’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), g iven 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., whether 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 reflects the distinct operating characteristics of peaking, cycling, and base load

units.

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

### , NO

x 

### , HCl, mercury, and CO 2 

### . Emission constraints can be implemented 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 el igible to

use and the supply regions that are eligible 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 short

- and long

- te rm projections of production activity in the

electric generation sector. The projections obtained using IPM are not statements of what will happen

, but they are estimates of what might happen given the assumptions and methodologies used. Chapters 3

to 11 contain detailed discussions of the cost and performance assumptions specific to the EPA Base

Case v.5.13. This section provides an overview of the essential methodological and structural features of

IPM that extend beyond the assumptions that are specifi c to EPA Base Case v.5.13.

### 2

- 5

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 existi ng units, and (3) to represent potential (new) units that the model can build.

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 acceptabl e limits, EPA utilizes model plants to

represent aggregations of actual individual generating units. The aggregation algorithm groups units with

similar characteristics for representation by 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 vari ations in

key features of those units that are critical in the base case and anticipated policy case runs. For EPA

Base Case v.5.13, IPM employed an aggregation algorithm which allowed 16,330 actual existing electric

generating units to be represented by 4,971 model plants. Section 4.2.6 describes the aggregation

procedure used in the EPA Base Case v.5.13.

Retrofit and Retirement Options : IPM also utilizes model plants to represent the retrofit and retirement

options that are available to existing units. EPA Base Case v.5.13 provides existing model plants with a

wide range of options for retrofitting with emission control equipment as well as with an option to retire

. (See Chapters 5 and section 7.3 in Chapter 7 for a detailed discussion of the options that are included in

the EPA Base Case v.5.13.) EPA Base Case v

. 5.13 model plants that represent potential (new) units are

not given the option to take on a retrofit or retire.

The op tions 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 ini tially assigned to a model plant which represents

existing generating units 2



### . In setting up IPM, parent

- child

- grandchild relationships are pre

- defined

between each existing model plant (parent) and the specific retrofit and retirement model plants (chi ldren

and grandchildren) that may replace the parent model plant during the course of a model run. The “child”

and “grandchild” model

- plants are inactive in IPM unless the model finds it economical to engage one of

the options provided, e.g., retrofit wit h particular emission controls or retire.

Theoretically, there are no limits on the number of “child,” “grandchild,” and even “great

- grandchild” model

plants (i.e., retrofit and retirement options) that can be associated with each existing model plant.

H owever, model size and computational considerations dictate that the number of successive retrofits be

limited. In EPA Base Case v.5.13, a maximum of three stages of retrofit options are provided (child,

grandchild and great

- grandchild). For example, an existing model plant may 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), with an activated carbon injection (ACI) for mercury control in the same or

subsequent run year (stage 2) and with a CCS for CO 2 

### control in the same or subsequent run year (stage

3). However, if it exercises this succession of retrofit options, no further retrofit or retirement options are

possible beyond the third stage.

Potential (New) Units : IPM also uses model plants to represent 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, regional location, and years available. When it is economically

advantageous to do so (or otherwise required by reserve margin constraints to maintain electric

reliability) , IPM “builds” one or more of these predefined model plants by raising its generation capacity

from z ero 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 technology

2 

### IPM has a linear programming structure whose decision variables can assume any value within the specifie d

bounds subject to the constraints. Therefore, IPM can generate solutions where model plants take retrofits/retire a

portion of the model plants capacity. IPM’s standard model plant outputs explicitly present these partial investment

decisions.

### 2

- 6

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.5.13 results are presented at the model plant level, EPA has developed a post

processor “parsing” tool designed to trans late results at the model plant level into generating unit

- specific

results. The parsing tool produces unit

- specific emissions, fuel use, emission control retrofit and capacity

projections based on model plant results. Another post

- processing activity in volves deriving inputs for air

quality modeling from IPM outputs. This entails using emission factors to derive the levels of pollutants

needed in EPA’s air quality models from emissions and other parameters generated by IPM. It also

involves using decis ion rules to assign point source locators to these emissions. (See Figure 1

- 1 for a

graphical representation of the relationship of the post

- processi ng tools to the overall IPM structure. The

air quality ready flat file documentation is available at http://www.epa.gov/airmar kets/progsregs/epa

ipm/BaseCase513 .html )

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 that 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 decision

- making will continue to take information into account from years beyond th e model’s

time horizon . Due to the number of model run years required by EPA for analytical purposes (seven in

the 2016

- 2050 time period) and a greatly expanded suite of modeling capabilities, such an approach

could not be used in EPA Base Case v.5.13 . I t would have increased the model ’s size beyond

acceptable solution time constraints . However

, boundary distort ions are a potential factor only for results

in 2050, the last modeled year. In addition, any tendency toward end

- year distortions should be reduced

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 ) 3



### . Nevertheless, the possibility of

residual boundary effects is something to bear in min d when interpreting the model ’s results from the

2050 run year

.

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 electricity demand subject to resource availability

and other operating and environmental constraints. 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 operati ng 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:

3 

### The prim ary impact of end year distortion occurs on the investment decisions as they are made by the model while

accounting for costs and revenues over a short (number of years mapped to that run year) time period. As the

number of years mapped to the last run yea r increases, more of the costs and revenues of the plant’s life are

captured and thus improving the quality of the decision.

The longer modeling horizon does not directly reduce the end year distortion. However, the discounting occurring

over a longer tim e period does reduce the impact of the end year results on the overall model solution.

### 2

- 7

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, discounting to a common base yea r ensures that IPM properly

captures complex inter

- temporal cost relationships.

Capital costs in IPM’s objective function are represented 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 outlays 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 approach

avoids pres enting artificially higher capital costs for investment decisions taken closer to the model’s time

horizon boundary simply because some of that cost would typically be serviced in years beyond the

model’s view. 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 informing IPM’s objective function represent the composite cost over all years in

the planning horizon rather than just the cost i n 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 ity Markets

Another important methodological feature worth noting about IPM is that it is desig ned to simulate

electricity production activity in a manner that would minimize production costs, as is the intended

outcome in wholesale electric ity markets . For this purpose

, t he model captures transmission costs and

losses between IPM model regions , bu t it is not designed to capture retail distribution costs. However,

the model implicitly includes distribution losses since net energy for load, 4 

### rather than delivered sales, 5 

### is

used to represent electric ity demand in the model. Additionally, the produc tion costs calculated by IPM

are the wholesale production costs. In reporting costs, the model does not include embedded costs, such

as carrying charges of existing units

, that may ultimately be part of the retail cost incurred by end

- use

consumers

.

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 electricity demand , the

LDCs are created by rea rranging the hourly chronological electric load data from the highest to lowest

(MW) value. In order to aggregate such load detail into a format enabling this scale of power sector

modeling , EPA applications of IPM use a 6

- step piecewise linear representa tion of the LDC.

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.5.13 contains two seasons:

summer (May 1

– September 30) and winter (Octob er 1

– April 30). Separate summer and winter LDCs

are created for each of IPM’s model regions. Figure 2

- 1 below presents side

- by

- side graphs of a

hyp othetical chronological hourly load curve and a corresponding load duration curve for a season

consisting of 3,672 hours.

4 

### Net energy for load is the electrical energy requirements of an electrical system, defined as system net generation,

plus energy received from others, less energy del ivered to others through interchange. It includes distribution losses.

5 

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

### 2

- 8

Figure 2

- 1 Hypothetical Chronological Hourly Load Curve and Seasonal Load Duration Curve

in EPA Base Case v.5.13

Regional forecasts of peak and total electricity demand (from AEO 2013 for EPA Base Case v5.13 ) and

hourly load curves from FERC Form 714 and ISO/RTOs (2011 for EPA Base Case v5.13 ) are used to

derive future seasonal load duration curves for each IPM run year in each IPM region. The results of this

process are individualized seasonal LDCs that capture the unique hourly electricity demand profile of

eac h region. The LDCs change over time to reflect projected changes in load factors

.

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.5.13 uses six load segments in its seasonal LDCs for model

run years 2016

- 2030 and four load segments in its LDCs for model run years 2040 and 2050. The

reduced number of load segmen ts 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

- 2 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 re presents 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

- 2 generally contains one percent of the hours in the period (i.e., "season") but

represents the highest load demand value. IPM has the flexibility 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 model 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 considerations and operating constraints. The most cost effective plants are

assigned to meet load in all 6 segments of the load duration curve. Th is is discussed in greater detail in

section 2.3.6 below. In 2040 and 2050 run years, segments 1 & 2 are aggregated into a single segment

and se gments 3 & 4 are aggregated into a single segment for a total of 4 segments.

Use of seasonal LDCs rather than annual LDCs allows 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 during the summer season. The use of seasonal LDCs also allows

IPM to capture seasonal variations in the generation resources available to respond to the customer

demand depicted in an LDC. F or 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. Furthe r, because of maintenance

### 2

- 9

scheduling for individual generating units, the capacity and utilization for these supply resources also vary

between seasons.

Attachment 2

- 1 contains data of the 2012 summer and winter load duration curves in each of the 64

model regions in the lower continental U.S. for EPA Base Case v.5.13.

Figure 2

- 2 Stylized Depiction of Lo ad Duration Curve Used in EPA Base Case v.5.13

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 ma rginal generating unit, i.e.,

the power plant that generates the last unit of electricity, sets the energy price. Physical operating

constraints also influence 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

with turndown constraints enable IPM to dispatch generation reso urces in a technically realistic fashion.

Figure 2

- 3 below depicts a highly stylized dispatch order based on the variable cost of generation of the

re source options included in the EPA Base Case v.5.13. In Figure 2

- 3 , a hypothetical load duration curve

is subdivided according to the type 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 are accordingly at th e 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 ( e.g ., peaking

turbines) are at the top of the “d ispatch stack,” since they are dispatched last and for the minimum

possible number of hours.

### 2

- 10 Figure 2

- 3 Stylized Dispatch Order in EPA Base Case v.5.13

Note: Figure 2

- 3 does not include all the plant ty pes that are modeled in EPA Base Case v.5.13.

Intermittent r enewable technologies such as wind and solar are considered non dispatchable and are

assigned a specific hourly generation profile.



### 2.3.7 Fuel Modeling

Another key methodological feature of IPM is its c apability 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.5.13 in one of

three alternative ways: (1) through an embedded modeling capability that dynamically balances supply

and demand to arrive at fuel prices (natural gas), (2) through a set of supply curves (coal and biomass) or

(3) through an exogenous price stre am (fuel oil and nuclear fuel). With the first and second approaches,

the model endogenously determines the price for that fuel by balancing the supply and demand. IPM

uses fuel quality information (e.g., the sulfur, chlorine or mercury content of differ ent types of coal from

different supply regions) to determine the emissions resulting from combustion of that fuel.

EPA Base Case v.5.13 includes coal, natural gas, fuel oil, nuclear fuel, biomass, and fossil and non

- fossil

waste as fuels for electric gen eration. The specific base case assumptions for these fuels are examined

in chapters 9 to 11.

2.3.8 Transmission Modeling

IPM includes a detailed representation of existing transmission capabilities between model regions. The

maximum transmission capabilities between regions are specified in IPM’s transmission constraints. Due

to uncertainty surrounding the building of new transmission lines in the U.S., EPA Base Case v5.13 does

not exercise IPM’s capability to model the building of new transmission lines. However, that capacity of

the model is described here in case it is applied in future analyses. Additions to transmiss ion lines are

represented by decision variables defined for each eligible link and model run year. In IPM’s objective

### 2

- 11

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. The specific transmission assumptions in EPA Base Case v.5.13 are described in section 3.3

.

2.3.9 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 production activity in wholesale electric mar kets 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. However, if desire d, appropriately designed sensitivity analyses or redefined model

parameters can be used to gauge the impact of market imperfections on the wholesale electric markets.

IPM’s assumption of perfect foresight implies that agents know precisely the nature and timing of

conditions in future years that affect the ultimate costs of decisions along the way . For example, under

IPM there is complete foreknowledge of future electricity demand, fuel supplies, and other variables

(including regulatory requirements ) that in reality are subject to uncertainty and limited foresight

. Modelers frequently assume perfect foresight in order to establish a decision

- making framework that can

estimate cost

- minimizing courses of action given the best

- guess expectations of the se future variables

that can be constructed at the time the projections are made.

2.3.10 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 IPM. 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 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

emissi ons trading policies , command

- and

- control policies, and renewable portfolio standards. IPM’s

representation of emissions trading policies can include allowance banking, trading, borrowing, bonus

allowance mechanisms, and progressive flow controls. Air regulations 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.5.13.



## 2.4 Hardware and Programming Features

IPM produces model files in sta ndard MPS linear programming format. IPM runs on most PC

- platforms.

Its hardware requirements are highly dependent on the size of a particular model run. For example, with

almost 16.6 million decision variables and 1.8 million constraints, EPA Base Case v.5.13 is run on a 64 bit

Enterprise Server

- Windows 2008 R2 platform with two Intel Xeon X5675 3.07 GHz processors and 72

GB of RAM. Due to the size of the EPA base case, a commercial grade solver is required.

(Benchmarking tests performed by EPA's N ational Environmental Scientific Computing Center using

research grade solvers yielded unacceptable results.) For current EPA applications of IPM, the FICO

Xpress Optimization Suite 7.5 (64 bit with multi

- threads barrier and MIP capabilities) linear progr amming

solvers are used.

Two data processors

-- a front

- end and the post

- processing tool

-- support the model. The front

- end

creates the necessary inputs to be used in IPM, while the post

- processing tool maps IPM model

- plant

level outputs to individual ge nerating units (a process called “parsing,” see section 2.3.1 ) and creates

input files in flat file format as needed by EPA’s air quality models.

### 2

- 12

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 model run s 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 inpu t 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.5.13. This section simply lists th e key input parameters required by IPM:

Electric System

Existing 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

Emissio ns Limits or Emission Rates for NO

x 

### , SO 2 

### , HCl, 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

Other System Requirements

Inter

- regional Transmission Capabilities

Reserve Margin Requirements for Reliability

Area Protection

System Specific Generation Requirements

Regional Specification

Economic Outlook

Electricity D emand

F irm Regional Electricity D emand

Load Curves

Financial Outlook

Capital Charge Rate

Discount Rate

Fuel Supply

Fuel Supply Curves for Coal and Biomass

### 2

- 13

Embedded Natural Gas Model

Fuel Price

Fuel Quality

Transportation Costs for Coal, Natural Gas, and Biomass

Air Regulatory Outlook

Air Regulations for NO

x 

### , SO 2 

### , HCl, CO 2 

### , and 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. Standard

IPM reports cover the following topics:

Generation

Capacity mix

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 

### , HCl, CO 2 

### , and Mercury)

Emission allowance pric es

### 2

- 14 

## Attachment 2

- 1 Load Duration Curves a 

## Used in EPA Base Case v.5.13

This is a small ex c erpt of the data in Attachment 2

- 1 . The complete data set in spreadsheet format can be downloaded via the link found at

www.epa.gov/airmarkets/prog sregs/epa

- ipm/BaseCasev513.htm

Month 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Hours 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Day

Hours 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2

ERC_

REST 2 9,087 2 9,081 2 9,428 30,073 31,131 32,672 33,837 34,243 33,469 32,063 30,593 2 9,370 2 8,122 2 7,173 2 6,764 2 7,133 2 9,534 32,765 33,125 33,279 32,700 31,281 2 9,582 2 8,464 2 8,103 2 8,113

ERC_

WEST 2 ,655 2 ,678 2 ,726 2 ,765 2 ,836 2 ,903 2 ,961 3,110 2 ,989 2 ,762 2 ,582 2 ,490 2 ,358 2 ,249 2 ,188 2 ,181 2 ,350 2 ,603 2 ,716 2 ,750 2 ,749 2 ,713 2 ,593 2 ,579 2 ,565 2 ,585

FRCC 17,709 16,861 16,336 16,045 15,996 16,303 16,879 17,602 19,095 2 0,808 2 1,921 2 2,566 2 2,995 2 3,070 2 2,956 2 2,784 2 2,750 2 3,609 2 5,136 2 4,741 2 3,876 2 2,478 2 0,971 19,160 17,682 16,839

MAP_

WAU

E 2 ,551 2 ,528 2 ,531 2 ,545 2 ,577 2 ,654 2 ,733 2 ,749 2 ,772 2 ,750 2 ,746 2 ,692 2 ,666 2 ,628 2 ,661 2 ,786 2 ,939 2 ,943 2 ,885 2 ,805 2 ,756 2 ,637 2 ,562 2 ,490 2 ,478 2 ,502

MIS_I

A 2 ,332 2 ,308 2 ,276 2 ,266 2 ,262 2 ,275 2 ,324 2 ,372 2 ,408 2 ,433 2 ,440 2 ,442 2 ,437 2 ,414 2 ,400 2 ,393 2 ,431 2 ,526 2 ,620 2 ,596 2 ,573 2 ,536 2 ,442 2 ,348 2 ,292 2 ,259

MIS_I

L 5,892 5,842 5,823 5,855 5,911 6,045 6,207 6,299 6,319 6,281 6,250 6,204 6,143 6,071 6,019 6,059 6,313 6,693 6,774 6,765 6,691 6,539 6,333 6,149 6,066 6,039

MIS_I

NKY 10,885 10,709 10,617 10,588 10,645 10,757 11,010 11,302 11,470 11,525 11,482 11,438 11,368 11,272 11,157 11,088 11,206 11,701 12,306 12,417 12,382 12,225 11,929 11,548 11,230 11,087

MIS_

LMI 10,211 9,916 9,712 9,605 9,552 9,557 9,629 9,793 10,034 10,198 10,389 10,547 10,623 10,680 10,675 10,675 10,665 10,839 11,503 11,984 11,971 11,813 11,458 11,025 10,605 10,292

MIS_

MAPP 967 943 927 919 91 6 920 930 956 980 993 1,000 1,001 994 992 984 977 985 1,022 1,080 1,089 1,073 1,051 1,020 976 945 926

MIS_

MIDA 3,205 3,172 3,128 3,115 3,109 3,127 3,194 3,260 3,310 3,345 3,354 3,357 3,350 3,318 3,299 3,289 3,341 3,472 3,602 3,569 3,536 3,4 86 3,356 3,227 3,150 3,105

MIS_

MNWI 10,338 10,083 9,916 9,828 9,799 9,841 9,949 10,226 10,474 10,620 10,697 10,701 10,631 10,611 10,519 10,451 10,532 10,928 11,552 11,648 11,470 11,235 10,904 10,430 10,108 9,898

MIS_

MO 5,133 5,049 5,004 4,988 5,022 5,073 5,205 5,358 5,446 5,465 5,423 5,391 5,345 5,289 5,218 5,170 5,213 5,462 5,820 5,892 5,882 5,810 5,664 5,4 67 5,299 5,225

MIS_

WUM

S 5,657 5,529 5,462 5,427 5,429 5,476 5,580 5,732 5,832 5,944 6,032 6,075 6,104 6,101 6,100 6,095 6,191 6,5 23 6,739 6,732 6,663 6,499 6,288 6,063 5,884 5,793

NENG

_CT 3,117 3,006 2 ,948 2 ,921 2 ,932 2 ,969 3,047 3,145 3,308 3,466 3,555 3,608 3,635 3,620 3,602 3,642 3,856 4,069 4,053 3,988 3,894 3,736 3,531 3,330 3,211 3,1 41

NENG

_ME 954 878 850 851 874 919 997 1,111 1,238 1,343 1,399 1,399 1,403 1,369 1,343 1,336 1,515 1,645 1,595 1,516 1,420 1,258 1,128 1,025 970 935

NENG

REST 7,761 7,478 7,318 7,265 7,278 7,413 7,680 7,987 8,432 8,906 9,194 9,357 9,419 9,401 9,354 9,432 10,064 10,580 10,519 10,300 9,987 9,496 8,896 8,332 7,986 7,797

NY_Z

_A&B 2 ,064 2 ,003 1,955 1,945 1,958 1,996 2 ,057 2 ,132 2 ,201 2 ,288 2 ,352 2 ,393 2 ,435 2 ,444 2 ,457 2 ,471 2 ,565 2 ,757 2 ,776 2 ,752 2 ,701 2 ,608 2 ,465 2 ,317 2 ,215 2 ,166

a The load curves for EPA Base Case v.5.13 are complied using data from 2011 FERC Form 714, ISO and RTO data.

### 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.5.13.



## 3.1 Model Regions

EPA Base Case v.5.13 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 network even though politically they constitute a single

province 6 

### ) as an integrated network.

There are 64 IPM model regions covering the US 48 states and District of Columbia. The IPM model

regions are approximately consistent with the configuration of the NERC assessment regions in the

NERC Long

- Term Reliability Assessmen ts. These IPM model regions reflect the administrative structure

of regional transmission organizations (RTOs) and independent system operators (ISOs). Further

disaggregation of the NERC assessment regions and RTOs allows a more accurate characterization o f

the operation of the US power markets by providing the ability to represent transmission bottlenecks

across RTOs and ISOs, as well as key transmission limits within them.

The IPM regions also provide disaggregation of the regions of the National Energy Modeling System

(NEMS) to provide for a more accurate correspondence with the demand projections of the Annual

Energy Outlook (AEO). Notable disaggregations are further described below:

NERC assessment regions MISO and PJM cover the areas of the correspo nding RTOs and are designed

to better represent transmission limits and dispatch in each area. In IPM, the MISO area is disaggregated

into 9 IPM regions and the PJM assessment area is disaggregated into 9 IPM regions, where the IPM

regions are selected to represent planning areas within each RTO and/or areas with internal transmission

limits.

New York is now disaggregated into 7 IPM regions, to better represent flows around New York City and

Long Island, and to better represent flows across New York state from Canada and other US regions.

The NERC assessment region SERC is divided into North, South, West and Southeast areas; IPM further

disaggregates the North and West areas to better represent transmission between areas, including

disaggregating SERC

- West into four IPM regions to reflect transmission constraints in Southern

Louisiana.

IPM retains the NERC assessment areas within the overall WECC regions, and further disaggregates

these areas using sub

- regions from the WECC Power Supply Assessment.

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

Figure 3

- 1 contains a map showing all the EPA Base Case 5.13 mod el regions. Using these shares of

each NEMS region net energy for load that falls in each IPM region, calculate the total net energy for load

for each IPM region from the NEMS regional load in AEO 2013

.

Table 3

- 1 defines the abbreviated region names appearing on the map and gives a crosswalk between

the IPM model regions, the NERC assessment regions, and regions used in the Energy Information

Administr ation’s (EIA’s) National Energy Model System (NEMS) which is the basis for EIA’s Annual

Energy Outlook (AEO) reports.

6 

### This results in a total of 11 Canadian model regions being rep resented in EPA Base Case v.5.13.

### 3

- 2



## 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 (peak demand) is

the maximum hourly demand within a given year after remov ing interruptible demand. Table 3

- 2 shows

the electricity demand assumptions (expressed as net energy for load) used in EPA Base Case v.5.13. It

is bas ed on the net energy for load in AEO 2013

. 7



### Figure 3

- 1 EPA Base Case v.5.13 Model Regions

For purposes of documentation, Table 3

- 2 presents the national net energy for load. However, EPA Base

Case v.5.13 models regional breakdowns of net energy for load in each of the 64 IPM US regions in the

following steps:

The net ene rgy for load in each of the 22 NEMS electricity regions is taken from the NEMS reference

case.

NERC balancing areas are assigned to both IPM regions and NEMS regions to determine the share

of the NEMS net energy for load in each NEMS regions that falls int o each IPM region. These shares

are calculated in the following steps.

Map the NERC Balancing Authorities/ Planning Areas in the US to the 64 IPM regions.

7 

### The electricity demand in EPA Base Case v.5.13 for the U.S. lower 48 states and the District of Columbia is

obtained for each IPM model region by disaggregating the "Total Net Energy for Load" projected for the

correspon ding NEMS Electric Market Module region as reported in the Electricity and Renewable Fuel Tables 7 3

-

120 " at http://www.eia.gov/forecasts/aeo/tables_ref.cfm

.

### 3

- 3 Map the Balancing Authorities/ Planning Areas in the US to the 22 NEMS regions.

Using the 2007 data from FERC Form 714 on net energy for load in each of the balancing areas,

calculate the proportional share of each of the net energy for load in 22 NEMS regions that falls in

each of the 64 IPM Regions.

Using these shares of each NEMS region net energy for load that falls in each IPM region, calculate

the total net energy for load for each IPM region from the NEMS regional load in AEO 2013

.

Table 3

- 1 Mapping of NERC Regions and NEMS Regions with EPA Base Case

v.5.13 Model Regions

NERC Assessment

Region AEO 2013 NEMS

Region Model

Region Model Region Description

ERCOT a 

### ERCT (1) ERC_FRNT ERCOT_Tenaska Frontier Generating Station

ERC_GWAY ERCOT_Tenaska Gateway Generating

Station

ERC_REST ERCOT_Rest

ERC_WEST ERCOT_West

FRCC FRCC (2) FRCC FRCC

MAPP MROW (4) MAP_WAUE MAPP_WAUE

MIS_MAPP MISO_MT, SD, ND

MISO MROE (3), RFCW (11) MIS_WUMS MISO_Wisconsin

- Upper Michigan (WUMS)

MROW (4) MIS_IA MISO_Iowa

MIS_MIDA MISO_Iowa

- MidAmerican

MIS_MNWI MISO_Minnesota and Western Wisconsin

RFCM (10) MIS_LMI MISO_Lower Michigan

RFCW (11), SRCE

(15) MIS_INKY MISO_Indiana (including parts of Kentucky)

SRGW (13) MIS_IL MISO_Illinois

MIS_MO MISO_Missouri

ISO

- NE NEWE (5) NENG_CT ISONE_Connecticut

NENG_ME ISONE_Maine

NENGREST ISONE_MA, VT, NH, RI (Rest of ISO New

England)

NYISO NYCW (6) NY_Z_J NY_Zone J (NYC)

NYLI (7) NY_Z_K NY_Zone K (LI)

NYUP (8) NY_Z_A&B NY_Zones A&B

NY_Z_C&E NY_Zone C&E

NY_Z_D NY_Zones D

NY_Z_F NY_Zone F (Capital)

NYUP (8), NYCW (6) NY_Z_G

- I NY_Zone G

- I (Downstate NY)

PJM RFCE (9) PJM_EMAC PJM_EMAAC

PJM_PENE PJM_PENELEC

PJM_SMAC PJM_SWMAAC

PJM_WMAC PJM_Western MAAC

RFCW (11) PJM_AP PJM_AP

PJM_ATSI PJM_ATSI

PJM_COMD PJM_ComEd

PJM_West PJM West

SRVC (16) PJM_Dom PJM_Dominion

SERC

- E SRVC (16) S_VACA SERC_VACAR

SERC

- N SRCE (15) S_C_KY SERC_Central_Kentucky

### 3

- 4

NERC Assessment

Region AEO 2013 NEMS

Region Model

Region Model Region Description

S_C_TVA SERC_Central_TVA

SERC

- SE SRSE (14) S_SOU SERC_Southeastern

SERC

- W SRDA (12) S_D_AMSO SERC_Delta_Amite South (including DSG)

S_D_WOTA SERC_Delta_WOTAB (including Western)

S_D_REST SERC_Delta_Rest of Delta (Central Arkansas)

SRDA (12), SRCE (15) S_D_N_AR SERC_Delta_Northern Arkansas (including

AECI)

SPP b 

### MROW (4) SPP_NEBR SPP Nebraska

SPNO (17), SRGW

(13) SPP_N SPP North

- (Kansas, Missouri)

SPSO (18) SPP_KIAM SPP_Kiamichi Energy Facility

SPP_SE SPP Southeast (Louisiana)

SPP_SPS SPP SPS (Texas Panhandle)

SPSO (18), SRDA (12) SPP_WEST SPP West (Oklahoma, Arkansas, Louisiana)

Basin (BASN) NWPP (21) WECC_ID WECC_Idaho

WECC_NNV WECC_Northern Nevada

WECC_UT WECC_Utah

Northern California

(CALN) CAMX (20) WEC_CALN WECC_Northern California (including SMUD)

WECC_SF WECC_San Francisco

Southern California

(CALS) AZNM (19) WECC_IID WECC_Imperial Irrigation District (IID)

CAMX (20) WEC_LADW WECC_LADWP

WEC_SDGE WECC_San Diego Gas and Electric

WECC_SCE WECC_Southern California Edison

Northwest (NORW) NWPP (21) WECC_MT WECC_Montana

WECC_PN

W WECC_Pacific Northwest

Rockies (Rock) NWPP (21), RMPA

(22) WECC_WY WECC_Wyoming

RMPA (22) WECC_CO WECC_Colorado

Desert Southwest (DSW) AZNM (19) WECC_AZ WECC_Arizona

WECC_NM WECC_New Mexico

WECC_SNV WECC_Southern Nevada

Canada CN_AB Alberta

CN_BC British Columbia

CN_MB Manitoba

CN_NB New Brunswick

CN_NF Newfoundland

CN_NL Labrador

CN_NS Nova Scotia

CN_ON Ontario

CN_PE Prince Edward Island

CN_PQ Quebec

CN_SK Saskatchewan

a ERCOT_Tenaska Frontier Generating Station (ERC_FRNT) and ERCOT_Tenaska Gateway Generating Station (ERC_GWAY)

regions in ERCOT are switching regions without any internal demand created to capture the ability to sell power to multiple p ower

markets.

b SPP_Kiamichi Energy Facility [SPP_KIAM] region in SPP is a swit ching region without any internal demand created to capture

the ability to sell power to multiple power markets



### .

### 3

- 5 Table 3

- 2 Electric Load Assumptions in EPA Base Case v.5.13

Year Net Energy for Load

(Billions of KWh)

2016 4,049

2018 4,135

2020 4,215

2025 4,390

2030 4,535

2040 4,887

2050 5,271

Notes:

This data is an aggregation of the model

- region

- specific

net energy loads used in the EPA Base Case v.5.13.



### 3.2.1 Demand Elasticity

EPA Base Case v.5.13 has the capability to consider endogenously the relationship of the price of power

to electricity demand. However, this capability is typically only exercised for sensitivity analyses where

different price elasticities of demand are specified for p urposes of comparative analysis. The default base

case assumption is that the electricity demand shown in Table 3

- 2 , which was originally derived fro m EIA

modeling that did consider price elasticity of demand, must be met as IPM solves for least

- cost electricity

supply. This approach maintains a consistent expectation of future load between the EPA Base Case and

the corresponding EIA Annual Energy Outl ook reference case (e.g., between EPA Base Case v5.13 and

the AEO2013 reference case).

3.2.2 Net Internal Demand (Peak Demand)

EPA Base Case v5.13 has separate regional winter and summer peak demand values, as derived from

each region’s seasonal load duration c urve (found in Attachment 2

- 1). Peak projections were estimated

based on AEO 2013 load factors and the estimated energy demand projections shown in Table 3

- 2

. Table 3

- 3 illustrates the national sum of each region’s winter and summer peak demand. Because each

region’s seasonal peak demand need not occur at the same t ime, the national peak demand is defined as

non

- coincidental (i.e., national peak demand is a summation of each region’s peak demand at whatever

point in time that region’s peak occurs across the given time period)

.

Table 3

- 3 National Non

- Coincidental Net Internal Demand

Year Peak Demand (GW)

Winter Summer

2016 657 746

2018 670 761

2020 686 780

2025 725 826

2030 763 873

2040 845 972

2050 916 1,053

Notes:

This data is an aggregation of the model

- region

- specific

peak demand loads used in the EPA Base Case v.5.13.

### 3

- 6

3.2.3 Regional Load Shapes

As of 2013, EPA has adopted year 2011 as the meteorological year in its air quality modeling. In order for

EPA Base Case v.5.13 to be consistent, the year 2011 was selected as the “normal weather year” 8 

### for all

IPM regions. The proximity of the 2011 cumulative annual heating degree days (HDDs) and cooling

degree days (CDDs) to the long

- term average cumulative annual HHDs and CDDs over the period 1981

to 2010 was estimated and found to be reasonably close. The 2011 chronological hourly load data were

assembled by aggregating individual utility load curves taken from Federal Energy Regulatory

Commission Form 714 data and individual ISOs and RTOs.



## 3.3 Transmissio n

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 5.13 characterizes the U.S. lower 48

states, the District of Columbia, and Canada into 75 diff erent model regions by means of 61 power

market regions and 3 power switching regions 9 

### in the U.S. and 11 power market regions in Canada. EPA

Base Case 5.13 includes explicit assumptions regarding the transmission grid connecting these modeled

power markets. This section details the assumptions about the transfer capabilities, wheeling cost s and

inter

- regional transmission used in EPA Base Case 5.13.

3.3.1 Inter

- regional Transmission Capability

Table 3

- 4 10 

### sh ows the firm and non

- firm Total Transfer Capabilitie s (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 signifi cant constraints. 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 ( a condition referred t o as N

- 1 , or “N minus one” ). 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 no rmal operation ( a condition referred to as N

- 0 , or “N minus zero” ). They specify the sum of the

maximum firm transfer capability between sub

- regions and incremental curtailable non

- firm transfer

capability. Non

- firm TTCs are used for energy transfers sin ce they provide a lower level of reliability than

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.5.13

From To 2016 2018

Transmission Tariff

(2011 mills/kWh) Capacity

TTC (MW) Energy

TTC (MW) Incremental

Capacity TTC

(MW) Incremental

Energy TTC (MW)

ERC_FRNT ERC_REST 860 860 0

SPP_WEST 860 860 6

8 

### The term “normal weather year” refers to a representative 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 long

- 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 ave raged 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.

9 

### Power switching regions are regions with no market load that represent individual generating facilities spec ifically

configured so they can sell directly into either ERCOT or SPP: these plants are implemented in IPM as regions with

transmission links only to ERCOT and to SPP.

10 

### In the column headers in Table 3

- 4 the term “Energy TTC (MW)” is equivalent to non

- firm TTCs and the term

“Capacity TTC (MW)” is equivalent to firm TTCs.

### 3

- 7 From To 2016 2018

Transmission Tariff

(2011 mills/kWh) Capacity

TTC (MW) Energy

TTC (MW) Incremental

Capacity TTC

(MW) Incremental

Energy TTC (MW)

ERC_GWAY ERC_REST 845 845 0

SPP_WEST 845 845 6

ERC_REST ERC_WEST 5,529 5,529 0

SPP_WEST 600 600 6

ERC_WEST ERC_REST 10,555 10,555 0

SPP_WEST 220 220 6

FRCC S_SOU 3,600 3,600 8

MAP_WAUE CN_SK 0 100 8

MIS_IA 0 100 6

MIS_MAPP 1,000 1,500 0

MIS_MIDA 600 1,000 6

MIS_MNWI 2,000 3,000 6

SPP_NEBR 700 1,000 6

MIS_IA MAP_WAUE 0 100 6

MIS_IL 0 100 0

MIS_MIDA 900 2,000 0

MIS_MNWI 1,195 2,000 0

MIS_MO 223 711 0

PJM_COMD 0 600 3

S_D_N_AR 0 100 1

MIS_IL MIS_IA 0 100 0

MIS_INKY 240 1,195 0

MIS_MIDA 0 100 0

MIS_MO 3,400 4,500 0

PJM_COMD 2,500 3,000 3

PJM_West 0 1,300 3

S_C_TVA 1,200 1,500 6

MIS_INKY MIS_IL 240 1,195 0

MIS_LMI 0 100 0

PJM_COMD 2,044 3,355 3

PJM_West 5,441 6,509 3

S_C_KY 2,257 3,787 6

S_C_TVA 300 500 6

MIS_LMI CN_ON 400 1,200 8

MIS_INKY 0 100 0

MIS_WUMS 0 100 0

PJM_ATSI 1,262 2,036 3

PJM_West 1,400 2,800 3

MIS_MAPP CN_MB 300 500 8

MAP_WAUE 1,000 1,500 0

MIS_MNWI 2,150 5,000 6

MIS_MIDA MAP_WAUE 600 1,000 6

MIS_IA 900 2,000 0

MIS_IL 0 100 0

MIS_MNWI 0 0 0

MIS_MO 0 500 0

PJM_COMD 2,000 3,000 3

S_D_N_AR 0 30 1

SPP_N 0 50 6

SPP_NEBR 1,000 2,000 6

MIS_MNWI CN_MB 200 1,700 8

### 3

- 8 From To 2016 2018

Transmission Tariff

(2011 mills/kWh) Capacity

TTC (MW) Energy

TTC (MW) Incremental

Capacity TTC

(MW) Incremental

Energy TTC (MW)

CN_ON 0 162 8

MAP_WAUE 2,000 3,000 6

MIS_IA 1,195 2,000 0

MIS_MAPP 2,150 5,000 6

MIS_MIDA 0 0 0

MIS_WUMS 1,480 2,400 0

MIS_MO MIS_IA 223 711 0

MIS_IL 3,400 4,500 0

MIS_MIDA 0 500 0

S_D_N_AR 2,100 2,804 1

SPP_N 300 1,000 6

MIS_WUMS MIS_LMI 0 100 0

MIS_MNWI 1,480 2,400 0

PJM_COMD 0 1000 3

NENG_CT NENGREST 2,600 2,600 800 800 0

NY_Z_G

- I 900 900 3

NY_Z_K 760 760 3

NENG_ME CN_NB 800 800 8

NENGREST 1,600 1,600 0

NENGREST CN_PQ 1,650 1,650 8

NENG_CT 2,600 2,600 800 800 0

NENG_ME 1,600 1,600 0

NY_Z_D 0 0 3

NY_Z_F 800 800 3

NY_Z_A&B CN_ON 1,200 1,200 8

NY_Z_C&E 1,550 1,550 0

PJM_PENE 500 1,000 6

NY_Z_C&E NY_Z_A&B 1,300 1,300 0

NY_Z_D 1,600 1,600 0

NY_Z_F 3,250 3,250 0

NY_Z_G

- I 1,700 1,700 0

PJM_PENE 755 1,500 6

NY_Z_D CN_PQ 1,200 1,200 8

NENGREST 150 150 3

NY_Z_C&E 2,650 2,650 0

NY_Z_F NENGREST 800 800 3

NY_Z_C&E 1,999 1,999 0

NY_Z_G

- I 3,450 3,450 0

NY_Z_G

- I NENG_CT 1,130 1,130 3

PJM_EMAC 1,000 1,000 0

NY_Z_C&E 1,600 1,600 0

NY_Z_F 1,999 1,999 0

NY_Z_J 4,350 4,350 0

NY_Z_K 1,290 1,290 0

NY_Z_J NY_Z_G

- I 3,500 3,500 0

NY_Z_K 175 175 0

PJM_EMAC 1,300 1,900 6

NY_Z_K NENG_CT 760 760 3

NY_Z_G

- I 530 530 0

NY_Z_J 283 283 0

PJM_EMAC 660 660 6

### 3

- 9 From To 2016 2018

Transmission Tariff

(2011 mills/kWh) Capacity

TTC (MW) Energy

TTC (MW) Incremental

Capacity TTC

(MW) Incremental

Energy TTC (MW)

PJM_AP PJM_ATSI 1,212 2,731 0

PJM_Dom 5,400 8,000 0

PJM_PENE 2,400 3,200 0

PJM_SMAC 1,100 2,200 0

PJM_West 4,800 6,300 0

PJM_ATSI MIS_LMI 1,262 2,036 3

PJM_AP 1,212 2,731 0

PJM_PENE 0 1,500 0

PJM_West 7,400 9,700 0

PJM_COMD MIS_IA 0 600 3

MIS_IL 2,500 3,000 3

MIS_INKY 3,840 5,098 3

MIS_MIDA 2,000 3,000 3

MIS_WUMS 0 1,000 3

PJM_West 980 4,000 0

PJM_Dom PJM_AP 5,400 8,000 0

PJM_SMAC 1,195 2,812 0

PJM_West 1,530 3,800 0

S_VACA 1,000 2,598 6

PJM_EMAC NY_Z_J 1,300 1,900 6

NY_Z_K 660 660 6

NY_Z_G

- I 500 500 0

PJM_SMAC 300 1,095 0

PJM_WMAC 6,900 6,900 0

PJM_PENE NY_Z_A&B 500 1,000 6

NY_Z_C&E 755 1,500 6

PJM_AP 2,400 3,200 0

PJM_ATSI 0 1,500 0

PJM_WMAC 3,565 3,565 0

PJM_SMAC PJM_AP 1,100 2,200 0

PJM_Dom 1,195 2,812 0

PJM_EMAC 300 1,095 0

PJM_WMAC 800 2,000 0

PJM_West MIS_IL 0 1,300 3

MIS_INKY 5,125 6,415 3

MIS_LMI 1,400 2,800 3

PJM_AP 4,800 6,300 0

PJM_ATSI 7,400 9,700 0

PJM_COMD 980 4,000 0

PJM_Dom 1,530 3,800 0

S_C_KY 1,255 2,074 6

S_C_TVA 2,119 3,118 6

S_VACA 700 1,000 6

PJM_WMAC PJM_EMAC 6,900 6,900 0

PJM_PENE 3,565 3,565 0

PJM_SMAC 800 2,000 0

S_C_KY MIS_INKY 2,257 3,787 6

PJM_West 1,255 2,074 6

S_C_TVA MIS_IL 1,200 1,500 6

MIS_INKY 300 500 6

PJM_West 2,119 3,118 6

### 3

- 10 From To 2016 2018

Transmission Tariff

(2011 mills/kWh) Capacity

TTC (MW) Energy

TTC (MW) Incremental

Capacity TTC

(MW) Incremental

Energy TTC (MW)

S_D_N_AR 1,732 3,019 8

S_D_REST 1,195 2,494 8

S_SOU 3,196 5,098 8

S_VACA 216 276 8

S_D_AMSO S_D_REST 2,450 2,450 0

S_SOU 420 700 8

SPP_SE 300 500 6

S_D_N_AR MIS_IA 0 100 1

MIS_MIDA 0 30 1

MIS_MO 2,100 2,804 1

S_C_TVA 1,732 3,019 8

SPP_N 1,792 2,955 6

SPP_WEST 2,000 3,000 6

S_D_REST S_C_TVA 1,195 2,494 8

S_D_AMSO 2,450 2450 0

S_D_WOTA 290 1,050 0

S_SOU 1,700 2,000 8

SPP_SE 1,639 3,136 6

SPP_WEST 100 900 6

S_D_WOTA S_D_REST 1,250 1,250 0

SPP_SE 1,491 2,835 6

S_SOU FRCC 3,600 3,600 8

S_C_TVA 4,411 5,893 8

S_D_AMSO 420 700 8

S_D_REST 1,700 2,000 8

S_VACA 1,400 3,000 8

S_VACA PJM_Dom 1,000 2,598 6

PJM_West 700 1,000 6

S_C_TVA 216 276 8

S_SOU 1,400 3,000 8

SPP_KIAM ERC_REST 1,178 1,178 6

SPP_WEST 1,178 1,178 0

SPP_N MIS_MIDA 0 50 6

MIS_MO 300 1,000 6

S_D_N_AR 1,792 2,955 6

SPP_NEBR 1,217 1,666 500 500 0

SPP_SPS 0 900 0

SPP_WEST 2,253 3,600 500 500 0

SPP_NEBR MAP_WAUE 700 1,000 6

MIS_MIDA 1,000 2,000 6

SPP_N 1,217 1,666 500 500 0

SPP_SE S_D_AMSO 300 500 6

S_D_REST 1,639 3,136 6

S_D_WOTA 1,491 2,835 6

SPP_WEST 0 852 0

SPP_SPS SPP_N 0 900 0

SPP_WEST 1,239 2,205 750 750 0

WECC_NM 610 610 6

SPP_WEST ERC_REST 600 600 6

ERC_WEST 220 220 6

S_D_N_AR 2,000 3,000 6

### 3

- 11 From To 2016 2018

Transmission Tariff

(2011 mills/kWh) Capacity

TTC (MW) Energy

TTC (MW) Incremental

Capacity TTC

(MW) Incremental

Energy TTC (MW)

S_D_REST 100 900 6

SPP_N 2,500 2,700 500 500 0

SPP_SE 0 688 0

SPP_SPS 1,239 2,205 750 750 0

WEC_CALN WECC_NNV 100 100 8

WECC_PNW 3,675 3,675 8

WECC_SCE 1,275 1,275 0

WECC_SF 1,272 1,272 0

WEC_LADW WECC_AZ 468 468 8

WECC_PNW 2,858 2,858 8

WECC_SCE 3,750 3,750 8

WECC_SNV 3,883 3,883 8

WECC_UT 1,400 1,400 8

WEC_SDGE WECC_AZ 1,168 1,168 8

WECC_IID 150 150 8

WECC_SCE 2,440 2,440 0

WECC_AZ WEC_LADW 362 362 8

WEC_SDGE 1,163 1,163 8

WECC_IID 195 195 8

WECC_NM 5,522 5,522 0

WECC_SCE 1,600 1,600 8

WECC_SNV 4,727 4,727 0

WECC_UT 250 250 8

WECC_CO WECC_NM 614 614 8

WECC_UT 650 650 8

WECC_WY 1,400 1,400 0

WECC_ID WECC_MT 200 200 8

WECC_NNV 350 350 0

WECC_PNW 1,800 1,800 8

WECC_UT 680 680 0

WECC_WY 0 0 8

WECC_IID WEC_SDGE 150 150 8

WECC_AZ 163 163 8

WECC_SCE 600 600 8

WECC_MT WECC_ID 325 325 8

WECC_PNW 2,000 2,000 0

WECC_WY 400 400 8

WECC_NM SPP_SPS 610 610 6

WECC_AZ 5,582 5,582 0

WECC_CO 664 664 8

WECC_UT 530 530 8

WECC_NNV WEC_CALN 100 100 8

WECC_ID 185 185 0

WECC_PNW 300 300 8

WECC_UT 235 235 0

WECC_PNW CN_BC 1,000 1,000 8

WEC_CALN 4,200 4,200 8

WEC_LADW 2,600 2,600 8

WECC_ID 500 500 8

WECC_MT 1,000 1,000 0

WECC_NNV 300 300 8

### 3

- 12 From To 2016 2018

Transmission Tariff

(2011 mills/kWh) Capacity

TTC (MW) Energy

TTC (MW) Incremental

Capacity TTC

(MW) Incremental

Energy TTC (MW)

WECC_SCE WEC_CALN 3,000 3,000 0

WEC_LADW 3,750 3,750 8

WEC_SDGE 2,200 2,200 0

WECC_AZ 1,082 1,082 8

WECC_IID 50 50 8

WECC_SNV 2,814 2,814 8

WECC_SF WEC_CALN 1,100 1,100 0

WECC_SNV WEC_LADW 2,300 2,300 8

WECC_AZ 4,785 4,785 0

WECC_SCE 1,700 1,700 8

WECC_UT 250 250 8

WECC_UT WEC_LADW 1,920 19,20 8

WECC_AZ 250 250 8

WECC_CO 650 650 8

WECC_ID 775 775 0

WECC_NM 600 600 8

WECC_NNV 360 360 0

WECC_SNV 140 140 8

WECC_WY 400 400 8

WECC_WY WECC_CO 1,400 1,400 0

WECC_ID 2,200 2,200 8

WECC_MT 200 200 8

WECC_UT 400 400 8



### 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 v.5.13. 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. The

maximum values for firm and non

- firm TTCs were obtained from public sources such as market reports

and regio nal transmission plans, wherever available. Where public sources were not available, the

maximum values for firm and non

- firm TTCs are based on ICF’s expert view. ICF analyzes the operation

of the grid under normal and contingency conditions, using indust ry

- standard methods, and calculates the

transfer capabilities between regions. ICF uses standard power flow data developed by the market

operators, transmission providers, or utilities, as appropriate.

It should be noted that each transmission link betwe en 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 , due to the physical nature of electron flow across the grid

.

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

- fir m (energy) transfers. The joint limits were obtained from public

sources where available, or based on ICF’s expert view. 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 market is connected to the New York market by four

transmission links. As shown in Table 3

- 4 , the transfer capabilities from New England to New York for the

individual links are:

### 3

- 13

NENG_CT to NY_Z_G

- I: 900 MW

NENGREST to NY_Z_F: 800 MW

NENG_CT to NY_Z_K: 760 MW

NENGREST to NY_Z_D: 0 MW

Without any simultaneous transfer limits, the total transfer capability from New England to New York

would be 2,460 MW. However, current system conditions and reliability requirements limit the total

simultaneous transfers from N ew England to New York to 1,730 MW. ICF uses joint limits to ensure that

this and similar reliability limits are not violated. Therefore each individual link can be utilized to its limit as

long as the total flow on all links does not exceed the joint li mit.

Table 3

- 5 Annual Joint Capacity and Energy Limits to Transmission Capabilities

Between Model Regions in EPA Base Case v.5.13

Region Connection Transmission Path Capacity TTC (MW) Energy TTC (MW)

NYISO to NYISO NY_Z_G

- I to NY_Z_K 1,465 NY_Z_J to NY_Z_K

NYISO to NYISO NY_Z_K to NY_Z_G

- I

285 NY_Z_K to NY_Z_J

NYISO to ISO

- NE NY_Z_G

- I to NENG_CT

1,730 NY_Z_F to NENGREST

NY_Z_K to NENG_CT

NY_Z_D to NENGREST

NYISO to ISO

- NE NY_Z_G

- I to NENG_CT

2,205 NY_Z_F to NENGREST

NY_Z_K to NENG_CT

NY_Z_D to NENGREST

ISO

- NE to NYISO NENG_CT to NY_Z_G

- I

1,730 NENGREST to NY_Z_F

NENG_CT to NY_Z_K

NENGREST to NY_Z_D

PJM to PJM PJM_West to PJM_ATSI

5,417 12,000 PJM_PENE to PJM_ATSI

PJM_AP to PJM_ATSI

PJM to PJM PJM_ATSI to PJM_West

5,417 12,000 PJM_ATSI to PJM_PENE

PJM_ATSI to PJM_AP

PJM to SERC

- E PJM_West to S_VACA 1,300 2,598 PJM_Dom to S_VACA

SERC

- E to PJM S_VACA to PJM_West 1,300 2,598 S_VACA to PJM_Dom

MAPP to MISO MIS_MAPP to MIS_MNWI 3,000 5,000 MAP_WAUE to MIS_MNWI

MISO to MAPP MIS_MNWI to MIS_MAPP 3,000 5,000 MIS_MNWI to MAP_WAUE

SERC

- N to PJM S_C_TVA to PJM_West 3,000 4,500 S_C_KY to PJM_West

PJM to SERC

- N PJM_West to S_C_TVA 3,000 4,500 PJM_West to S_C_KY

SERC

- N to MISO S_C_TVA to MIS_INKY 2,257 4,000 S_C_KY to MIS_INKY

### 3

- 14

Region Connection Transmission Path Capacity TTC (MW) Energy TTC (MW)

MISO to SERC

- N MIS_INKY to S_C_TVA 2,257 4,000 MIS_INKY to S_C_KY

MISO to PJM MIS_INKY to PJM_COMD 4,586 6,509 MIS_INKY to PJM_West

PJM to MISO PJM_COMD to MIS_INKY 5,998 8,242 PJM_West to MIS_INKY



### 3.3.3 Transmission Link Wheeling Charge

Transmission wheeling charge is the cost of transferring electric power from one region to another using

the transmission link. The EPA Base Case 5.13 has no charges within individual IPM regions and no

charges between IPM regions that fall within the same RTO. Charges between other regions vary to

reflect the cost of wheeling. The wheeling charges in 2011 mills/kWh are shown in Table 3

- 4 in the

column lab e led “Transm ission Tariff”.

3.3.4 Transmission Losses

The EPA Base Case 5.13 assumes a 2.8 percent inter

- regional transmission loss of energy

transferred.This is based on the average loss factor for the transmission grid calculated from the U.S

Energy Information Administra tion (EIA) State Electricity Profiles 2010 report. 11 

### The results were

validated using average loss factors derived from standard power flow data developed by the market

operators, transmission providers, and utilities.



## 3.4 International Imports

The U.S. electr ic 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.5.13 but Mexico is not. International electric tradi ng between the U.S. and Mexico is

represented by an assumption of net imports based on information from AEO 2013. Table 3

- 6

summarizes the assumption s on net imports into the US from Mexico.

Table 3

- 6 International Electricity Imports in EPA Base Case v.5.13

2016 2018 2020 2025 2030 2040 2050

Net Imports from Mexico

(billions kWh) 0.67 0.55 0.31

- 0.29

- 0.53

- 0.53

- 0.53

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

Source: AEO 2013



## 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.5.13 can be found in the National Electrical Energy Data System (NEEDS v.5.13), a

database which provides IPM with information on a ll currently operating and planned

- committed electric

generating units. NEEDS v.5.13 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 ec onomic dispatch profile given the operating and physical constraints imposed

11 

### State Electricity Profiles 2010

, Table 3

- 10 , U.S. Energy Information Administration , January 2012.

( http://www.eia.gov/electricity/state/pdf/sep2010.pdf ).

### 3

- 15

on the unit. In EPA Base Case v.5.13 unit specific operational and physical constraints are generally

represented through availability and turndown constraints. However, for som e 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 ge nerating 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.5.13. They are based on data from

NERC Generating Availability Data System (GADS) 2007

- 2011 and AEO 2012. Table 3

- 18 shows the

availability assumptions for all generating units in EPA Base Case v.5.13.

Table 3

- 7 Availability Assumptions in the EPA Base Case v.5.13

Unit Type Annual Availability (%)

Biomass 82

- 86

Coal Steam 77

- 90

Combined Cycle 84

- 90

Combustion Turbine 85

- 93

Fossil Waste 90

Fuel Cell 87

Geothermal 97

- 98

Hydro 81

- 91

IGCC 79

- 88

Landfill Gas 90

Municipal Solid Waste 90

Non

- Fossil Waste 90

Nuclear 58

– 100

O/G Steam 70

– 92

Pumped Storage 83

– 90

Solar PV 90

Solar Thermal 90

Tires 90

Wind 95

Notes:

Values shown are a range of all of the values modeled within the EPA Base Case v.5.13

. The range depends on the

source of information: GADS data vary by size, AEO 2012 data may vary by projected year.



### In the EPA Base Case v.5.13, 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

variations 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 genera tion profiles, not

availabilities, to define the upper bound on the generation obtainable from the unit. The capacity factor is

### 3

- 16

the percentage of the maximum possible power generated by the unit. For example, a photovoltaic solar

unit would have a capaci ty factor of 27% if the usable sunlight were only available that percent of the

time. For such 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

- 923 data 2007

- 2011. A discussion of capacity factors and generation profiles for wind and solar

technologies i s contained in section 4.4.5 and Table 4

- 32 and Table 4

- 33

.

Table 3

- 8 Seasonal Hydro Capacity Factors (%) in the EPA Base Case v.5.13

Model Region Winter Capacity Factor Summer Capacity Factor Annual Capacity Factor

ERC_REST 12.9% 25.0% 18.0%

FRCC 44.0% 31.8% 38.9%

MIS_MAPP 81.0% 87.4% 83.7%

MAP_WAUE 29.2% 40.2% 33.8%

MIS_IL 55.0% 64.3% 58.9%

MIS_INKY 76.2% 95.9% 84.4%

MIS_IA 38.5% 48.6% 42.7%

MIS_MIDA 41.6% 49.6% 44.9%

MIS_LMI 68.8% 44.4% 58.6%

MIS_MO 42.7% 58.7% 49.4%

MIS_WUMS 66.2% 61.5% 64.2%

MIS_MNWI 33.0% 36.3% 34.4%

NENG_CT 47.3% 40.2% 44.4%

NENGREST 45.8% 34.5% 41.1%

NENG_ME 65.5% 58.1% 62.4%

NY_Z_C&E 56.9% 55.0% 56.1%

NY_Z_F 67.0% 58.9% 63.6%

NY_Z_G

- I 35.8% 34.6% 35.3%

NY_Z_A&B 70.4% 65.0% 68.1%

NY_Z_D 88.3% 83.3% 86.2%

PJM_WMAC 41.5% 20.3% 32.6%

PJM_EMAC 48.3% 24.6% 38.4%

PJM_West 33.8% 28.0% 31.4%

PJM_AP 64.6% 45.5% 56.6%

PJM_COMD 36.5% 48.0% 41.3%

PJM_ATSI 23.5% 32.8% 27.4%

PJM_Dom 21.1% 12.9% 17.7%

PJM_PENE 63.0% 34.1% 50.9%

S_VACA 21.1% 14.2% 18.2%

S_C_KY 29.2% 30.2% 29.6%

S_C_TVA 38.8% 28.3% 34.4%

S_SOU 22.8% 14.5% 19.3%

S_D_WOTA 20.1% 23.0% 21.3%

S_D_N_AR 23.9% 26.7% 25.1%

S_D_REST 49.2% 56.6% 52.3%

SPP_NEBR 32.1% 43.7% 36.9%

SPP_N 15.7% 22.8% 18.7%

SPP_WEST 32.1% 39.9% 35.4%

WECC_ID 32.3% 52.3% 40.7%

WECC_NNV 49.6% 62.6% 55.1%

WECC_UT 30.1% 42.5% 35.3%

WEC_CALN 26.9% 45.1% 34.5%

WECC_IID 45.7% 78.5% 59.5%

### 3

- 17

Model Region Winter Capacity Factor Summer Capacity Factor Annual Capacity Factor

WEC_LADW 17.1% 27.9% 21.6%

WEC_SDGE 30.8% 53.7% 40.4%

WECC_SCE 28.3% 52.9% 38.6%

WECC_MT 34.4% 52.2% 41.9%

WECC_PNW 41.7% 46.5% 43.7%

WECC_CO 28.8% 36.8% 32.2%

WECC_WY 22.8% 54.1% 36.0%

WECC_AZ 28.9% 33.3% 30.8%

WECC_NM 30.1% 43.0% 35.5%

WECC_SNV 20.4% 25.6% 22.6%

Notes:

Annual capacity factor is provided for information purposes only. It is not directly used in modeling.



### Capacity factors are also used to define the 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, consequent ly, the

availabilities) of existing nuclear units in EPA Base Case v.5.13 vary from region to region and over time.

Further discussion of the nuclear capacity factor assumptions in EPA Base Case v.5.13 is contained in

Section 4.5

.

In EPA Base Case v5.13 capacity factors for oil/gas ( O/G ) steam units are treated separately and

assigned minimum capacity factors under certain conditions. These capacity factors are a result of

stakeholder comments that many of the O/G steam units in the national fleet may no t operate under the

economic conditions reflected in EPA power sector modeling. These comments note that these units

often operate due to local transmission constraints, unit

- specific grid reliability requirements, or other

drivers that are not captured i n EPA’s modeling. EPA examined its modeling treatment of these units and

has introduced minimum capacity factor constraints in EPA Base Case v5.13 to reflect better the real

world behavior of these units where drivers of that behavior are not fully repres ented in the model itself

. This approach is designed to balance the continued operation of these units in the near term while also

allow ing for economic forces to influence decision

- making over the modeling time horizon ; as a result, the

minimum capacity factor limitations are phased out over time and are completely removed if the capacity

in question reaches 60 years of age Review of the historical operation of these units indicate that units

with high capacity factors continue at similar levels over tim e; in order to reflect persistent operation of

these units, minimum capacity factors for higher capacity factor units are phased out more slowly than

lower capacity factor units. The steps followed in assigning these capacity constraints are as follows:

1 ) For each O/G steam unit, calculate an seasonal capacity factor over a six year baseline (2007

- 2012).

2) Identify the minimum capacity factor over this baseline period for each unit.

3) R emove the minimum capacity factor limit ation when the unit reaches 60 year old.

4) For units less than 60 year s old, remove the constraints based on the assigned minimum capacity

factor and the model year, on the following schedule:

For model year 2016, keep minimum capacity factor unless unit > 60 year s old.

For model year 2018, remove minimum constraint from units with capacity factor < 2 .5 %

For model year 2020, remove minimum constraint from units with capacity factor < 5%

For model year 2025, remove minimum constraint from units with capac ity factor < 15%

For model year 2030, remove minimum constraint from units with capacity factor < 25%

For model year 2040, remove minimum constraint from units with capacity factor < 45%

### 3

- 18

3.5.3 Turndown

Turndown assumptions in EPA Base Case v.5.13 are used t o 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.5.13 require coal steam units to dispatch no

less tha n 50% of the unit capacity 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 less than 25% of the unit capacity in the five base

- and mid

- load segments of the LDC in

order to dispatch 100% of the unit capacity in the peak load segment of the LDC. These turndown

constraints were developed by ICF through detailed assessments of the historical experience and

operating ch aracteristics 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) requirement. 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.

The reserve margin capacity contribution for renewable units is described in Section 4.4.5; the reserve

margin capacity contribution for other units is the dependable capacity in the NEEDS for existing units or

the capacity build by IPM for new units. 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 th at are in effect in each NERC region.

Individual reserve margins for each NERC region are derived either directly or indirectly from NERC’s

electric reliability reports. They are based on reliability standards such as loss of load expectation

(LOLE), whic h is defined as the expected number of days in a specified period in which the daily peak

load will exceed the available capacity. EPA Base Case v.5.13 reserve margin assumptions are shown in

Table 3

- 9

.

Table 3

- 9 Planning Reserve Margins in EPA Base Case v.5.13

Model Region Reserve Margin

- Summer Reserve Margin

- Winter

CN_AB 12.2% 11.7%

CN_BC 12.5% 16.2%

CN_MB 12.0% 12.0%

CN_NB 20.0% 20.0%

CN_PE 20.0% 20.0%

CN_NS 20.0% 20.0%

CN_NF 20.0% 20.0%

CN_NL 20.0% 20.0%

CN_ON 19.2% 20.0%

CN_PQ 11.4% 12.2%

CN_SK 11.0% 11.0%

ERC_FRNT 13.8% 13.8%

ERC_GWAY 13.8% 13.8%

ERC_REST 13.8% 13.8%

ERC_WEST 13.8% 13.8%

FRCC 19.3% 19.3%

MAP_WAUE 15.0% 15.0%

MIS_IA 16.3% 16.3%

MIS_IL 16.3% 16.3%

MIS_INKY 16.3% 16.3%

MIS_LMI 16.3% 16.3%

MIS_MAPP 15.0% 15.0%

### 3

- 19 Model Region Reserve Margin

- Summer Reserve Margin

- Winter

MIS_MIDA 16.3% 16.3%

MIS_MNWI 16.3% 16.3%

MIS_MO 16.3% 16.3%

MIS_WUMS 16.3% 16.3%

NENG_CT 15.0% 15.0%

NENG_ME 15.0% 15.0%

NENGREST 15.0% 15.0%

NY_Z_A&B 16.0% 16.0%

NY_Z_C&E 16.0% 16.0%

NY_Z_D 16.0% 16.0%

NY_Z_F 16.0% 16.0%

NY_Z_G

- I 16.0% 16.0%

NY_Z_J 16.0% 16.0%

NY_Z_K 16.0% 16.0%

PJM_AP 15.4% 15.4%

PJM_ATSI 15.4% 15.4%

PJM_COMD 15.4% 15.4%

PJM_Dom 15.4% 15.4%

PJM_EMAC 15.4% 15.4%

PJM_PENE 15.4% 15.4%

PJM_SMAC 15.4% 15.4%

PJM_West 15.4% 15.4%

PJM_WMAC 15.4% 15.4%

S_C_KY 15.0% 15.0%

S_C_TVA 15.0% 15.0%

S_D_AMSO 15.0% 15.0%

S_D_N_AR 15.0% 15.0%

S_D_REST 15.0% 15.0%

S_D_WOTA 15.0% 15.0%

S_SOU 15.0% 15.0%

S_VACA 15.0% 15.0%

SPP_KIAM 13.6% 13.6%

SPP_N 13.6% 13.6%

SPP_NEBR 13.6% 13.6%

SPP_SE 13.6% 13.6%

SPP_SPS 13.6% 13.6%

SPP_WEST 13.6% 13.6%

WEC_CALN 14.7% 11.9%

WEC_LADW 15.1% 11.0%

WEC_SDGE 15.1% 11.0%

WECC_AZ 13.5% 14.0%

WECC_CO 14.7% 15.7%

WECC_ID 12.6% 13.5%

WECC_IID 15.1% 11.0%

WECC_MT 17.9% 19.9%

WECC_NM 13.5% 14.0%

WECC_NNV 12.6% 13.5%

WECC_PNW 17.9% 19.9%

WECC_SCE 15.1% 11.0%

WECC_SF 14.7% 11.9%

WECC_SNV 13.5% 14.0%

WECC_UT 12.6% 13.5%

WECC_WY 14.7% 15.7%

### 3

- 20



## 3.7 Power Plant Lifetimes

EPA Base Case v5.13 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 if their retention is deemed uneconomic. Other

types of units are not provided an economic retirement option.

Nuclear Retirement at Age 60 : EPA Base Case v.5.13 assumes that c ommercial nuclear reactors will

be retired upon license expiration

, which includ es a 20 year operating extension that is assumed to be

granted for each reactor by the N uclear R egulatory C ommission (NRC) . EPA Base Case v.5.13

continues the assumption of a 60 year life from the previous base case platforms. EPA Base Case v.5.13

modeli ng uses a maximum 60 year lifetime for nuclear reactors based on the current NRC licensing

extension program, which states; “Based on the Atomic Energy Act, the Nuclear Regulatory Commission

(NRC) issues licenses for commercial power reactors to operate fo r up to 40 years and allows these

licenses to be renewed for up to another 20 years. Economic and antitrust considerations, not limitations

of nuclear technology, determined the original 40

- year term for reactor licenses. “ 12 

### Today’s nuclear fleet

totals m ore than 100 GW. A ssuming a 60

- year lifetime 13 

### reduces the current fleet to under 5 GW in 2050.

This is illustrated in Figure 3

- 2 . For a complete li sting of the existing nuclear units including their online

year and other characteristics, see Table 4

- 34

.

Figure 3

- 2 Scheduled Retirements of Existing Nuclear Capacity Under 60

- Year Life Assumption



## 3.8 Heat Rates

12 

### For more info regarding the NRC’s licensing extension program, see NRC website:

http://www.nrc.gov/reading

- rm/doc

- collections/fact

- sheets/fs

- reactor

- li cense

- renewal.html

. For an up to date list regarding license renewal status, see “Status of License Renewal Applications and Industry

Activities”; NRC website: http://w ww.nrc.gov/reactors/operating/licensing/renewal/applications.html .”

13 

### R eal

- world r etirement decisions affecting some nuclear units such as Oyster Creek and San Onofre have occurred

prior to those units reaching 60 year s in service

. 

### - 20 0 20 40 60 80 100 120

0 1 2 3 4 5 6 7 8 9 10

2012 2017 2022 2027 2032 2037 2042 2047 2056 Total Capacity (GW) Remaining Capacity (GW) Retired in Year



### Year 

## Impact of 60

- Year Lifetime on Existing Nuclear Fleet

Capacity Retired Capacity Remaining

### 3

- 21

Heat rates, expressed in BTUs per kWh

, are a metric of the efficiency of a generating unit. As in previous

versions of NEEDS, it is assumed in NEEDS v.5.13 that heat rates of existing units will remain constant

over time. This assumption reflects two offsetting factors: (1) plant efficiencies tend to degrade over time

and (2) increased maintenance and component replacement work to maintain or improve plant effic iency.

The heat rates in EPA Base Case v.5.13 are based on values from AEO 2013. These values were

screened and adjusted using a procedure developed by EPA to ensure that the heat rates used in EPA

Base Case v.5.13 are within the engineering capabilities o f 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/g as steam, combined cycle, combustion 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.5.13

Plant Type Heat Rate (Btu/kWh)

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 8,700 18,700

Combustion Turbine

- Natural Gas < 80 MW 8,700 36,800

Combustion Turbine

- Oil and Oil/Gas

- ≥ 80 MW 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, HCl and CO 2 

### emissions regulations that are represented in the EPA Base Case v.5.13. The first four subsections

discuss national and regional regulations. The next two sub sections describe state level environmental

regulations and a variety of legal settlements. The last subsection presents emission assumptions for

potential units.

3.9.1 SO 2 

### Regulations

Unit

- level Regulatory SO 2 

### Emission Rates and Coal Assignments : Before discu ssing 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 mode l set

- up in the coal choices given to coal fired existing units in EPA Base Case

v.5.13. The SIP requirements define “regulatory SO 2 

### emission rates.” Since SO 2 

### emissions are

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

### emission ra tes 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. I n EPA Base Case v.5.13

there are six different sulfur grades of bituminous coal, four different grades of subbituminous coal, f ive

different grades of lignite, and one sulfur grade of residual fuel oil. There are two different SO 2 

### scrubber

options and one DSI option for coal units. Further discussion of fuel types and sulfur content is contained

in Chapter 9 . Further discuss ion of SO 2 

### control technologies is contained in Chapter 5

.

### 3

- 22

National and Regional SO 2 

### Regulations : The national program affecting SO 2 

### emissions in EPA Base

Case v.5.13 is the Acid Rain 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 across all

affected electric generation sources.

The annual SO 2 

### caps over the modeling time horizon in EPA Base Case v.5.13 reflect the provisions in

Title IV. Since EPA Base Case v.5.13 uses year 2016 as the first analysis year, a projection of allowance

banking behavior through the end of 2015 and specification of the available 2016 allowances are neede d

to initialize the modeling. EPA developed the projection of the banked allowances (30.6 million) going

into 2016. Calculating the available 2016 allowances involved deducting allowance surrenders due to

NSR settlements and state regulations from the 20 16 SO 2 

### cap of 8.95 million tons. The surrenders

totaled 142 thousand tons in allowances, leaving 8.808 million of 2016 allowances remaining. Table 7

- 4

shows the ini tial bank and 2016 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 Table 3

- 13 and Table 3

- 14

.

EPA Base Case v.5.13 also includes a representation of the Western Regional Air Partnership (WRAP)

Program, a regional initiative involving New Mexico, Utah, and Wyoming directed toward addr essing

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 

### Regulation s

Much like SO 2 

### regulations, existing NO

x 

### regulations are represented in EPA Base Case v.5.13 through a

combination of system level NO

x 

### programs and generation unit

- level NO

x 

### limits. T he NO

x 

### SIP Call trading

program is no longer represented since it was re placed by the requirements of the Clean Air Interstate

Rule (CAIR), described in section 3.9.4 below. Rhode Island is the only state from the NOx SIP Call that

is not covered in CAIR. Its NO

x 

### emission obligations under the NO

x 

### SIP Call are still included in EPA Base

Case v.5.13

.

By assigning unit

- specific NO

x 

### rates based on 2011 data, EPA Base Case v.5.13 is implicitly representing

Title IV unit

- specific rate limits and Clean Air Act Reasonably Available Control Technology (RAC T)

requirements for controlling NO

x 

### emissions from electric generating units in ozone non

- attainment areas

or in the Ozone Transport Region (OTR). 14 

### 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.5.13 the NO

x 

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

x 

### pollution control equipment or if it is assumed to install state

- of

- the

- art NO

x 

### combustion con trols

.

NO

x 

### Emission Rates

Future emission projections for NO

x 

### are a product of a unit’s utilization (heat input) and emission rate

(lbs/mmbtu). A unit’s NO

x 

### emission rate can vary significantly depending on the NO X 

### reduction

requirements to which it is su bject. For example, a unit may have a post

- combustion control installed

(e.g., SCR or SNCR), but only operate it during the particular time of the year in which it is subject to NO

x 

### reduction requirements (i.e., the unit only operates its post

- combustion control during the ozone season).

Therefore, its ozone

- season NO

x 

### emission rate would be lower than its non

- ozone

- season NO

x 

### emission

rate. Because the same individual unit can have such large variation in its emission rate, the model

needs a suite of em ission rate “modes” from which it can select the value most appropriate to the

conditions in any given model scenario. The different emission rates reflect the different operational

conditions a unit may experience regarding upgrades to its combustion con trols and operation of its

14 

### The OTR consists of th e following states: Maine, New Hampshire, Vermont, Massachusetts, Rhode Island,

Connecticut, New York, New Jersey, Pennsylvania, Delaware, Maryland, District of Columbia, and northern Virginia.

### 3

- 23

existing post

- combustion controls. Four modes of operation are developed for each unit , with each mode

carrying a potentially different NO

x 

### emission rate for that unit under those operational conditions

.

The emission rates assigned to each mode are derived from historic data (where available) and

presented in the NEEDS file. When the model is run, IPM selects one of these four modes through a

decision process depicted in

### 3

- 24

Figure 3

- 4 below

. The four modes address whether or not units upgrade combustion controls and/or

operate existing post

- combustion controls; the modes themselves do not address what happens to the

unit’s NOx rate if it i s projected to add a new post

- combustion NOx control. In such cases

, after the

model selects the appropriate mode, the emission rate originally assigned to that mode is further adjusted

downward to reflect the retrofit of a SCR or SNCR. In this case, an emission rate is assumed that reflects

a percentage removal from the mode ’s emission rate or a n emission rate floor (whichever is greater). The

full process for determining the NOx rate of units in EPA Base Case v.5.13 model projections is

summarized in Figure 3

- 3 below.

Figure 3

- 3 Modeling Process for Obtaining Projected NO

x 

### Emission Rates

NO

x 

### Emission Ra tes in NEEDS, v.5.13 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 2011. The

emission rates themselves reflect the im pact of 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 NO X 

### reduction requirements affecting that unit in a model run.

The reason for having a framework of four potential NO

x 

### rate “modes” applicable to each unit in NEEDS is

to enable the model to select from a range of NO

x 

### rates possible at a unit, given its configuration of NO

x 

### combustion controls and its assumed operation of existing post

- combustion controls. There are up to four

basic operating states for a given unit that significantly impact its NO

x 

### rate , and thus there are four NO X 

### rate “ modes ”

.

Mode 1: No post

- combustion control operating; existing combustion controls

Mode 2: Post

- combustion control operating, existing combustion controls

Mode 3: No post

- combustion control operating; state

- of

- the

- art (SOA) combustion controls (where

applicab le)

Mode 4: Post

- combustion control operating; state

- of

- the

- art (SOA) combustion controls (where

applicable)

Emission rates derived for each unit operating under each of these four modes are presented in the

NEEDS file. Note, not every unit has a differe nt emission rate for each mode, because certain units

cannot in practice change their NO

x 

### rates to conform to all potential operational states described above.

For instance, a unit without a post

- combustion control will not have different emission rates b etween

modes 1 and 2, or between modes 3 and 4, as there is no post

- combustion control that would potentially

turn on or off at these units. For such units, the mode 2 rate will simply equal the mode 1 rate, and the

mode 4 rate will equal the mode 3 rate. Historic NOx

Emission Rate Data

(e.g., 2011, 2009) NEEDS

Assignment of emission rates

(derived from historic data) to

each of four NO

x Modes. Modes

reflect different potential

operational conditions at a unit. Model Projections

Assignment of NO

x emission rate

based on one of four NEEDS

“modes” rates with potential

adjustment if the unit is projected to

add post

- combustion retrofit control

technology



## .

### 3

- 25 Figure 3

- 4 How One of the Four NO

x 

### Modes Is Ultimately Selected for a Unit

State

- of

- the

- art combustion controls (SOA combustion controls)

The definition of “state

- of

- the

- art” varies depending on the unit type and configuration. Table 3

- 11

indicates the incremental combustion controls that are required to achieve a “state

- of

- the

- art” combustion

control configuration for each unit. For instanc e if a wall

- fired boiler (highlighted b elow) currently has

LNB, the “ state

- of

- the

- art ” rate calculated for such a unit would assume a NO

x 

### emission rate reflective of

overfire air being added at the unit. The cost assumptions behind such an upgrade are described in

chapter 5. As described in t he attachment of this chapter, the “state

- of

- the

- art” combustion controls

reflected in the modes are only assigned to a unit if it is subject to a new (post

- 2011) NO

x 

### reduction

requirement (i.e., a NO

x 

### reduction requirement that did not apply to the unit d uring its 2011 operation that

forms the historic basis for deriving NO

x 

### rates for units in Base Case v.5.13) . Existing reduction

requirements as of 2011 (e.g., NO

x 

### SIP Call) under which units have already made combustion control

decision s would not trigge r the assignment of the “ state

- of

- the

- art ” mode s that reflect additional

combustion controls.

Table 3

- 11 State

- of

- the

- Art Combustion Control Configurations by Boiler Type

Boiler Type Existing NO

x



### Combustion Control Incremental Combustion Control N ecessary

to A chieve “ S tate

- of

- the

- A rt”

Cell LNB NGR OFA

LNB AND OFA

Cyclone

-- OFA

Stoker/SPR

-- OFA

Tangential --

LA

LNB

LNB + OFA

LNC1 a



### LNC2

OFA

ROFA LNC3

LNC3

CONVERSION FROM LNC1 TO LNC3

CONVERSION FROM LNC1 TO LNC3

CONVERSION FROM LNC1 TO LNC3

CONVERSION FROM LNC2 TO LNC3

LNC1

LNB

Vertical

-- NO

x 

### Combustion Control

- Vertically Fired Units

Wall -- LNB AND OFA

LA LNB AND OFA Is the unit subject

to any new (post

2011) NOx

reduction

requirement?

Is it a seasonal or annual

requirement? Did the source operate a

p ost

- combustion control

in 2011? Mode 1: Existing combustion controls, no post

combustion control operating

Mode 2 : Existing combustion controls, post

combustion control operating (where applicable)

Mode 3 :

If SNCR

– SOA combustion controls, no po st

combustion control operating

If SCR

– Mode 3 = Mode 1

Existing combustion controls, no post

- combustion

contro ls operating (where applicable)

Mode 4:

If SNCR

– SOA combustion controls, post

combustion controls operating

If SCR

– Mode 4 = Mode 2

Existing combustion controls, post

- combustion

controls operating (where applicable) No No

Yes

Yes

Annual Winter For what season is

the model

assigning the

mode rate?

Seasonal Summer

### 3

- 26

Boiler Type Existing NO

x



### Combustion Control Incremental Combustion Control N ecessary

to A chieve “ S tate

- of

- the

- A rt”

LNB OFA

LNF OFA

OFA LNB

a LNC1 = low NO

x coal

- and air nozzles with close

- coupled overfire air, LNC2 = Low NO

x Coal

- and

- Air Nozzles with Separated

Overfire Air, LNC3 = Low NO

x Coal

- and

- Air Nozzles with Close

- Coupled and Separated Overfire Air



### The emission rates for each generating unit under each mode are included in the NEEDS v5.13

database, described in Chapter 4. Attachment 3

- 1 and accompanying Tables 3

- 1.1 and 3

- 1.2 give further

information on the procedures employed to derive the four NO

x 

### modes

.

Additional NO

x 

### rate assumptions include default NO

x 

### rates of 0.23 lbs/ MMBtu for existing biomass units

and 0.044 lbs/MMBtu for existing landfill gas units.

Because of the complexity of the fleet and the completeness/incompleteness of historic data, there are

instances where the derivation of a unit’s modeled NO

x 

### emission ra te is more detailed than the

description provided above. For a more complete step

- by

- step description of the decision rules used to

develop the NO

x 

### rates, please see attachment 3

- 1.

3.9.3 Multi

- Pollutant Environmental Regulations

CAIR

The Clean Air Interstate Rule (CAIR) uses a cap and trade system to reduce the target pollutants

— SO 2 

### and NO

x 

### — for 27 eastern states and DC. 15 

### CAIR uses Title IV SO 2 

### allowances as currency for the SO 2 

### trading program. The initial bank and allowance totals for CAIR are the same as fo r the Acid Rain

Program above. For the Annual NO

x 

### trading program, the total Annual NO

x 

### allowances issued for 2016

was 1.2 million and the initial bank for 2016 was projected to be 1.5 million allowances. For the Ozone

Season NO

x 

### trading program, the total seasonal NO

x 

### allowances was 0.48 million and the initial bank

going into 2016 was projected to be 0.74 million. Table 7

- 4 shows the initial bank and 2016 allowance

specification along with the caps for the entire modeling time horizon.

In 2008, the U.S. Court of Appeals for the District of Columbia Circuit remand ed CAIR to EPA to correct

legal flaws in the proposed regulations as cited in the Court’s July 2008 ruling. The Court allowed EPA to

proceed with implementation of the CAIR trading programs while EPA works on a replacement rule

addressing the Court’s find ings. CAIR’s provisions were still in effect when EPA Base Case v.5.13 was

released and were included in the modeling. For more information on CAIR, go to

http://www.epa.gov/cair/.

MATS

Finalized in 2011, the Mercury and Air Toxics Rule (MATS) establishes National Emissions Standards for

Hazardous Air Pollutants (NESHAPS) for the “electric utility steam generating unit” source category,

which includes those units that combust coal or oil for the purpose of generating electricity for sale and

distribution t hrough the electric grid to the public. EPA v

. 5.13 applies the input

- based (lbs/MMBtu) MATS

control requirements for mercury and hydrogen chloride to covered units. Treatment of the filterable PM

standar d in the model is detailed in section 5.6.1. EPA Base Case v.5.13 does not model the alternative

SO 2 

### standard offered under MATS for units to demonstrate compliance with the rule’s HCl control

requirements . Coal steam units with access to lignite in the modeling are required to meet the “ e xisting

15 

### The states included in the Clean Air Interstate Rule are Ala bama, Arkansas, Connecticut, Delaware, Florida,

Georgia, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maryland, Massachusetts, Michigan, Mississippi, Missouri, New

Jersey, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Texas, Vir ginia, West Virginia

and Wisconsin.

### 3

- 27

coal

- fi red unit low Btu virgin coal” standard. For more information on MATS, go to

http://www.epa.gov/mats/.

Regional Haze

The Clean Air Act establishes a national goal for returning visibility to natural conditions through the

“prevention of any future, and the remedying of any existing impairment of visibility in Class I areas [156

national parks and wilderness areas], where impairment results from manmade air pollution.” On July 1,

1999, EPA established a comprehensive visibility protection program with the is suance of the regional

haze rule (64 FR 35714). This rule implements the requirements of section 169B of the CAAA and

requires states to submit State Implementation Plans (SIPs) establishing goals and long

- term strategies

for reducing emissions of air poll utants (including SO 2 

### and NO

x 

### ) that cause or contribute to visibility

impairment. The requirement to submit a regional haze SIP applies to all 50 states, the District of

Columbia, and the Virgin Islands. Among the components of a long

- term strategy is the requirement for

states to establish emission limits for visibility

- impairing pollutants emitted by certain source types

(including EGUs) that were placed in operation between 1962 and 1977. These emission limits are to

reflect Best Available Retrofit Techn ology (BART). States may perform individual point source BART

determinations, or meet the requirements of the rule with an approved BART alternative. An alternative

regional SO 2 

### cap for EGUs under Section 309 of the regional haze rule is available to certa in western

states whose emission sources affect Class 1 areas on the Colorado Plateau.

Since 2010, EPA has approved or, in a very few cases, put in place regional haze Federal

Implementation Plans for several states. The BART limits approved in these plan s (as of August 29,

2013) that will be in place for EGUs are represented in the EPA Base Case v.5.13 as follows.

Source

- specific NO

x 

### or SO 2 

### BART emission limits, minimum SO 2 

### removal efficiency requirements for

FGDs, limits on sulfur content in fuel oil, c onstraints on fuel type (e.g., natural gas only or prohibition

of certain fuels such as petroleum coke), or commitments to retire units are applied to the relevant

EGUs.

EGUs in states that rely on CAIR trading programs to satisfy BART must meet the requir ements of

CAIR.

EGUs in states that rely on state power plant rules to satisfy BART must meet the emission limits

imposed by those state rules.

For the three western states (New Mexico, Wyoming, and Utah) with approved Section 309 SIPs for

SO 2 

### BART, emissi on constraints were not applied as current and projected emissions are well under

the regional SO 2 

### cap.

Table 3

- 19 lists the NO

x 

### and SO 2 

### limits applied to specific EGUs and other implementations applied in

IPM. For more information on Regional Haze Rule, go to: http://www.epa.gov/visibility/ program.html

3.9.4 CO 2 

### Regulations

The Regional Greenhouse Gas Initiative (RGGI) is a CO 2 

### cap and trade program affecting fossil fired

electric power plants 25 MW or larger in Connecticut, Delaware, Maine, Maryland, Massachusetts, New

Hampshire, New York, Rhode Island, and Vermont. Table 7

- 4 shows the specifications for RGGI that are

impl emented in EPA Base Case v.5.13.

As part of California’s Assembly Bill 32 (AB32), the Global Warming Solutions Act, a multi

- sector GHG

cap

- and

- trade program was established that targets 1990 emission levels by 2020. The cap begins in

2013 for electric uti lities and large industrial facilities, with distributors of transportation, natural gas and

other fuels joining the capped sectors in 2015. In addition to in

- state sources, the cap

- and

- trade program

also covers the emissions associated with qualifying, o ut

- of

- state EGUs that sell power into California.

Due to the inherent complexity in modeling a multi

- sector cap

- and

- trade program where the participation

of out

- of

- state EGUs is determined based on endogenous behavior (i.e. IPM determines whether

### 3

- 28

qualifyi ng out

- of

- state EGUs are projected to sell power into California), EPA has developed a simplified

methodology to model California’s cap

- and

- trade program:

Adopt the AB32 cap

- and

- trade allowance price from EIA’s AEO2013 Reference Case, which fully

represent s the non

- power sectors. All qualifying fossil

- fired EGUs in California are subject to this

price signal.

Estimate a marginal CO 2 

### emission rate for each IPM region that exports power to California. This rate

is assumed to be the CO 2 

### rate of the model plan t with the highest variable cost in EPA Base Case

v.5.13.

For each IPM region that exports power to California, convert the $/ton CO 2 

### allowance price

projection into a mills/kWh transmission wheeling charge using the marginal emission rate from the

previou s step. The additional wheeling charge for qualifying out

- of

- state EGUs is equal to the

allowance price imposed on affected in

- state EGUs. Applying the charge to the transmission link

ensures that power imported into California from out

- of

- state EGUs mus t account for the cost of CO 2 

### emissions represented by its generation, such that the model may clear the California market in a

manner consistent with AB32 policy treatment of CO 2 

### emissions.

3.9.5 State

- Specific Environmental Regulations

EPA Base Case v.5.13 rep resents enacted laws and regulations in 26 states affecting emissions from the

electricity sector. Table 3

- 13 summarizes the provisions of state laws and regulations that are

represented in EPA Base Case v.5.13.

3.9.6 New Source Review (NSR) Settlements

New Source Review (NSR) settlements refer to legal agreements with companies resulting from the

permitting process under the CAAA which requires industry to undergo an EPA pre

- construction review of

proposed environmental controls 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.5.13 includes NSR

sett lements with 31 electric power companies. A summary of the units affected and how the settlements

were modeled can be found in Table 3

- 14

.

Eight state settlements and nine citizen settlements are also represented in EPA Base Case v.5.13.

These are summarized in Table 3

- 15 and Table 3

- 16 respectively.

3.9.7 Emission Assumptions for Potential (New) Units

Emissions from existing and planned/comm itted units vary from installation to installation based on the

performance of the generating unit and the emissions regulations that are in place. In contrast, there are

no location

- specific variations in the emission and removal rate capabilities of pot ential new units. In IPM,

potential new units are modeled as additional capacity and generation that may come online in each

model region. Across all model regions the emission and removal rate capabilities of potential new units

are the same, and they r eflect applicable federal emission limitations on new sources. The specific

assumptions regarding the emission and removal rates of potential new units in EPA Base Case v.5.13

are presented in Table 3

- 12 . (Note: Nuclear, wind, solar, and fuel cell technologies are not included in

Table 3

- 12 because they do not emit any of the listed pollutants.) For additional details on the modeling

of potential new units, see Chapter 4

.

3.9.8 Energy Efficiency an d Renewable Portfolio Standards

Renewable Portfolio Standards (RPS) generally refer s to various state

- level policies that require the

addition of renewable generation to meet a specified share of state

- wide generation. In EPA Base Case

v.5.13 the state R PS requirements are represented at a regional level utilizing the aggregate regional

### 3

- 29

representation of RPS requirements that is implemented in AEO 2013 as shown in Table 3

- 17

. 16 

### This

table shows the RPS requirements that apply to the NEMS (National Energy Modeling System) regions

used in AEO. In addition, state level solar carve

- out requirements have been implemented at a NEMS

region level in EPA Base Case v.5.13.



## 3.10 Capacity Deployment Constraints

EPA Base Case v

. 5.13 includes capacity deployment constraints for the more capital intensive generation

technologies and retrofits (new nuclear, advanced coal with carbon capture, and carbon capture retrofits).

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 the real world in any gi ven model run year over the

modeling time horizon. Such limiting factors include:

production capacity limitations (including the 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 fin ancing simultaneously at an acceptable

level of risk),

workforce limitations (limitations in the skilled engineering and construction labor force, replacement

challenges caused by an aging 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 constr aints 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 particular 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 ass umed 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 d eployment limit for the

subsequent period.

16 

### Energy Information Administration, U.S. Department of Energy, Assumptions to Annual Energy Outlook 201 3 :

Renewable Fuels Module (DOE/EIA

- 0554(2010)), April 15 , 201 3 , Table 13. 2 “Aggregate Regional Renewable

Portfolio Standard Requirements, ” http://www.eia.gov/forecasts/aeo/assumptions/pdf/renewable.pdf

.

### 3

- 30

8. If necessary, prevent sudden spikes in capacity in later periods when there has been little or no build

up in preceding periods by tying the amount of capacity that can be built in a given period to the

amount o f capacity built in preceding periods.

Attachment 3

- 2 shows the joint capacity deployment constraint on advanced coal with carbon capture and

storage ( CCS) and new nuclear. Attachment 3

- 3 shows the capacity deployment constraint on new

nuclear in itself. The bar graph in Attachment 3

- 3 illustrates how building capacity in earlier years

increases the maximum capacity that can be built over the entire modeling time horizon.

### 3

- 31 Table 3

- 12 Emission and Removal Rate Assumptions for Potential (New) Units in EPA Base Case v.5.13

Controls,

Removal,

and

Emissions

Rates Supercritical

Pulverized

Coal Integrated

Gasification

Combined

Cycle Integrated

Gasification

Combined Cycle

with Carbon

Sequestration Advanced

Combined

Cycle Advanced

Combined Cycle

with Carbon

Sequestration Advanced

Combustion

Turbine Biomass

Bubbling

Fluidized

Bed (BFB) Geothermal Landfill

Gas

SO 2 

### Removal /

Emissions

Rate 96% with a

floor of 0.06

lbs/MMBtu 99% 99% None None None 0.08

lbs/MMBtu None None

NO

x 

### Emission

Rate 0.07

lbs/MMBtu 0.013

lbs/MMBtu 0.013 lbs/MMBtu 0.011

lbs/MMBtu 0.011 lbs/MMBtu 0.011

lbs/MMBtu 0.02

lbs/MMBtu None 0.09

lbs/MMBtu

Hg Removal /

Emissions

Rate 90% 90% 90% Natural Gas:

0.000138

lbs/MMBtu

Oil:

0.483

lbs/MMBtu Natural Gas:

0.000138

lbs/MMBtu

Oil:

0.483 lbs/MMBtu Natural Gas:

0.000138

lbs/MMBtu

Oil:

0.483

lbs/MMBtu 0.57

lbs/MMBtu 3.70 None

CO 2 

### Removal /

Emissions

Rate 202.8

- 215.8

lbs/MMBtu 202.8

- 215.8

lbs/MMBtu 90% Natural Gas:

117.08

lbs/MMBtu

Oil:

161.39

lbs/MMBtu 90% Natural Gas:

117.08

lbs/MMBtu

Oil:

161.39

lbs/MMBtu None None None

HCL Removal /

Emissions

Rate 99% 0.0001

lbs/MMBtu 99% 0.0001

lbs/MMBtu 99% 0.0001

lbs/MMBtu

### 3

- 32 

## Attachment 3

- 1 NO

x 

## Rate Development in EPA Base Case v.5.13

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

x 

### rates found in the NEEDS v5.13 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 w ith only its current configuration of NO

x 

### combustion and post

- combustion controls under

all the conceivable operating conditions 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 scenario being modeled

c onsistent 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 and the general

contr ol states they reflect

Mode 1= Existing combustion controls, no post

- combustion control operation

Mode 2= Existing combustion controls, post

- combustion control operation (where applicable)

Mode 3= SOA combustion controls (where applicable), no post

- comb ustion control operation

Mode 4 = SOA combustion controls, post

- combustion control operation (where applicable)

Please see Figure 3

- 4 in Section 3.9.2 for an explanation of how the model selects the appropriate NOx

mode for each unit in the projection sce nario.

Q 3 : How are emission rates calculated for each unit for each of the four NO

x 

### modes ?

A 3 : 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 

### Budge t 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 consistent with what eng ineering theory tells us its values should be.

Otherwise, we apply additional screens. Explaining th e additional steps involved in those anamolous

case

- by

- case evaluations is beyond the scope of this illustration. However, the process below describes

ho w the values would generally be derived:

The procedure employs the following hierarchy of NO

x 

### rate data sources:

1. 20 11 ETS

2. Comments on NO

x 

### rate

3. 20 09 ETS

4. 20 10 EIA Form 860

5. Defaults

The existing coal steam boilers in US are categorized into three groups depending on the configuration of

NO

x 

### combustion and post

- combustion controls.

Group 1

- Coal boilers without post

- combustion NO

x 

### controls

Mode 1 = 2011 ETS Annual Average NO

x 

### Rate

Mode 2 = Mode 1

Mode 3

Mode 3 calculation follows Steps 1

- 7:

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

### 3

- 33

Step 2: 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

- 1.1 ), then Mode 1 = Base NO

x 

### rate. Go to Step 6

If Mode 1 ≤ Cut

- off (in Table 3

- 1.1 ), then the unit has SOA control and

Mode 3 = Mode 1

Step 3: For units listed as having SOA combustion controls.

Mode 3 = Mode 1

Step 4: For units listed as not having SOA 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

- 1.1 ), then the unit has SOA cont rol and

Mode 3 = Mode 1

If Mode 1 > Cut

- off (in Table 3

- 1.1 ), then go to Step 5

Step 5: Determine the unit’s Base NO

x 

### rate, i.e., the unit’s uncontrolled emission rate without combustion

controls, using the appropriate equation (not in boldface italics) in Table 3

- 1. 2 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 6.

Step 6: Use the appropriate equations (in boldface italics) in Table 3

- 1. 2 to calculate the NO

x 

### rate with

SOA combustion controls.

Step 7: Compare the value calculated in Step 6 to the applicable NO

x 

### floor rate in Table 3

- 1. 1

.

If the value from Step 6 is ≥ floor, use the Step 6 value as Mode 3. Otherwise, use the floor as the M ode 3

NO

x 

### rate.

Mode 4

Mode 4 =Mode 3

Group 2

- Coal boilers with SCR

Pre

- screen coal boilers with 2011 ETS NO

x 

### rates into the following four operating regimes. A coal boiler is

assumed to be operating its SCR when the seasonal NO

x 

### rate is less than 0.2 lbs/MMBtu

Group 2.1 SCR is not operating in both summer and winter seasons

Follow the NO

x 

### rate rules summarized for Group 1 boilers. No state of the art combustion controls are

implemented.

Mode 1 = 2011 ETS Annual Average NO

x 

### Rate Mode 2 = maximum {(1

- 0.9) * Mode 1, 0.07} Mode 3 =

Mode 1

Mode 4 = Mode 2

Group 2.2 SCR is operating in summer only

Mode 1 = 2011 ETS Winter NO

x 

### Rate

Mode 2 = 2011 ETS Summer NO

x 

### Rate

Mode 3 = Mode 1

Mode 4 = Mode 2

Group 2.3 SCR is operating in winter only

Mode 1 = 2011 ETS Summer NO

x 

### Rate

Mode 2 = 2011 ETS Winter NO

x 

### Rate

Mode 3 = Mode 1

Mode 4 = Mode 2

### 3

- 34

Group 2.4 SCR is operating year

- round

Mode 1 = if (2009 ETS Winter NO

x 

### Rate > 0.2, 2009 ETS Winter NO

x 

### Rate, 2011 ETS Annual Average

NO

x 

### Rate) 17



### Mo de 2 = 2011 ETS Annual Average NO

x 

### Rate

Mode 3 = Mode 1

Mode 4 = Mode 2

Group 3

- Coal boilers with SNCR

Step 1: Pre

- screen coal boilers with 2011 ETS NO

x 

### rates to verify if they have not operated their SNCR in

both summer and winter seasons. A coal boiler is assumed to be not operating its SNCR when the NO

x 

### rate is greater than 0.3 lbs/MMBtu in both summer and winter seasons.

Group 3.1 SNCR is not operat ing in both summer and winter seasons

Follow the NO

x 

### rate rules summarized for Group 1 boilers

Step 2: Pre

- screen coal boilers with 20 11 ETS NO

x 

### rates into the following three operating regimes. First

estimate the implied removal for a coal boiler using t he following equation:

Implied Removal (%) = ((Winter NO

x 

### Rate

– Summer NO

x 

### Rate)/ Winter NO

x 

### Rate) * 100

Second, assign the coal boiler to a specific operating regime based on the following logic.

If Implied Removal > 20% then SNCR is operating in summe r season only

, Else if Implied Removal <

- 20% then SNCR is operating in winter season only,

Else SNCR is operating year

- round

Second, assign the coal boiler to a specific operating regime based on the following logic.

Group 3.2 SNCR is operating in summ er only

Mode 1 = 2011 ETS Winter NO

x 

### Rate

Mode 2= 2011 ETS Summer NO

x 

### Rate

Mode 3 = same as Group 1 Mode 3

Mode 4 = maximum {(1

- 0.25) * Mode 3, 0.1} for non FBC units

Mode 4 = maximum {(1

- 0.50) * Mode 3, 0.08} for FBC units

Note: The (1

- .25) and (1

- 0.5) terms in the equations above represents the NO

x 

### removal efficiencies of

SNCR for non FBC and FBC boilers.

Group 3.3 SNCR is operating in winter only

Mode 1= 2011 ETS Summer NO

x 

### Rate

Mode 2 = 2011 ETS Winter NO

x 

### Rate

Mode 3 = same as Group 3.2 Mode 3

Mode 4 = same as Group 3.2 Mode 4

Group 3.4 SNCR is operating year

- round

Mode 1= if (2009 ETS Winter NO

x 

### Rate > 0.3, 2009 ETS Winter NO

x 

### Rate, 2011 ETS Annual Average

NO

x 

### Rate)

Mode 2 = 2011 ETS Annual Average NO

x 

### Rate

Mode 3 = same as Group 3.2 Mode 3

M ode 4 = Mode 3

Other things worth noting are:

17 This equation implies that if a unit with a SCR operates year round i n ETS 2011 and in winter in ETS 2009, then Mode 1 NO

x rate

will reflect SCR operation.

### 3

- 35

(a) In general, winter NO

x 

### rates reported in EPA’s Emission Tracking System were used as proxies for

assigning emission rates to Mode 1

. (b) If a unit does not report having combustion controls, but has an em ission rate below a specific cut

- off

rate (shown in Table 3

- 1. 1 ), it is considered to have combustion controls.

(c) For units with combustion controls that were not state

- of

- the

- art, the derivation of an emission rate

reflecting an up grade to state

- of

- the

- art combustion controls necessitated calculating (as an interim step)

the unit’s emission rate if it were to “uninstall” its existing combustion controls. That interim “no

combustion controls” emission rate becomes the departure poin t for calculating the unit’s emission rate

assuming a state

- of

- the

- art combustion control configuration.

(d) The NO

x 

### rates achievable by state

- of

- the

- art combustion controls vary by coal rank (bituminous and

subbituminous ) and boiler type. The equations us ed to derive these rates are shown in Table 3

- 1. 2

.

### 3

- 36 Table 3

- 1 .1 Cutoff and Floor NO

x 

### Rates (lb/MMBtu) in EPA Base Case v.5.13

Boiler Type Cutoff Rate (lbs/MMBtu) Floor Rate (lbs/MMBtu)

Bituminous Subbituminous Lignite Bituminous Subbituminous Lignite

Wall

- Fired Dry

- Bottom 0.43 0.33 0.29 0.32 0.18 0.18

Tangentially

- Fired 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 0.49

Vertically

- Fired 0.57 0.44 0.44 0.49 0.25 0.25



### Table 3

- 1 .2 NO

x 

### Removal Efficiencies for Different Combustion Control Configurations in EPA Base Case v.5.13

(State of the art configurations are shown in bold italic.)

Boiler Type Coal Type Combustion Control Technology Fraction of Removal Default Removal

Dry Bottom Wall

- Fired Bituminous LNB 0.163 + 0.272* Base NO

x 

### 0.568

LNB + OFA 0.313 + 0.272* Base NO

x 

### 0.718

Dry Bottom Wall

- Fired Subbituminous/Lignite LNB 0.135 + 0.541* Base NO

x 

### 0.574

LNB + OFA 0.285 + 0.541* Base NO

x 

### 0.724

Tangentially

- Fired Bituminous LNC1 0.162 + 0.336* Base NO

x 

### 0.42

LNC2 0.212 + 0.336* Base NO

x 

### 0.47

LNC3 0.362 + 0.336* Base NO

x 

### 0.62

Tangentially

- Fired Subbituminous/Lignite LNC1 0.20 + 0.717* Base NO

x 

### 0.563

LNC2 0.25 + 0.717* Base NO

x 

### 0.613

LNC3 0.35 + 0.717* Base NO

x 

### 0.713

Notes:

LNB = Low NO

x Burner

OFA = Overfire Air

LNC = Low NO

x Control

### 3

- 37 

## Attachment 3

- 2 Capacity Deployment Limits for Advanced Coal with CCS and

New Nuclear in EPA Base Case v.5.13

Run

Year Advanced Coal with

CCS (MW) New Nuclear

(MW) Notes:

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. 2016

- -

2018

- -

2020 6,500 5,000

2025 17,254 13,272

2030 31,750 24,423

2040 106,211 81,701

2050 301,097 231,613

2020 2025 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 New Nuclear



### Advanced Coal with CCS 

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

### 3

- 38 

## Attachment 3

- 3 Nuclear Capacity Deployment Constraint in EPA Base Case v.5.13

Run

Year Base New

Nuclear

Capacity Base New Nuclear Capacity Deployment

Equation Possible Additional New Nuclear

Capacity Deployment Equation 1 Maximum Annual Incremental New Nuclear Capacity

Deployment Allowed Equation

2020 5,000 5,000 0 5,000

2025 4,400 0.88 * 2020_Base_Capacity + 0.88 * 2020_Incremental_Capacity = 0.88 * ( 2020_Base_Capacity + 2020_Incremental_Capacity )

2030 3,872 0.88 * 2025_Base_Capacity + 0.88 * 2025_Incremental_Capacity = 0.88 * ( 2025_Base_Capacity + 2020_Incremental_Capacity )

2040 19,208 4.96 * 2030_Base_Capacity + 4.96 * 2030_Incremental_Capacity = 4.96 * ( 2030_Base_Capacity + 2030_Incremental_Capacity )

2050 37,648 1.96 * 2040_Base_Capacity + 1.96 * 2040_Incremental_Capacity = 1.96 * ( 2040_Base_Capacity + 2040_Incremental_Capacity )

Run

Year Maximum Possible New Nuclear Capacity Deployment Allowed

Deployment Starts 2020 Deployment Starts 2025 Deployment Starts 2030 Deployment Starts 2040 Deployment Starts 2050

Incremental Cumulative Incremental Cumulative Incremental Cumulative Incremental Cumulative Incremental Cumulative

2020 5,000 5,000 0 0 0 0 0 0 0 0

2025 8,272 13,272 4,400 4,400 0 0 0 0 0 0

2030 11,151 24,423 7,744 12,144 3,872 3,872 0 0 0 0

2040 57,278 81,701 43,010 55,154 26,797 30,669 19,208 19,208 0 0

2050 149,912 231,613 121,948 177,102 90,170 120,839 75,295 94,503 37,648 37,648

Notes:

No nuclear deployment is allowed before 2020

1 Addtional new nuclear capacity deployment is only possible if nuclear capacity has been built in the previous run year.

### 3

- 39 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 2020 2025 2030 2040 2050

Capacity (MW) Run Years 

## Maximum Possible Cumulative New Nuclear Capacity Each Run Year

Deployment Starts 2050 Deployment Starts 2040 Deployment Starts 2030 Deployment Starts 2025 Deployment Starts 2020

### 3

- 40 Table 3

- 13 State Power Sector Regulations included in EPA Base Case v.5.13

State/Region Bill Emission Type Emission Specifications Implementation

Status Notes

Alabama Alabama Administrative Code

Chapter 335

- 3

- 8 NO

x 0.02 lbs/MMBtu for combined cycle EGUs which commenced

operation after April 1, 2003; For combined

- cycle electric

generating units fired by natural gas: 4.0 ppmvd at 15% O2

(0.0178 lbs/MMBtu), by fuel oil

- 15.0 ppmvd at 15% O2 (0.0667

lbs/MMBtu) 2003

Arizona Title 18, Chapter 2, Article 7 Hg 90% removal of Hg content of fuel or 0.0087 lbs/GWh annual

reduction for all non

- cogen coal units > 25 MW 2017

California CA Reclaim Market 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)

1994 Since the Reclaim Trading

Credits are applicable to entities

besides power plants, we

approximate by hardwiring the

NO

x and SO 2 allowance prices

for the calendar year 2006. 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)

CA AB 32 CO 2 Power sector and Non

- power Sector Cap in Million metric tons:

382.40 in 2016, 358.30 in 2018 and 334.20 2020 onwards. 2012 Refer to Section 3.9.4 for details

Colorado 40 C.F.R. Part 60 Hg 2012 & 2013: 80% reduction of Hg content of fuel or 0.0174

lbs/GWh annual reduction for Pawnee Station 1 and Rawhide

Station 101.

2014 through 2016: 80% reduction of Hg content of fuel or 0.0174

lbs/GWh annual reduction for all coal units > 25 MW

2017 onwards: 90% reduction of Hg content of fuel or 0.008 7

lb/GWh annual reduction for all coal units > 25 MW 2012

Clean Air, Clean Jobs Act NO

x, SO 2 , Hg Retire Arapahoe 3 by 2014; Cherokee 1 & 2 by 2012, Cherokee 3

by 2017; Cameo 1 & 2; Valmont 5 by 2018; W N Clark 55 & 59

by 2015

Convert following units to natural gas: Arapahoe 4 by 2015;

Cherokee 4 by 2018

Install SCRs in Hayden 1 & 2 by 2016; SCR + FGD in Pawnee 1

[already installed] 2010

Connecticut Executive Order 19 and Regulations

of Connecticut State Agencies

(RCSA) 22a

- 174

- 22 NO

x 0.15 lbs/MMBtu annual rate limit for all fossil units > 15 MW

2003 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 > 25 MW (Title

IV Sources)

0.55 lbs/MMBtu annual rate limit for all non

- fossil units > 15 MW

and fossil units < 25MW and > 15MW (Non

- Title IV Sources)

Public Act No. 03

- 72 & RCSA 22a

198 Hg 90% removal of Hg content of fuel or 0.0087 lbs/GWh annual

reduction for all coal

- fired units 2008

Delaware 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

Regulation No. 1146: Electric

Generating Unit (EGU) Multi

Pollutant Regulation NO

x 0.125 lbs/MMBtu rate limit of NO

x annually for all coal and

residual

- oil fired units > 25 MW

2009 The following units have

specific NO

x , SO 2 , and Hg

annual caps in MTons:

Edge Moor 3: 0.773 NO

x , 1.391 SO 2 0.26 lbs/MMBtu annual rate limit for coal and residual

- oil fired

units > 25 MW

### 3

- 41 State/Region Bill Emission Type Emission Specifications Implementation

Status Notes

Hg 2012: 80% removal of Hg content of fuel or 0.0174 lbs/GWh

annual reduction for all coal units > 25 MW

2013 onwards: 90% removal of Hg content of fuel or 0.0087

lbs/GWh annual reduction for all coal units > 25 MW 2012 SO 2 , & 2012: 0.0000083 Hg,

2013 onwards: 0.0000033 Hg

Edge Moor 4: 1.339 NO

x , 2.41

SO 2 , & 2012: 0.0000144 Hg,

2013 onwards: 0.0000057 Hg

Edge More 5: 1.348 NO

x &

2.427 SO 2 Indian River 3: 0.977 NO

x , 1.759 SO 2 , & 2012: 0.0000105

Hg, 2013 onwards: 0.0000042

Hg

Indian River 4: 2.032 NO

x , 3.657 SO 2 , & 2012: 0.0000219

Hg, 2013 onwards: 0.0000087

Hg

McKee Run 3 0.244 NO

x &

0.439 SO 2

Regulation 1108: Distillate Fuel Oil

rule SO 2 Any relevant units are to use 0.3% sulfur distillate fuel oil Fuel rule modeled through unit

emission rates

Georgia Multi

- pollutant 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

Illinois Title 35, Section 217.706 NO

x 0.25 lbs/MMBtu summer season rate limit for all fossil units > 25

MW 2003

Title 35, Part 225, Subpart B

225.230 Hg 90% removal of Hg content of fuel; or a standard of .0080 lb

Hg/GWh for sources at or above 25 MW; If facility commenced

operation on or before December 31, 2008, start date for

implementation must be July 1, 2009 2009 Not Ameren Specific

Title 35 Part 225 Subpart B 225.233 NO

x 0.11 lbs/MMBtu annual rate limit and ozone season rate limit for

all coal steam units > 25 MW 2012

Not Ameren Specific SO 2 2015 onwards: 0.25 lbs/MMBtu annual rate limit for all coal steam

units > 25 MW or a rate equivalent to 35% of the base SO 2 emissions (whichever is more stringent) 2015

Hg 90% removal of Hg content of fuel or 0.08 lbs/GWh annual

reduction for all coal units > 25 MW 2015

Title 35 Part 225 Subpart B 225.233

(MPS Ameren specific) NOx 0.11 lbs/MMBtu annual rate limit and ozone season rate limit

Ameren coal steam units > 25 MW 2012

SO 2 2015 & 2016 onwards: 0.25 lbs/MMBtu annual rate limit for all

Ameren coal steam units > 25 MW

2017 onwards: 0.23 lbs/MMBtu annual rate limit for all Ameren

coal steam units > 25 MW 2015

Title 35 Part 225; Subpart F:

Combined Pollutant Standards

(REPEALED) NO

x 0.11 lbs/MMBtu ozone season and annual rate limit for all

specified Midwest Gen coal steam units 2012

REPEALED SO 2 0.44 lbs/MMBtu annual rate limit in 2013, decreasing annually to

0.11 lbs/MMBtu in 2019 for all specified Midwest Gen coal steam

units 2013

Hg 90% removal of Hg content of fuel or 0.08 lbs/GWh annual

reduction for all specified Midwest Gen coal steam units 2015

### 3

- 42 State/Region Bill Emission Type Emission Specifications Implementation

Status Notes

Louisiana Title 33 Part II

- Chapter 22, Control

of Nitrogen Oxides NO

x For units >/= 80 MMBtu/hr, rate limit in lbs/MMBtu:

Coal fired : 0.21

Oil

- fired: 0.18

All others (gas or liquid): 0.1

Stationary Sources >/= 10 MMBtu/hr, rate limit in lbs/MMBtu:

Oil

- fired: 0.3

Gas

- fired: 0.2 Applicable for all units in Baton

Rouge Nonattainment Area &

Region of Influence.

Willow Glenn, located in

Iberville, obtained a permit that

allows its gas

- fired units to

maintain a cap. These units are

separately modeled.

Title 33, Part III

- Chapter 15,

Emission Standards for Sulfur

Dioxide SO 2 1.2 lbs/MMBtu ozone season ppmvd for all single point sources

that emit or have the potential to emit 5 tons or more of SO 2 2005

Maine 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 exchang ers, primary boilers, and resource recovery units

with heat input capacity > 250 MMBtu/hr 2005

38 MRSA Section 603

- A Low Sulfur

in Fuel Rule SO 2 All fossil units require the use of 0.5% sulfur residual oil [0.52

lbs/MMBtu] 2018 Fuel rule modeled through unit

emission rates

Statue 585

- B Title 38, Chapter 4:

Protection and Improvement of Air Hg 25 lbs annual cap for any facility including EGUs 2010

Maryland Maryland Healthy Air Act NO

x 7.3 MTons summer cap and 16.7 MTons annual cap for 15

specific existing coal steam units

2009 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

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

existing coal steam units

Massachusett

s 310 CMR 7.29 NO

x 1.5 lbs/MWh annual GPS for Brayton Point, Mystic Generating

Station, Mount Tom, Canal, and Salem Harbor

2006 SO 2 3.0 lbs/MWh annual GPS for Brayton Point, Mystic Generating

Station, Mount Tom, Canal, and Salem Harbor

Hg 2012: 85% removal of Hg content of fuel or 0.00000625 lbs/MWh

annual GPS for Brayton Point, Mystic Generating Station, Mount

Tom, Canal, and Salem Harbor

2013 onwards: 95% removal of Hg content of fuel or 0.0000025

lbs/MWh annual GPS for Brayton Point, M ystic Generating

Station, Mount Tom, Canal, and Salem Harbor Brayton units 1 through 3 have

an annual Hg cap of 0.0000733

MTons

Mt. Tom 1 has an annual Hg

cap of 0.00000205 MTons

Salem Harbor units 1 through 3

have an annual Hg cap of

0.0000106 MTons

310 CMR 7.04 SO 2 Sulfur in Fuel Oil Rule requires the use of 0.5% sulfur residual oil

[0.52 lbs/MMBtu] by July 1, 2014 for units greater than 250

MMBtu energy input; by July 1, 2018 for all residual oil units

except for those located in the Berkshire APCD. 2014 Fuel rule modeled through unit

emission rates

### 3

- 43 State/Region Bill Emission Type Emission Specifications Implementation

Status Notes

Michigan Part 18 Rules

– R 336.1801 (2) (a) NO

x For all fossil units > 25 MW, and annual PTE of NO

x >25 tons,.25

lbs/MMBtu ozone season rate, OR 65% NO

x reductions from

1990 levels 2004

Part 18 Rules

– R 336.1801 (2) (a) SO 2 SO 2 ppmvd rates in 50% excess air for units in Wayne county:

Pulverized coal: 550;Other coal: 420;Distillate oil Nos. 1 & 2:

120;Used oil: 300;Crude and Heavy oil: 400

2012 Not modeled in IPM as limits

are within SIP rates For all other units,

with 0

- 500,000 lbs Steam per Hour Plant Capacity: 2.5

with >500,000 lbs Steam per Hour Plant Capacity: 1.67

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 facilities >

500 MW combined; Dry scrubbed units must implement by

December 31, 2010; Wet scrubbed units must implement by

December 31, 2014. 2006

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. Fra ncois, 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

New

Hampshire 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

ENV

- A2900 Multiple pollutant

annual budget trading and banking

program 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 2007

SO 2 7.29 MTons annual cap for Merrimack 1 & 2, Newington 1, and

Schiller 4 through 6

Env

- A 2300

- Mitigation of Regional

Haze SO 2 90% SO 2 control at Merrimack 1 & 2; 0.5 lb SO 2 /MMBtu 30 day

rolling average at Newington 1

2013

NO

x 0.30 lb NO

x /MMBtu 30

- day rolling average at Merrimack 2; 0.35 lb

NO

x /MMBtu when burning oil and 0.25 lb NO

x /MMBtu when

burning oil and gas at Newington 1(permit condition).

New Jersey 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

or <= 3.0 mg/MWh (net)

95% removal of Hg content of fuel annually for all MSW

incinerator units or <= 28 ug/dscm 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 2007

### 3

- 44 State/Region Bill Emission Type Emission Specifications Implementation

Status Notes

N.J. A. C. Title 7, Chapter 27,

Subchapter 19, Table 2 NO

x Annual rate limits in lbs/MMBtu for the following technologies:

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 2007

N.J. A. C. Title 7, Chapter 27,

Subchapter 19, Table 3 NO

x Annual rate limits in lbs/MMBtu for the following technologies:

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 cyclo ne

- fired oil and/or gas boilers serving an EGU 2007

N.J. A. C. Title 7, Chapter 27,

Subchapter 19, Table 6; non

- High

Electricity demand Day (HEDD) unit 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

N.J. A . C. Title 7, Chapter 27,

Subchapter 19, Table 7; High

Electricity demand Day (HEDD) unit NO

x 1.0 lbs/MWh annual GPS for gas

- burning simple cycle

combustion turbine units

1.6 lbs/MWh annual GPS for oil

- burning simple cycle combustion

turbine units

0.75 lbs/MWh annual GPS for gas

- burning combined cycle CT or

regenerative cycle CT units

1.2 lbs/MWh annual GPS for oil

- burning combined cycle CT or

regenerative cycle CT units 2007 On and after May 1, 2015, the

owner or operator of a

stationary combustion turb ine

that is a HEDD unit or a

stationary combustion turbine

that is capable of generating 15

MW or more and that

commenced operation on or

after May 1, 2005 shall comply

with limits outlines “in Table 7

during operation on high

electricity demand days,

rega rdless of the fuel

combusted, unless combusting

gaseous fuel is not possible due

to gas curtailment."

New York Part 237 NO

x 39.91 Mtons [Thousand tons] non

- ozone season cap for fossil fuel

units > 25 MW 2004

Part 238 SO 2 131.36 MTons [Thousand tons] annual cap for fossil fuel units >

25 MW 2005

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

Subpart 227

- 2 Reasonably

Available Control Technology

(RACT) For Major Facilities of

Oxides Of Nitrogen (NO

x ) NO

x Annual rate in lbs/MMBtu for very large boilers >250 MMBtu/hr

that commenced operation prior to July 1, 2014;

Gas only, tangential & wall fired : 0.2

Gas/oil tangential & wall fired : 0.25; cyclone: 0.43

Coal Wet Bottom, tangential & wall fired : 0.1; cyclone: 0.6

Coal Dry Bottom, tangential: 0.42; wall fired : 0.45; st okers: 0.301

Annual rate in lbs/MMBTu for very large boilers >250 MMBtu/hr

that commenced operation after July 1, 2014;

Gas only, tangential & wall fired : 0.8

Gas/oil tangential & wall fired : 0.15; cyclone: 0.2

Coal Wet Bottom, tangential & wall fired : 0.12; cyclone: 0.2

Coal Dry Bottom, tangential & wall fired : 0.12; stokers: 0.08 2004

### 3

- 45 State/Region Bill Emission Type Emission Specifications Implementation

Status Notes

Annual rate in lbs/MMBTu for large boilers between 100 and 250

MMBtu/hr that commenced operation prior to July 1, 2014;

Gas Only: 0.20

Gas/Oil: 0.30

Pulverized Coal: 0.50

Coal (Overfeed Stoker):0.301

Annual rate in lbs/MMBTu for large boilers between 100 and 250

MMBtu/hr that commenced operation after July 1, 2014;

Gas Only: 0.06

Gas/Oil: 0.15

Pulverized Coal: 0.20

Coal (Overfeed Stoker/FBC): 0.8

Annual rate in lbs/MMBTu for mid

- size boilers between 25 and

100 MMBtu/hr that commenced operation prior to July 1, 2014;

Gas Only: 0.10

Distillate Oil/Gas: 0.12

Residual Oil/Gas: 0.30

Annual rate in lbs/MMBTu for mid

- size boilers between 25 and

100 MMBtu/hr that commenced operation after July 1, 2014;

Gas Only: 0.05

Distillate Oil/Gas: 0.08

Residual Oil/Gas: 0.20

For simple cycle and regenerative combustion turbines:

(i) 50 parts per million on a dry volume basis (ppmvd), corrected

to 15 percent oxygen, for sources designed to burn gaseous fuels

(gaseous fuels include, but are not limited to, natural gas, landfill

gas, and digester gas) only; and

(ii) 100 ppmvd, corre cted to 15 percent oxygen, for sources

capable of firing distillate oil or more than one fuel. Compliance with these emission

limits must be determined with a

one hour average during the

ozone season and a 30

- day

average during the non

- ozone

season unless the owner or

operator chooses to use a

CEMS under the provisions of

section 227

- 2.6(b) of this

Subpart.

For combined cycle combustion turbines :

(i) prior to July 1, 2014, 42 ppmvd (0.1869 lbs/MMBtu), corrected

to 15 percent oxygen, when firing gas; and

(ii) prior to July 1, 2014, 65 ppmvd (0.2892 lbs/MMBtu), corrected

to 15 percent oxygen, when firing oil.

Stationary internal combustion engines having a maximum

mechanical output => 200 brake horsepower in a severe ozone

nonattainment area or having a maximum mechanical output

rating =>400 brake horsepower outside a severe ozone

nonattainment:

(1) For internal combustion engines fired solely with natural gas:

1.5 grams per brake hors epower

- hour.

(2) For internal combustion engines fired with landfill gas or

digester gas (solely or in combination with natural gas): 2.0 grams

per brake horsepower

- hour.

(3) For internal combustion engine fired with distillate oil (solely or

in combinatio n with other fuels): 2.3 grams per brake horsepower

hour.

Part 251 CO 2 1450 lbs/MWh rate limit for New Combustion Turbines =>25MW

925 lbs/MWh rate limit for New Fossil Fuel except CT =>25MW 2012

### 3

- 46 State/Region Bill Emission Type Emission Specifications Implementation

Status Notes

North Carolina NC Clean Smokestacks Act: Statute

143

- 215.107D 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

SO 2 2012: 100 MTons annual cap for Progress Energy coal plants >

25 MW and 150 MTons annual cap for Duke Energy coal plants >

25 MW

2013 onwards: 50 MTons annual cap for Progress Energy coal

plants > 25 MW and 80 MTons annual cap for Duke Energy coal

plants > 25 MW 2009

SECTION .2500

– Mercury Rules

for Electric Generators Hg Coal

- fired electric steam >25 MW to comply with the mercury

emission caps of 1.133 tons (36,256 ounces) per year between

2010 and 2017

inclusive and 0.447 tons (14,304 ounces) per year for 2018 and

thereafter 2010 Vacated

15A NCAC 02D .2511 Hg Duke Energy and Progress Energy Hg control plans submitted on

January 1, 2013 and are awaiting approval. All control

technologies and limitations must be implemented by December

31, 2017. 2017

Oregon Oregon Administrative Rules,

Chapter 345, Division 24 CO 2 675 lbs/MWh annual rate limit for new combustion turbines

burning 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 Utility Mercury Rule

-

Potential Units Hg 25 lbs limit for all potential coal units > 25 MW 2009

Texas Senate Bill 7 Chapter 101 SO 2 273.95 MTons cap of SO 2 for all grandfathered units built before

1971 in East Texas Region

2003

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

Chapter 117 NO

x 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

2007 Units are also allowed to

comply by reducing the same

amount of NO

x on a monthl y

basis using a system cap or by

purchasing credits.

East and Central Texas,

Dallas/Fort Worth Area,

Beaumont

- Port Arthur region

units are assumed to be in

compliance based on their

reported 2011 ETS rates. The

regulations for these regions

are not model ed. 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

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

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

Washington Washington State House Bill 3141 CO 2 $1.45/MTons cost (2004$) for all new fossil

- fuel power plant 2004

### 3

- 47 State/Region Bill Emission Type Emission Specifications Implementation

Status Notes

Washington State House Bill 5769 CO 2 1100 lbs/MWh rate limit for new coal plants 2011

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: 2013

onwards: 0.10

Arch fired: 2009 onwards: 0.18

2009 Annual rate limits in lbs/MMBtu for coal fired boilers between 500

and 1,000 MMBtu/hr:

Wall

- fired with a heat release rate=> 17,000 Btu per cubic feet per

hour; 2013 onwards: 0.17 ; if heat input is lesser:

Tangential fired: 2009 onwards: 0.15

Cyclone fired: 2013 onwards: 0.15

Fluidized bed: 2013 onwards: 0.10

Arch fired: 2009 onwards: 0.18

Annual rate limits in lbs/MMBtu for coal fired boilers between 250

and 500 MMBtu/hr:

Same as for coal boiled between 500 and 1000 MMBtu/hr in

addition to:

Stoker Fired: .20

Annual rate limits in lbs/MMBtu for coal fired boilers between 50

and 250 MMBtu/hr:

Same as for coal boiled between 500 and 1000 MMBtu/hr in

addition to:

Stoker Fired: .25

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: 0.42

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: 0.19

Biologically derived fuel CCs > 25 MWs: 0.15

Natural gas CCs between 10 and 24 MW: 0.19

Chapter NR 44.12/446.13 Control of

Mercury Emissions Hg Large (150MW capacity or greater) or small (between 25 and 150

MW) coal

- fired EGU, 2015 onwards: 90% removal of Hg content

of fuel or 0.0080 lbs/GWh reduction in coal fired EGUs > 150 MW 2015

Chapter NR 446.14 Multi

- pollutant

reduction alternative for coal

- fired

electrical generating units Hg All Coal>25MW;

70% reduction in fuel, or .0190 lbs per GW

- hr from CY 2015

– CY

2017 (0.00005568 lbs/MMBtu)

80% reduction in fuel, or .0130 lbs per GW

- hr from CY2018

– CY

2020 (0.0000381 lbs/MMBtu)

90% reduction in fuel, or .0080 lbs per GW

- hr from January 1,

2021 onwards (0.00000234 lbs/MMBtu) 2015 Alternative already modeled in

IPM

SO 2 All Coal>25MW; .10 lbs per mmBTU by January 1, 2015

NO

x All Coal>25MW; 07 lbs per mmBTU by January 1, 2015

### 3

- 48 Table 3

- 14 New Source Review (NSR) Settlements in EPA Base Case v.5.13

Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Alabama Power

James H.

Miller Alabama Unit 3 Inst all and

operate FGD

continuously 95% 12/31/11 Operate existing

SCR

continuously 0.1 05/01/08 0.03 12/31/06

Within 45 days of

settlement entry,

APC must retire

7,538 SO 2 emission

allowances. APC shall not sell,

trade, or otherwise

exchange any Plant

Miller excess SO 2

emission allowances

outside of the APC

system 1/1/21 1) Settlement requires 95%

removal efficiency for SO 2 or 90%

in the event that the unit combust a

coal with sulfur content gre ater than

1% by weight. 2) The settlements

require APC to retire $4,900,000 of

SO 2 emission allowances within 45

days of consent decree entry. 3)

EPA assumed a retirement of 7,

538 SO 2 allowances based on a

current allowance price of $650. http://www2.

epa.gov/enfo

rcement/alab

ama

- power

company

clean

- air

- act

settlement Alabama Unit 4 Install and

operate FGD

continuously 95% 12/31/11 Operate existing

SCR

continuously 0.1 05/01/08 0.03 12/31/06 1/1/21

Minnkota Power Cooperative

Beginning 1/01/2006, Minnkota shall not emit more than 31,000 tons of SO 2 /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 b y 12/31/2015, then beginning 1/01/2014, the plant wide emission shall not exceed 8,500.

Milton R.

Young North Dakota Unit 1 Install and

continuously

operate FGD 95% if wet

FGD, 90% if

dry 12/31/11 Install and

continuously

operate Over

- fire

AIR, or

equivalent

technology with

emission rate <

.36 0.36 12/31/09 0.03 if wet

FGD, .015

if dry FGD Plant will surrender

4,346 allowances for

each year 2012

– 2015, 8,693

allowances for years

2016

– 2018, 12,170

allowances for year

2019, and 14,886

allowances/year

thereafter if Units 1

– 3 are operational by

12/31/2015. If only

Units 1 and 2 are

operati onal

by12/31/2015, the

plant shall retire

17,886 units in 2020

and thereafter. Minnkota shall not sell

or trade NO

x allowances allocated to

Units 1, 2, or 3 that

would otherwise be

available for sale or

trade as a result of the

actions taken by the

settli ng defendants to

comply with the

requirements 1) Settlement requires 95%

removal efficiency for SO 2 at Unit 1

if a wet FGD is installed, or 90% if a

dry FGD is installed. The FGD for

Units 1 and 2 and the NO

x control

for Unit 1 are modeled as emission

c onstraints in EPA Base Case, the

NO

x control for Unit 2 is hardwired

into EPA Base Case. 2) Beginning

12/31/2010, Unit 2 will achieve a

phase II average NO

x emission rate

established through its NO

x BACT

determination. Beginning

12/31/2011, Unit 1 will achieve a

phase II NO

x emission rate

established by its BACT

determination. http://www2.

epa.gov/enfo

rcement/min

nkota

- power

cooperative

and

- square

butte

electric

cooperative

settlement North Dakota Unit 2 Design,

upgrade, and

continuou sly

operate FGD 90% 12/31/10 Install and

continuously

operate over

- fire

AIR, or

equivalent

technology with

emission rate <

.36 0.36 12/31/07 0.03 Before

2008

SIGECO

FB Culley Indiana Unit 1 Repower to

natural gas

(or retire) 12/31/06

The provision did not

specify an amount of

SO 2 allowances to be

surrendered. It only

provided that excess

allowances resulting

from compliance with

NSR settlement

provisions must be

retired. http://www2.

epa.gov/enfo

rcement/sout

hern

- indiana

gas

- and

electric

company

sigeco

- fb

culley

- plant

clean

- air

- act

caa Indiana Unit 2 Improve and

continuously

operate

existing FGD

(shared by

Units 2 and 3) 95% 06/30/04

Indiana Unit 3 Improve and

continuously

operate

existing FGD

(shared by

U nits 2 and 3) 95% 06/30/04 Operate Existing

SCR

Continuously 0.1 09/01/03 Install and

continuously

operate a

Baghouse 0.015 06/30/07

PSEG FOSSIL

Bergen New Jersey Unit 2 Repower to

combined

cycle 12/31/02 The provision did not

specify an amount of

SO 2 allowances to be

surrendered. It only

provided that excess

allowances resulting

from compliance with

NSR settlement

provisions must be

retired. http://www2.

epa.gov/enfo

rcement/pse

g

- fossil

- l lc

settlement Hudson New Jersey Unit 2 Install Dry FGD

(or approved

alt. technology)

and continually

operate 0.15 12/31/06 Install SCR (or

approved tech)

and continually

operate 0.1 05/01/07 Install

Baghouse (or

approved

technology) 0.015 12/31/06 The settlement requires coal with

monthly average sulfur content no

greater than 2% at units operating

FGD

-- this limit is modeled as a

coal choice exception in EPA Base

Case.

### 3

- 49 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Mercer New Jersey Unit 1 Install Dry FGD

(or approved

a lt. technology)

and continually

operate 0.15 12/31/10 Install SCR (or

approved tech)

and continually

operate 0.1 01/01/07 0.015 12/31/10 The settlement requires coal with

monthly average sulfur content no

greater than 2% at units operating

FGD

-- this limit is modeled as a

coal choice exception in EPA Base

Case.

New Jersey Unit 2 Install Dry FGD

(or approved

alt. technology)

and continually

operate 0.15 12/31/12 Install SCR (or

approved tech)

and continually

operate 0.1 01/01/07 0.015 12/31/10 The settlement requires coal with

monthly average sulfur content no

greater than 2% at units operating

FGD

-- this limit is modeled as a

coal choice exception in EPA Base

Case.

TECO

Big Bend Florida Unit 1 Existing

Scrubber

(shared by

Units 1 & 2) 95% (95% or

.25) 09/1/00

(01/01/13) Install SCR 0.1 05/01/09

The provision did not

specify an amount of

SO 2 allowances to be

surrendered. It only

provided that excess

allowances resulting

from compliance with

NSR settlement

provisions must be

retired. http://www2.

epa.gov/enfo

rcement/tam

pa

- electric

company

teco

- clean

air

- act

- caa

settlement Florida Unit 2 Existing

Scrubber

(shared by

Units 1 & 2) 95% (95% or

.25) 09/1/00

(01/01/13) Install SCR 0.1 05/01/09

Florida Unit 3 Existing

Scrubber

(shared by

Units 3 & 4) 93% if Units

3 & 4 are

operating 2000

(01/01/10) Install SCR 0.1 05/01/09

Florida Unit 4 Existing

Scrubber

(shared by

Units 3 & 4) 93% if Units

3 & 4 are

operating 06/22/05 Install SCR 0.1 07/01/07

Gannon Florida Six units Retire all six

coal units

and repower

at least 550

MW of coal

capacity to

natural gas 12/31/04

WEPCO

WEPCO shall comply with the following system wide average NO

x emission rates and total NO

x tonnage 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 0.17 and 17, 400 tons. For SO 2 emissions, 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 0.61 and 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 0.32 and 33,300 tons.

http://www2.

epa.gov/enfo

rc ement/wisc

onsin

electric

power

company

wepco

- clean

air

- act

- civil

settlement Presque Isle Wisconsin Units

1

– 4 Retire or

install SO 2 and NO

x controls 12/31/12 Install and

continuously

operate FGD

(or approved

equiv. tech) 95% or 0.1 12/31/12 Install SCR (or

approved tech)

and continually

operate 0.1 12/31/12

The provision did not

specify an amount of

SO 2 allowances to be

surrendered. It only

provided that excess

allowances resulting

from compliance with

NSR settlement

provisions must be

retired. Wisconsin Units 5, 6 Install and

operate low NO

x burners 12/31/03

Wisconsin Units 7, 8 Operate existing

low NO

x burners 12/31/05 Install

B aghouse

Wisconsin Unit 9 Operate existing

low NO

x burners 12/31 /06 Install

Baghouse

Pleasant

Prairie Wisconsin Unit 1 Install and

continuously

operate FG D

(or approved

control tech) 95% or 0.1 12/31/06 Install and

continuously

operate SCR (or

approved tech) 0.1 12/31/06

### 3

- 50 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Wisconsin Unit 2 Install and

continuously

operate FGD

(or approved

control tech) 95% or 0.1 12/31/07 Install and

continuously

operate SCR (or

approved tech) 0.1 12/31/03

Oak Creek Wisconsin Units 5, 6 Install and

continuously

op erate FGD

(or approved

control tech) 95% or 0.1 12/31/12 Install and

continuously

operate SCR (or

approved tech) 0.1 12/31/12

Wisconsin Unit 7 Install and

continuously

operate FGD

(or approved

control tech) 95% or 0.1 12/31/12 Install and

continuously

operate SCR (or

approved tech) 0.1 12/31/12

Wisconsin Unit 8 Install and

continuously

operate FGD

(or approved

control tech) 95% or 0.1 12/31/12 Install and

continuously

operate SCR (or

approved tech) 0.1 12/31/12

Port

Washington Wisconsin Units

1

– 4 Retire 12/31/04

for Unit s 1

– 3. Unit 4

by entry of

consent

decree

Valley Wisconsin Boilers

1

– 4 Operate existing

low NO

x burner 30 day s after entry

of consent

decree

VEPCO

The Total Permissible NO

x Emissions (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 thereafter. Beginning 1/1/2013 they will have a syste m wide emission rate no greater than 0.15 lbs/mmBTU.

http://www2.

epa.gov/enfo

rcement/virgi

nia

- electric

and

- power

company

vepco

- clean

air

- act

- caa

settlement Mount Storm West Virginia Units

1

– 3 Construct or

improve FGD 95% or 0.15 01/01/05 Install and

continuously

operate SCR 0.11 01/01/08

On or before March

31 of every year

beginning in 2013

and continuing

thereafter, VEPCO

shall surrender

45,000 SO 2

allowances. Chesterfield Virginia Unit 4 Install and

continuously

operate SCR 0.1 01/01/13

Virginia Unit 5 Construct or

improve FGD 95% or 0.13 10/12/12 Install and

continuously

operate SCR 0.1 01/01/12

Virginia Unit 6 Construct or

improve FGD 95% or 0.13 01/01/10 Install and

continuously

operate SCR 0.1 01/01/11

Chesapeake

Energy Virginia Units 3, 4 Install and

continuously

operate SCR 0.1 01/ 01/13

Clover Virginia Units 1, 2 Improve FGD 95% or 0.13 09/01/03

Possum

Point Virginia Units 3, 4 Retire and

repower to

natural gas 05/02/03

Santee Cooper

Santee Cooper shall comply with the following system wide averages for NO

x emission rates and combined tons for emission of: by 1/01/2005 facility shall comply with an emission rate of 0.3 and 30,000

tons, by 1/1/2007 an emission rate of 0.18 and 25,000 tons, by 1/1/2010 and emission rate of 0.15 and 20,000 tons. For SO 2 emiss ion 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 rate of 0.53 and 70 tons, and by 1/1/2011 and emission rate of 0.5 and 65 tons. http://www2.

epa.gov/enfo

rcement/sout

h

- carolina

public

service

Cross South

Carolina Unit 1 Upgrade and

continuously

operate FGD 95% 06/30/06 Install and

continuously

operate SCR 0.1 05/31/04 The provision did not

specify an amount of

SO 2 allowances to be

### 3

- 51 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

South

Carolina Unit 2 Upgrade and

continuously

operate FGD 87% 06/30/06 Install and

c ontinuously

operate SCR 0.11/0.1 05/31/04

and

05/31/07 surrendered. It only

provided that excess

allowances resulting

from compliance with

NSR settlement

provisions must be

retired. authority

santee

cooper

settlement

Winyah South

Carolina Unit 1 Install and

continuously

operate FGD 95% 12/31/08 In stall and

continuously

operate SCR 0.11/0.1 11/30/04

and

11/30/04

South

Carolina Unit 2 Install and

continuously

operate FGD 95% 12/31/08 Install and

continuously

operate SCR 0.12 11/30/04

South

Carolina Unit 3 Upgrade and

continuously

operate

existing FGD 90% 12/31/08 Install and

continuously

operate SCR 0.14/0.12 11/30/200

5 and

11/30/08

South

Carolina Unit 4 Upgrade and

continuously

operate

existing FGD 90% 12/31/07 Install and

continuously

operate SCR 0.13/0.12 11/30/05

and

11/30/08

Grainger South

Carolina Unit 1 Operate low NO

x burner or more

stringent

technology 06/25/04

South

Carolina Un it 2 Operate low NO

x burner or more

stringent

technology 05/01/04

Jeffries South

Carolina Units 3, 4 Operate low NO

x burner or more

stri ngent

technology 06/25/04

OHIO EDISON

Ohio Edison shall achieve reductions of 2,483 tons NO

x between 7/1/2005 an d 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 NO

x required for the Sammis Plant. Ohio Edison must reduce 24,600 tons

system

- wide of SO 2 by 12/31/2010.

http://www2.

epa.gov/enfo

rcement/ohio

- edison

company

- wh

sammis

power

station

- clean

air

- act

- 2005

-

settlement

and

- 2009 No later than 8/11/2005, Ohio Edison shall install and operate low NO

x burners on Sammis Units 1, 2,4,5,6, and 7 and overfired air on Sammis Units 1,2,3,6, and 7. No later than 12/1/2005, Ohio Ed ison shall

install advanced combustion control optimization with software to minimize NO

x emissions from Sammis Units 1

– 5.

W.H.

Sammis

Plant Ohio Unit 1 Install Induct

Scrubber (or

approved

equiv.

control tech) 50% removal

or 1.1

lbs/mmBTU 12/31/08 Install SNCR

(or approved

alt. tech) &

operate

continuously 0.25 10/31/07 Beginning on

1/1/2006, Ohio

Edison may use, sell

or transfer any

restricted SO 2 only to

satisfy the

Operational Needs at

the Sammis, Burger

and Mansfield Plant,

or new units within

the FirstEnergy

System that comply

with a 96% removal

for SO 2 . For

calendar year 2006

through 2017, Ohio

Edison may

accumulate SO 2 allowances for use at

the Sammis, Burger,

and Mansfield plants,

or FirstEnergy units

equipped with SO 2 Emission Control

Standards.

Beginning in 2018,

Ohio Edison shall

surrender unus ed

restricted SO 2 Plant

- wide NOx Annual Caps:

11,371 tons 7/1/2005

– 12/31/2005;

21,251 tons 2006; 20,596 tons

2007; 18,903 tons 2008; 17,328

tons 2009

– 2010; 14,845 tons

2011; 11,863 2012 onward.

Sammis Plant

- Wide Annual SO 2 Caps: 58,000 tons SO 2 7/1/2005

- 12/31/2005; 116,000 tons 1/1/2006

– 12/31/2007; 114,000 tons

1/1/2008

- 12/31/2008; 101,500 tons

1/1/2009

– 12/31/2010; 29,900 tons

1/1/2011 onward. Sammis Units 1

– 5 are also subject to the following

SO 2 Monthly Caps if Oh io Edison

installs the improved SO 2 control

technology (Unit 5's option A):

3,242 tons May, July, and August

2010; 3,137 tons June and

September 2010. Ohio Edison has

installed the required SO 2 technology (Unit 5's option B), so

the Monthly Caps are: 2,5 33 tons

May, July, and August 2010; 2,451

tons June and September 2010.

Add'l Monthly Caps are: 2,533 tons

May, July, and August 2011; 2,451

tons June and September 2011

thereafter. Ohio Unit 2 Install Induct

Scrubber (or

approved

equiv.

control tech) 50% removal

or 1.1

lbs/mmBTU 12/31/08 Operate

existing SNCR

continuously 0.25 02/15/06

Ohio Unit 3 Install Induct

Scrubber (or

approved

equiv.

control tech) 50% removal

or 1.1

lbs/mmBTU 12/31/08 Operate low NO

x burners and

overfire air by

12/1/05; install

SNCR

(or approved

alt. tech) &

operate

continuously by

12/31/07 0.25 12/01/05

and

10/31/07

Ohio Unit 4 Install Induct

Scrubber (or

approved

equiv.

control tech) 50% r emoval

or 1.1

lbs/mmBTU 06/30/09 Install SNCR

(or approved

alt. tech) &

operate

continuously 0.25 10/31/07

### 3

- 52 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Ohio Unit 5 Install Flash

Dryer Absorber

or E CO 2 (or

approved

equ iv.

control tech) &

operate

continuously 50% removal

or 1.1

lbs/mmBTU 06/29/09 Install SNCR

(or approved

alt. tech) &

Operate

Continuously 0.29 03/31/08 allowances.

Ohio Unit 6 Install FGD 3 (or

approved

equiv.

control tech) &

operate

continuously 95% removal

or 0.13

lbs/mmBTU 06/30/11 Install SNCR

(or approved

alt. tech) &

operate

continuously "Minimum

Extent

Practicable" 06/30/05 Operate

Existing

ESP

Continuously 0.03 01/01/10 In addition to SNCR, settlement

requires installation of first SCR (or

approved alt tech) on either Unit 6

or 7 by 12/31/2010; second

installation by 12/31/2011. Both

SCRs must achieve 90% Design

Removal Efficiency by 180 days

after installation date. Each SCR

must provide a 30

- Day Rolling

average. NO

x Emission Rate o f 0.1

lbs/mmBTU starting 180 days after

installation dates above. Ohio Unit 7 Install FGD (or

approved

equiv.

control tech) &

operate

continuously 95% removal

or 0.13

lbs/mmBTU 06/30/11 Operate

existing SNCR

Continuously "Minimum

Extent

Practicable" 08/11/05 Operate

Existing

ESP

Continuously 0.03 01/01/10

Mansfield

Plant Pennsylvania Unit 1 Upgrade

existing FGD 95% 12/31/05 Additional Mansfield Plant

- wide

SO 2 reductions are as follows:

4,000 tons in 2006, 8,000 tons in

2007, and 12,000 tons/yr for every

year after. Settlement allows

relinquishment of SO 2 requirement

upon shutdown of unit, after which

the SO 2 reductions must be made

by another plant(s). Pennsylvania Unit 2 Upgrade

existing FGD 95% 12/31/06

Pennsylvania Unit 3 Upgrade

existing FGD 95% 10/31/07

Eastlake Ohio Unit 5 Install low NO

x burners, over

f ired

air and SNCR &

operate

continuously "Minimize

Emissions

to the

Extent

Practicable" 12/31/06 Settlement requires Eastlake Plant

to achieve additional reductions of

11,000 tons of NO

x per year

commencing in calendar year 2007,

and no less than 10,000 tons must

come from this unit. The extra

1,000 tons may come from this unit

or another unit in the region. Upon

shutdown of Eastlake, another plant

must achieve these reductions.

Burger Ohio Unit 4 Repower

with at least

80% biomass

fuel, up to

20% low

sulfur coal

OR Retire by

12/31/2010 12/31/11

Ohio Unit 5 12/31/11

MIRANT 1,6

System

- wide NO

x Emission 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 NO

x Emission 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 continuing for

each and every Ozone Season thereafter, the Mirant System shall not exceed a System

- wide Ozone Season Emission Rate of 0.150 lbs/mmBTU NO

x

.

http://www2.

epa.gov/enfo

rcement/mira

nt

- clean

- air

settlement

Potomac

River Plant Virginia Unit 1

Virginia Unit 2

Virginia Unit 3 Install low NO

x burners (or more

effective tech) &

operate

continuously 05/01/04 Settlement requires installation of

Separated Overfire Air tech (or

more effective technology) by

5/1/2005. Plant

- wide Ozone

Season NO

x Caps: 1,750 tons

2004; 1,625 tons 2005; 1,600 tons

2006

– 2009; 1,475 tons 2010

thereafter. Plant

- wide annual NO

x Caps are 3,700 tons in 2005 and

each ye ar thereafter. Virginia Unit 4 Install low NO

x burners (or more

effective tech) &

operate

continuously 05/01/04

### 3

- 53 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Virginia Unit 5 Install low NO

x burners (or more

effective tech) &

operate

continuously 05/01/04

Morgantown

Plant Maryland Unit 1 Install SCR

(or approved

alt. tech) &

operate

continuously 0.1 05/01/07

Maryland Unit 2 Install SCR

(or approved

alt. tech) &

operate

continuously 0.1 05/01/08

Chalk Point Maryland Unit 1 Install and

continuously

operate F GD

(or equiv.

technology) 95% 06/01/10 For each year after

Mirant commences

FGD operation at

Chalk Point, Mirant

shall surrender the

number of SO 2 Allowances equal to

the amount by which

the SO 2 Allowances

allocated to the Units

at the Chalk Point

Plant are greater

than the total amount

of SO 2 emissions

allowed under this

Section XVIII. Mirant must install and operate

FGD by 6/1/2010 if authorized by

court to reject ownership interest in

Morg antown Plant, or by no later

than 36 months after they lose

ownership interest of the

Morgantown Plant. [Installed] Maryland Unit 2 Install and

continuously

operate FGD

(or equiv.

technology) 95% 06/01/10

ILLINOIS POWER

System

- wide NO

x Emission Annual Caps: 15,000 tons 2 005; 14,000 tons 2006; 13,800 tons 2007 onward. System

- wide SO 2 Emission 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 tons 2013 onward.

http://www2.

epa.gov/enfo

rcement/illin o

is

- power

company

and

- dynegy

midwest

generation

settlement Baldwin Illinois Unit 1 Install wet or

dry FGD (or

approved

equiv.

alt. tech) &

operate

continuously 0.1 12/31/11 Operate OFA &

existing SCR

continuously 0.1 08/11/05 Install &

continuously

operate

Baghouse 0.015 12/31/10

By year end 2008,

Dynegy will surrender

12,000 SO 2 emission

allowances, by year

end 2009 it will

surrender 18,000, by

year end 2010 it will

surrender 24,000,

any by year end 2011

and each year

thereafter it will

surrender 30,000

allowances. If the

surrendered

allowances result in

insufficient remaining

allowances allocated

to the units

comprising the DMG

system, DMG can

request to surrender

fewer SO 2

allowances. Illinois Unit 2 Install wet or

dry FGD (or

approved

equiv.

alt. tech) &

operate

continuously 0.1 12/31/11 Operate OFA &

existing SCR

continuously 0.1 08/11/05 Install &

continuously

operate

Baghouse 0.015 12/31/10

Illinois Unit 3 Install wet or

dry FGD (or

approved

equiv.

alt. tech) &

operate

continuously 0.1 12/31/11 Operate OFA

and/or low NO

x burners 0.12 until

12/30/12;

0.1

from

12/31/12 08/11/05

and

12/31/12 Install &

continuously

operate

Baghouse 0.015 12/31/10

Havana Illinois Unit 6 Install wet o r dry FGD (or

approved

equiv.

alt. tech) &

operate

continuously 1.2

lbs/mmBTU

until

12/30/2012;

0.1

lbs/mmBTU

from

12/31/2012

onward 08/11/05

and

12/31/12 Operate OFA

and/or low NO

x burners &

operate existing

SCR

continuously 0.1 08/11/05 Install &

con tinuously

operate

Baghouse,

then install

ESP or alt.

PM equip For Bag

house:

.015

lbs/mmBT

U; For

ESP: .03

lbs/mmBT

U For

Baghous

e:

12/31/12;

For ESP:

12/31/05

### 3

- 54 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Hennepin Illinois Unit 1 1.2 07/27/05 Operate OFA

and/or low NO

x burners "Mi nimum

Extent

Practicable" 08/11/05 Install ESP

(or equiv. alt.

tech) &

continuously

operate

ESPs 0.03 12/31/06 Settlement requires first installation

of ESP at either Unit 1 or 2 on

12/31/2006; and on the other by

12/31/2010.

Illinois Unit 2 1.2 07/27/05 Operate OFA

and/or low NO

x burners "Minimum

Extent

Practicable" 08/11/05 Install ESP

(or equiv. alt.

tech) &

continuously

operate

ESPs 0.03 12/31/06

Vermilion Illinois Unit 1 1.2 01/31/07 Operate OFA

and/or low NO

x burners "Minimum

Extent

Practicable" 08/11/05 Install ESP

(or equiv. alt.

tech) &

continuously

operate

ESPs 0.03 12/31/10

Illinois Unit 2 1.2 01/31/07 Operate OFA

and/or low NO

x burners "Minimum

Extent

Practicable" 08/11/05 Install ESP

(or equiv. alt.

tech) &

continuously

operate

ESPs 0.03 12/31/10

Wood River Illinois Unit 4 1.2 07/27/05 Operate OFA

and/or low NO

x burners "Minimum

Extent

Practicable" 08/11/05 Install ESP

(or equiv. alt.

tech) &

continuously

operate

ESPs 0.03 12/31/05 Settlement requires first installation

of ESP at either Unit 4 or 5 on

12/31/2005; and on the other by

12/31/2007.

Illinois Unit 5 1.2 07/27/05 Operate OFA

and/or low NO

x burners "Minimum

Extent

Practicable" 08/11/05 Install ESP

(or equiv. alt.

tech) &

continuously

operate

ESPs 0.03 12/31/05

Kentucky Utilities Company

### 3

- 55 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

EW Brown

Generating

Station Kentucky Unit 3 Install FGD 97% or 0.100 12/31/10 Install and

continuously

operate SCR by

12/31/2012,

continuously

operate low NO

x boiler and OFA. 0.07 12/31/12 Continuously

operate ESP 0.03 12/31/10 KU must surrender

53,000 SO 2 allowances of 2008

or earlier vintage by

March 1, 2009. All

surplus NO

x allowances must be

surrendered through

2020. SO 2 and NO

x

allowances may not be

used for compliance,

and emissions

decreases for purposes

of complying with the

Consent Decree do not

earn credits. Annual SO 2 cap is 31,998 tons

through 2010, th en 2,300 tons each

year thereafter. Annual NO

x cap is

4,072 tons. http://www2.

epa.gov/enfo

rcement/kent

ucky

- utilities

company

clean

- air

- act

settlement

Salt River Project Agricultural Improvement and Power District (SRP)

Coronado

Generating

Station Arizona Unit 1 or

Unit 2 Immediately

begin

continuous

operation of

existing FGDs

on both units,

install new

FGD. 95% or 0.08 New FGD

installed by

1/1/2012 Install and

continuously

operate low NO

x burner and SCR 0.32 prior to

SCR

installation,

0.080 after LNB by

06/01/200

9, SCR by

06/01/201

4

Optimization

and

continuous

operation of

existing

ESPs. 0.03 Optimiza

tion

begins

immediat

ely, rat e

limit

begins

01/01/12

(date of

new

FGD

installatio

n) Beginning in 2012, all

surplus SO 2 allowances for both

Coronado and

Springerville Unit 4

must be surrendered

through 2020. The

allowances limited by

this condition may,

however, be used for

compliance at a

prospective future

plant using BACT

and otherwise

specified in par. 54 of

the consent decree. SO 2 and NO

x

allowances may not be

used for compliance,

and emissions

decreases for purposes

of complying with the

Consent Decree do not

earn credits. Annu al plant

- wide NO

x cap is 7,300

tons after 6/1/2014. http://www2.

epa.gov/enfo

rcement/salt

river

- project

agriculture

improvement

- and

- power

district

settlement

Arizona Unit 1 or

Unit 2 Install new

FGD 95% or 0.08 01/01/13 Install and

continuously

operate low NO

x burner 0.32 06/01/11 Optimiza

tion

begins

immediat

ely, rate

limit

begins

01/01/13

(date of

new

FGD

installatio

n)

Americ an Electric Power

Eastern System

- Wide [Modified

Limits for SO 2 ] Annual Cap

(tons) Year

http://www.ct.

gov/ag/lib/ag/

press_releas

es/2013/201

30225_aep_

cdmod.pdf 145,000 2016

- 2018

113,000 2019

- 2021

110,000 2022

- 2025

102,000 2026

- 2028

94,000 2029 and

thereafter

Eastern System

- Wide Annual Cap

(tons) Year Annual Cap

(tons) Year

NO

x and SO 2 allowances that

would have been

made available by

emission reductions

pursuant to the

Consent Decree

must be surrendered. NO

x and SO 2

allowances may not be

used to comply with

any of the limits

imposed by the

Consent Decree. The

Consent Decree

includes a formula for

calculating excess NO

x allowances relative to

the CAIR Allocations, http://www2.

epa.gov/enfo

rcement/ame

rican

- electric

power

service

corporation 450,000 2010 96,000 2009

450,000 2011 92,500 2010

420,000 2012 92,500 2011

350,000 2013 85,000 2012

### 3

- 56 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

340,000 2014 85,000 2013 and restricts the use of

some. See par. 74

- 79

for details. Reducing

emissions below the

Eastern System

- Wide

Annual Tonnage

Limitations for NO

x and

SO 2 earns

supercompliant

allo wances. 2 75,000 2015 85,000 2014

2 60,000 2016 75,000 2015

2 35,000 2017 72,000 2016 and

thereafter

184,000 2018

174,000 2019 and

thereafter

At least

600MW from

various units West Virginia Sporn

1

– 4

Retire,

retrofit, or re

power 12/31/18 Virginia Clinch

River

1

– 3

Indiana Tanners

Creek

1

– 3

West Virginia Kammer

1

– 3

Amos West Virginia Unit 1 Install and

continuously

operate FGD 12/31/09 Install and

continuously

operate SCR 01/01/08

West Virginia Unit 2 Install and

continuously

operate FGD 12/31/10 Install and

continuously

operate SCR 01/01/09

West Virginia Unit 3 Install and

continuously

operate FGD 12/31/09 Install and

continuously

operate SCR 01/01/08

Big Sandy Kentucky Unit 1 Burn only coal

with no more

than 1.75

lbs/mmBTU

annual average Date of

entry Continuously

operate low NO

x burners Date of

entry

Kentucky Unit 2 Install and

continuously

operate FGD 12/31/15 Install and

continuously

operate SCR 01/01/09

Cardinal Ohio Unit 1 Install and

continuously

operate FGD 12/31/08 Install and

continuously

operate SCR 01/01/09 Continuously

operate ESP 0.03 12/31/09

Ohio Unit 2 Install and

continuously

ope rate FGD 12/31/08 Install and

continuously

operate SCR 01/01/09 Continuously

operate ESP 0.03 12/31/09

Ohio Unit 3 Install and

continuously

operate FGD 12/31/12 Install and

continuously

operate SCR 01/01/09

Clinch River Virginia Units

1

– 3 Plant

- wide

annual cap:

21,700 tons

from 2010 to

2014, then

16,300 after

1/1/2015 2010

–

2014 , 2015

and

thereafter Continuously

operate low NO

x burners Date of

entry

### 3

- 57 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Conesville Ohio Unit 1 Retire,

retrofit, or re

power Date of

entry

Ohio Unit 2 Retire,

retrofit, or re

power Date of

entry

Ohio Unit 3 Retire,

retrofit, or re

power 12/31/12

Ohio Unit 4 Install and

continuously

operate FGD 12/31/10 Install and

continuously

operate SCR 12/31/10

Ohio Unit 5 Upgrade

existing FGD 95% 12/31/09 Continuously

operate low NO

x burners Date of

entry

Ohio Unit 6 Upgrade

existing FGD 95% 12/31/09 Continuously

operate low NO

x burners Date of

entry

Gavin Ohio Unit 1 Install and

continuously

operate FGD Date of

entry Install and

continuously

operate SCR 01/01/09

Ohio Unit 2 Install and

continuously

operate FGD Date of

entry Install and

continuously

operate SCR 01/01/09

Glen Lynn Virginia Units

1

– 3

Virginia Units 5, 6 Burn only coal

with no more

than 1.75

lbs/mmBTU

annual average Date of

entry Continuously

operate low NO

x burners Date of

entry

Kammer West Virginia Units

1

– 3 Plant

- wide

annual cap:

35,000 01/01/10 Continuously

operate over

- fire

air Date of

entry

Kanawha

River West Virginia Units 1, 2 Burn only coal

with no more

than 1.75

lbs/mmBTU

annual average Date of

entry Continuously

ope rate low NO

x burners Date of

entry

Mitchell West Virginia Unit 1 Install and

continuously

operate FGD 12/31/07 Install and

continuously

operate SCR 01/01/09

West Virginia Unit 2 Install and

continuously

operate FGD 12/31/07 Install and

continuously

operate SCR 01/01/09

Mountaineer West Virginia Unit 1 Install and

continuously

operate FGD 12/31/07 Install and

continuously

operate SCR 01/01/08

Muskingum

River Ohio Units

1

– 4 Retire,

retrofit, or re

power 12/31/15

Ohio Unit 5 Install and

continuously

operate FGD 12/31/15 Install and

continuously

operate SCR 01/01/08 Continuously

operate ESP 0.03 12/31/02

Picway Ohio Unit 9 Continuously

operate low NO

x burners Date of

entry

### 3

- 58 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Rockport Rockport Units 1 & 2 shall not exceed a n Annual Tonnage Limit of 28 MTons of SO 2 in 2016

- 2017, 26 Mtons in 2018

- 2019, 22 M T ons in 2020

- 2025, 18 M T ons in 2026

- 202 8 and 10 MTons in 2029 and each

year thereafter.

Indiana Unit 1 Install DSI

__ Install and

continuously

operate FGD 4/16/2015

__ 12/31/2025 Install and

continuously

operate SCR 12/31/25

Indiana Unit 2 Install DSI

__ Install and

continuously

operate FGD 4/16/2015

__ 12/31/2028 Install and

continuously

operate SCR 12/31/28

Sporn West Virginia Unit 5 Retire,

retrofit, or re

power 12/31/13

Tanners

Creek Indiana Units

1

– 3 Burn only coal

with no more

than 1.2

lbs/mmBTU

annual average Date of

entry Continuously

operate low NO

x burners Date of

entry

Indiana Unit 4 Burn only coal

with no more

than 1.2%

sulfur content

annual average Date of

entry Continuously

operate over

- fire

air Date of

entry

East Kentucky Power Cooperative Inc.

Dale Plant Kentucky Unit 1 Install and

continuously

operate low NO

x burners by

10/31/2007 0.46 01/01/08 EKPC must

surrender 1,000 NO

x allowances

immediately under

the ARP, and 3,107

under the NO

x SIP

Call. EKPC must

also surrender

15,311 SO 2

allowances. Date of

entry

http://www2.

epa.gov/enfo

rcement/east

- kentucky

power

cooperative

settlement Kentucky Unit 2 Install and

continuously

operate low NO

x burners by

10/31/2007 0.46 01/01/08

System

- wide Kentucky By 12/31/2009, EKPC shall choose whether to: 1) install and continuously operate NO

x controls at Cooper 2 by 12/31/2012 and SO 2 controls by 6/30/2012 or 2) retire Dale 3 and Dale 4 by 12/31/2012.

12

- month

rolling limit

(tons) Start of 12

-

month

cycle 12

- month

rolling limit

(tons) Start of

12

- month

cycle

System

- wide

12

- month

rolling tonnage

limits apply 57,000 10/01/08

All units must

operate low NO

x boilers 11,500 01/01/08 PM control

devices must

be operated

continuously

system

- wide,

ESPs must

be optimized

within 270

days of entry

date, or

EKPC may

choose to

submit a PM

Pollution

Control

Upgrade

Analysis. 0.03 1 year

from

entry

date All surplus SO

2

allowances must be

surrendere d each

year, beginning in

2008. SO 2 and NO

x

allowances may not be

used to comply with the

Consent Decree. NO

x allowances that would

become available as a

result of compliance

with the Consent

Decree may not be

sold or traded. SO 2 and NO

x allowances

alloc ated to EKPC

must be used within the

EKPC system.

Allowances made

available due to

supercompliance may

be sold or traded. 40,000 07/01/11 8,500 01/01/13

28,000 01/01/13 8,000 01/01/15

### 3

- 59 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Spurlock Kentucky Unit 1 Install and

continuously

operate FGD 95% or 0.1 6/30/2011 Continuously

operate SCR 0.12 for Unit

1 until

01/01/2013,

at which

point the

unit limit

drops to

0.1. Prior to

01/01/2013,

the

combined

average

when both

units a re

operating

must be no

more than

0.1 60 days

after entry

Kentucky Unit 2 Install and

continuously

operate FGD

by 10/1/2008 95% or 0.1 1/1/2009 Continuously

operate SCR and

OFA 0.1 for Unit

2, 0.1

combined

average

when both

units are

operating 60 days

after entry

Dale Plant Kentucky Unit 3 EKPC may

choose to

retire Dale 3

and 4 in lieu

of installing

controls in

Cooper 2 12/31/2012

Kentucky Unit 4

Cooper Kentucky Unit 1

Kentucky Unit 2 If EKPC opts to

install controls

rather than

retiring Dale, it

must install

and

continuously

operate FGD

or equiv.

technology 95% or 0.10 If EKPC elects to

install controls, it

must

continuously

operate SCR or

install equiv.

technology 0.08 (or

90% if non

SCR

technology

is used) 12/31/12 EKPC has installed a DFGD on

this unit and Dale continues to

operate.

Nevada Power Company

Beginning 1/1/2010, combined NO

x emissions from Units 5, 6, 7, and 8 must be no more than 360 tons per year.

Clark

Generating

Station Nevada Unit 5

Units may

only fire

natural gas Increase water

injection

immediately,

then install and

operate ultra

- low

NO

x burners

(ULNBs) or

equivalent

technology. In

2009, Units 5

and 8 may not

emit more than

180 tons

combined 5ppm 1

-

hour

average 12/31/08

(ULNB

installation

),

01/30/09

(1

- hour

average) Allowances may not be

used to comply with the

Consent Decree, an d

no allowances made

available due to

compliance with the

Consent Decree may

be traded or sold. http://www2.

epa.gov/enfo

rcement/nev

ada

- power

company

clean

- air

- act

caa

settlement Nevada Unit 6 5ppm 1

- hour

average 12/31/09

(ULNB

installatio n

),

01/30/10

(1

- hour

average)

### 3

- 60 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Ne vada Unit 7 5ppm 1

- hour

average 12/31/09

(ULNB

installation

),

01/30/10

(1

- hour

average)

Ne vada Unit 8 5ppm 1

-

hour

average 12/31/08

(ULNB

installation

),

01/30/09

(1

- hour

average)

Dayton Power & Light

Non

- EPA Settlement of 10/23/2008

Stuart

Generating

Station Ohio Station

wide Complete

installation of

FGDs on each

unit. 96% or 0.10 07/31/09 Owners may not

purchase any

new catalyst with

SO 2 to SO 3

conversion rate

greater than

0.5% 0.17

station

- wide 30 days

after entry

0.030 lbs

per unit 07/31/09

NO

x and SO 2 allowances may not be

used to comply with the

monthly rates specified

in the Consen t Decree. 0.17

station

- wide 60 days

after entry

date

82%

including

data from

periods of

malfunctions 7/31/09

through

7/30/11 Install control

technology on

one unit 0.10 on any

single unit 12/31/12

Install rigid

type

electro

- des

in each

unit's ESP 12/31/15

82%

including

data fr om periods of

malfunctions after

7/31/11 0.15

station

- wide 07/01/12

0.10

station

- wide 12/31/14

PSEG FOSSIL, Amended Consent Decree of November 2006

Kearny New Jersey Unit 7 Retire unit 01/01/07 Allowances allocated

to Kearny, Hudson,

and Mercer may only

be used for the

operational needs of

those units, and all

surplus allowances

must be surrendered.

Within 90 days of

amended Consent

Decree, PSEG must

surrender 1,230 NO

x Allowances and

8,568 SO 2

Allowances not

already allocated to

or generated by the

units listed here.

Kearny allowances

must be surrendered

with the shutdown of

those units. http://www2.

epa.gov/enfo

rcement/pse

g

- fossil

- llc

settlement

New Jersey Unit 8 R etire unit 01/01/07

Hudson New Jersey Unit 2 Install Dry FGD

(or approved

alt. technology)

and continually

operate 0.15 12/31/10 Install SCR (or

approved tech)

and continually

operate 0.1 12/31/10 Install

Baghouse (or

approved

technology) 0.015 12/31/10

### 3

- 61 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Annual Cap

(tons) Year Annual Cap

(tons) Year

5,547 2007 3,486 2007

5,270 2008 3,486 2008

5,270 2009 3,486 2009

5,270 2010 3,486 2010

Mercer New Jersey Unit 1 Install Dry FGD

(or approved

alt. technology)

and continually

operate 0.15 12/31/10 Install SCR (or

approved tech)

and continually

operate 0.1 01/01/07 Install

Baghouse (or

approved

technology) 0.015 12/31/10

New Jersey Unit 2 Install Dry FGD

(or approved

alt. technology)

and continually

operate 0.15 12/31/10 Install SCR (or

approved tech)

and continually

operate 0.1 01/01/07 Install

Baghouse (or

approved

technology) 0.015 12/31/10

Westar Energy

Jeffrey

Energy

Center Kansas All units Units 1, 2, and 3 have a total annual limit

of 6,600 tons of SO 2 starting 2011

Units 1, 2, and 3 must all install FGDs by

2011 and operate them continuously.

FGDs must maintain a 30

- Day Rolling

Average Unit Removal Efficiency for SO 2 of at least 97% or a 30

- Day Rolling

Average Unit Emission Rate for SO 2 of no

greater than 0.070 lbs/mmBTU. Units 1

- 3 must continuously operate Low

NO

x Combustion Systems by 2012 and

achieve and maintain a 30

- Day Rolling

Average Unit Emission Rate for NO

x of no

greater than 0.180 lbs/mmBTU.

One of the three units must install an

SCR by 2015 and operate it continuously

to maintain a 30

- Day Rolling Average

Unit Emission Rate for NO

x of no greater

than 0.080 lbs/mmBTU.

By 2013 Westar shall elect to either (a)

install a second SCR on one of the other

JEC Units by 2017 or (b) meet a 0.100

lbs/mmBTU Plant

- Wide 12

- Month Rolling

Average Emission Rate for NO

x by 2015 Units 1, 2, and 3 must operate each

ESP and FGD system continuously

by 2011 and maintain a 0.030

lbs/mmBTU PM Emissio ns Rate.

Units 1 and 2’s ESPs must be

rebuilt by 2014 in order to meet a

0.030 lbs/mmBTU PM Emissions

Rate http://www2.

epa.gov/enfo

rcement/west

ar

- energy

inc

settlement

Duke Energy

Gallagher Indiana Units 1 &

3 Retire or

repower as

natural gas 1/1/2012 http://www2.

epa.gov/enfo

rcement/duk

e

- energy

gallagher

plant

- clean

air

- act

settlement Units 2 &

4 Install Dry

sorbent

injection

technology 80% 1/1/2012

American Municipal Power

Gorsuch

Station Ohio Units 2 &

3

Elected to Retire Dec 15,

2010 (must retire by Dec

31, 2012) http://www2.

epa.gov/enfo

rcement/ame

rican

municipal

power

- clean

air

- act

settlement Units 1 &

4

Hoosier Energy Rural Electric Cooperative

Ratts Indiana Units 1 &

2 Install &

continually

operate SNCRS 0.25 12/31/201

1 Continuously operate ESP Annually surrender any NO

x and SO 2 allowances that

Hoosier does not need in order to meet its regulatory

obligations http://www2.

epa.gov/enfo

rcement/hoo

### 3

- 62 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Merom Indiana Unit 1 Continuously

run current

FGD for 90%

removal and

update FGD for

98% removal

by 2012 98% 2012

Continuously

operate existing

SCRs 0.12 Continuously operate ESP and

achieve PM rate no greater than

0.007 by 6/1/12 sier

- energy

rural

- electric

cooperative

inc

settlement

Unit 2 Continuously

run current

FGD for 90%

removal and

update FGD for

98% removal

by 2014 98% 2014 Continuously operate ESP and

achieve PM rate no greater than

0.007 by 6/1/13

Northern Indiana Public Service Co.

Bailly Indiana Units 7 &

8 Upgrade

existing FGD 95% by 01/01/11

97% by 01/01/14 (95% if

low sulfur coal only is

burned) OFA & SCR 0.15 lbs/mmBTU by

12/31/10

0.13 lbs/mmBTU by

12/31/13

0.12 lbs/mmBTU by

12/31/15 0.3

lbs/mmBT

U (0.015 if

a

Baghouse

is installed) 12/31/20

10

http://www2.

epa.gov/enfo

rcement/nort

hern

- indiana

public

service

company

clean

- air

- act

settlement Michigan

City Indiana Unit 12 FGD 0.1

lbs/mmBTU 12/31/2018 OFA & SCR 0.14 lbs/mmBTU by

12/31/10

0.12 lbs/mmBTU by

12/31/11

0.10 lbs/mmBTU by

12/31/13 0.3

lbs/mmBT

U (0.015 if

a

Baghouse

is installed) 12/31/20

18

Schahfer Indiana Unit 14 FGD 0.08

lbs/mmBTU 12/31/2013 OFA & SCR 0.14 lbs/mmBTU by

12/31/10

0.12 lbs/mmBTU by

12/31/12

0.10 lbs/mmBTU by

12/31/14 0.3

lbs/mmBT

U (0.015 if

a

baghouse

is installed) 12/31/20

13

Indiana Unit 15 FGD 0.08

lb s/mmBTU 12/31/2015 LNB/OFA 0.16 3/31/2011 0.3

lbs/mmBT

U (0.015 if

a

baghouse

is installed) 12/31/20

15 Either: SCR or

SNCR 0.08 12/31/201

5

0.15 12/31/201

2

Indiana Units 17

& 18 Upgrade

existing FGD 97% 1/31/2011 LNB/OFA 0.2 3/31/2011 0.3

lbs/mmBT

U (0.015 if

a

baghouse

is installed) 12/31/20

10

Dean H

Mitchell Indiana Units 4,

5, 6, &

11 Retire 12/31/2010

Tennessee Valley Authority

Colbert Alabama Units 1

-

4 FGD 6/30/2016 SCR 6/30/2016

Shall surrender all

calendar year NO

x and SO 2 Allowances

allocated to TVA that

are not needed for

compliance with its

own CAA reqts.

Allocated allowances

may be used for

TVA’s own

compliance with CAA

reqts. Shall not use NO

x or

SO 2 Allowances to

comply with any

requirement of the

Consent Decree,

No thing prevents TVA

from purchasing or

otherwise obtaining

NO

x and SO 2 allowances from other

sources for its

compliance with CAA

reqts.

TVA may sell, bank,

use, trade, or transfer 2011 http://www2.

epa.gov/enfo

rcement/tenn

essee

- valley

authority

clean

- air

- act

settlement l Unit 5 FGD 12/31/15 SCR Effective

Date

Widows

Creek Alabama Units 1

-

6 Retire 2 units 7/31/13

Retire 2 units 7/31/14

Retire 2 units 7/31/15

Unit 7 SCR Effective

Date

Unit 8 SCR Effective

Date

Paradise Kentucky Units 1 &

2 Upgrade FGD 93% 12/31/12 SCR Effective

Date

Unit 3 Wet FGD Effective

Date SCR Effective

Date

### 3

- 63 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Shawnee Kentucky Units 1 &

4 FGD 1.2 12/31/17 SCR 12/31/17 any NO

x and SO 2 Super

- Compliance”

Allowances resulting

from meeting System

wide limits. Except that

reductions used to

support new CC/CT will

not be Super

Allowances in that year

and thereafter. Units 5

-

10 1.2 Effective

Date

Allen Tennessee Units 1

-

3 FGD 12/31/18 0.3 12/31/18

Bull Run Tennessee Unit 1 Wet FGD Effective

Date 0.3 Effective

Date

Cumberland Tennessee Units 1 &

2 Wet FGD Effective

Date

Gallatin Tennessee Units 1

-

4 FGD 12/31/17 SCR 12/31/17 0.3 12/31/17

John Sevier Tennessee Units 1 &

2 Retire 2 Units 12/31/12

and 12/31/15

Units 3 &

4 FGD 12/31/15 SCR 12/31/15

Johnsonville Tennessee Units 1

-

10 Retire 6 Units 12/31/15

Retire 4 Units 12/31/17

Kingston Tennessee Units 1

-

9 FGD Effective

Date SCR Effective

Date 0.3 Effective

Date

Wisconsin Public Service

Pulliam Wisconsin Units 5

- 6 Retire, refuel

or repower

as natural

gas 6/1/2015 0.750

lbs/mmBTU 1/1/2013

until

retirement

http://www2.

epa.gov/enfo

rcement/wisc

onsin

- public

service

corporation

settlement Wisconsin Units 7

- 8 0.750

lbs/mmBTU

& plant

- wide

cap of 2100

tons starting

2016 1/1/2013 0.250

lbs/mmBTU

& plant

wide cap of

1500 tons

starting

2016 12/31/12 The modeled SO 2 rate in IPM is

lower; only tonnage limitation

imposed through a constraint.

Weston Wisconsin Unit 1 0.750

lbs/mmBTU 1/1/2013

until

retirement 0.250

lbs/mmBTU 12/31/201

2 until

retirement

Wisconsin Units 2 Retire, refuel

or repower

as natural

gas 6/1/2015 0.750

lbs/mmBTU 1/1/2013

until

retirement 0.280

lbs/mmBTU 12/31/201

2 until

retirement

Wisconsin Units 3 ReACT by

12/31/2016 0.750

lbs/mmBTU

until 2016

0.080

lbs/mmBTU

2016

onwards 12/31/16 ReACT by

12/31/2016 0.130

lbs/mmBTU

until 2016

0.100

lbs/mmBTU

2016

onwards 12/31/16

Wisconsin Units 4 Continuously

Operate the

existing DFGD

& burn only

Powder River

Basin Coal 0.080

lbs/mmBTU 2/31/2013 Continuously

Operate the

existing SCR 0.060

lbs/mmBTU 2/31/2013

Louisiana Generating LLC

Plant

- Wide Annual Tonnage Limitations for SO 2 is 18,950 tons in Plant

- Wide Annual Tonnage Limitations

### 3

- 64 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

2016 and thereafter for NO

x is 8,950 tons in 2015 and

thereafter

Big Cajun 2 Louisiana Unit 1 Retirement,

Refueling,

Repowering,

or Retro fit 04/01/25 install and

Continuously

Operate DSI

__

install and

Continuously

Operate Dry

FGD 0.380

lbs/mmBTU

[2015]

__

0.070

lbs/mmBTU 4/15/2015

[DSI]

__

4/1/2025

[DFGD] install and

Continuously

Operate SNCR 0.150

lbs/mmBTU 05/01/14 Continuously

Oper ate

each ESP 0.030

lbs/mmBT

U 04/15/15 May trade Super

- Compliant

Allowances, may buy external

allowances to comply.

“Commencing January 1, 2013, and

continuing thereafter, Settling

Defendant shall burn only coal with

no greater sulfur content than 0.45

percent by weight on a dry basis at

Big Cajun II Units 1 and 3. “ http://www2.

epa.gov/enfo

rcement/louis

iana

generating

settlement Unit 2 Refuel/conve

rt to NG fired 04/15/15 install and

Continuou sly

Operate SNCR 0.150

lbs/mmBTU 05/01/14

Unit 3 install and

Continuously

Operate SNCR 0.135

lbs/m mBTU 05/01/14 Continuously

Operate

each ESP 0.030

lbs/mmBT

U 04/15/15

Dairyla nd Power Cooperative

Dairyland Power Cooperative shall not exceed an Annual Plant

- wide Tonnage Limitation of 6800 tons of NO

x in calendar years 2016, 3700 tons 2017

- 2019, and 3200 tons in 2020 and thereafter; and an

Annual Plant

- wide Tonnage Limitation of 6070 tons of SO 2 in 2016, 6060 tons 2017

- 2019 and 4580 tons in 2020 and thereafter.

Alma Wisconsin Unit 1 Cease

Burning Coal 06/30/12

http://www2.

epa.gov/enfo

rcement/dair

yland

- power

cooperative

settlement Unit 2 Cease

Burning Coal 06/30/12

Unit 3 Cease

Burning Coal 06/30/12

Unit 4

Option 2 :

Retrofit and

Regulate

both units

more

stringently 12/31/14 I nstall and

continuously

operate DFGD

or DSI at Alma

4 1.00

lbs/mmBTU

at Alma 4

And a joint

cap of 3,737

tons until

2019, and

2,242 tons

thereafter. In

the event

that one

retires,

Tonnage

Cap of 2,136

tons for the

remaining

unit until

2019 and

1,282 tons

t hereafter 12/31/2014 Continuously

Operate the

existing Low NO

x Combustion

System

(including OFA)

and SNCR 0.350

lbs/mmBTU

__

Joint cap of

1308 tons

for

- until

2019, and

785 tons

thereafter.

In the event

that one

retires,

Tonnage

Cap of 746

tons for

rema ining

unit until

2019 and

449 tons

thereafter 8/1/2012

__

12/31/201

4 Continuously

Operate an

ESP or FF

on

Alma Unit 4 0.030

lbs/mmBT

U [with

ESP]

0.015

lbs/mmBT

U [with FF]

at Alma 4.

Joint cap of

112 tons

until 2019,

and 67

tons

thereafter.

In the

event that

one retires,

Tonnage

Cap of 64

tons for the

remaining

unit until

2019 and

39 tons

thereafter 12/31/14 Dairyland was provided with two

options for compliance. It chose

Option 2 and it is the one modeled

in IPM. Details on Option 1 can be

fou nd in the settlement document

referenced in the adjoining column.

Unit 5

J.P. Madgett Wisconsin Unit 1 Install and

continuously

oper ate DFGD 0.090

lbs/mmBTU 12/31/14 Continuously

Operate existing

Low NO

x Combustion

System

__

Install an SCR 0.30

lbs/mmBTU

__

0.080

lbs/mmBTU 8/1/2012

__

6/30/2016 Continuously

Operate the

existing

Baghouse 0.0150

lbs/mmBT

U 07/01/13

### 3

- 65 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Genoa Wisconsin Unit 1 Continuously

Operate the

FGD 0.090

lbs/mmBTU 12/31/12 Contin uously

Operate existing

Low NO

x Combustion

System including

OFA

__

Install an SNCR 0.14

lbs/mmBTU

__

Annual

Tonnage

Cap of

1,140 tons 12/31/201

4

__

6/1/2015 Continuously

Operate the

existing

Baghouse 0.0150

lbs/mmBT

U 07/01/13

Dominion Energy, Inc.

In calendar year 2014, and in each calendar year thereafter, Kincaid shall not exceed a Plant

- Wide Annual Tonnage Limitation of 3,500 tons of NO

x & 4,400 tons of SO 2 , and Brayton Point shall not exceed a

Plant

- Wide Annual Tonnage Limitation of 4,600 tons of NO

x & 4,100 tons of SO 2

.

Brayton

Point Massachuset

ts Unit 1 Continuously

Operate the

existing dry

FGD 0.150

lbs/mmBTU 06/01/13 Continuously

Operate the

SCR, OFA, and

LNB 0.080

lbs/mmBTU 05/01/13

Install/Contin

uously

Operate a

Baghouse 0.015

lbs/mmBT

U [PM by

2013]

0.01

lb s /mmBT

U [PM

post

- 2013] 06/01/13 ht tp://www2.

epa.gov/enf

orcement/d

ominion

energy

- inc Unit 2 Continuously

Operate the LNB

and OFA 0.280

lbs/mmBTU 05/02/13

Unit 3 Continuously

Operate dry

FGD 0.080

lbs/mmBTU 07/01/13 Continuously

Operate the

SCR, OFA, and

LNB 0.080

lbs/mmBTU 05/01/13 Install/Contin

uously

Operate a

Baghouse 0.015

lbs/mmBT

U [PM by

2013]

0.01

lbs/mmBT

U [PM

post

- 2013] 07/01/13

Kincaid

Power

Station Illinois Unit 1

Continuously

Operate D SI 0.100

lbs/mmBTU 01/01/14 Continuously

Operate each

SCR and OFA 0.080

lbs/mmBTU 05/01/13 Continuously

Operate the

ESP 0.030

lbs/mmBT

U [PM by

2013]

0.015

lbs/mmBT

U [PM by

post

- 2013] 06/01/13

Unit 2

State Line

Power

Station In diana Unit 3

Retire 06/01/12

Unit 4

Wisco nsin Power and Light

Edgewater 3

- 5

- shall not exceed an Annual Tonnage Limitation of 2,500 tons of NO

x in calendar years 2016

- 2018, and

1100 tons 2019 onwards & an Annual Tonnage Limitation of 12,500 tons of SO 2 in 2016, 6000 tons 2017

- 2018 and 1100

tons 2019 onwards. Columbia 1 & 2 shall not exceed an Annual Tonnage Limitation of 5,600 tons of NO

x in calendar

years 2016

- 2018, and 4300 tons 2019 onwards & an Annual Tonnage Limitation of 3290 tons of SO 2 in 2016 and

thereafter.

Edgewater

Generating

Station Wisconsin Unit 3 Retire,

Refuel, or

Repower 12/31/15 Unit

- Specific

Annual

Tonnage

Cap of 700

Tons of SO 2 05/21/13 Unit

Specific

Annual

Tonnage

Cap of 250

tons of NO

x 05/21/13

http://www2.

epa.gov/enfo

rcement/wisc

onsin

- power

and

- light

- et

al

- settlement

Unit 4 Retire,

Refuel, or

Repower 12/31/18 0.700

lbs/mmBTU 05/21/13 Operate SNCR

and LNB 0.150

lbs/mmBTU 01/01/14 Continuous

Operation of

the existing

ESP 0.030

lbs/mmBT

U 12/31/13

### 3

- 66 Company

and Plant State Unit Settlement Actions

Notes Reference Retire/Repower SO 2 control NO

x Control PM or Mercury Control Allowance

Retirement Allowance Restriction

Action Effective

Date Equipment Percent

Removal or

Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Retirement Restriction Effective

Date

Unit 5 Install and

continuously

operate DFGD 0.075

lbs/mmBTU 12/31/16 Install and

continuously

operate SCR 0.070

lbs/mmBTU 05/01/13 Install and

continuously

operate

Fabric Filter 0.015

lbs/mmBT

U 12/31/16

Columbia

Generating

Station Wisconsin Unit 1

Install and

continuously

operate DFGD 0.075

lbs/mmBTU

01/01/15 Operation of the

Low NO

x Combustion

System 0.150

lbs/mmBTU 07/21/13

Install and

continuously

operate

Fabric Filter 0.015

lbs/mmBT

U 12/31/14

Unit 2 0.075

lbs/mmBTU Operation of the

Low NO

x Combustion

System

__

Install and

continuously

operate SCR 0.150

lbs/mmBTU

__

0.070

lbs/mmBTU 7/21/2013

__

12/31/201

8 0.015

lbs/mmBT

U 12/31/14

Nelson

Dewey

Generating

Station Wisconsin Unit 1 Retire,

Refuel, or

Repower 12/31/15 commence

burning 100%

Powder River

Basin or

equivalent fuel

containing ≤

1.00

lbs/mmBTU of

SO 2 0.800

lbs/mmBTU 05/22/13 0.300

lbs/mmBTU 04/22/13 0.100 lbs/

mmBTU 04/22/13 Cease Burning Petcoke and

Commence Burning 100% PRB

Coal or

Equivalent at Nelson Dewey Units 1

and 2. Unit 2 Retire,

Refuel, or

Repower 12/31/15

### 3

- 67 Table 3

- 15 State Settlements in EPA Base Case v.5.13

Company

and Plant State Unit State Enforcement Actions

Notes Retire/Repower SO 2 Control NO

x Control PM Control Mercury Control

Action Effective

Date Equipment Percent

Removal or

Rate Effective Date Equipment Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective Date

AES

If the MPC project is discontinued at Greenidge Unit 4 by 12/31/2009, Unit 4 will be subject to the following SO 2 emission caps: 2005 will be 12,125 tons, 2006 will be 11,800 tons, 2007 will be 11,475 tons,

2008 will be 11,150 tons, 2009 will be 10,825 to ns. By 12/31/2009, AES shall control, repower, or cease operations at Westover Unit 7. Beginning in 2005, Unit 8 will be su bject to the following SO 2 emission

caps: 2005 is 9500 tons, 2006 is 9250, 2007 is 9000, 2008 is 8750, 2009 is 8500 tons. http://w ww.ag.ny.gov/press

release/governor

- and

- attorney

- general

announce

- new

- yorks

- largest

- coal

- plants

slash

- pollution

Greenidge New York Unit 4 Update: as of May 2009, CONSOL and AES describe the Greenidge Unit 4 MPC effort as a success. http://investor.aes.com/phoenix.zhtml?c=

202639&p=irol

newsArticle&ID=1274075&highlight=

Install FGD 90% 09/01/07 Install SCR 0.15 09/01/07 1) Except when Greenidge Unit 4 is

operating below minimum operating load,

it will make good faith efforts to achieve a

NO

x emission rate of 0.1 lbs/mmBtu. If

this level cannot be achieved, the

emission limit shall be the level achieved

within one year of commencement of

operation, no less stringent than 0.15

lbs/mmBtu. 2) Unit 4 will make good f aith

efforts to achieve a SO 2 removal

efficiency of 95%. If this removal

efficiency cannot be achieved, the

emission limit shall be the level achieved

by 9/1/2007, but no less stringent than

90% removal efficiency, resulting in a

0.38 lbs/mmBtu permitted limit.

New York Unit 3 Install

BACT,

repower, or

cease

operations Install BACT 12/31/09 Install B ACT 12/31/09

Westover Update: as of May 2009, NO

x emissions appear to be above the specified 0.15 lbs/m mBtu http://www.powermag.com/print/environm

ental/Apply

- the

- fundamentals

- to

- improve

emissions

- performance_574.html

New York Unit 8 90% 12/31/10 Install SCR 0.15 12/31/10 1) Except when Westover Unit 8 is

operating below minimum operating load,

it will make good faith efforts to achieve a

NO

x emission rate of 0.1lbs/mmBtu. If

this level cannot be achieved, the

emission limit will be the level achieved

within one year of operation that is no

less stringent than 0.15 lbs/mmBtu. 2)

Unit 8 w ill make good faith efforts to

achieve a SO 2 removal efficiency of 95%.

If this level cannot be achieved, a removal

efficiency no less than 90% will be used,

resulting in a 0.34 lbs/mmBtu permit.

New York Unit 7 Install

BACT,

repower, or

cease

operations Install BACT 12/31/09 Install BACT 12/31/09

Hickling New York Unit 1 Install

BACT,

repower, or

cease

operations Install BACT 05/01/07 Install BACT 05/01/0 7

New York Unit 2 Install

BACT,

repower, or

cease

operations Install BACT 05/01/07 Install BACT 05/01/07

Jennison New York Unit 1 Install

BACT,

repower, or

cease Install BACT 05/01/07 Install BACT 05/01/07

### 3

- 68 Company

and Plant State Unit State Enforcement Actions

Notes Retire/Repower SO 2 Control NO

x Control PM Control Mercury Control

Action Effective

Date Equipment Percent

Removal or

Rate Effective Date Equipment Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective Date

operations

New York Unit 2 Install

BACT,

repower, or

cease

operations Install BACT 05/01/07 Instal l BACT 05/01/07

Niagara Mohawk Power

NRG shall comply with the below annual tonnage limitations for its Huntley and Dunkirk Stations : In 2005 59,537 tons of SO 2 and 10,777 tons of NO

x , in 2006 34,230 of SO 2 and 6,772 of NO

x , in 2007 30,859

of SO 2 and 6,211 of NO

x , in 2008 22,733 tons of SO 2 and 6,211 tons of NO

x , in 2009 19,444 of SO 2 and 5,388 of NO

x , in 2010 and 2011 19,444 of SO 2 and 4,861 of NO

x , in 2012 16,807 of SO 2 and 3,241 of

NO

x , 2013 and 14,169 of SO 2 and 3,241 of NO

x thereafter. http://www.ag.ny.gov/press

release/governor

- and

- attorney

- general

announce

- new

- yorks

- largest

- coal

- plants

slash

- pollution

Huntley New York Units

63

– 66 Retire Before

2008

Public Service Co. of NM

San Juan New Mexico Unit 1

State

- of

- the

- art

technology 90% 10/31/08

State

- of

- the

- art

technology 0.3 10/31/08

Operate

Baghouse and

demister

technology 0.015 12/31/09 Design

activated

carbon

injection

technology

(or

comparable

tech) 12/31/09 All four units have installed Wet

Scrubbers. Unit 1 and 4 NO

x controls

[SNCR] are hardwired into EPA Base

Case.

http://nmsierraclub.org/sites/default/files/2

0055

- 10SanJuanfinaldecreeasentered%20%28

2%29.pdf New Mexico Unit 2 03/31/09 03/31/09 12/31/09 12/31/09

New Mexico Unit 3 04/30/08 04/30/08 04/30/08 04/30/08

New Mexico Unit 4 10/31/07 10/31/07 10/31/07 10/31/07

Public Service Co of Colorado

Comanche Colorado Unit 1 Install and

operate FGD 0.1

lbs/mmBtu

combined

average 07/01/09 Install low

- NO

x emission controls

0.15 lbs/mmBtu

combined

average 07/01/09 Install

sorbent

injection

technology 07/01/09 Comanche units 1 and 2 taken together

shall not exceed a 0.15 heat rate for NO

x , nor 0.10 for SO 2

, no later than 180 days

after initial start

- up of control equipment,

or by 7/01/2009, whichever is earlier.

http://content.sierraclub.org/coal/sites/con

tent.sierraclub.org.coal/files/elp/docs/co

comanche_agree

- sign_2004

- 12

- 02.pdf Colorado Unit 2 Install and

operate FGD 07/01/09 Install low

- NO

x emission controls 07/01/09 Install

sorbent

injection

technology 07/01/09

Colorado Unit 3 Install and

operate FG D 0.1

lbs/mmBtu Install and operate

SCR 0.08 Install and

operate a fabric

filter dust

collection

system 0.013 Install

sorbent

injection

technology Within 180

days of start

- up

Rochester Gas & Electric

Russell Plant New York Units

1

– 4 Retire all

units http://www.ag.ny.gov/press

release/cuomo

- announces

- settlement

close

- rochester

- gas

- electrics

- coal

burning

- russell

- power

Mirant New York

Lovett Plant New York Unit 1 Retire 05/07/07 http://www.nytimes.com/2007/05/11/nyreg

ion/11plant.html?_r=1&pagewanted=print

New York Unit 2 Retire 04/30/08 Retirements are pursuant to a 2003

consent decree, and the plant's failure to

comply with the required reductions.

TVA

Allen Tennessee Units 1

- 3 Remove from

Service, FGD, or

Retire 12/31/2015 Install SCR Effective

Date

http://www2.epa.gov/sites/production/files

/documents/tvacoal

- fired

- cd.pdf Bull Run Tennessee Unit 1 Install Wet FGD Effective Date Install SCR Effective

Date

Colbert Alabama Units 1

- 4 Remove from

Service, FGD,

Repower to

Renewable

Biomass, or

Retire 6/30/2016 Remove from

Service, SCR,

Repower to

Renewable

Biomass, or Retire 6/30/2016

### 3

- 69 Company

and Plant State Unit State Enforcement Actions

Notes Retire/Repower SO 2 Control NO

x Control PM Control Mercury Control

Action Effective

Date Equipment Percent

Removal or

Rate Effective Date Equipment Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective Date

Unit 5 Remove from

Service, FGD, or

Retire 12/31/2015 Install SCR Effective

Date

Cumberland Tennessee Units 1 & 2 Install Wet FGD Effective Date Install SCR Effective

Date

Gallatin Tennessee Units 1

- 4 FGD, Repower to

Renewable

Biomass, or

Retire 12/31/2017 Install SCR,

Repower to

Renewable

Biomass, or Retire 12/31/2017

John Sevier Tennessee Units 1 & 2 Retire 12/31/2012

Units 3 & 4 Remove

from

Service 12/31/2012 FGD, Repower to

Renewable

Biomass, or

Retire 12/31/2015 Install SCR,

Repower to

Renewable

Biomass, or Retire 12/31/2015

John sonville Tennessee Units 1

- 10 Retire 6 Units by

12/31/15, 4

Units by

12/31/18

Kingston Tennessee Units 1

- 9 Install Wet FGD Effective Date Install SCR Effective

Date

Paradise Kentucky Units 1 & 2 Upgrade FGD 93%

Removal 12/31/2012 Install SCR Effective

Date

Unit 3 Install Wet FGD Effective Date Install SCR Effective

Date

Shawnee Kentucky Units 1 & 4 FGD, Repower to

Renewable

Biomass, or

Retire 12/31/2017 Install SCR,

Repower to

Renewable

Biomass, or Retire 12/31/2017

Widows

Creek Alabama Units 1 & 2 Retire 7/31/2013

Unit 3 & 4 Retire 7/31/2014

Units 5 & 6 Retire 7/31/2015

Units 7 & 8 Install Wet FGD Effective Date Install SCR Effective

Date

RC Cape May Holdings, LLC

B L England New Jersey Unit 1 Retire/Rep

ower 05/01/14

http://www.nj.gov/dep/docs/20120613104

728.pdf

Unit 2 Retire/Rep

ower

[Decision

to be made

b y

December

2013] 05/01/14

### 3

- 70 Table 3

- 16 Citizen Settlements in EPA Base Case v.5.13

Company and

Plant State Unit Citizen Suits Provided by DOJ

Notes Retire/Repower SO 2 control NO

x Control PM Control Mercury Control

Action Effective

Date Equipment Percent Removal

or Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date

SWEPCO (AEP)

Welsh Texas Units

1

- 3 Install and

operate CEMs 12/31/2010 SWEPCO may attempt to demonstrate that PM

CEMs are infeasible after two years of operation.

http://www.ocefoundation.org/PDFs/ConsentDecr

ee&CLtoDOJ.pdf

Allegheny Energy

Hatfield's Ferry Pennsylvania Unit 1

Install and

operate

wet FGD 6/30/2010 Install and

operate sulfur

trioxide injection

systems, improve

ESP

performance 0.1 lbs/mmBtu in

2006, then 0.075

lbs per hour

(filterable) and 0.1

lbs/mmBtu for

particles less than

ten microns in

2010 7/31/2006

and

6/30/2010 http://www.environmentalintegrity.org/law_library/

PennFuture_EIP_Lawsuit.php Pennsylvania Unit 2

Pennsylvania Unit 3 11/31/2006

and

6/30/2010

Wis consin Public Service Corp

Pulliam Wisconsin Unit 3

Retire 12/31/2007 http://milwaukee.bizjournals.com/milwaukee/stori

es/2006/10/23/daily29.html Wisconsin Unit 4

University of Wisconsin

Charter Street

Heating Plant Wisconsin Repower to

burn 100%

biomass 12/31/2012 Sierra Club suit was based on NSR.

http://wisconsin.sierraclub.org/PDF/press/112607

_PR_WIStateOwnedCoalSettlement.pdf

Tucson Electric Power

Springerville

Plant Arizona Unit 1

Dry FGD,

85%

reduction

required 0.27 lbs/mmBtu 12/31/2006

SCR, LNB 0.22 lbs/mmBtu 12/31/2006

Baghouse 0.03 lbs/mmBtu 1/1/2006

Lawsuit filed by Grand Canyon Trust. Consent

decree is not published. For the compliance

details, see the EPA's own copy of the plant's

permit revisions:

http://xrl.us/springerville and

http://xrl.us/springerville2 Arizona Unit 2

Arizona Unit 3

Arizona Unit 4 Four

- unit cap of

10,662 tons per

year once units 3

and 4 are

operational Four

- unit cap of

8,940 tons per

year once units

3 and 4 are

operational

Kansas Cit y Board of Public Utilities

Quindaro Kansas Units 1 Cease burning

coal/Convert to

natural gas 04/16/15

http://www.bpu.com/AboutBPU/MediaNewsRelea

ses/BPUUnifiedGovernmentSettleThreatenedLaw

suit.aspx

http://www.platts.com/RSSFeedDetailedNews/RS

SFeed/ElectricPower/21193551

"end coal

- fired operations at two coal units

totaling 167 MW at its Quindaro station by April

2015 and to install a baghouse at its 232

- MW

Nearman

- 1 coal unit by September 2017."

"BPU spokesman David Mehlhaff said the muni

plans to convert the Quindaro

- 1 and

- 2 coal units

to only natural gas firing, probably by April 2015;

both units current ly have dual

- fuel capabilities." Kansas Units 2

Nearman Kansas Unit 1 Install and

continuously

operate a

baghouse 0.01 lbs/mmBtu 09/01/17

### 3

- 71 Company and

Plant State Unit Citizen Suits Provided by DOJ

Notes Retire/Repower SO 2 control NO

x Control PM Control Mercury Control

Action Effective

Date Equipment Percent Removal

or Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date Equipment Rate Effective

Date

MidAmerican Energy Company

Walter Scott, Jr

Energy Center Iowa Units 1

Cease burning

coal/Convert to

natural gas 04/16/16 http://www.sec.gov/Archives/edgar/data/928576/0

00092857613000014/llcmec33113form10

- q.htm

"MidAmerican Energy has committed to cease

burning solid fuel, su ch as coal, at its Walter

Scott, Jr. Energy Center Units 1

and 2, George Neal Energy Center Units 1 and 2

and Riverside Energy Center by April 16,

2016...The George Neal Energy Center Unit 1

and Riverside Energy Center currently have the

capability to burn natural gas in the production of

electricity, although under current operating and

economic conditions, production utilizing natural

gas would be very limited" Iowa Units 2

George Neal

Energy Center Iowa Units 1

Iowa Units 2

Riverside

Energy Center Iowa Units 7

Iowa Units 8

Iowa Units 9

Dominion Energy

Salem Harbor Massachusetts Unit 1

-

4 Retire 12/31/2011

for units

1&2

6/1/2014

for units

3&4 http://www.clf.org/wp

content/uploads/2012/02/Signed

- Consent

Decree

- 12_11.pdf

Duke Energy

Wabash River Indiana Unit 2

-

5 Retire 2014

http://www.duke

- energy.com/about

- us/retired

coal

- units

- potential

- retirements.asp

Wabash River Indiana Unit 6 Coal to Gas

Conversion 6/12018

### 3

- 72 Table 3

- 17 Renewable Portfolio Standards in EPA Base Case v.5.13

Regional Renewable Portfolio Standards

- AEO 2013

NEMS Region IPM Regions Covered Units 2016 2018 2020 2025 2030

-

2050

ERCOT (1) ERC_REST, ERC_FRNT, ERC_GWAY, ERC_WEST % 4.5% 4.5% 4.4% 4.4% 4.4%

MORE (3) MIS_WUMS (42%) % 10.1% 10.0% 10.0% 9.9% 10.0%

MROW (4) MAP_WAUE, MIS_IA, MIS_MIDA, MIS_MNWI, MIS_MAPP,

SPP_NEBR % 8.9% 9.6% 10.3% 11.3% 11.4%

NEWE (5) NENG_CT, NENGREST, NENG_ME % 11.6% 13.0% 14.3% 14.5% 14.6%

NYCW (6), NYLI (7),

NYUP (8) NY_Z_J, NY_Z_K, NY_Z_C&E, NY_Z_F, NY_Z_G

- I, NY_Z_A&B % 25.0% 24.8% 24.6% 24.5% 24.6%

RFCE (9) PJM_EMAC, PJM_PENE, PJM_SMAC, PJM_WMAC % 9.7% 11.6% 13.6% 14.7% 14.8%

RFCM (10) MIS_LMI % 10.1% 10.1% 10.0% 9.9% 10.0%

RFCW (11) MIS_INKY (90%), MIS_WUMS (58%), PJM_West, PJM_AP,

PJM_ATSI, PJM_COMD % 5.0% 6.0% 7.1% 9.2% 9.3%

SRDA (12) S_D_AMSO, S_D_N_AR, S_D_REST, S_D_WOTA, SPP_WEST

(10%) % 0.7% 0.6% 0.6% 0.6% 0.6%

SRGW (13) MIS_IL, MIS_MO, SPP_N (3%) % 7.3% 10.2% 11.2% 15.7% 15.8%

SRCE (15) S_C_KY, S_C_TVA, MIS_INKY (10%) % 0.0% 0.0% 0.0% 0.1% 0.1%

SRVC (16) PJM_Dom, S_VACA % 3.3% 4.2% 5.0% 5.5% 5.5%

SPNO (17) SPP_N (97%) % 8.5% 9.7% 11.9% 13.1% 13.2%

SPSO (18) SPP_SE, SPP_SPS, SPP_WEST (90%), SPP_KIAM % 1.8% 1.9% 2.1% 2.2% 2.2%

AZNM (19) WECC_AZ, WECC_IID, WECC_NM, WECC_SNV % 7.4% 8.0% 9.4% 11.1% 11.1%

CAMX (20) WEC_LADW, WEC_CALN, WEC_SDGE, WECC_SF, WECC_SCE % 25.6% 29.3% 33.0% 32.9% 33.0%

NWPP (21) WECC_ID, WECC_MT, WECC_NNV, WECC_PNW, WECC_UT,

WECC_WY (58%) % 7.2% 7.2% 10.1% 10.9% 11.0%

RMPA (22) WECC_CO, WECC_WY (42%) % 10.6% 13.1% 15.5% 15.3% 15.5%

Regional RPS Solar Carve

- outs

NEMS Region IPM Regions Covered Units 2016 2018 2020 2025 2030

-

2050

ERCOT (1) ERC_REST, ERC_FRNT, ERC_GWAY, ERC_WEST %

- - - - -

MORE (3) MIS_WUMS (42%) %

- - - - -

MROW (4) MAP_WAUE, MIS_IA, MIS_MIDA, MIS_MNWI, MIS_MAPP,

SPP_NEBR % 0.01% 0.01% 0.58% 0.58% 0.59%

NEWE (5) NENG_CT, NENGREST, NENG_ME % 0.08% 0.08% 0.08% 0.08% 0.08%

NYCW (6), NYLI (7),

NYUP (8) NY_Z_J, NY_Z_K, NY_Z_C&E, NY_Z_F, NY_Z_G

- I, NY_Z_A&B % 0.00% 0.00% 0.00% 0.00% 0.00%

### 3

- 73 Regional Renewable Portfolio Standards

- AEO 2013

NEMS Region IPM Regions Covered Units 2016 2018 2020 2025 2030

-

2050

RFCE (9) PJM_EMAC, PJM_PENE, PJM_SMAC, PJM_WMAC % 0.30% 0.49% 0.67% 0.71% 0.71%

RFCM (10) MIS_LMI %

- - - - -

RFCW (11) MIS_INKY (90%), MIS_WUMS (58%), PJM_West, PJM_AP,

PJM_ATSI, PJM_COMD % 0.18% 0.25% 0.32% 0.43% 0.45%

SRDA (12) S_D_AMSO, S_D_N_AR, S_D_REST, S_D_WOTA, SPP_WEST

(10%) %

- - - - -

SRGW (13) MIS_IL, MIS_MO, SPP_N (3%) % 0.29% 0.39% 0.46% 0.68% 0.72%

SRCE (15) S_C_KY, S_C_TVA, MIS_INKY (10%) % 0.001% 0.001% 0.001% 0.001% 0.001%

SRVC (16) PJM_Dom, S_VACA % 0.06% 0.09% 0.09% 0.09% 0.09%

SPNO (17) SPP_N (97%) % 0.03% 0.05% 0.05% 0.08% 0.08%

SPSO (18) SPP_SE, SPP_SPS, SPP_WEST (90%), SPP_KIAM % 0.10% 0.10% 0.14% 0.14% 0.14%

AZNM (19) WECC_AZ, WECC_IID, WECC_NM, WECC_SNV % 0.48% 0.47% 0.58% 0.60% 0.61%

CAMX (20) WEC_LADW, WEC_CALN, WEC_SDGE, WECC_SF, WECC_SCE %

- - - - -

NWPP (21) WECC_ID, WECC_MT, WECC_NNV, WECC_PNW, WECC_UT,

WECC_WY (58%) % 0.05% 0.05% 0.06% 0.06% 0.06%

RMPA (22) WECC_CO, WECC_WY (42%) % 0.01% 0.01% 0.02% 0.02% 0.02%

### 3

- 74 Table 3

- 18 Complete Availability Assumptions in EPA Base Case v.5.13

This is a small excerpt of the data in Table 3

- 18 . The complete data set in spreadsheet format can be

downl oaded via the link found at www.epa.gov/airmarkets/progsregs/epa

- ipm/BaseCasev513.html

. Please see Table 3

- 19 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

### 3

- 75 Table 3

- 19 BART Regulations included in EPA Base Case v.5.13

BART Affected Plants UniqueID BART Status / CAIR/

Shutdown/ Coal

- to

- Gas NO

x BART Limit SO 2 BART

Limit NO

x

Compliance

Date SO 2

Compliance

Date

Colstrip 6076_B_1 BART NO

x 0.15 lb/MMBtu 2018 2018

Colstrip 6076_B_2 BART NO

x 0.15 lb/MMBtu 2018 2018

Comanche 470_B_1 BART NO

x 0.20 lb/MMBtu 2018 2018

Comanche 470_B_2 BART NO

x 0.20 lb/MMBtu 2018 2018

Craig 6021_B_C1 BART NO

x 0.27 lb/MMBtu 2018 2018

Craig 6021_B_C2 BART NO

x 0.08 lb/MMBtu 2018 2018

Four Corners 2442_B_1 BART NO

x 0.05 lb/MMBtu Acutal

emissions 2018 2018

Four Corners 2442_B_2 BART NO

x 0.05 lb/MMBtu Acutal

emissions 2018 2018

Four Corners 2442_B_3 BART NO

x 0.05 lb/MMBtu Acutal

emissions 2018 2018

Four Corners 2442_B_4 BART NO

x 0.05 lb/MMBtu Acutal

emissions 2018 2018

Four Corners 2442_B_5 BART NO

x 0.05 lb/MMBtu Acutal

emissions 2018 2018

Gerald Gentleman 6077_B_1 BART NO

x 0.23 lb/MMBtu TBD 2018 2018

Gerald Gentleman 6077_B_2 BART NO

x 0.23 lb/MMBtu TBD 2018 2018

Hayden 525_B_H1 BART NO

x 0.08 lb/MMBtu 2018 2018

Hayden 525_B_H2 BART NO

x 0.07 lb/MMBtu 2018 2018

J E Corette Plant 2187_B_2 BART NO

x 0.35 lb/MMBtu 2018 2018

Martin Drake 492_B_5 BART NO

x 0.31 lb/MMBtu 2018 2018

Martin Drake 492_B_6 BART NO

x 0.32 lb/MMBtu 2018 2018

Martin Drake 492_B_7 BART NO

x 0.32 lb/MMBtu 2018 2018

Nebraska City 6096_B_1 BART NO

x 0.23 lb/MMBtu 2018 2018

Reid Gardner 2324_B_1 BART NO

x 0.20 lb/MMBtu 2018 2018

Reid Gardner 2324_B_2 BART NO

x 0.20 lb/MMBtu 2018 2018

Reid Gardner 2324_B_3 BART NO

x 0.20 lb/MMBtu 2018 2018

San Juan 2451_B_1 BART NO

x 0.11 lb/MMBtu Acutal

emissions 2018 2018

San Juan 2451_B_2 BART NO

x 0.11 lb/MMBtu Acutal

emissions 2018 2018

San Juan 2451_B_3 BART NO

x 0.11 lb/MMBtu Acutal

emissions 2018 2018

San Juan 2451_B_4 BART NO

x 0.11 lb/MMBtu Acutal

emissions 2018 2018

Tecumseh Energy Center 1252_B_10 BART NO

x 0.18 lb/MMBtu 2018 2018

Apache Station 160_B_2 BART NO

x & BART SO 2 0.07 lb/MMBtu

across 2 units 0.15

lb/MMBtu 12/1/17 12/1/16

Apache Station 160_B_3 BART NO

x & BART SO 2 0.07 lb/MMBtu

across 2 units 0.15

lb/MMBtu 12/1/17 12/1/16

Cherokee 469_B_4 BART NO

x & BART SO 2 0.12 lb/MMBtu 7.81 tpy (12

month rolling) 2018 2018

Cholla 113_B_2 BART NO

x & BART SO 2 0.055 lb/MMBtu

across 3 units 0.15

lb/MMBtu 12/1/17 12/5/13

Cholla 113_B_3 BART NO

x & BART SO 2 0.055 lb/MMBtu

across 3 units 0.15

lb/MMBtu 12/1/17 12/5/13

Cholla 113_B_4 BART NO

x & BART SO 2 0.055 lb/MMBtu

across 3 units 0.15

lb/MMBtu 12/1/17 12/5/13

Coal Creek 6030_B_1 BART NO

x & BART SO 2 0.13 lb/MMBtu

(combined both

units) 0.15

lb/MMBtu or

95%

efficiency 2018 2018

Coal Creek 6030_B_2 BART NO

x & BART SO 2 0.13 lb/MMBtu

(combined both

units) 0.15

lb/MMBtu or

95%

efficiency 2018 2018

Coronado 6177_B_U1B BART NO

x & BART SO 2 0.065 lb/MMBtu

across 2 units 0.08

lb/MMBtu 12/1/17 6/5/13

Coronado 6177_B_U2B BART NO

x & BART SO 2 0.065 lb/MMBtu

across 2 units 0.08

lb/MMBtu 12/1/17 6/5/13

Jeffrey Energy Center 6068_B_1 BART NO

x & BART SO 2 0.15 lb/MMBtu 0.15

lb/MMBtu 2018 2018

Jeffrey Energy Center 6068_B_2 BART NO

x & BART SO 2 0.15 lb/MMBtu 0.15

lb/MMBtu 2018 2018

### 3

- 76 BART Affected Plants UniqueID BART Status / CAIR/

Shutdown/ Coal

- to

- Gas NO

x BART Limit SO 2 BART

Limit NO

x

Compliance

Date SO 2

Compliance

Date

La Cygne 1241_B_1 BART NO

x & BART SO 2 0.13 lb/MMBtu

(combined both

units) 0.15

lb/MMBtu 6/1/15 6/1/15

La Cygne 1241_B_2 BART NO

x & BART SO 2 0.13 lb/MMBtu

(combined both

units) 0.15

lb/MMBtu 6/1/15 6/1/15

Leland Olds 2817_B_1 BART NO

x & BART SO 2 0.19 lb/MMBtu 0.15

lb/MMBtu or

95%

efficiency 2018 2018

Leland Olds 2817_B_2 BART NO

x & BART SO 2 0.35 lb/MMBtu 0.15

lb/MMBtu or

95%

efficiency 2018 2018

Merrimack 2364_B_2 BART NO

x & BART SO 2 0.30 lb/MMBtu 90 % control 2018 2018

Milton R Young 2823_B_B1 BART NO

x & BART SO 2 0.36 lb/MMBtu 0.15

lb/MMBtu or

95%

efficiency 2018 2018

Milton R Young 2823_B_B2 BART NO

x & BART SO 2 0.35 lb/MMBtu 0.15

lb/MMBtu or

95%

efficiency 2018 2018

Muskogee 2952_B_4 BART NO

x & BART SO 2 0.15 lb/MMBtu 0.06

lbs/MMBtu 2018 2018

Muskogee 2952_B_5 BART NO

x & BART SO 2 0.15 lb/MMBtu 0.06

lbs/MMBtu 2018 2018

Pawnee 6248_B_1 BART NO

x & BART SO 2 0.07 lb/MMBtu 0.12

lb/MMBtu 2018 2018

Ray D Nixon 8219_B_1 BART NO

x & BART SO 2 0.21 lb/MMBtu 0.11

lb/MMBtu 2018 2018

Sooner 6095_B_1 BART NO

x & BART SO 2 0.15 lb/MMBtu 0.06

lbs/MMBtu 2018 2018

Sooner 6095_B_2 BART NO

x & BART SO 2 0.15 lb/MMBtu 0.06

lbs/MMBtu 2018 2018

Stanton 2824_B_1 BART NO

x & BART SO 2 0.29 lb/MMBtu 0.24

lb/MMBtu 2018 2018

Lansing Smith 643_B_1 BART NO

x & BART SO 2 4700 tpy across 2

units 0.74

lb/MMBtu 2018 2018

Lansing Smith 643_B_2 BART NO

x & BART SO 2 4700 tpy across 2

units 0.74

lb/MMBtu 2018 2018

Northeastern 2963_B_3313 BART NO

x & BART SO 2 ; Shutdown by 2016 0.23 lb/MMBtu 0.60

lb/MMBtu 2018 2018

Boardman 6106_B_1SG BART NO

x & BART SO 2 ; Shutdown by 2020 0.7 lb/MMBtu 1.2 lb/MMBtu 2018 2018

Northeastern 2963_B_3314 BART NO

x & BART SO 2 ; Shutdown by 2024 0.15 lb/MMBtu 0.40

lb/MMBtu 2018 2018

Seminole 136_B_1 BART SO 2 0.25

lb/MMBtu 2018 2018

Seminole 136_B_2 BART SO 2 0.25

lb/MMBtu 2018 2018

Northside Generating Station 667_B_1 BART SO 2 3600 tpy

across 3 units 2018 2018

Northside Generating Station 667_B_2 BART SO 2 3600 tpy

across 3 units 2018 2018

Northside Generating Station 667_B_3 BART SO 2 3600 tpy

across 3 units 2018 2018

Deerhaven Generating Station 663_B_B2 BART SO 2 5500 tpy 2018 2018

Merrimack 2364_B_2 BART SO 2 Actual

Emissions

[with FGD] 2018 2018

Yates 728_B_Y6BR Coal

- to

- Gas by 2016

Yates 728_B_Y7BR Coal

- to

- Gas by 2016

George Neal North 1091_B_1 Coal

- to

- Gas by 4/16/2016

George Neal North 1091_B_2 Coal

- to

- Gas by 4/16/2016

George Neal North 1091_B_3 Coal

- to

- Gas by 4/16/2016

Walter Scott Jr. Energy Center 1082_B_3 Coal

- to

- Gas by 4/16/2016

A B Brown 6137_B_1 CAIR

Ames Electric Services Power Plant 1122_B_7 CAIR

### 3

- 77 BART Affected Plants UniqueID BART Status / CAIR/

Shutdown/ Coal

- to

- Gas NO

x BART Limit SO 2 BART

Limit NO

x

Compliance

Date SO 2

Compliance

Date

Asbury 2076_B_1 CAIR

Bailly 995_B_7 CAIR

Bailly 995_B_8 CAIR

Barry 3_B_4 CAIR

Barry 3_B_5 CAIR

Belle River 6034_B_1 CAIR

Belle River 6034_B_2 CAIR

Big Brown 3497_B_1 CAIR

Big Brown 3497_B_2 CAIR

Big Cajun 2 6055_B_2B1 CAIR

Big Stone 6098_B_1 CAIR

Blue Valley 2132_B_3 CAIR

Bowen 703_B_1BLR CAIR

Bowen 703_B_2BLR CAIR

Bowen 703_B_3BLR CAIR

Bowen 703_B_4BLR CAIR

Bridgeport Station 568_B_BHB3 CAIR

Bruce Mansfield 6094_B_1 CAIR

Bruce Mansfield 6094_B_2 CAIR

Bruce Mansfield 6094_B_3 CAIR

Bull Run 3396_B_1 CAIR

Burlington 1104_B_1 CAIR

Capitol Heat and Power 54406_G_1 CAIR

Capitol Heat and Power 54406_G_2 CAIR

Cardinal 2828_B_1 CAIR

Cardinal 2828_B_2 CAIR

Cardinal 2828_B_3 CAIR

Cayuga 1001_B_1 CAIR

Cayuga 1001_B_2 CAIR

Charles R Lowman 56_B_1 CAIR

Charles R Lowman 56_B_2 CAIR

Charles R Lowman 56_B_3 CAIR

Chesterfield 3797_B_5 CAIR

Chesterfield 3797_B_6 CAIR

Cheswick 8226_B_1 CAIR

Colbert 47_B_5 CAIR

Coleto Creek 6178_B_1 CAIR

Columbia 8023_B_1 CAIR

Columbia 8023_B_2 CAIR

Conemaugh 3118_B_1 CAIR

Conemaugh 3118_B_2 CAIR

Conesville 2840_B_4 CAIR

Conesville 2840_B_5 CAIR

Conesville 2840_B_6 CAIR

Cooper 1384_B_1 CAIR

Cooper 1384_B_2 CAIR

Crawfordsville 1024_B_6 CAIR

Cumberland 3399_B_1 CAIR

Cumberland 3399_B_2 CAIR

Dean H Mitchell 996_B_11 CAIR

Dolphus M Grainger 3317_B_1 CAIR

Dolphus M Grainger 3317_B_2 CAIR

Dover 2914_B_4 CAIR

E C Gaston 26_B_4 CAIR

### 3

- 78 BART Affected Plants UniqueID BART Status / CAIR/

Shutdown/ Coal

- to

- Gas NO

x BART Limit SO 2 BART

Limit NO

x

Compliance

Date SO 2

Compliance

Date

E C Gaston 26_B_5 CAIR

E W Brown 1355_B_2 CAIR

E W Brown 1355_B_3 CAIR

East Bend 6018_B_2 CAIR

Eckert Station 1831_B_4 CAIR

Eckert Station 1831_B_5 CAIR

Eckert Station 1831_B_6 CAIR

Elmer Smith 1374_B_1 CAIR

Elmer Smith 1374_B_2 CAIR

Erickson Station 1832_B_1 CAIR

F B Culley 1012_B_2 CAIR

F B Culley 1012_B_3 CAIR

Fair Station 1218_B_2 CAIR

Fayette Power Project 6179_B_1 CAIR

Fayette Power Project 6179_B_2 CAIR

Fort Martin Power Station 3943_B_1 CAIR

Fort Martin Power Station 3943_B_2 CAIR

General James M Gavin 8102_B_1 CAIR

General James M Gavin 8102_B_2 CAIR

Genoa 4143_B_1 CAIR

George Neal South 7343_B_4 CAIR

Ghent 1356_B_1 CAIR

Ghent 1356_B_2 CAIR

Ghent 1356_B_3 CAIR

Gibson 6113_B_1 CAIR

Gibson 6113_B_2 CAIR

Gibson 6113_B_3 CAIR

Gibson 6113_B_4 CAIR

Gorgas 8_B_10 CAIR

Greene County 10_B_1 CAIR

Greene County 10_B_2 CAIR

H L Spurlock 6041_B_1 CAIR

H L Spurlock 6041_B_2 CAIR

Hamilton 2917_B_8 CAIR

Hamilton 2917_B_9 CAIR

Hammond 708_B_4 CAIR

Harding Street 990_B_70 CAIR

Harrington 6193_B_061B CAIR

Harrington 6193_B_062B CAIR

Harrington 6193_B_063B CAIR

Harrison Power Station 3944_B_1 CAIR

Harrison Power Station 3944_B_2 CAIR

Harrison Power Station 3944_B_3 CAIR

Hatfields Ferry Power Station 3179_B_1 CAIR

Hatfields Ferry Power Station 3179_B_2 CAIR

Hatfields Ferry Power Station 3179_B_3 CAIR

Henderson 2062_B_H3 CAIR

HMP&L Station Two Henderson 1382_B_H2 CAIR

Homer City Station 3122_B_1 CAIR

Homer City Station 3122_B_2 CAIR

Homer City Station 3122_B_3 CAIR

Iatan 6065_B_1 CAIR

J H Campbell 1710_B_1 CAIR

J H Campbell 1710_B_2 CAIR

### 3

- 79 BART Affected Plants UniqueID BART Status / CAIR/

Shutdown/ Coal

- to

- Gas NO

x BART Limit SO 2 BART

Limit NO

x

Compliance

Date SO 2

Compliance

Date

J H Campbell 1710_B_3 CAIR

J M Stuart 2850_B_1 CAIR

J M Stuart 2850_B_2 CAIR

J M Stuart 2850_B_3 CAIR

J M Stuart 2850_B_4 CAIR

Jack McDonough 710_B_MB1 CAIR

Jack McDonough 710_B_MB2 CAIR

Jack Watson 2049_B_4 CAIR

Jack Watson 2049_B_5 CAIR

James De Young 1830_B_4 CAIR

James De Young 1830_B_5 CAIR

James H Miller Jr 6002_B_2 CAIR

James H Miller Jr 6002_B_1 CAIR

James River Power Station 2161_B_4 CAIR

James River Power Station 2161_B_5 CAIR

Jasper 2 6225_B_1 CAIR

John E Amos 3935_B_1 CAIR

John E Amos 3935_B_2 CAIR

John E Amos 3935_B_3 CAIR

John P Madgett 4271_B_B1 CAIR

Kenneth C Coleman 1381_B_C1 CAIR

Kenneth C Coleman 1381_B_C2 CAIR

Kenneth C Coleman 1381_B_C3 CAIR

Keystone 3136_B_1 CAIR

Keystone 3136_B_2 CAIR

Labadie 2103_B_1 CAIR

Labadie 2103_B_2 CAIR

Labadie 2103_B_3 CAIR

Labadie 2103_B_4 CAIR

Lake Road 2098_B_6 CAIR

Lake Road 2908_G_11 CAIR

Lake Shore 2838_B_18 CAIR

Lansing 1047_B_4 CAIR

Logansport 1032_B_6 CAIR

Manitowoc 4125_B_7 CAIR

Marshall 2144_B_5 CAIR

Martin Lake 6146_B_1 CAIR

Martin Lake 6146_B_2 CAIR

Martin Lake 6146_B_3 CAIR

McIntosh 6124_B_1 CAIR

Merom 6213_B_1SG1 CAIR

Merom 6213_B_2SG1 CAIR

Miami Fort 2832_B_7 CAIR

Miami Fort 2832_B_8 CAIR

Michigan City 997_B_12 CAIR

Mill Creek 1364_B_1 CAIR

Mill Creek 1364_B_2 CAIR

Mill Creek 1364_B_3 CAIR

Mill Creek 1364_B_4 CAIR

Milton L Kapp 1048_B_2 CAIR

Mitchell 3948_B_1 CAIR

Mitchell 3948_B_2 CAIR

Mitchell Power Station 3181_B_33 CAIR

Monroe 1733_B_1 CAIR

### 3

- 80 BART Affected Plants UniqueID BART Status / CAIR/

Shutdown/ Coal

- to

- Gas NO

x BART Limit SO 2 BART

Limit NO

x

Compliance

Date SO 2

Compliance

Date

Monroe 1733_B_2 CAIR

Monroe 1733_B_3 CAIR

Monroe 1733_B_4 CAIR

Monticello 6147_B_1 CAIR

Monticello 6147_B_2 CAIR

Monticello 6147_B_3 CAIR

Montrose 2080_B_3 CAIR

Mountaineer 6264_B_1 CAIR

Mt Storm 3954_B_1 CAIR

Mt Storm 3954_B_2 CAIR

Mt Storm 3954_B_3 CAIR

Muscatine Plant #1 1167_B_8 CAIR

Muskingum River 2872_B_5 CAIR

New Madrid 2167_B_1 CAIR

New Madrid 2167_B_2 CAIR

Orrville 2935_B_13 CAIR

Ottumwa 6254_B_1 CAIR

Paradise 1378_B_1 CAIR

Paradise 1378_B_2 CAIR

Paradise 1378_B_3 CAIR

Petersburg 994_B_1 CAIR

Petersburg 994_B_2 CAIR

Petersburg 994_B_3 CAIR

Pleasant Prairie 6170_B_1 CAIR

Pleasants Power Station 6004_B_1 CAIR

Pleasants Power Station 6004_B_2 CAIR

PPL Brunner Island 3140_B_2 CAIR

PPL Brunner Island 3140_B_3 CAIR

PPL Montour 3149_B_1 CAIR

PPL Montour 3149_B_2 CAIR

Prairie Creek 1073_B_4 CAIR

Presque Isle 1769_B_5 CAIR

Presque Isle 1769_B_6 CAIR

Presque Isle 1769_B_7 CAIR

Presque Isle 1769_B_8 CAIR

Presque Isle 1769_B_9 CAIR

Pulliam 4072_B_8 CAIR

R D Green 6639_B_G1 CAIR

R D Green 6639_B_G2 CAIR

R D Morrow 6061_B_1 CAIR

R D Morrow 6061_B_2 CAIR

R M Schahfer 6085_B_14 CAIR

R M Schahfer 6085_B_15 CAIR

R S Nelson 1393_B_6 CAIR

Robert A Reid 1383_B_R1 CAIR

Rodemacher 6190_B_2 CAIR

Rush Island 6155_B_1 CAIR

Rush Island 6155_B_2 CAIR

Sandow 6648_B_4 CAIR

Scherer 6257_B_1 CAIR

Scherer 6257_B_2 CAIR

Shelby Municipal Light Plant 2943_B_1 CAIR

Shelby Municipal Light Plant 2943_B_2 CAIR

Shiras 1843_B_2 CAIR

### 3

- 81 BART Affected Plants UniqueID BART Status / CAIR/

Shutdown/ Coal

- to

- Gas NO

x BART Limit SO 2 BART

Limit NO

x

Compliance

Date SO 2

Compliance

Date

Sibley 2094_B_2 CAIR

Sibley 2094_B_3 CAIR

Sikeston Power Station 6768_B_1 CAIR

Sioux 2107_B_1 CAIR

Sioux 2107_B_2 CAIR

South Oak Creek 4041_B_7 CAIR

South Oak Creek 4041_B_8 CAIR

Southwest Power Station 6195_B_1 CAIR

St Clair 1743_B_7 CAIR

St Marys 2942_B_6 CAIR

Streeter Station 1131_B_6 CAIR

Streeter Station 1131_B_7 CAIR

Tanners Creek 988_B_U4 CAIR

Thomas Hill 2168_B_MB1 CAIR

Thomas Hill 2168_B_MB2 CAIR

Trenton Channel 1745_B_9A CAIR

Valley 4042_B_1 CAIR

Valley 4042_B_2 CAIR

Valley 4042_B_3 CAIR

Valley 4042_B_4 CAIR

Victor J Daniel Jr 6073_B_1 CAIR

Victor J Daniel Jr 6073_B_2 CAIR

W A Parish 3470_B_WAP5 CAIR

W A Parish 3470_B_WAP6 CAIR

W A Parish 3470_B_WAP7 CAIR

W H Sammis 2866_B_4 CAIR

W H Sammis 2866_B_5 CAIR

W H Sammis 2866_B_6 CAIR

W H Sammis 2866_B_7 CAIR

Wabash River 1010_B_6 CAIR

Wansley 6052_B_1 CAIR

Wansley 6052_B_2 CAIR

Warrick 6705_B_2 CAIR

Warrick 6705_B_3 CAIR

Warrick 6705_B_4 CAIR

Wateree 3297_B_WAT1 CAIR

Wateree 3297_B_WAT2 CAIR

Welsh 6139_B_1 CAIR

Weston 4078_B_3 CAIR

Whitewater Valley 1040_B_2 CAIR

Widows Creek 50_B_8 CAIR

Williams 3298_B_WIL1 CAIR

Winyah 6249_B_1 CAIR

Winyah 6249_B_2 CAIR

Asheville 2706_B_1 CAIR/State EGU Rule

Asheville 2706_B_2 CAIR/State EGU Rule

Belews Creek 8042_B_1 CAIR/State EGU Rule

Belews Creek 8042_B_2 CAIR/State EGU Rule

Cliffside 2721_B_5 CAIR/State EGU Rule

Marshall 2727_B_1 CAIR/State EGU Rule

Marshall 2727_B_2 CAIR/State EGU Rule

Marshall 2727_B_3 CAIR/State EGU Rule

Marshall 2727_B_4 CAIR/State EGU Rule

Roxboro 2712_B_1 CAIR/State EGU Rule

### 3

- 82 BART Affected Plants UniqueID BART Status / CAIR/

Shutdown/ Coal

- to

- Gas NO

x BART Limit SO 2 BART

Limit NO

x

Compliance

Date SO 2

Compliance

Date

Roxboro 2712_B_2 CAIR/State EGU Rule

Roxboro 2712_B_3A CAIR/State EGU Rule

Roxboro 2712_B_3B CAIR/State EGU Rule

Roxboro 2712_B_4A CAIR/State EGU Rule

Roxboro 2712_B_4B CAIR/State EGU Rule

Lee 2709_B_3 CAIR/State EGU Rule;

Shutdown by 2013

L V Sutton 2713_B_3 CAIR/State EGU Rule;

Shutdown by 2017

Portland 3113_B_2 CAIR; Shutdown by 1/7/2015

Harllee Branch 709_B_2 CAIR; Shutdown by 10/1/13

Canadys Steam 3280_B_CAN1 CAIR; Shutdown by

12/1/2017

Canadys Steam 3280_B_CAN2 CAIR; Shutdown by

12/1/2017

Canadys Steam 3280_B_CAN3 CAIR; Shutdown by

12/1/2017

Harllee Branch 709_B_1 CAIR; Shutdown by 12/31/13

Chesapeake 3803_B_4 CAIR; Shutdown by 12/31/14

Welsh 6139_B_2 CAIR; Shutdown by 12/31/14

Conesville 2840_B_3 CAIR; Shutdown by

12/31/2012

HMP&L Station Two Henderson 1382_B_H1 CAIR; Shutdown by 2008

Menasha 4127_B_B24 CAIR; Shutdown by 2009

Pella 1175_B_6 CAIR; Shutdown by 2012

Pella 1175_B_7 CAIR; Shutdown by 2012

Jefferies 3319_B_3 CAIR; Shutdown by 2013

Jefferies 3319_B_4 CAIR; Shutdown by 2013

Big Sandy 1353_B_BSU2 CAIR; Shutdown by 2015

Frank E Ratts 1043_B_1SG1 CAIR; Shutdown by 2015

Frank E Ratts 1043_B_2SG1 CAIR; Shutdown by 2015

Harbor Beach 1731_B_1 CAIR; Shutdown by 2015

Nelson Dewey 4054_B_2 CAIR; Shutdown by 2015

Cane Run 1363_B_4 CAIR; Shutdown by 2016

Cane Run 1363_B_5 CAIR; Shutdown by 2016

Cane Run 1363_B_6 CAIR; Shutdown by 2016

Harllee Branch 709_B_3 CAIR; Shutdown by 2016

Harllee Branch 709_B_4 CAIR; Shutdown by 2016

Kraft 733_B_3 CAIR; Shutdown by 2016

J T Deely 6181_B_1 CAIR; Shutdown by 2018

J T Deely 6181_B_2 CAIR; Shutdown by 2018

State Line 981_B_4 CAIR; Shutdown by 3/25/12

Avon Lake 2836_B_12 CAIR; Shutdown by 4/1/2015

Walter C Beckjord 2830_B_5 CAIR; Shutdown by 4/1/2015

Walter C Beckjord 2830_B_6 CAIR; Shutdown by 4/1/2015

New Castle 3138_B_5 CAIR; Shutdown by

4/16/2015

Big Sandy 1353_B_BSU1 CAIR; Shutdown by 6/1/2015

Bay Shore 2878_B_3 CAIR; Shutdown by 9/1/2012

Bay Shore 2878_B_4 CAIR; Shutdown by 9/1/2012

Eastlake 2837_B_5 CAIR; Shutdown by 9/1/2012

Edgewater 4050_B_4 CAIR; Shutdown or Coal

- to

Gas by 12/31/2018

Dave Johnston 4158_B_BW43 Proposal 5/23/13

Dave Johnston 4158_B_BW44 Proposal 5/23/13

Jim Bridger 8066_B_BW71 Proposal 5/23/13

Jim Bridger 8066_B_BW72 Proposal 5/23/13

Jim Bridger 8066_B_BW73 Proposal 5/23/13

### 3

- 83 BART Affected Plants UniqueID BART Status / CAIR/

Shutdown/ Coal

- to

- Gas NO

x BART Limit SO 2 BART

Limit NO

x

Compliance

Date SO 2

Compliance

Date

Jim Bridger 8066_B_BW74 Proposal 5/23/13

Laramie River Station 6204_B_1 Proposal 5/23/13

Laramie River Station 6204_B_2 Proposal 5/23/13

Naughton 4162_B_1 Proposal 5/23/13

Naughton 4162_B_2 Proposal 5/23/13

Naughton 4162_B_3 Proposal 5/23/13

Neil Simpson 4150_B_5 Proposal 5/23/13

Wyodak 6101_B_BW91 Proposal 5/23/13

Navajo 4941_B_1 Proposed

Navajo 4941_B_2 Proposed

Navajo 4941_B_3 Proposed

Indian River Generating Station 594_B_3 Shutdown by 12/31/13; State

EGU Rule

Cherokee 469_B_3 Shutdown by 12/31/16

Valmont 477_B_5 Shutdown by 12/31/17

Crystal River 628_B_1 Shutdown by 2020

Crystal River 628_B_2 Shutdown by 2020

Transalta Centralia Generation 3845_B_BW21 Shutdown by 2020

Transalta Centralia Generation 3845_B_BW22 Shutdown by 2025

Brayton Point 1619_B_1 State Alternative Program

Brayton Point 1619_B_2 State Alternative Program

Brayton Point 1619_B_3 State Alternative Program

Baldwin Energy Complex 889_B_1 State EGU Rule

Baldwin Energy Complex 889_B_2 State EGU Rule

Baldwin Energy Complex 889_B_3 State EGU Rule

C P Crane 1552_B_2 State EGU Rule

Chalk Point LLC 1571_B_1 State EGU Rule

Chalk Point LLC 1571_B_2 State EGU Rule

Coffeen 861_B_01 State EGU Rule

Coffeen 861_B_02 State EGU Rule

Dallman 963_B_31 State EGU Rule

Dallman 963_B_32 State EGU Rule

Dallman 963_B_33 State EGU Rule

Dickerson 1572_B_3 State EGU Rule

Duck Creek 6016_B_1 State EGU Rule

E D Edwards 856_B_2 State EGU Rule

E D Edwards 856_B_3 State EGU Rule

Edge Moor 593_B_4 State EGU Rule

Havana 891_B_9 State EGU Rule

Herbert A Wagner 1554_B_3 State EGU Rule

Indian River Generating Station 594_B_4 State EGU Rule

Joliet 29 384_B_71 State EGU Rule

Joliet 29 384_B_72 State EGU Rule

Joliet 29 384_B_81 State EGU Rule

Joliet 29 384_B_82 State EGU Rule

Kincaid Generation LLC 876_B_1 State EGU Rule

Kincaid Generation LLC 876_B_2 State EGU Rule

Marion 976_B_4 State EGU Rule

Marion 976_B_123 State EGU Rule

Morgantown Generating Plant 1573_B_1 State EGU Rule

Morgantown Generating Plant 1573_B_2 State EGU Rule

Newton 6017_B_1 State EGU Rule

Newton 6017_B_2 State EGU Rule

Pearl Station 6238_B_1A State EGU Rule

Powerton 879_B_51 State EGU Rule

### 3

- 84 BART Affected Plants UniqueID BART Status / CAIR/

Shutdown/ Coal

- to

- Gas NO

x BART Limit SO 2 BART

Limit NO

x

Compliance

Date SO 2

Compliance

Date

Powerton 879_B_52 State EGU Rule

Powerton 879_B_61 State EGU Rule

Powerton 879_B_62 State EGU Rule

PSEG Hudson Generating Station 2403_B_2 State EGU Rule

Waukegan 883_B_8 State EGU Rule

Will County 884_B_4 State EGU Rule

Wood River 898_B_5 State EGU Rule

Austin Northeast 1961_B_NEPP TBD

Clay Boswell 1893_B_3 TBD

Clay Boswell 1893_B_4 TBD

H Wilson Sundt GS 126_B_4 TBD

Hibbing 1979_B_1 TBD

Hibbing 1979_B_2 TBD

Hibbing 1979_B_3 TBD

Hoot Lake (Otter Tail) 1943_B_3 TBD

Sherburne County 6090_B_1 TBD

Sherburne County 6090_B_2 TBD

Silver Bay Power 10849_B_BLR2 TBD

Silver Lake 2008_B_3 TBD

Silver Lake 2008_B_4 TBD

Allen S King 1915_B_1 TBD

Big Bend 645_B_BB01 TBD Proposed

Big Bend 645_B_BB02 TBD Proposed

Big Bend 645_B_BB03 TBD Proposed

Crist 641_B_6 TBD Proposed

Crist 641_B_7 TBD Proposed

Crystal River 628_B_4 TBD Proposed

Crystal River 628_B_5 TBD Proposed

Deerhaven Generating Station 663_B_B2 TBD Proposed

Lansing Smith 643_B_1 TBD Proposed

Lansing Smith 643_B_2 TBD Proposed

Flint Creek 6138_B_1 TBD State SIP disapproved

Hunter 6165_B_1 TBD State SIP disapproved

Hunter 6165_B_2 TBD State SIP disapproved

Huntington 8069_B_1 TBD State SIP disapproved

Huntington 8069_B_2 TBD State SIP disapproved

White Bluff 6009_B_1 TBD State SIP disapproved

White Bluff 6009_B_2 TBD State SIP disapproved

### 4-1



# 4. Generating Resources

“Existing”, “planned-committed”, and “potential” are the three general types of generating units modeled in

EPA Base Case v.5.13. 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 “plannedcommitted”. “Potential” units refer to new generating options used in IPM for capacity expansion

projections of the 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.5.13,

(2) Section 4.2 provides detailed information on existing non-nuclear generating units modeled in EPA

Base Case v.5.13,

(3) Section 4.3 provides detailed information pertaining to planned-committed units which are assumed in

EPA Base Case v.5.13

,

(4) Section 4.4 provides detailed information pertaining to the EPA Base Case assumptions for potential

plants, and

(5) Section 4.5 describes the handling of existing and potential nuclear units in EPA Base Case v.5.13



## 4.1 National Electric Energy Data System (NEEDS)

EPA Base Case v.5.13 uses the NEEDS database as its source for data on all existing and plannedcommitted units. Table 4-1 below summarizes the resources used in developing data on existing units in

NEEDS v.5.13. The data sources for 

### planned-committed units in NEEDS are discussed below in Section

0 . The population of existing units in NEEDS v.5.13 represents generating units that were in operation

through the end of 2010. The population of planned-committed includes any units online or scheduled to

come online from 2011 to the end of 2015 (with five exceptions listed in the note under Table 4-2 below).



## 4.2 Existing Unit s

EPA Base Case v.5.13 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 configuration. 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.5.13.

4.2.1 Population of Existing Units

The population of existing units was taken primarily from EIA 860 (2010). 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 EPA Base Case v.5.13. Table 4-2 below summarizes the rules used

in populating the NEEDS v.5.13 database. Excerpt from Table 4- 35 lists all units that were not included

in the NEEDS v.5.13 database based on these criteria.

### 4

- 2 Table 4

- 1 Data Sources for NEEDS v. 5.13 for EPA Ba se Case v. 5.13

Data Source a 

### Data Source Documentation

DOE's Form EIA

- 860 DOE's Form EIA

- 860 is an annual survey of utility and non

- utility power plants at the

generator level. It contains data such as summer, winter and nameplate capacity,

location (state and county), operating status, prime mover, energy sources and in

service date of existing and proposed generators. NEEDS v.5.13 uses EIA Form 860

(2010, 2011) data as one of the primary generator data inputs.

DOE's Form EIA

- 860 also collects data of steam boilers such as energy sources, boiler

identification, location, operating status and design information; and associated

environmental equipment such as NO

x 

### combustion and post

- combustion control, FGD

scrubber, mercury control and particulate collector device information. Note that boilers

in plants with less than 10 MW do not report all data elements. The association

between boilers and generators is also pr ovided. Note that boilers and generators are

not necessarily in a one

- to

- one correspondence. NEEDS v.5.13 uses EIA Form 860

(2010, 2011) data as one of the primary boiler 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.5.13 uses NERC ES&D (2011) data as one of

the data inputs.

DOE’s Annual Energy

Outlook (AEO) The Energy Information Administration (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 Nation al Energy Modeling

System (NEMS). Information from AEO 2012 such as heat rates, planned committed

units were used in NEEDS v.5.13. Nuclear unit capacities and uprates are from AEO

2013.

Ventyx's New Entrants

database Ventyx's New Entrants database has inf ormation on new power plant builds, rerates

and retirements. NEEDS v.5.13 uses the dataset downloaded on April 13, 2012 and

April 23, 2013, as one of the sources of developm ent of committed generating units.

EPA's Emission Tracking

System The Emission Tra cking 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. NEEDS v.5.13 uses annual and seasonal ETS (2011) data as one of

the primary data input s for NO

x 

### rate development and environmental equipment

assignment.

Utility and Regional EPA

Office Comments Comments from utilities and regional EPA offices regarding the population in NEEDS

(retirements, new units) as well as unit characteristics were in corporated in NEEDS

v.5.13.

Note:

a 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 data were not available for the indicated issued date or where there were

methodological reasons for using other vintages of the data.



### Table 4

- 2 Rules Used in Populating NEEDS v.5.13 for EPA Base Case v.5.13

Scope Rule

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

blank.

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 assoc iated

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 2015; one geothermal unit and four nucle ar units that are scheduled to come online

after 2015 were also included a

### 4

- 3

Scope Rule

Firm/Non

- firm

Electric Sales Excluded non

- utility onsite generators that do not produce electricity for sale to the grid on a net

basis

Excluded all mobile and distributed generators

Note:

a The geothermal unit is Bonnett, unit ST2; the four nuclear units are Vogtle, units 3&4, and V C Summer, units 2&3



### As with previous versions of the database, NEEDS v. 5.13 includes steam units at the boiler level and

non

- steam units at the generator level (nuclear units are also at the generator level) . A unit in NEEDS

v. 5.13 , therefore, refers to a boiler in the case of a steam unit and a generator in the case of a non

- ste am

unit. Table 4

- 3 provides a summary of the population and capacity of the existing units included in

NEED S v.5.13 through 2010. EIA Form 860 (2010 ) is the starting point and largest component of the

existing unit population in NEEDS v. 5.13 but the final population of existing units is supplemented based

on information from other sources, including comments from utilities, submissions to EPA's Emissi on

Tracking System , Annual Energy Outlook

, and reported capacity in Ventyx's New Entrants database.

EPA removed capacity from the NEEDS inventory based on public announcements of future closures.

Removal of such capacity from the NEEDS inventory pre

- empts the model itself from making any

decisions regarding that capacity’s future status or configuration; such capacity is simply no longer

available for the model to consider in optimizing electricity supply to meet demand. The list of units

considered for re moval from NEEDS is built from several data sources including:

1. Edison Electric Institute (EEI), “Coal Fleet Retirement Announcements”, July 29, 2011

2. PJM, “Future Deactivation Requests”, “PJM Generator Deactivations”, 2012 (updated frequently)

3. EIA, “Retired U.S. Electric Generating Units by Operating Company, Plant and Month, 2012

4. Research by EPA and ICF staff

EPA only removed units from the NEEDS inventory if a high degree of certainty could be assigned to

future implementation of the announced action. The available retirement

- related information was

reviewed for each unit individually, and a determination was made regarding the removal of the unit from

NEEDS v.5.13 . This assessment is based on the rules below, applied in the following order:

1. All units that are listed as retired in the 2010, 2011, 2012 and February 2013 versions of EIA Electric

Power Monthly are flagged for removal from NEEDS.

2. All units with a status flag of “RE” or with a planned retirement year prior to 2016 in 2011 EIA 860 are

flagged for removal from NEEDS.

3. All units that have been cleared by a regional transmission operator (RTO) or independent system

operator (ISO) to retire before 2016 , or whose RTO/ISO clearance to retire is contingent on actions

that can be completed before 2016, are flagged for removal from NEEDS.

4. All units that have committed specifically to retire before 2016 under federal or state enforcement

actions or regulatory requirements are flagged for removal from NEEDS.

5. Finally, if a retirement announcement for a given un it can be corroborated by other available

information then the unit is flagged for removal from NEEDS.

Note that units which are required to retire pursuant to enforcement actions or state rules in 2016 or later

are retained in the NEEDS database. Such 201 6

- and

- later retirements are captured as constraints on

those units in IPM modeling, and the capacity is retired in future year projections per the terms of the

related requirements.

### 4

- 4

Table 4

- 36 lists all units that were removed from EPA’s inventory based on announcements that were

reviewed using the rules outlined above.

Table 4

- 3 Summary Population (through 2010 ) of Existing Units in NEEDS v. 5.13

Plant Type Number of Units Capacity (MW)

Biomass 161 3,140

Coal Steam 949 275,568

Combined Cycle 1659 203,181

Combustion Turbine 5419 135,353

Fossil Waste 58 372

Fuel Cell 15 3

Geothermal 201 2,304

Hydro 3749 77,946

IGCC 6 539

Landfill Gas 1315 1,437

Municipal Solid Waste 174 2,142

Non

- Fossil Waste 100 1,328

Nuclear 99 98,173

O/G Steam 529 92,909

Pumped Storage 151 22,310

Solar PV 151 390

Solar Thermal 15 548

Tires 2 46

Wind 665 39,150

US Total 15,418 956,837



### 4.2.2 Capacity

The NEEDS unit capacity values implemented in EPA Base Case v.5.13 reflect net summer dependable

capacity 18 

### , to the extent possible. Table 4

- 4 summarizes the hierarchy of primary data sources used in

compiling capacity data for NEEDS v.5.13 ; in other words, data sources are evaluated in this order, and

capacity values are taken from a particular source only if the sources listed above it do not provide

adequate data for the unit in question. 19



### Table 4

- 4 Hierarchy of Data Sources for Capacity in NEEDS v. 5.13

Sources Presented in Hierarchy

Summer Net Dependable Capacity from Comments

2010 EIA 860 Summer Capacity

2011 EIA 860 Summer Capacity

2010 EIA 860 Winter Capacity

2011 EIA 860 Winter Capacity

2010 EIA 860 Nameplate Capacity

2011 EIA 860 Nameplate Capacity

Notes :

Presented in hierarchical order that applies.

If capacity is zero, unit is not included.



### As noted earlier, NEEDS v. 5.13 includes boiler level data for steam units, and generator level data for

non

- steam units. Capacity data in EIA 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.

18 

### 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 p eak season, after accounting for station or auxiliary

services.

19 

### EIA 860 2010 was reviewed before 2011 because 2010 was the most recent data year available at the time

NEEDS development began.

### 4

- 5

The capacity

- parsing algorithm used for steam units in NEEDS v. 5.13 took into account boiler

- generator

mapping. Fossil 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 ge nerator 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 860 , steam units report the maximum steam flow from the boiler to the

generator . There is, however, no further data on the steam flow of each boiler

- generator link. Instead,

EIA 860 contains only the maximum steam flow for each boiler. Table 4

- 5 summarizes the algorithm

used for parsing capacity with data on maximum steam flow and boiler

- generator 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. 5.13

Type of Boiler

- Generator Links

For Boiler B 1 

### to B N 

### linked to Generators

G 1 

### to G N 

### One

- to

- One One

- to

- Many Many

- to

- One Many

- to

- Many

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



### Since EPA Base Case v. 5.13 uses net energy for load as demand, NEEDS v. 5.13 only includes

generators that sell the majority of their power to the electric grid ; this approach is intended to be broadly

consistent with the generating capacity used in the AEO projections used as the s ource where demand is

net energy for load. The generators that should be in NEEDS v. 5.13 by this qualification are determined

from the 2010 EIA Form 923 non

- utility source and disposition data set

.

4.2.3 Plant Location

NEEDS v. 5.13 uses state, county and model r egion data to represent the physical location of each plant.

State and County

NEEDS v. 5.13 used the state and county data in EIA 860 ( 2010, 2011 )

.

Model Region

For each unit the associated model region was derived based on NERC assessment regions reporte d

in NERC ES&D 2011 for that unit. For units with no NERC assessment region data, state and county

were used to derive associated model regions. Using these shares of each NEMS region net energy

for load that falls in each IPM region, calculate the total net energy for load for each IPM region from

the NEMS regional load in AEO 2013

.

Table 3

- 1 in Chapter 3 provides a summary of the mapping between NERC assessment regions and EPA

Base Case v

. 5.13 model regions.

4.2.4 Online Year

The EPA Base Case v. 5.13 uses online year to capture when the unit entered service. NEEDS includes

online years for all units in the database. In NEEDS v. 5.13 , online years for boilers, utility and non

- utility

generators w ere primarily derived from reported in

- service dates in EIA Form 860 (2010, 2011).

### 4

- 6

EPA Base Case v. 5.13 include s constraints to set the retirement year for generating units that are firmly

committed to retire after 2015 based on state or federal regulation s and enforcement actions . In addition,

existing nuclear units must retire when they reach age 60. (See section 0 for a discussion of the nuclear

lifetime ass umption.) EPA Base Case v. 5.13 also provide s economic retirement options to coal, oil and

gas steam, combined cycle, combustion turbines and nuclear units. This means that the model may elect

to retire these units if it is economical to do so. In IPM, a r etired plant ceases to incur FOM and VOM

costs. However, retired units do continue to service debt on any previously incurred c apital cost for

model

- installed retrofits if the model projected a retrofit on the unit prior to retirement.

4.2.5 Unit Configuration

Unit configuration refers to the physical specification of a unit’s design. Unit configuration in EPA Base

Case v. 5.13 drives model plant aggregation, modeling of pollution control options and mercury emission

modification factors. NEEDS v. 5.13 contains information on the firing and bottom type of coal steam

boilers in the database. Great effort was taken to ensure that the inventory of existing and committed

controls represented in EPA Base Case v. 5.13 was comprehensive and as up

- to

- date as possible. Th e

hierarchy of data sources used is shown in Table 4

- 6

.

Table 4

- 6 Data Sources for Unit Configuration in NEEDS v. 5.13 for EPA Base Case v. 5.13

Unit

Component Primary Data Source Secondary Data

Sourc e Tertiary

Data

Source Other

Sources Default

Firing Type 2010 EIA 860 EPA’s Emission

Tracking System

(ETS)

– 2011 -- -- --

Bottom Type 2010 EIA 860 EPA’s Emission

Tracking System

(ETS)

– 2011 -- -- Dry

SO 2 

### Pollution

Control NSR Settlement or

Comments EPA’s Emission

Tracking System

(ETS)

- 2011 2010 EIA

860 See Note No

Control

NO

x 

### Pollution

Control NSR Settlement or

Comments EPA’s Emission

Tracking System

(ETS)

- 2011 2010 EIA

860 See Note No

Control

Mercury Control NSR Settlement or

Comments 2010 EIA 860

-- -- No

Control

Particulate

Matter Control NSR Settlement or

Comments EPA’s Emission

Tracking System

(ETS)

- 2011 2010 EIA

860 -- No

Control

HCl Control NSR Settlement or

Comments -- -- See Note No

Control

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: Reports filed with the Securities and Exchange Commissio n; websites of generating unit owners and operators; GenerationHub; state public uti lity service commissions; state permitting

agencies; architecture and engineering firm announcements (eg.: Shaw, URS, Stanley, Black &Veatch, Peter Kewit, etc.);

equipment supplier announcements (Alstom, B&W, Babcock Power); Power

- Eng.com; McILVAINE Utili ty Upgrade Database; ICAC

(Institute of Clean Air Companies).



### 4.2.6 Model Plant Aggregation

While EPA Base Case using IPM is comprehensive in representing all the units contained in NEEDS, an

aggregation scheme is used to combine existing units with similar 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. 5.13 aggregation

scheme is design ed so that each model plant only represents generating units from a single state. This

### 4

- 7

design makes it possible to obtain state

- level results directly from IPM outputs. In addition, the

aggregation scheme supports modeling 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 

### emission rates and

environmental regulations among others. Units are aggregated together only if they match on all the

different categories specified for the aggregation. The 1 1 major categories used for the aggregation

scheme in EPA Base Case v. 5.13 are the following:

(1) Model Region

(2) Unit Technology Type

(3) Fuel Demand Region

(4) Applicable Environmental Regulations

(5) State

(6) Facility (ORIS) for fossil units

(7) Unit Configuration

(8) Emission Rates

(9) Heat Rates

(10) Fuel

(11) Size

Table 4

- 7 shows the number of actual units by generation technology type and the related number of

aggregated “model plants” used in the EPA Base Case v.5.13. 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.5.13. 20



### Table 4

- 7 Aggregation Profile of Model Plants as Provided at Set Up of EPA Base Case v.5.13

Existi ng and Planned/Committed Units

Plant Type Number of Units Number of IPM Model

Plants

Biomass 194 119

Coal Steam 1,003 759

Combined Cycle 1,727 702

Combustion Turbine 5,552 2,200

Fossil_Other 60 18

Fuel Cell 25 12

20 

### (1 ) The “Number of IPM Model Plants” shown for many of the “Plant Types” in the “Retrofits” block in Table 4

- 7

exceeds the “Number of IPM Model Plants” shown for “Plant Type” “Coal 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 i n Stage 2 + Technology C in Stage 3 , the

reverse timing, or multiple technologies simultaneously in Stage 1). (3) Since only a subset of coal plants is eligible

for certain retrofits, many of the “Plant Types” in the “Retrofits” block that represent only a single retrofit technology

(e.g., “Retrofit Coal with Selective Non

- catalytic Reduction (SNCR)”) have a “Number of IPM Model Plants” that is a

smaller than the “Number of IPM Model Plants” shown for “Plant Type” “Coal Steam”. (4) The total number of mo del

plants representing different types of new units often exceeds the 64 US model regions and var ies from technology to

technology for several reasons. First, some technologies have multiple vintages (i.e., different cost and/or

performance parameters de pending on which run

- year in which the unit is created) , 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 reg ions).

### 4

- 8 Existi ng and Planned/Committed Units

Plant Type Number of Units Number of IPM Model

Plants

Geothermal 219 28

Hydro 3,807 160

Import 1 1

Integrated Gas Combined Cycle 10 5

Landfill Gas 1,414 225

Non Fossil_Other a 

### 308 135

Nuclear b 

### 105 105

Oil/Gas Steam 532 347

Pumped Storage 152 24

Solar PV 370 47

Solar Thermal 27 10

Wind 824 74

Total 16,330 4,971

New Units

Plant Type Number of Units Number of IPM Model

Plants

New Advanced Coal with CCS

-- 51

New Biomass

-- 123

New Combined Cycle

-- 61

New Combined Cycle with Carbon Capture

-- 61

New Combustion Turbine

-- 61

New Fuel Cell

-- 122

New Future Technology

-- 305

New Geothermal 64

New IGCC 56

New Landfill Gas

-- 369

New Nuclear

-- 122

New Offshore Wind

-- 714

New Onshore Wind

-- 1480

New Solar PV

-- 228

New Solar Thermal 91

New SPC

- WetFGD_SCR

-- 51

Total

-- 3,959

Retrofits

Plant Type Number of Units Number of IPM Model Plants

Retrofit Coal with ACI

-- 414

Retrofit Coal with ACI + CCS

-- 164

Retrofit Coal with ACI + CCS + HRI

-- 158

Retrofit Coal with ACI + CCS + HRI + SCR

-- 78

Retrofit Coal with ACI + CCS + HRI + SCR + Scrubber

-- 138

Retrofit Coal with ACI + CCS + HRI + Scrubber

-- 152

Retrofit Coal with ACI + CCS + SCR

-- 78

Retrofit Coal with ACI + CCS + SCR + Scrubber

-- 138

Retrofit Coal with ACI + CCS + Scrubber

-- 152

Retrofit Coal with ACI + DSI

-- 389

Retrofit Coal with ACI + DSI + HRI

-- 385

### 4

- 9 Retrofits

Plant Type Number of Units Number of IPM Model Plants

Retrofit Coal with ACI + DSI + HRI + SCR

-- 525

Retrofit Coal with ACI + DSI + HRI + SCR + Scrubber

-- 354

Retrofit Coal with ACI + DSI + HRI + Scrubber

-- 362

Retrofit Coal with ACI + DSI + HRI + SNCR

-- 151

Retrofit Coal with ACI + DSI + HRI + SNCR + Scrubber

-- 74

Retrofit Coal with ACI + DSI + SCR

-- 528

Retrofit Coal with ACI + DSI + SCR + Scrubber

-- 356

Retrofit Coal with ACI + DSI + Scrubber

-- 364

Retrofit Coal with ACI + DSI + Scrubber + SNCR

-- 76

Retrofit Coal with ACI + DSI + SNCR

-- 173

Retrofit Coal with ACI + HRI

-- 406

Retrofit Coal with ACI + HRI + SCR

-- 570

Retrofit Coal with ACI + HRI + SCR + Scrubber

-- 883

Retrofit Coal with ACI + HRI + Scrubber

-- 737

Retrofit Coal with ACI + HRI + SNCR

-- 162

Retrofit Coal with ACI + HRI + SNCR + Scrubber

-- 302

Retrofit Coal with ACI + SCR

-- 576

Retrofit Coal with ACI + SCR + Scrubber

-- 886

Retrofit Coal with ACI + Scrubber

-- 742

Retrofit Coal with ACI + Scrubber + SNCR

-- 307

Retrofit Coal with ACI + SNCR

-- 166

Retrofit Coal with C2G

-- 621

Retrofit Coal with C2G + SCR

-- 621

Retrofit Coal with CCS

-- 410

Retrofit Coal with CCS + HRI

-- 352

Retrofit Coal with CCS + HRI + SCR

-- 124

Retrofit Coal with CCS + HRI + SCR + Scrubber

-- 168

Retrofit Coal with CCS + HRI + Scrubber

-- 200

Retrofit Coal with CCS + SCR

-- 122

Retrofit Coal with CCS + SCR + Scrubber

-- 168

Retrofit Coal with CCS + Scrubber

-- 200

Retrofit Coal with DSI

-- 239

Retrofit Coal with DSI + HRI

-- 473

Retrofit Coal with DSI + HRI + SCR

-- 658

Retrofit Coal with DSI + HRI + SCR + Scrubber

-- 383

Retrofit Coal with DSI + HRI + Scrubber

-- 333

Retrofit Coal with DSI + HRI + SNCR

-- 129

Retrofit Coal with DSI + SCR

-- 661

Retrofit Coal with DSI + SCR + Scrubber

-- 385

Retrofit Coal with DSI + Scrubber

-- 334

Retrofit Coal with DSI + SNCR

-- 200

Retrofit Coal with HRI

-- 646

Retrofit Coal with HRI + SCR

-- 782

Retrofit Coal with HRI + SCR + Scrubber

-- 1,347

Retrofit Coal with HRI + Scrubber

-- 1,034

Retrofit Coal with HRI + Scrubber + SNCR

-- 440

Retrofit Coal with HRI + SNCR

-- 209

Retrofit Coal with SCR

-- 399

Retrofit Coal with SCR + Scrubber

-- 1,353

Retrofit Coal with Scrubber

-- 524

### 4

- 10 Retrofits

Plant Type Number of Units Number of IPM Model Plants

Retrofit Coal with Scrubber + SNCR

-- 452

Retrofit Coal with SNCR

-- 106

Retrofit Combined Cycle with CCS

-- 424

Retrofit Oil/Gas steam with SCR

-- 227

Total

-- 25,670

Retirements

Plant Type Number of Units Number of IPM Model Plants

CC Retirement

-- 702

Coal Retirement

-- 5,372

CT Retirement

-- 2,200

IGCC Retirement

-- 5

Non

- Fossil Retirement

-- 680

Nuke Retirement

-- 105

O/G Retirement

-- 1,195

Total

-- 10,259

Grand Total (Existing and Planned/Committed + New + Retrofits + Retirements): 44,859

Notes:

a Non Fossil_Other includes units whose fuel is municipal solid waste, tires, and other non

- fossil waste.

b The 105 nuclear units include 99 currently operating units, 1 unit scheduled to retire in 2014 (Vermont Yankee), plus Watts

Bar Nuclear Plant (Unit 2), Vogtle (Units 3&4), and V C Summer (Units 2&3), which are scheduled to come online during 2015

- 2018. All except Vermont Yankee Nuclear unit are listed in Table 4

- 34



### 4.2.7 Cost and Performance Characteristics of Existing Units

In EPA Base Case v. 5.13 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. 5.13 . For existing units, only the cost of mainta ining (FOM) and running (VOM)

the unit are modeled. Embedded costs, such as carrying capital charges, are not modeled ; however,

because such historically invested capital costs are sunk costs, they are economically irrelevant for

projecting least

- cost inv estment and operational decisions for electricity supply going forward

. The

section below contains a discussion of the cost and performance assumptions for existing units used in

the EPA Base Case v. 5.13

.

Variable Operating and Maintenance Cost (VOM)

VOM represents the non

- fuel variable cost associated with producing 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. 5.13 . The values shown in this table

were obtained using a procedure developed jointly by EPA’s power sector engineering staff and ICF.

Table 4

- 8 VOM Assumptions in EPA Base Case v. 5.13

Capacity Type SO 2 Control NO

x Control Hg Control Variable O&M ( 2011$/ mills/kWh)

Biomass

-- -- -- 2.41

Coal Steam No SO 2 Control No NO

x Control No Hg Control 0.84

ACI 2.28

SCR No Hg Control 1.29

ACI 2.73

SNCR No Hg Control 1.85

ACI 3.29

### 4

- 11 Capacity Type SO 2 Control NO

x Control Hg Control Variable O&M ( 2011$/ mills/kWh)

Dry FGD No NO

x Control No Hg Control 4.29

ACI 5.73

SCR No Hg Control 4.74

ACI 6.18

SNCR No Hg Control 5.30

ACI 6.74

Wet FGD No NO

x Control No Hg Control 4.77

ACI 6.21

SCR No Hg Control 5.22

ACI 6.66

SNCR No Hg Control 5.78

ACI 7.22

DSI No NO

x Control No Hg Control 10.07

ACI 11.51

SCR No Hg Control 10.52

ACI 11.96

SNCR No Hg Control 11.08

ACI 12.52

Combined Cycle No SO 2 Control No NO

x Control

No Hg Control 2.82

- 5.96

SCR 2.95

- 6.09

SNCR 3.41

- 6.55

Combustion Turbine No SO 2 Control No NO

x Control

No Hg Control 3.35

- 22.44

SCR 3.48

- 22.57

SNCR 3.94

- 23.03

Fuel Cell

-- -- -- 0.00

Geothermal

-- -- -- 2.86

Hydro

-- -- -- 1.70

IGCC

-- -- -- 3.24

- 6.38

Landfill Gas / Municipal Solid Waste

-- -- -- 2.44

O/G Steam No SO 2 Control No NO

x Control

No Hg Control 0.76

SCR 0.89

SNCR 1.35

Wet FGD No NO

x Control 0.76

SCR 0.89

SNCR 1.35

Pumped Storage

-- -- -- 9.43

Solar PV

-- -- -- 0.00

Solar Thermal

-- -- -- 3.51

Wind

-- -- -- 2.20



### Fixed Operation and Maintenance Cost (FOM)

FOM represents the annual fixed cost of maintaining a unit. FOM costs are incurred independent of

generation levels and signify the fixed cost of operating and maintaining the unit ’s availability to provide

generation.

### 4

- 12

Table 4

- 9 summarizes the FOM assumptions used in EPA Base Case v. 5.13 . Note that FOM varies by

the age of the unit , and the total FOM cost incurred by a unit depends on its capacity size. The values

appearing in this table include the cost of maintaining any associated pollution control equipment. The

values in

### 4

- 13

Table 4

- 9 are based on FERC (Federal Energy Regulatory Commission) Form 1 data maintained by

Ventyx and ICF research

.

### 4

- 14 Table 4

- 9 FOM Assumptions Used in EPA Base Case v.5.13

Plant Type SO 2 

### Control NO

x 

### Control Hg Control Age of Unit FOM (2011$ /kW

Yr)

Biomass

-- -- -- All Years 35.28

Coal Steam No SO 2



### Control No NO

x



### Control No Hg

Control 0 to 30 Years 36.49

30 to 40 Years 38.61

40 to 50 Years 49.13

Greater than 50

Years 50.83

ACI 0 to 30 Years 36.58

30 to 40 Years 38.70

40 to 50 Years 49.22

Greater than 50

Years 50.92

SCR No Hg

Control 0 to 30 Years 37.13

30 to 40 Years 39.26

40 to 50 Years 49.78

Greater than 50

Years 51.48

ACI 0 to 30 Years 37.22

30 to 40 Years 39.35

40 to 50 Years 49.86

Greater than 50

Years 51.57

SNCR No Hg

Control 0 to 30 Years 37.11

30 to 40 Years 39.23

40 to 50 Years 49.75

Greater than 50

Years 51.45

ACI 0 to 30 Years 37.20

30 to 40 Years 39.32

40 to 50 Years 49.84

Greater than 50

Years 51.54

Dry FGD No NO

x



### Control No Hg

Control 0 to 30 Years 46.51

30 to 40 Years 48.63

40 to 50 Years 59.15

Greater than 50

Years 60.85

ACI 0 to 30 Years 46.60

30 to 40 Years 48.72

40 to 50 Years 59.24

Greater than 50

Years 60.94

SCR No Hg

Control 0 to 30 Years 47.15

30 to 40 Years 49.28

40 to 50 Years 59.80

Greater than 50

Years 61.50

ACI 0 to 30 Years 47.24

30 to 40 Years 49.37

### 4

- 15

Plant Type SO 2 

### Control NO

x 

### Control Hg Control Age of Unit FOM (2011$ /kW

Yr)

40 to 50 Years 59.88

Greater than 50

Years 61.59

SNCR No Hg

Control 0 to 30 Years 47.13

30 to 40 Years 49.25

40 to 50 Years 59.77

Greater than 50

Years 61.47

ACI 0 to 30 Years 47.22

30 to 40 Years 49.34

40 to 50 Years 59.86

Greater than 50

Years 61.56

Wet FGD No NO

x



### Control No Hg

Control 0 to 30 Years 45.90

30 to 40 Years 48.02

40 to 50 Years 58.54

Greater than 50

Years 60.24

ACI 0 to 30 Years 45.99

30 to 40 Years 48.11

40 to 50 Years 58.63

Greater than 50

Years 60.33

SCR No Hg

Control 0 to 30 Years 46.54

30 to 40 Years 48.67

40 to 50 Years 59.19

Greater than 50

Years 60.89

ACI 0 to 30 Years 46.63

30 to 40 Years 48.76

40 to 50 Years 59.27

Greater than 50

Years 60.98

SNCR No Hg

Control 0 to 30 Years 46.52

30 to 40 Years 48.64

40 to 50 Years 59.16

Greater than 50

Years 60.86

ACI 0 to 30 Years 46.61

30 to 40 Years 48.73

40 to 50 Years 59.25

Greater than 50

Years 60.95

DSI No NO

x



### Control No Hg

Control 0 to 30 Years 37.99

30 to 40 Years 40.11

40 to 50 Years 50.63

Greater than 50

Years 52.33

ACI 0 to 30 Years 38.08

30 to 40 Years 40.20

### 4

- 16

Plant Type SO 2 

### Control NO

x 

### Control Hg Control Age of Unit FOM (2011$ /kW

Yr)

40 to 50 Years 50.72

Greater than 50

Years 52.42

SCR No Hg

Control 0 to 30 Years 38.63

30 to 40 Years 40.76

40 to 50 Years 51.28

Greater than 50

Years 52.98

ACI 0 to 30 Years 38.72

30 to 40 Years 40.85

40 to 50 Years 51.36

Greater than 50

Years 53.07

SNCR No Hg

Control 0 to 30 Years 38.61

30 to 40 Years 40.73

40 to 50 Years 51.25

Greater than 50

Years 52.95

ACI 0 to 30 Years 38.70

30 to 40 Years 40.82

40 to 50 Years 51.34

Greater than 50

Years 53.04

Combined Cycle No SO 2



### Control No NO

x



### Control No Hg

Control - 24.29

SCR No Hg

Control - 25.56

SNCR No Hg

Control - 24.42

Combustion

Turbine No SO 2



### Control No NO

x



### Control No Hg

Control - 16.47

SCR No Hg

Control - 18.41

SNCR No Hg

Control - 16.91

Fuel Cell

-- -- -- All Years 370.36

Geothermal

-- -- -- All Years 40.07

Hydro

-- -- -- All Years 19.28

IGCC No SO 2



### Control No NO

x



### Control -- All Years 36.89

Landfill Gas /

Municipal Solid

Waste -- -- -- All Years 46.10

O/G Steam No SO 2



### Control No NO

x



### Control No Hg

Control 0 to 30 Years 20.31

30 to 40 Years 21.22

40 to 50 Years 23.68

Greater than 50

Years 23.68

SCR No Hg

Control 0 to 30 Years 21.34

30 to 40 Years 22.25

40 to 50 Years 24.71

### 4

- 17

Plant Type SO 2 

### Control NO

x 

### Control Hg Control Age of Unit FOM (2011$ /kW

Yr)

Greater than 50

Years 24.71

SNCR No Hg

Control 0 to 30 Years 20.47

30 to 40 Years 21.39

40 to 50 Years 23.84

Greater than 50

Years 23.84

Wet FGD No NO

x



### Control No Hg

Control 0 to 30 Years 20.31

30 to 40 Years 21.22

40 to 50 Years 23.68

Greater than 50

Years 23.68

SCR No Hg

Control 0 to 30 Years 21.34

30 to 40 Years 22.25

40 to 50 Years 24.71

Greater than 50

Years 24.71

SNCR No Hg

Control 0 to 30 Years 20.47

30 to 40 Years 21.39

40 to 50 Years 23.84

Greater than 50

Years 23.84

Pumped Storage

-- -- -- All Years 7.37

Solar PV

-- -- -- All Years 20.53

Solar Thermal

-- -- -- All Years 31.94

Wind

-- -- -- All Years 19.54



### Heat Rates

Heat Rates describe the efficiency of the unit expressed as BTUs per kWh. The treatment of heat rates

in EPA Base Case v. 5.13 is discussed in Section 3.8

.

Lifet imes

Unit lifetime assumptions in EPA Base Case v. 5.13 are detailed in Sections 0 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 emissions (mercury outlet) to the mercury content of the fuel

(mercury inlet). Section 5.4.2 contains a detailed discuss ion of the EMF assumptions in EPA Base Case

v. 5.13

.

### 4

- 18

4.2.8 Life Extension Costs for E xisting U nits

T he modeling time horizon in EPA Base Case 5.13 extends to 2050 and covers a period of almost 40

years. This time horizon requires consideration in EPA Base Case v

. 5.13 of investments

, beyond routine

maintenance, necessary to extend the life of existing units

. 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 reported annual capital expenditures made by older units

.

Table 4

- 10 Life Extension Cost Assumptions Used in EPA Base Case v. 5.13

Plant Type Lifespan without Life

Extension

Expenditures Life Extension Cost as

Proportion of New Unit

Capital Cost (%) Capital Cost of

New Unit

(2011$/kW) Life Extension

Cost (2011$/kW)

Biomass

-

Fluidized Bed 40 6.6% 4,429 291

Coal Steam 40 7.0% 3,160 221

Combined Cycle 30 9.3% 1,047 98

Combustion

Turbine & IC

Engine 30

4.2% 691 29

Oil/Gas Steam 40 3.4% 2,923 98

IGCC 40 7.4% 3,254 241

Nuclear 40 9.0% 6,168 555

Landfill Gas 20 9.1% 9,023 823

Notes:

Life extension expenditures double the lifespan of the unit.



## 4.3 Planned

- Committed Units

EPA Base Case v.5.13 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 201 6.

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.5.13 using the

information sources listed in Table 4

- 1

. Table 4

- 11 summari zes the extent of inventory of planned

committed units in EPA Base Case v.5.13 indicating its generating capacity by unit types.

Table 4

- 11 Summary of Planned

- Committed Units in NEEDS v. 5.13 for EPA Base Case v. 5.13

Type Capacity (MW) Year Range Described

Renewables/Non

- conventional

Biomass 901 2011

- 2015

Fuel Cell 33 2011

- 2015

Geothermal 332 2011

- 2016

Hydro 689 2011

- 2015

Landfill Gas 576 2011

- 2015

Municipal Solid Waste 119 2011

- 2015

Non

- Fossil Waste 254 2011

- 2015

Pumped Storage 40 2015

- 2015

Solar PV 5,262 2011

- 2015

Solar Thermal 1,777 2012

- 2015

Wind 18,951 2011

- 2015

Subtotal 28,933

### 4

- 19 Type Capacity (MW) Year Range Described

Fossil/Conventional

Coal Steam 9

, 4 98 2011

- 2015

Combined Cycle 18,597 2011

- 2015

Combustion Turbine 8,899 2011

- 2015

Fossil Waste 40 2015

- 2015

IGCC 1,168 2012

- 2014

Nuclear 5,522 2015

- 2018

O/G Steam 4 2015

- 2015

Subtotal 44,528

Grand Total 7 2

, 6 61



### Due to data confidentiality restrictions, NEEDS v. 5.13 does not list the planned

- committed units on a unit

by

- unit basis. Rather, all units having similar technologies and located within the same model region are

aggregated together as one record. Table 4

- 12 gives a breakdo wn of planned

- committed units by IPM

region, unit type, number of units, and capacity in cluded in EPA Base Case v.5.13.

Table 4

- 12 Planned

- Committed Units by Model Region in NEEDS v. 5.13 for EPA Base Case v. 5 .13

IPM Region Plant Type Capacity (MW)

ERC_REST Biomass 144

Coal Steam 2,869

Combined Cycle 620

Combustion Turbine 166

Hydro 0.19

Landfill Gas 15

Non

- Fossil Waste 4

Solar PV 78

Wind 311

ERC_WEST Wind 585

FRCC Biomass 110

Combined Cycle 2,388

Combustion Turbine 465

Landfill Gas 10

Non

- Fossil Waste 15

Solar PV 74

MAP_WAUE Combustion Turbine 60

Wind 102

MIS_IA Combustion Turbine 5

Wind 460

MIS_IL Biomass 15

Coal Steam 1,600

Combustion Turbine 20

Wind 415

MIS_INKY Hydro 162

IGCC 586

Landfill Gas 6

Non

- Fossil Waste 4

Solar PV 17

MIS_LMI Landfill Gas 26

Non

- Fossil Waste 4

Solar PV 1

Wind 391

### 4

- 20 IPM Region Plant Type Capacity (MW)

MIS_MAPP Coal Steam 99

Geothermal 23

Non

- Fossil Waste 6

Solar PV 0

Wind 431

MIS_MIDA Wind 956

MIS_MNWI Combined Cycle 300

Combustion Turbine 5

Hydro 10

Landfill Gas 2

Non

- Fossil Waste 6

Wind 807

MIS_MO Combustion Turbine 6

Landfill Gas 15

Non

- Fossil Waste 3

MIS_WUMS Biomass 57

Coal Steam 615

Combustion Turbine 58

Landfill Gas 23

Wind 162

NENG_CT Combined Cycle 628

Combustion Turbine 763

NENG_ME Combined Cycle 25

Hydro 2

Landfill Gas 3

Wind 131

NENGREST Biomass 77

Combustion Turbine 40

Hydro 2

Landfill Gas 42

Non

- Fossil Waste 1

Solar PV 54

Wind 288

NY_Z_A&B Biomass 15

Landfill Gas 5

Wind 141

NY_Z_C&E Combustion Turbine 2

Landfill Gas 13

Wind 129

NY_Z_D Wind 21

NY_Z_F Hydro 2

Non

- Fossil Waste 20

NY_Z_G

- I Landfill Gas 2

NY_Z_J Combined Cycle 540

Combustion Turbine 466

Fuel Cell 5

NY_Z_K Combustion Turbine 0.23

Solar PV 75

PJM_AP Coal Steam 700

Combined Cycle 570

Hydro 0.01

Landfill Gas 10

Solar PV 20

Wind 253

### 4

- 21 IPM Region Plant Type Capacity (MW)

PJM_ATSI Combined Cycle 666

Fossil Waste 23

Landfill Gas 21

Non

- Fossil Waste 135

Solar PV 19

Wind 5

PJM_COMD Biomass 55

Landfill Gas 7

Solar PV 23

Wind 996

PJM_Dom Biomass 50

Combined Cycle 589

Combustion Turbine 52

Landfill Gas 42

Solar PV 31

PJM_EMAC Biomass 30

Combined Cycle 545

Combustion Turbine 990

Hydro 137

Landfill Gas 32

Non

- Fossil Waste 1

Solar PV 431

Wind 5

PJM_PENE Biomass 1

Combustion Turbine 2

Landfill Gas 7

O/G Steam 4

Wind 511

PJM_SMAC Biomass 4

Combustion Turbine 5

Landfill Gas 5

Solar PV 7

PJM_West Coal Steam 585

Combined Cycle 539

Hydro 47

Landfill Gas 14

Non

- Fossil Waste 3

Solar PV 3

Wind 806

PJM_WMAC Biomass 30

Combined Cycle 100

Combustion Turbine 10

Landfill Gas 16

Solar PV 27

Wind 69

S_C_KY Coal Steam 732

Hydro 105

Landfill Gas 2

S_C_TVA Biomass 13

Combined Cycle 878

Hydro 66

IGCC 582

Landfill Gas 8

Nuclear 1,122

Solar PV 23

### 4

- 22 IPM Region Plant Type Capacity (MW)

S_D_AMSO Municipal Solid Waste 115

S_D_N_AR Combined Cycle 495

S_D_WOTA Hydro 24

S_SOU Biomass 122

Combined Cycle 2,552

Landfill Gas 18

Non

- Fossil Waste 2

Nuclear 2,200

Solar PV 3

S_VACA Biomass 58

Coal Steam 800

Combined Cycle 4,203

Combustion Turbine 727

Hydro 33

Landfill Gas 73

Municipal Solid Waste 2

Non

- Fossil Waste 2

Nuclear 2,200

Solar PV 124

SPP_N Coal Steam 279

Hydro 5

Landfill Gas 3

Municipal Solid Waste 2

Solar PV 0.09

Wind 1,274

SPP_NEBR Coal Steam 220

Wind 244

SPP_SE Combustion Turbine 33

Non

- Fossil Waste 21

SPP_SPS Combustion Turbine 507

Solar PV 55

Wind 458

SPP_WEST Coal Steam 609

Combustion Turbine 20

Hydro 20

Wind 1,673

WEC_CALN Biomass 21

Combined Cycle 1,240

Combustion Turbine 1,252

Fuel Cell 3

Hydro 8

Landfill Gas 3

Non

- Fossil Waste 9

Solar PV 1,057

Solar Thermal 30

Wind 468

WEC_LADW Combined Cycle 560

Combustion Turbine 1,133

Fuel Cell 1

Solar PV 178

WEC_SDGE Biomass 2

Combustion Turbine 38

Fuel Cell 6

Pumped Storage 40

Solar PV 35

### 4

- 23 IPM Region Plant Type Capacity (MW)

WECC_AZ Combustion Turbine 516

Landfill Gas 6

Non

- Fossil Waste 0.15

Solar PV 1,153

Solar Thermal 250

Wind 109

WECC_CO Combined Cycle 200

Combustion Turbine 200

Hydro 8

Landfill Gas 8

Solar PV 89

Solar Thermal 1

Wind 546

WECC_ID Combined Cycle 299

Hydro 4

Non

- Fossil Waste 10

Solar PV 10

Wind 593

WECC_IID Combined Cycle 94

Geothermal 92

Solar PV 249

WECC_MT Biomass 12

Combustion Turbine 172

Hydro 13

Landfill Gas 2

Wind 221

WECC_NM Combined Cycle 142

Fossil Waste 17

Geothermal 10

Hydro 3

Solar PV 75

Solar Thermal 1

Wind 50

WECC_NNV Combustion Turbine 1

Geothermal 138

Landfill Gas 6

Solar PV 3

Solar Thermal 110

Wind 150

WECC_PNW Biomass 86

Geothermal 30

Hydro 39

Landfill Gas 83

Non

- Fossil Waste 10

Solar PV 10

Wind 3,057

WECC_SCE Combustion Turbine 1,158

Fuel Cell 5

Landfill Gas 37

Solar PV 1,185

Solar Thermal 1,385

Wind 2,027

WECC_SF Fuel Cell 13

### 4

- 24 IPM Region Plant Type Capacity (MW)

WECC_SNV Combined Cycle 424

Landfill Gas 11

Solar PV 155

WECC_UT Combustion Turbine 28

Geothermal 40

Wind 104

WECC_WY Coal Steam 390

Wind 2

Note:

Any unit in NEEDS v.5.13 that has an online year of 2011 or later was considered a Planned and Committed Unit



### 4.3.2 Capacity

The capacity of planned

- committed units in NEEDS v.5.13 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.5.13 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.

4.3.4 Online and Retirement Year

As noted above, planned

- committed units included in NEEDS v.5.13 are only those units which are likely

to come on

- line before 201 6. All planned

- committed units were given a default online year of 2015 since

2016 is the first analysis year in EPA Base Case v.5.13.

4.3.5 Unit Configuration

, Cost and Performance

All planned

- commi tted units in NEEDS v.5.13 assume the cost, performance, and unit configuration

characteristics of potential units that are available in 2015. A detailed description of potential unit

assumptions is provided below in Section 4.4

.



## 4.4 Potential Units

The EPA Base Case v.5.13 includes options for developing a variety of potential units that may be “built”

at a future date in response to electricity demand and the constra ints represented in the model. Defined

by region, technology, and the year 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 th e type and number of potential units

provided in EPA Base Case v. 5.13 . The following sections describe the cost and performance

assumptions for the potential units represented in the EPA Base Case v. 5.13

.

### 4

- 25

4.4.1 Methodology Used to Derive the Cost and Performan ce Characteristics of Conventional

Potential Units

The cost and performance characteristics of conventional potential units in EPA Base Case v.5.13 are

derived primarily from assumptions used in the Annual Energy Outlook (AEO) 2013 published by the U.S.

D epartment of Energy’s Energy Information Administration. The capital costs for IGCC and IGCC+CCS

technologies in Table 4

- 13 are derived from a recently updated study 21 

### by DOE’s National Energy

Technology Laboratory (NETL).

4.4.2 Cost and Performance for Potential Conventional Units

EPA’s assumed cost and performance characteristics for potential conventional units are shown in Table

4

- 13 . The cost and performance assumptions are based on the size (i.e., net electrical 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 include s 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 refer red 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, lic enses, 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 n IPM 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 .5.13 are discussed in

Chapter 8 under financial assumptions.

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 non

- fuel costs incurred in run ning an electric generating unit. It is proportional

to the electrical energy produced and is expressed in units of $ per MWh.

In addition to the three components of cost, Table 4

- 13 indicates the first run 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, whereas new er 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

(kWh). Availability indicates the percentage of time that a generating u nit 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

- 12

.

4.4.3 Short

- Term Ca pital Cost Adder

In addition to the capital costs shown in Table 4

- 13 and

21 

### http://www.netl.doe.gov/energy

- analyses/pubs/BaselineCostUpdate.pdf

.

### 4

- 26

Table 4

- 16 EPA Base Case v. 5.13 includes a short

- term capital cost adder that kicks in if the new

capacity deployed in a specific model run year exceeds certain upper bounds. This adder 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 that time market adjustments in anticipation of such longer

term dep loyment patterns will have eliminated the short term scarcity experienced in earlier years.

The column labeled “Step 1” in Table 4

- 14 indicates the total amount of cap acity 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 2016 for coal steam potential units is 6

, 913 MW. If no more than this total new coal steam capacity is

built in 2016 , only the capital cost shown in Table 4

- 13 is incurred. Between 6

, 913 and 11,522 MW (the

sum of the Step 1 and Step 2 upper bounds, i.e., 6

, 913 MW + 4

, 609 MW = 11,522 MW), the Step 2 cost

adder of $916/kW 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 bo und of 6,913 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 1 1

, 522 MW, then the Step 3 capacity adder of $2, 370 / k W is incurred.

The short

- term capital cost adders shown in Table 4

- 14 were derived from AEO assumptions.

4.4.4 Regional Cost Adjustment

The capital costs reported in Table 4

- 14 are generic. Before EPA implements these capital cost values

values they are converted to region

- specific costs. T his is done through the application of regional cost

cost adjustment factors which capture regional differences in labor, material, and construction costs and

and ambient conditions . The regional adjustment factors used in EPA Base Case v. 5.13 are shown in

in Table 4

- 15 . They were developed from AEO 2013 by multiplying the regional and ambient multipliers

and are applied to both conventional technologies shown in Table 4

- 13 and renewable and non

conventional technologies shown in

### 4

- 27

Table 4

- 16 below

.

### 4

- 28 Table 4

- 13 Performance and Unit Cost Assumptions for Potential (New) Capacity from Conventional Technologies

in EPA Base Case v.5.13

Advanced

Combined

Cycle Advanced

Combustion

Turbine Nuclear Integrated

Gasification

Combined Cycle Integrated Gasification

Combined Cycle with Carbon

Sequestration Supercritical

Pulverized Coal

Size (MW) 400 210 2236 600 520 1300

First Run Year Available 2016 2016 2020 2018 2020 2018

Lead Time (Years) 3 2 6 4 4 4

Availability 87% 92% 90% 85% 85% 85%

Vintage #1 (2016

- 2054)

Heat Rate

(Btu/kWh) 6,430 9,750 10,452 8,700 10,700 8,800

Capital

(2011$/kW) 1,006 664 5,429 2,969 4,086 2,883

Fixed O&M (2011$/kW/yr) 15.1 6.9 91.7 62.3 70.6 30.6

Variable O&M (2011$/MWh) 3.2 10.2 2.1 7.2 8.2 4.4

Notes:

a Capital cost represents overnight capital cost.



### Table 4

- 14 Short

- Term Capital Cost Adders for New Power Plants in EPA Base Case v.5.13 (2011$)

ID # Plant Type 2016 2018 2020 2025 2030

Step 1 Step 2 Step 3 Step 1 Step 2 Step 3 Step 1 Step 2 Step 3 Step 1 Step 2 Step 3 Step 1 Step 2 Step 3

1 Biomass Upper Bo und (MW) 600 400

- 1,200 800

- 1,200 800

- 3,000 2 ,000

- 3,000 2 ,000

-

Adder ($/kW)

- 1,285 3,322

- 1,285 3,322

- 1,285 3,322

- 1,285 3,322

- 1,285 3,322

2 Coal Steam Upper Bound (MW) 6,913 4,609

- 13,826 9,218

- 13,826 9,218

- 34,566 2 3,044

- 34,566 2 3,044

-

Adder ($/kW)

- 916 2 ,370

- 916 2 ,370

- 916 2 ,370

- 916 2 ,370

- 916 2 ,370

3 Combined Cycle Upper Bound (MW) 46,157 30,771

- 92,314 61,542

- 92,314 61,542

- 2 30,784 153,856

- 2 30,784 153,856

-

Adder ($/kW)

- 313 809

- 313 809

- 313 809

- 313 809

- 313 809

4 Combustion Turbine Upper Bound (MW) 2 3,668 15,778

- 47,335 31,557

- 47,335 31,557

- 118,338 78,892

- 118,338 78,892

-

Adder ($/kW)

- 2 00 518

- 2 00 518

- 2 00 518

- 2 00 518

- 2 00 518

5 Fuel Cell Upper Bound (MW) 600 400

- 1,200 800

- 1,200 800

- 3,000 2 ,000

- 3,000 2 ,000

-

Adder ($/kW)

- 2 ,215 5,727

- 2 ,215 5,727

- 2 ,215 5,727

- 2 ,215 5,727

- 2 ,215 5,727

6 Geothermal Upper Bound (MW) 2 05 137

- 410 2 74

- 410 2 74

- 1,026 684

- 1,026 684

-

Adder ($/kW)

- 2 ,268 5,865

- 2 ,268 5,865

- 2 ,268 5,865

- 2 ,268 5,865

- 2 ,268 5,865

### 4

- 29 ID # Plant Type 2016 2018 2020 2025 2030

Step 1 Step 2 Step 3 Step 1 Step 2 Step 3 Step 1 Step 2 Step 3 Step 1 Step 2 Step 3 Step 1 Step 2 Step 3

7 IGCC and

Advanced Coal with

Carbon Capture Upper Bound (MW) 2 ,400 1,600

- 4,800 3,200

- 4,800 3,200

- 12,000 8,000

- 12,000 8,000

-

Adder ($/kW)

- 944 2 ,441

- 944 2 ,441

- 944 2 ,441

- 944 2 ,441

- 944 2 ,441

8 Landfill Gas Upper Bound (MW) 600 400

- 1,200 800

- 1,200 800

- 3,000 2 ,000

- 3,000 2 ,000

-

Adder ($/kW)

- 2 ,669 6,904

- 2 ,669 6,904

- 2 ,669 6,904

- 2 ,669 6,904

- 2 ,669 6,904

9 Nuclear Upper Bound (MW) 11,244 7,496

- 2 2,488 14,992

- 2 2,488 14,992

- 56,220 37,480

- 56,220 37,480

-

Adder ($/kW)

- 1,789 4,626

- 1,789 4,626

- 1,789 4,626

- 1,789 4,626

- 1,789 4,626

10 Solar Thermal Upper Bound (MW) 90 60

- 180 120

- 180 120

- 450 300

- 450 300

-

Adder ($/kW)

- 1,439 3,722

- 1,439 3,722

- 1,439 3,722

- 1,439 3,722

- 1,439 3,722

11 Solar PV Upper Bound (MW) 2 86 190

- 571 381

- 571 381

- 1,428 952

- 1,428 952

-

Adder ($/kW)

- 1,025 2 ,651

- 1,025 2 ,651

- 1,025 2 ,651

- 1,025 2 ,651

- 1,025 2 ,651

12 Onshore Wind Upper Bound (MW) 11,618 7,746

- 2 3,237 15,491

- 2 3,237 15,491

- 58,092 38,728

- 58,092 38,728

-

Adder ($/kW)

- 694 1,794

- 694 1,794

- 694 1,794

- 694 1,794

- 694 1,794

13 Offshore Wind Upper Bound (MW) 600 400

- 1,200 800

- 1,200 800

- 3,000 2 ,000

- 3,000 2 ,000

-

Adder ($/kW)

- 2 ,256 5,833

- 2 ,256 5,833

- 2 ,256 5,833

- 2 ,256 5,833

- 2 ,256 5,833



### Table 4

- 15 Regional Cost Adjustment Factors for Conventional and Renewable Generating Technologies in EPA Base Case v. 5.13

Model Region Pulverized

Coal Integrated

Gasification

Combined

Cycle Integrated

Gasifi cation

Combined

Cycle with

Carbon

Capture Advanced

Combustion

Turbine Advanced

Combined

Cycle Fuel

Cell Advanced

Nuclear Biomass Geothermal Landfill

Gas Onshore

Wind Offshore

Wind Solar

Thermal Solar

Photovoltaic

ERC_REST 0.905 0.943 0.959 0.985 0.954 0.963 0.960 0.925 1.000 0.927 0.952 0.918 0.858 0.871

ERC_WEST 0.905 0.9 43 0.959 0.985 0.954 0.963 0.960 0.925 1.000 0.927 0.952 0.918 0.858 0.871

FRCC 0.921 0.961 0.981 0.977 0.959 0.972 0.966 0.940 1.000 0.944 0.963 1.000 0.891 0.901

MIS_MAPP 0.952 0.956 0.956 0.994 0.971 0.981 0.980 0.961 1.000 0.964 1.032 1.008 0.953 0.954

MAP_WAUE 0.952 0. 956 0.956 0.994 0.971 0.981 0.980 0.961 1.000 0.964 1.032 1.008 0.953 0.954

MIS_IL 1.072 1.067 1.052 1.057 1.059 1.017 1.028 1.029 1.000 1.030 1.036 1.000 1.057 1.051

MIS_INKY 1.049 1.056 1.042 1.078 1.061 1.001 1.029 1.017 1.000 1.000 1.019 1.011 1.000 0.999

MIS_IA 0.952 0.95 6 0.956 0.994 0.971 0.981 0.980 0.961 1.000 0.964 1.032 1.008 0.953 0.954

MIS_MIDA 0.952 0.956 0.956 0.994 0.971 0.981 0.980 0.961 1.000 0.964 1.032 1.008 0.953 0.954

### 4

- 30 Model Region Pulverized

Coal Integrated

Gasification

Combined

Cycle Integrated

Gasifi cation

Combined

Cycle with

Carbon

Capture Advanced

Combustion

Turbine Advanced

Combined

Cycle Fuel

Cell Advanced

Nuclear Biomass Geothermal Landfill

Gas Onshore

Wind Offshore

Wind Solar

Thermal Solar

Photovoltaic

MIS_LMI 0.980 0.968 0.959 0.992 0.978 0.994 0.992 0.982 1.000 0.985 0.998 0.981 0.965 0.968

MIS_MO 1.072 1.067 1.052 1.057 1.059 1.017 1.028 1.029 1.000 1.030 1.036 1.000 1.057 1.051

MIS_WUMS 1.049 1.042 1.021 1.057 1.040 1.001 1.029 1.017 1.000 1.000 1.019 1.011 1.000 0.999

MIS_MNWI 0.952 0.956 0.956 0.994 0.971 0.981 0.980 0.961 1.000 0.964 1.032 1.008 0.953 0.954

NENG_CT 1.096 1.056 1.008 1.147 1.105 1.009 1.054 1.038 1.000 1.016 1.058 1.031 1.035 1.028

NENGREST 1.096 1.056 1.008 1.147 1.105 1.009 1.054 1.038 1.000 1.016 1.058 1.031 1.035 1.028

NENG_ME 1.096 1.056 1.008 1.147 1.105 1.009 1.054 1.038 1.000 1.016 1.058 1.031 1.035 1.028

NY_Z_C&E 1.107 1.071 1.012 1.180 1.119 0.996 1.067 1.034 1.000 0.996 1.008 0.988 0.976 0.977

NY_Z_F 1.107 1.071 1.012 1.180 1.119 0.996 1.067 1.034 1.000 0.996 1.008 0.988 0.976 0.977

NY_Z_G

- I 1.107 1.071 1.012 1.180 1.119 0.996 1.067 1.034 1.000 0.996 1.008 0.988 0.976 0.977

NY_Z_J 1.326 1.267 1.243 1.651 1.631 1.141 1.136 1.246 1.000 1.263 1.246 1.294 1.501 1.449

NY_Z_K 1.326 1.267 1.243 1.651 1.631 1.141 1.136 1.246 1.000 1.263 1.246 1.294 1.501 1.449

NY_Z_A&B 1.107 1.071 1.012 1.180 1.119 0.996 1.067 1.034 1.000 0.996 1.008 0.988 0.976 0.977

NY_Z_D 1.107 1.071 1.012 1.180 1.119 0.996 1.067 1.034 1.000 0.996 1.008 0.988 0.976 0.977

PJM_WMAC 1.152 1.123 1.068 1.232 1.184 1.018 1.085 1.070 1.000 1.034 1.048 1.026 1.055 1.048

PJM_EMAC 1.152 1.123 1.068 1.232 1.184 1.018 1.085 1.070 1.000 1.034 1.048 1.026 1.055 1.048

PJM_SMAC 1.152 1.123 1.068 1.232 1.184 1.018 1.085 1.070 1.000 1.034 1.048 1.026 1.055 1.048

PJM_West 1.049 1.042 1.021 1.057 1.040 1.001 1.029 1.017 1.000 1.000 1.019 1.011 1.000 0.999

PJM_AP 1.049 1.042 1.021 1.057 1.040 1.001 1.029 1.017 1.000 1.000 1.019 1.011 1.000 0.999

PJM_COMD 1.049 1.042 1.021 1.057 1.040 1.001 1.029 1.017 1.000 1.000 1.019 1.011 1.000 0.999

PJM_ATSI 1.049 1.042 1.021 1.057 1.040 1.001 1.029 1.017 1.000 1.000 1.019 1.011 1.000 0.999

PJM_Dom 0.885 0.925 0.932 0.959 0.918 0.956 0.954 0.906 1.000 0.911 0.947 0.921 0.824 0.841

PJM_PENE 1.152 1.123 1.068 1.232 1.184 1.018 1.085 1.070 1.000 1.034 1.048 1.026 1.055 1.048

S_VACA 0.885 0.925 0.932 0.959 0.918 0.956 0.954 0.906 1.000 0.911 0.947 0.921 0.824 0.841

S_C_KY 0.927 0.948 0.954 0.970 0.944 0.970 0.968 0.938 1.000 0.940 0.963 1.000 0.883 0.894

S_C_TVA 0.927 0.948 0.954 0.970 0.944 0.970 0.968 0.938 1.000 0.940 0.963 1.000 0.883 0.894

S_SOU 0.919 0.957 0.973 1.011 0.979 0.969 0.964 0.933 1.000 0.937 0.961 0.930 0.877 0.888

S_D_WOTA 0.917 0.950 0.962 0.993 0.964 0.969 0.965 0.933 1.000 0.941 0.962 1.000 0.879 0.890

### 4

- 31 Model Region Pulverized

Coal Integrated

Gasification

Combined

Cycle Integrated

Gasifi cation

Combined

Cycle with

Carbon

Capture Advanced

Combustion

Turbine Advanced

Combined

Cycle Fuel

Cell Advanced

Nuclear Biomass Geothermal Landfill

Gas Onshore

Wind Offshore

Wind Solar

Thermal Solar

Photovoltaic

S_D_AMSO 0.917 0.950 0.962 0.993 0.964 0.969 0.965 0.933 1.000 0.941 0.962 1.000 0.879 0.890

S_D_N_AR 0.917 0.950 0.962 0.993 0.964 0.969 0.965 0.933 1.000 0.941 0.962 1.000 0.879 0.890

S_D_REST 0.917 0.950 0.962 0.993 0.964 0.969 0.965 0.933 1.000 0.941 0.962 1.000 0.879 0.890

SPP_NEBR 0.952 0.956 0.956 0.994 0.971 0.981 0.980 0.961 1.000 0.964 1.032 1.008 0.953 0.954

SPP_N 1.072 1.082 1.073 1.078 1.080 1.017 1.028 1.029 1.000 1.030 1.036 1.000 1.057 1.051

SPP_SE 0.980 1.002 1.007 1.032 1.016 0.991 0.992 0.979 1.000 0.982 1.018 1.000 0.974 0.974

SPP_WEST 0.980 1.007 1.014 1.039 1.024 0.991 0.992 0.979 1.000 0.982 1.018 1.000 0.974 0.974

SPP_SPS 0.980 1.002 1.007 1.032 1.016 0.991 0.992 0.979 1.000 0.982 1.018 1.000 0.974 0.974

WECC_ID 1.015 1.044 1.045 1.079 1.059 0.994 1.007 1.004 1.000 0.984 1.047 1.017 0.990 0.987

WECC_NNV 1.015 1.044 1.045 1.079 1.059 0.994 1.007 1.004 1.000 0.984 1.047 1.017 0.990 0.987

WECC_UT 1.015 1.044 1.045 1.079 1.059 0.994 1.007 1.004 1.000 0.984 1.047 1.017 0.990 0.987

WECC_SF 1.193 1.186 1.139 1.311 1.267 1.030 1.093 1.083 1.000 1.057 1.119 1.049 1.129 1.111

WEC_CALN 1.193 1.186 1.139 1.311 1.267 1.030 1.093 1.083 1.000 1.057 1.119 1.049 1.129 1.111

WECC_IID 1.000 1.092 1.135 1.188 1.166 0.995 1.001 1.000 1.000 0.988 1.035 1.000 0.993 0.991

WEC_LADW 1.193 1.186 1.139 1.311 1.267 1.030 1.093 1.083 1.000 1.057 1.119 1.049 1.129 1.111

WEC_SDGE 1.193 1.186 1.139 1.311 1.267 1.030 1.093 1.083 1.000 1.057 1.119 1.049 1.129 1.111

WECC_SCE 1.193 1.186 1.139 1.311 1.267 1.030 1.093 1.083 1.000 1.057 1.119 1.049 1.129 1.111

WECC_MT 1.015 1.044 1.045 1.079 1.059 0.994 1.007 1.004 1.000 0.984 1.047 1.017 0.990 0.987

WECC_PNW 1.015 1.044 1.045 1.079 1.059 0.994 1.007 1.004 1.000 0.984 1.047 1.017 0.990 0.987

WECC_CO 0.989 1.103 1.142 1.239 1.185 0.976 1.005 0.973 1.000 0.954 1.033 1.000 0.929 0.931

WECC_WY 1.015 1.126 1.174 1.239 1.190 0.994 1.007 1.004 1.000 0.984 1.047 1.017 0.990 0.987

WECC_AZ 1.000 1.092 1.135 1.188 1.166 0.995 1.001 1.000 1.000 0.988 1.035 1.000 0.993 0.991

WECC_NM 1.000 1.092 1.135 1.188 1.166 0.995 1.001 1.000 1.000 0.988 1.035 1.000 0.993 0.991

WECC_SNV 1.000 1.092 1.135 1.188 1.166 0.995 1.001 1.000 1.000 0.988 1.035 1.000 0.993 0.991

### 4

- 32

Table 4

- 16 Performance and Unit Cost Assumptions for P otential (New) Renewable and Non

- Conventional Technology Capacity

in EPA Base Case v.5.13

Biomass

Bubbling

Fluidized Bed

(BFB) Geothermal Landfill Gas

Fuel Cells Solar Photovoltaic Solar Thermal Onshore Wind Offshore Wind LGHI LGLo LGVLo

Size (MW) 50 50 50 10 150 100 100 400

First Run Year Available 2018 2018 2016 2016 2016 2016 2016 2018

Lead Time (Years) 4 4 3 3 2 3 3 4

Availability 83% 87% 90% 87% 90% 90% 95% 95%

Generation Capability Economic Dispatch Economic Dispatch Economic Dispatch Economic Dispatch Generation Profile Generation Profile Generation Profile Generation Profile

Vintage #1 (2016

- 2054) Vintage #1 (2016)

Heat Rate (Btu/kWh) 13,500 30,000 13,648 13,648 13,648 9,246 9,756 9,756 9,756 9,756

Capital (2011$/kW) 4,041 1,187

- 15,752 8,408 10,594 16,312 7,117 3,364 4,690 2,258 6,298

Fixed O&M (2011$/kW/yr) 103.79 50

- 541 381.74 381.74 381.74 357.47 21.37 66.09 38.86 72.71

Variable O&M (2011$/MWh) 5.17 0.00 8.51 8.51 8.51 0.0 0.0 0.0 0.0 0.0

Vintage #2 (2018)

Heat Rate (Btu/kWh) 8,738 9,756 9,756 9,756 9,756

Capital (2011$/kW) 6995 3,281 4,636 2,250 6233

Fixed O&M (2011$/kW/yr) 357.5 21.4 66.1 38.9 72.7

Variable O&M (2011$/MWh) 0.0 0.0 0.0 0.0 0.0

Vintage #3 (2020)

Heat Rate (Btu/kWh) 8,230 9,756 9,756 9,756 9,756

Capital (2011$/kW) 6806 3,217 4,594 2,220 6108

Fixed O&M (2011$/kW/yr) 357.5 21.4 66.1 38.9 72.7

Variable O&M (2011$/MWh) 0.0 0.0 0.0 0.0 0.0

Vintage #4 (2025)

Heat Rate (Btu/kWh) 6,960 9,756 9,756 9,756 9,756

Capital (2011$/kW) 6276 3,027 4,470 2,123 5739

Fixed O&M (2011$/kW/yr) 357.5 21.4 66.1 38.9 72.7

Variable O&M (2011$/MWh) 0.0 0.0 0.0 0.0 0.0

Vintage #5 (2030)

Heat Rate (Btu/kWh) 6,960 9,756 9,756 9,756 9,756

Capital (2011$/kW) 5,799 2,859 4,360 2,039 5411

Fixed O&M (2011$/kW/yr) 357.5 21.4 66.1 38.9 72.7

Variable O&M (2011$/MWh) 0.0 0.0 0.0 0.0 0.0

Vintage #6 (2040)

### 4

- 33 Biomass

Bubbling

Fluidized Bed

(BFB) Geothermal Landfill Gas

Fuel Cells Solar Photovoltaic Solar Thermal Onshore Wind Offshore Wind LGHI LGLo LGVLo

Heat Rate (Btu/kWh) 6,960 9,756 9,756 9,756 9,756

Capital (2011$/kW) 4,872 2,533 4,147 1,864 4,759

Fixed O&M (2011$/kW/yr) 357.5 21.4 66.1 38.9 72.7

Variable O&M (2011$/MWh) 0.0 0.0 0.0 0.0 0.0

Vintage #7 (2050)

Heat Rate (Btu/kWh) 6,960 9,756 9,756 9,756 9,756

Capital (2011$/kW) 4872 2,533 4,147 1,864 4759

Fixed O&M (2011$/kW/yr) 357.5 21.4 66.1 38.9 72.7

Variable O&M (2011$/MWh) 0.0 0.0 0.0 0.0 0.0

Notes:

a Assumptions for Biomass Co

- firing for Coal Plants can be found in Table 5

- 9

### 4

- 34

4.4.5 Cost and Performance for Potential Renewable Generating and Non

- Conventional

Technologies

### 4

- 35

Table 4

- 16 summarizes the cost and performance assumptions in EPA Base Case v. 5.13 for potential

renewable and non

- conventional tech nology generating units. T he parameters shown in

### 4

- 36

Table 4

- 16 are based on AEO 2013. The size (MW) presented in

### 4

- 37

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

### 4

- 38

Table 4

- 16 are averages or ranges that are discussed in further detail in the following subsections. Also

discussed below are additional types of data from sources other than AEO 2013 that play a role in the

representation of these types of generation in EPA Base Case v. 5.13

It should be noted that the short te rm 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 d o to

the conventional generation technologies

Wind Generation

EPA Base Case v. 5.13 includes onshore wind

, offshore

- shallow and offshor e

- 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 Resou rce Potential : EPA worked with the U.S. Department of Energy’s (DOE) National

Renewable Energy Laboratory (NREL), to conduct a complete update of the wind resource assumptions

for use in EPA Base Case v.5.13. The result is a complete representation of the potential onshore,

offshore (shallow and deep) wind generating capacity (in MW) broken into five wind quality classes

(descri bed 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

EP A Base Case v.5.13. Wind resources in EPA Base Case v.5.13 are aggregated into five wind classes

, ranging from class 3 (designated to be the least productive for wind generation) to class 7 (designated to

be the most productive for wind generation).

Table 4

- 17 Onshore Regional Potential Wind Capacity (MW) by Wind and Cost Class

in EPA Base Case v.5.13

IPM Region State Wind Class Cost Class

1 2 3 5

ERC_REST TX 3 3,091 12,363 1,236 601,483

4 309 1,237 124 60,176

5 52 208 21 10,112

6 7 27 3 1,318

7 0.061 0.244 0.024 12

ERC_WEST TX 3 1,910 7,642 764 371,768

4 1,215 4,860 486 236,421

5 611 2,445 244 118,943

6 222 890 89 43,298

7 63 250 25 12,181

FRCC FL 3 0.202 0.398 0.204 0.396

MAP_WAUE MN 3 45 190 63 8,728

4 12 52 17 2,398

MT 3 45 190 63 8,731

4 79 330 110 15,191

5 25 106 35 4,869

6 4 16 5 757

7 0.411 2 1 79

ND 3 19 80 27 3,662

4 49 205 68 9,441

5 52 220 73 10,144

6 26 110 37 5,078

7 1 2 1 112

SD 3 42 175 58 8,078

### 4

- 39 IPM Region State Wind Class Cost Class

1 2 3 5

4 337 1,416 472 65,220

5 125 526 175 24,219

6 35 147 49 6,746

7 14 58 19 2,658

MIS_IA IA 3 834 3,503 1,168 161,316

4 399 1,676 559 77,184

5 141 593 198 27,319

6 40 167 56 7,676

7 1 3 1 131

MN 3 7 31 10 1,411

4 24 101 34 4,660

5 37 155 52 7,149

6 30 128 43 5,891

7 8 35 12 1,628

MIS_IL IL 3 10,275 25,688 14,128 78,349

4 39 98 54 298

MIS_INKY IN 3 8,570 19,045 17,141 50,470

4 420 934 840 2,475

KY 3 0.054 0.120 0.108 0.318

MIS_LMI MI 3 1,620 10,798 10,798 30,774

4 10 67 67 190

5 1 3 3 10

6 0.201 1 1 4

7 0.102 1 1 2

MIS_MAPP MT 3 739 3,103 1,034 142,878

4 633 2,660 887 122,473

5 391 1,644 548 75,689

6 148 621 207 28,585

7 30 126 42 5,795

ND 3 462 1,940 647 89,326

4 1,468 6,167 2,056 283,981

5 1,112 4,669 1,556 215,001

6 556 2,336 779 107,578

7 105 440 147 20,282

SD 3 412 1,731 577 79,691

4 1,141 4,792 1,597 220,646

5 1,171 4,920 1,640 226,544

6 625 2,623 874 120,798

7 125 525 175 24,168

MIS_MIDA IA 3 509 2,138 713 98,454

4 419 1,759 586 81,018

5 333 1,398 466 64,366

6 165 692 231 31,867

7 5 21 7 990

IL 3 57 241 80 11,089

4 0.215 1 0.301 42

MIS_MNWI MI 3 1 4 1 170

MN 3 1,652 6,939 2,313 319,537

### 4

- 40 IPM Region State Wind Class Cost Class

1 2 3 5

4 311 1,304 435 60,052

5 136 571 190 26,285

6 154 648 216 29,818

7 36 153 51 7,046

SD 3 0.137 1 0.192 26

4 11 45 15 2,078

5 39 164 55 7,564

6 35 146 49 6,702

7 6 23 8 1,081

WI 3 109 456 152 20,991

MIS_MO IA 3 140 351 193 1,070

MO 3 12,356 30,891 16,990 94,218

4 326 814 448 2,483

5 5 13 7 40

MIS_WUMS MI 3 25 42 4 4,142

4 0.214 0.356 0.036 35

5 0.029 0.049 0.005 5

6 0.006 0.010 0.001 1

WI 3 494 824 82 80,959

4 2 4 0.403 396

5 0.259 0.432 0.043 42

6 0.055 0.092 0.009 9

NENG_CT CT 3 2 4 4 11

NENG_ME ME 3 1,093 2,186 2,186 5,464

4 60 120 120 300

5 23 46 46 115

6 15 30 30 75

7 19 39 39 96

NENGREST MA 3 75 150 150 374

4 26 52 52 130

5 14 27 27 68

6 6 12 12 29

7 5 10 10 26

NH 3 177 354 354 886

4 19 38 38 94

5 9 17 17 44

6 5 10 10 24

7 5 10 10 24

RI 3 4 7 7 18

4 1 1 1 3

5 3 5 5 13

VT 3 248 496 496 1,239

4 24 49 49 122

5 10 21 21 52

6 6 12 12 30

7 6 12 12 31

NY_Z_A&B NY 3 2,095 2,095 2,095 4,189

4 4 4 4 8

### 4

- 41 IPM Region State Wind Class Cost Class

1 2 3 5

NY_Z_C& E NY 3 1,847 1,847 1,847 3,694

4 19 19 19 38

5 4 4 4 8

6 2 2 2 4

7 3 3 3 5

NY_Z_D NY 3 570 570 570 1,139

4 28 28 28 55

5 8 8 8 17

6 4 4 4 7

7 4 4 4 7

NY_Z_F NY 3 472 472 472 944

4 17 17 17 34

5 5 5 5 10

6 3 3 3 6

7 4 4 4 9

NY_Z_G

- I NY 3 66 66 66 132

4 1 1 1 3

5 1 1 1 1

6 0.400 0.400 0.400 1

7 0.240 0.240 0.240 0.480

NY_Z_K NY 3 55 55 55 110

4 12 12 12 23

5 2 2 2 5

PJM_AP MD 3 45 101 91 267

4 1 1 1 4

PA 3 52 116 105 308

4 1 3 3 8

5 1 1 1 3

6 0.018 0.040 0.036 0.106

VA 3 17 39 35 103

4 3 7 7 19

5 2 4 3 10

6 0.360 1 1 2

7 0.162 0.360 0.324 1

WV 3 142 316 284 837

4 14 31 28 82

5 3 7 6 18

6 1 2 2 6

7 1 2 2 6

PJM_ATSI OH 3 2,019 4,486 4,037 11,887

4 1 2 2 7

PJM_COMD IL 3 9,743 21,651 19,486 57,375

4 99 220 198 583

PJM_Dom NC 3 34 34 34 68

VA 3 71 71 71 141

4 5 5 5 10

5 2 2 2 4

6 1 1 1 1

### 4

- 42 IPM Region State Wind Class Cost Class

1 2 3 5

7 0.120 0.120 0.120 0.240

PJM_EMAC DE 3 2 2 2 4

MD 3 313 313 313 626

NJ 3 51 51 51 101

VA 3 362 362 362 724

4 2 2 2 3

PJM_PENE PA 3 415 415 415 831

4 16 16 16 31

5 2 2 2 5

6 0.020 0.020 0.020 0.040

PJM_SMAC MD 3 1 1 1 2

PJM_West IN 3 4,353 9,674 8,707 25,637

KY 3 1 3 2 7

MI 3 202 449 404 1,191

OH 3 2,931 6,513 5,862 17,260

TN 3 2 4 4 11

4 0.288 1 1 2

5 0.108 0.240 0.216 1

VA 3 36 80 72 212

4 3 7 6 19

5 1 3 3 9

6 0.297 1 1 2

WV 3 4 10 9 26

4 0.387 1 1 2

5 1 1 1 3

6 0.414 1 1 2

7 0.261 1 1 2

PJM_WMAC PA 3 107 107 107 213

4 2 2 2 3

S_C_KY KY 3 10 10 10 19

S_C_TVA AL 3 10 10 10 20

GA 3 7 7 7 15

4 0.160 0.160 0.160 0.320

KY 3 0.200 0.200 0.200 0.400

NC 3 31 31 31 62

4 6 6 6 11

5 3 3 3 5

6 2 2 2 4

7 1 1 1 2

TN 3 55 55 55 110

4 1 1 1 2

5 1 1 1 1

6 0.040 0.040 0.040 0.080

7 1 1 1 1

VA 3 0.080 0.080 0.080 0.160

S_D_AMSO LA 3 48 192 192 529

S_D_N_AR AR 3 38 152 152 417

4 0.495 2 2 5

### 4

- 43 IPM Region State Wind Class Cost Class

1 2 3 5

5 0.050 0.200 0.200 1

MO 3 49 196 196 539

S_D_REST AR 3 0.220 1 1 2

S_D_WOTA LA 3 10 40 40 109

TX 3 98 392 392 1,079

S_SOU AL 3 13 13 13 26

GA 3 18 18 18 35

4 1 1 1 2

S_VACA NC 3 122 122 122 244

4 3 3 3 5

5 1 1 1 1

6 1 1 1 2

7 0.340 0.340 0.340 1

SC 3 48 48 48 97

4 0.040 0.040 0.040 0.080

SPP_N KS 3 1,949 8,230 3,899 202,509

4 2,297 9,697 4,594 238,608

5 2,687 11,346 5,374 279,175

6 1,426 6,021 2,852 148,161

7 211 889 421 21,880

MO 3 1,021 4,312 2,043 106,102

4 6 25 12 619

SPP_NEBR NE 3 811 3,404 1,135 156,753

4 1,436 6,031 2,010 277,691

5 1,412 5,930 1,977 273,063

6 738 3,100 1,033 142,746

7 133 559 186 25,741

SPP_SE LA 3 0.406 2 1 55

SPP_SPS NM 3 1,128 5,318 1,612 153,108

4 253 1,192 361 34,322

5 45 210 64 6,045

6 20 95 29 2,724

7 3 15 5 433

OK 3 25 117 35 3,372

4 123 580 176 16,699

5 225 1,060 321 30,520

6 85 398 121 11,470

7 32 149 45 4,296

TX 3 696 3,282 995 94,481

4 582 2,744 831 78,989

5 462 2,176 659 62,645

6 663 3,126 947 90,002

7 256 1,206 365 34,718

SPP_WEST AR 3 58 273 83 7,859

4 1 5 2 148

5 0.129 1 0.184 17

6 0.032 0.149 0.045 4

7 0.022 0.102 0.031 3

### 4

- 44 IPM Region State Wind Class Cost Class

1 2 3 5

MO 3 0.042 0.198 0.060 6

OK 3 2,002 9,437 2,860 271,662

4 816 3,846 1,165 110,705

5 240 1,131 343 32,558

6 70 331 100 9,515

7 6 30 9 864

TX 3 0.099 0.465 0.141 13

WEC_CALN CA 3 187 623 218 2,088

4 5 18 6 59

5 1 5 2 16

6 1 2 1 7

7 0.360 1 0.420 4

WEC_LADW CA 3 111 369 129 1,235

4 41 137 48 460

5 7 22 8 75

6 5 18 6 59

7 3 12 4 39

WEC_SDGE CA 3 55 183 64 613

4 14 47 17 159

5 4 13 5 44

6 1 2 1 7

7 0.384 1 0.448 4

WECC_AZ AZ 3 98 392 218 10,170

4 0.233 1 1 24

5 0.014 0.058 0.032 1

WECC_CO CO 3 1,071 4,284 2,678 259,744

4 314 1,257 786 76,222

5 145 578 361 35,052

6 18 72 45 4,388

7 1 5 3 307

WECC_ID ID 3 50 451 669 15,547

4 1 11 16 382

5 0.323 3 4 100

6 0.104 1 1 32

7 0.038 0.346 1 12

WECC_IID CA 3 26 103 57 2,674

4 0.325 1 1 34

5 0.015 0.061 0.034 2

WECC_MT MT 3 1,260 11,340 16,800 390,608

4 229 2,065 3,059 71,112

5 60 542 803 18,673

6 17 157 232 5,396

7 8 76 113 2,629

WECC_NM NM 3 1,464 5,855 3,253 152,057

4 567 2,269 1,260 58,928

5 365 1,460 811 37,926

6 142 569 316 14,774

7 27 108 60 2,802

### 4

- 45 IPM Region State Wind Class Cost Class

1 2 3 5

TX 3 219 875 486 22,725

4 9 38 21 982

5 2 6 4 168

6 1 2 1 58

7 0.326 1 1 34

WECC_NNV NV 3 21 188 278 6,471

4 0.456 4 6 141

5 0.068 1 1 21

6 0.013 0.119 0.176 4

WECC_PNW CA 3 1 5 7 173

4 0.036 0.324 0.480 11

5 0.007 0.065 0.096 2

6 0.001 0.011 0.016 0.372

ID 3 2 15 23 527

4 0.252 2 3 78

5 0.123 1 2 38

6 0.105 1 1 33

7 0.088 1 1 27

OR 3 76 681 1,008 23,440

4 4 34 50 1,158

5 1 10 15 355

6 1 5 7 168

7 0.355 3 5 110

WA 3 51 462 685 15,930

4 3 25 37 861

5 1 7 10 238

6 0.368 3 5 114

7 0.241 2 3 75

WECC_SCE CA 3 989 3,295 1,153 11,039

4 69 229 80 768

5 34 112 39 375

6 15 50 18 169

7 28 93 33 313

WECC_SF CA 3 237 790 277 2,648

4 28 92 32 309

5 26 87 30 290

6 4 12 4 39

WECC_SNV NV 3 1 5 3 138

4 0.004 0.014 0.008 0.374

WECC_UT UT 3 39 350 519 12,062

4 0.279 3 4 86

5 0.039 0.351 1 12

6 0.006 0.054 0.080 2

7 0.003 0.027 0.040 1

WECC_WY NE 4 2 22 32 745

5 15 138 204 4,748

6 16 146 216 5,026

7 0.471 4 6 146

### 4

- 46 IPM Region State Wind Class Cost Class

1 2 3 5

SD 3 125 1,124 1,665 38,719

4 38 344 510 11,863

5 11 95 141 3,286

6 1 13 19 447

7 0.018 0.162 0.240 6

WY 3 918 8,261 12,238 284,541

4 338 3,046 4,513 104,927

5 188 1,694 2,509 58,337

6 114 1,027 1,521 35,370

7 98 878 1,301 30,238



### Table 4

- 18 Offshore Shallow Regional Potential Wind Capacity (MW) by Wind and

Cost Class in EPA Base Case v.5.13

IPM Region State Wind Class Cost Class

1 2 4

ERC_REST TX 3 850 1,700 1,700

4 6,423 12,846 12,846

5 1,079 2,158 2,158

6 2,625 5,251 5,251

FRCC FL 3 57,921 115,842 115,842

4 7 13 13

MIS_INKY IN 3 63 125 125

4 259 517 517

5 85 169 169

MIS_LMI MI 3 1,739 3,478 3,478

4 3,784 7,567 7,567

5 1,899 3,799 3,799

6 416 831 831

MIS_MNWI MI 3 118 236 236

4 14 29 29

MN 3 134 269 269

WI 3 911 1,822 1,822

4 141 282 282

MIS_WUMS MI 3 2,275 4,550 4,550

4 3,095 6,189 6,189

5 477 953 953

6 59 117 117

7 92 185 185

WI 3 525 1,049 1,049

4 1,472 2,944 2,944

5 737 1,473 1,473

6 84 167 167

NENG_CT CT 3 287 574 574

4 162 323 323

NENG_ME ME 3 619 1,238 1,238

4 419 837 837

5 166 331 331

### 4

- 47 IPM Region State Wind Class Cost Class

1 2 4

6 234 469 469

7 16 33 33

NENGREST MA 3 181 363 363

4 579 1,158 1,158

5 661 1,321 1,321

6 2,307 4,615 4,615

7 3,112 6,224 6,224

NH 3 24 48 48

4 52 103 103

5 31 62 62

RI 3 43 87 87

4 89 177 177

5 85 170 170

6 225 449 449

NY_Z_A&B NY 3 205 410 410

4 1,092 2,184 2,184

5 2 4 4

NY_Z_C&E NY 3 249 499 499

4 524 1,048 1,048

5 2 5 5

NY_Z_G

- I NY 3 1 1 1

NY_Z_J NY 3 46 93 93

4 118 237 237

5 4 8 8

NY_Z_K NY 3 258 517 517

4 881 1,763 1,763

5 787 1,573 1,573

6 1,533 3,067 3,067

PJM_ATSI OH 3 173 347 347

4 2,628 5,256 5,256

5 1,261 2,523 2,523

PJM_COMD IL 3 100 200 200

4 267 534 534

5 418 836 836

6 2 4 4

PJM_Dom NC 3 706 1,413 1,413

4 2,776 5,551 5,551

5 3,843 7,687 7,687

6 553 1,107 1,107

VA 3 809 1,619 1,619

4 979 1,958 1,958

5 1,313 2,626 2,626

6 1 1 1

PJM_EMAC DE 3 214 428 428

4 1,079 2,159 2,159

5 170 340 340

MD 3 1,303 2,607 2,607

4 1,696 3,392 3,392

### 4

- 48 IPM Region State Wind Class Cost Class

1 2 4

5 366 732 732

NJ 3 365 729 729

4 1,626 3,253 3,253

5 2,981 5,962 5,962

6 1,953 3,907 3,907

VA 3 365 730 730

4 3,555 7,110 7,110

5 1,525 3,050 3,050

PJM_PENE PA 3 23 45 45

4 649 1,297 1,297

5 427 853 853

PJM_SMAC MD 3 567 1,134 1,134

4 0.040 0.080 0.080

PJM_West MI 3 62 123 123

4 440 880 880

S_D_AMSO LA 3 8,846 17,693 17,693

S_D_WOTA LA 3 3,590 7,181 7,181

4 586 1,172 1,172

TX 3 639 1,278 1,278

4 1,145 2,290 2,290

AL 3 1,939 3,877 3,877

S_SOU FL 3 4,827 9,654 9,654

GA 3 5,135 10,270 10,270

4 4,146 8,292 8,292

MS 3 1,056 2,113 2,113

S_VACA NC 3 1,437 2,874 2,874

4 8,366 16,733 16,733

5 6,468 12,935 12,935

6 94 188 188

SC 3 381 762 762

4 9,932 19,864 19,864

5 2,993 5,986 5,986

SPP_SE LA 3 1,828 3,656 3,656

WEC_CALN CA 3 196 391 391

4 37 73 73

5 11 23 23

6 4 9 9

WEC_LADW CA 3 10 21 21

WECC_PNW CA 3 122 243 243

4 43 86 86

5 24 48 48

6 2 4 4

OR 3 876 1,753 1,753

4 150 300 300

5 46 92 92

6 64 128 128

7 9 18 18

WA 3 610 1,220 1,220

### 4

- 49 IPM Region State Wind Class Cost Class

1 2 4

4 404 808 808

5 1 1 1

WECC_SCE CA 3 170 339 339

4 55 109 109

5 7 15 15

6 0.080 0.160 0.160

WECC_SF CA 3 326 652 652

4 1 3 3



### Table 4

- 19 Offshore Deep Regional Potential Wind Capacity (MW) by Wind and

Cost Class in EPA Base Case v.5.13

IPM Region State Wind Class Cost Class

1 2 4

ERC_REST TX 3 10,991 21,982 21,982

4 7,963 15,926 15,926

5 97 194 194

FRCC FL 3 61,964 123,927 123,927

MIS_INKY IN 3 298 596 596

MIS_LMI MI 3 5,068 10,136 10,136

4 16,868 33,736 33,736

5 259 518 518

MIS_MNWI MI 3 464 928 928

MN 3 4,795 9,590 9,590

WI 3 3,608 7,216 7,216

MIS_WUMS MI 3 9,225 18,450 18,450

4 7,779 15,558 15,558

5 8,557 17,114 17,114

6 5,572 11,145 11,145

WI 3 1,427 2,854 2,854

4 8,953 17,906 17,906

5 300 599 599

NENG_CT CT 3 19 38 38

NENG_ME ME 3 499 999 999

4 962 1,924 1,924

5 1,789 3,579 3,579

6 7,377 14,755 14,755

7 7,582 15,165 15,165

NENGREST MA 3 279 558 558

4 817 1,633 1,633

5 8,923 17,845 17,845

6 15,734 31,467 31,467

NH 3 70 140 140

4 369 737 737

5 359 717 717

6 660 1,319 1,319

RI 3 205 411 411

4 300 600 600

### 4

- 50 IPM Region State Wind Class Cost Class

1 2 4

5 2,624 5,248 5,248

NY_Z_A&B NY 3 4,384 8,767 8,767

4 37 74 74

NY_Z_C&E NY 3 1,377 2,754 2,754

4 0.160 0.320 0.320

NY_Z_J NY 3 1 2 2

4 0.240 0.480 0.480

NY_Z_K NY 3 432 865 865

4 981 1,963 1,963

5 10,948 21,896 21,896

6 69 137 137

PJM_ATSI OH 3 4 7 7

PJM_COMD IL 3 491 981 981

4 1,269 2,538 2,538

PJM_Dom NC 3 938 1,875 1,875

4 11,658 23,316 23,316

5 588 1,177 1,177

VA 3 142 284 284

4 713 1,426 1,426

PJM_EMAC DE 3 469 938 938

4 10 19 19

MD 3 3,802 7,603 7,603

4 29 58 58

NJ 3 1,281 2,562 2,562

4 5,085 10,171 10,171

5 3,280 6,560 6,560

VA 3 3,637 7,274 7,274

4 568 1,135 1,135

PJM_PENE PA 3 230 461 461

PJM_SMAC MD 3 6 13 13

PJM_West MI 3 1,462 2,925 2,925

4 355 710 710

S_D_AMSO LA 3 10,146 20,293 20,293

S_D_WOTA LA 3 222 444 444

S_SOU AL 3 3,564 7,129 7,129

FL 3 14,264 28,527 28,527

GA 3 2,379 4,757 4,757

MS 3 5 10 10

S_VACA NC 3 4,338 8,677 8,677

4 9,454 18,909 18,909

SC 3 7,383 14,766 14,766

4 91 182 182

SPP_SE LA 3 125 249 249

WEC_CALN CA 3 12,809 25,617 25,617

4 5,277 10,555 10,555

5 6,043 12,087 12,087

6 12,939 25,878 25,878

7 3,678 7,356 7,356

### 4

- 51 IPM Region State Wind Class Cost Class

1 2 4

WEC_LADW CA 3 6,527 13,054 13,054

WECC_PNW CA 3 299 598 598

4 322 644 644

5 469 938 938

6 1,103 2,206 2,206

7 1,538 3,076 3,076

OR 3 6,530 13,061 13,061

4 12,517 25,035 25,035

5 3,759 7,518 7,518

6 4,667 9,334 9,334

7 4,598 9,197 9,197

WA 3 6,716 13,431 13,431

4 6,304 12,607 12,607

WECC_SCE CA 3 17,439 34,877 34,877

4 10,699 21,398 21,398

5 5,028 10,056 10,056

WECC_SF CA 3 3,883 7,766 7,766

4 3,907 7,814 7,814

5 4,064 8,127 8,127

6 49 98 98



### Generation Profiles : Unlike other renewable generation technologies, which dispatch on an economic

basis subject to their availability constraint, 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.5.13 uses generation pro files 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 Hour 1 through Hour 24 the generation profile indicates the amount of generation (kWh) per MW of

available capacity. The wind generation profiles were prepared with data from NREL. This provided the

separate winter and summer generation profiles for wind c lasses 3

- 7 for onshore and offshore (shallow

and deep) generation in each IPM region. As an illustrative example

, Excerpt of Table 4

- 20 shows the

generation profile for onshore wind in model region WECC_CO. In IPM the seasonal average “kWh of

generation per MW” (shown in the last row of the Excerpt of Table 4

- 20 ) is used to derive the generation

from a particular wind class in a specific model region.

Excerpt of Table 4

- 20 Representative Wind Generation Profiles in EPA Base Case v. 5.13

Illustrative Hourly Wind Generation Profile (kWh of Generation per MW of Electricity)

The complete data set in spreadsheet format can be downloaded via the link found at

www.epa.gov/airmarkets/pr ogsregs/epa

- ipm/BaseCasev513.html

Winter Hour Wind Class Summer Hour Wind Class

1 2 3 4 5 1 2 3 4 5

01 410 483 504 521 539 01 273 326 385 407 431

02 404 478 499 517 536 02 263 314 373 396 422

03 400 474 497 514 533 03 252 303 362 386 412

04 388 462 486 504 526 04 234 285 343 366 394

05 366 439 465 485 511 05 208 257 312 335 364

06 351 423 449 471 499 06 187 234 286 308 339

### 4

- 52 07 348 419 443 467 495 07 170 213 263 284 315

08 355 427 448 471 500 08 166 206 254 276 306

09 346 420 440 463 493 09 159 198 243 265 294

10 331 406 426 452 483 10 162 204 244 267 298

11 319 394 415 443 475 11 175 220 254 280 312

12 317 391 412 441 474 12 191 238 267 293 326

13 317 388 409 440 473 13 202 248 273 298 332

14 319 389 410 441 475 14 210 255 276 300 334

15 322 389 411 441 475 15 216 260 279 303 336

16 319 384 405 436 471 16 218 261 279 303 336

17 308 373 390 423 459 17 215 257 275 299 330

18 309 374 389 422 458 18 220 262 280 304 333

19 332 399 412 444 478 19 241 284 306 330 357

20 369 438 450 478 506 20 268 313 342 367 391

21 395 467 480 503 525 21 284 332 370 394 417

22 410 483 497 517 536 22 289 341 386 408 430

23 415 488 504 522 540 23 286 340 392 413 435

24 413 486 505 522 540 24 281 335 391 412 435

Winter

Average 357 428 448 472 500 Summer

Average 224 270 310 333 362

Notes:

Based on Onshore Wind in Model Region WECC_CO.

This is an example of the wind data used in EPA Base Case v.5.13



### 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 gen eration that are

used in EPA Base Case v. 5.13 were obtained from NREL and are shown in Table 4

- 21

, Table 4

- 2 2

, and. Table 4

- 23

.

Reserve Margin Contribution (also referred to as capacity credit) : EPA Base Case v.5.13 uses reserve

margins, discussed in detail in Section 3.6 , to model reliability. Each region has a reserve margin

requirement which is used to determine 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 t he 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 p ercent contribution towards reserve margin, then only the designated

share 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. Th is means that wind and solar have limited (less than 100 percent) contribution s toward reserve

margin s in the EPA Base Case v. 5.13

.

Table 4

- 21

, Table 4

- 2 2 , and Table 4

- 23 present the reserve margin contributions apportioned to new wind

plants in the EPA Base Case v. 5.13 as derived from AEO 201 2 and NREL

. NREL is the source for

capacity factors; AEO 2012 Reference Case output s are used to develop a ratio of capacity factors to

reserve contribution. The tables show the onshore and offshore (shallow and deep) reserve margins for

each wind class.

Table 4

- 21 Onshore Reserve Margin Contribution an Average Capacity Factor by Wind Class

Wind Class

1 2 3 4 5

### 4

- 53

Capacity Factor 30% 36% 39% 41% 44%

Reserve Margin Contribution a 

### 20% 24% 26% 27% 29%

Note:

a Reserve Margin Contribution for ERC_REST and ERC_WEST is 8.7%.



### Table 4

- 2 2 Offshore Shallow Reserve Margin Contribution an

Average Capacity Factor by Wind Class

Wind Class

1 2 3 4 5

Capacity Factor 31% 40% 43% 46% 50%

Reserve Margin Contribution a 

### 20% 26% 28% 30% 33%

Note:

a Reserve Margin Contribution for ERC_REST and ERC_WEST is 8.7%.



### Table 4

- 23 Offshore Deep Reserve Margin Contribution an

Average Capacity Factor by Wi nd Class

Wind Class

1 2 3 4 5

Capacity Factor 36% 45% 49% 51% 53%

Reserve Margin Contribution a 

### 24% 30% 32% 34% 35%

Note:

a Reserve Margin Contribution for ERC_REST and ERC_WEST is 8.7%.



### Capital cost calculation : EPA Base Case v.5.13 uses multipliers similar to the LT (long term) multipliers

from the Energy Information Administration’s NEMS model 22 

### 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 degr adation, site accessibility challenges, population proximity, competing

land uses, aesthetics, and environmental factors. Five cost classes are used in EPA Base Case v. 5.13

with class 1 having the lowest cost adjustment factor (1) and class 5 having the hi ghest adjustment factor

(ranging from 2

. 00 to 2. 50 depending on whether the wind resource is onshore, offshore shallow or

offshore deep), as shown in Table 4

- 24

. To obtain the capital cost for a particular new wind model plant,

the base capital costs shown in

22 

### 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

- 54

Table 4

- 16 are multiplied by the cost adjustment factor for the wind cos t class applicable to the new plant.

Table 4

- 24 Capital Cost Adjustment Factors for New Wind Plants in Base Case v.5.13

Cost Class

1 2 3 4 5

Onshore 1 1.1 1.25

-- 2.00

Offshore Deep Water 1 1.35

-- 2.5

--

Offshore Shallow Water 1 1.35

-- 2.5

--



### Many factors figure in whether the model determines that adding wind capacity yields the greatest

incremental improvement in the system

- wide (least 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 co nstraints o n 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

- 25 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 3 , cost class 2 in the WECC_CO model region in run year 2020.

Table 4

- 25 Example Calculations o f Wind Generation Potential, Reserve Margin Contribution, a nd

Capital Cost f or Onshore Wind i n WECC_CO a t Wind Class 3 , Cost Class 2 Required Data

Table 4

- 17 Potential wind capacity ( C ) = 578 MW

Table 4

- 20 Winter average generation ( G W 

### ) per available MW = 448 kWh/MW

Table 4

- 20 Summer average generation ( G S 

### ) per available MW = 310 kWh/MW

Hours in Winter ( H W



### ) season (October

– April) = 5

, 088 h ours

Hours in Summer ( H S 

### )season (May

– September) = 3

, 672 hours

Table 4

- 21 Reserve Margin Contribution ( RM ) WECC_CO, Wind Class 3 = 26 percent

Table 4

- 16 Capital Cost ( Cap 2020 

### ) in vintage range for year 2020 = $2

, 220/kW

Table 4

- 24 Capital Cost Adjustment Factor ( CAF ON,C2 

### ) for onshore cost class 2 = 1.1

Table 4

- 15 Regional Factor (RF) 1.033

Calculations

### 4

- 55

Solar Generation

EPA Base Case v.5.13 includes solar PV and solar thermal generation technologies . The following

sections describe four key aspects of the representation of solar generation: solar resource potential,

generation profiles, reserve margin contribution, and capital cost calculation.

Solar Resource Potenti al : The resource potential estimates for solar PV and solar thermal technologies

were developed by NREL by model region and state . These are summarized in Table 4

- 26 and

### 4

- 56

Table 4

- 27

.

Table 4

- 26 Solar PV Regional Potential Capacity (MW) in EPA Base Case v.5.13

Model Region State Class

1 2 3 4 5 6

FRCC FL

-- 466,717 2,016,007 2

-- --

ERC_REST TX

-- 5,330,140 6,734,722 17,494

-- --

ERC_WEST TX

-- -- 4,352,761 1,172,478 1,508,010 960,326

MAP_WAUE MN 13,256 97,901

-- -- -- --

MT

-- 177,744

-- -- -- --

ND

-- 248,206

-- -- -- --

SD

-- 991,602

-- -- -- --

MIS_IA IA 846 2,792,414

-- -- -- --

MN

-- 205,901

-- -- -- --

MIS_IL IL

-- 3,840,608

-- -- -- --

MIS_INKY IN 11,692 2,660,403

-- -- -- --

KY

-- 454,286

-- -- -- --

MIS_LMI MI 1,187,823 1,910,779

-- -- -- --

MIS_MAPP MT

-- 2,526,463

-- -- -- --

ND 575,717 5,813,548

-- -- -- --

SD

-- 5,361,555

-- -- -- --

MIS_MIDA IA

-- 2,636,683

-- -- -- --

IL

-- 151,384

-- -- -- --

MIS_MNWI MI 38,128 32,825

-- -- -- --

MN 1,628,979 5,292,787

-- -- -- --

SD

-- 167,192

-- -- -- --

WI 215,333 1,174,605

-- -- -- --

MIS_MO IA

-- 52,502

-- -- -- --

MO

-- 2,718,802

-- -- -- --

MIS_WUMS MI 437,954 465,821

-- -- -- --

WI 541,446 2,293,924

-- -- -- --

NENG_CT CT 6,300 74,375

-- -- -- --

NENG_ME ME 1,174,023 305,524

-- -- -- --

NENGREST MA 30,548 118,009

-- -- -- --

NH 34,503 101,436

-- -- -- --

RI 49 34,073

-- -- -- --

VT 88,147 11,168

-- -- -- --

NY_Z_A&B NY 321,929 152,829

-- -- -- --

NY_Z_C&E NY 482,549 176,823

-- -- -- --

NY_Z_D NY 223,636 75,990

-- -- -- --

NY_Z_F NY 82,765 64,662

-- -- -- --

NY_Z_G

- I NY 5,299 58,250

-- -- -- --

NY_Z_J NY

-- 676

-- -- -- --

NY_Z_K NY

-- 25,646

-- -- -- --

PJM_AP MD 2,017 59,871

-- -- -- --

PA 76,636 78,377

-- -- -- --

VA

-- 122,956

-- -- -- --

WV 7,588 76,925

-- -- -- --

### 4

- 57 Model Region State Class

1 2 3 4 5 6

PJM_ATSI OH 474,342 869,085

-- -- -- --

PA 151,543 2,456

-- -- -- --

PJM_COMD IL 546 1,576,218

-- -- -- --

PJM_Dom NC

-- 441,910

-- -- -- --

VA

-- 1,742,725

-- -- -- --

PJM_EMAC DE

-- 175,165

-- -- -- --

MD

-- 329,397

-- -- -- --

NJ 183 316,902

-- -- -- --

PA

-- 146,075

-- -- -- --

VA

-- 53,494

-- -- -- --

PJM_PENE PA 276,816 30,194

-- -- -- --

PJM_SMAC DC

-- 35

-- -- -- --

MD

-- 180,702

-- -- -- --

PJM_West IN 11,570 745,754

-- -- -- --

KY

-- 18,542

-- -- -- --

MI 52,303 87,422

-- -- -- --

OH 88,741 1,452,563

-- -- -- --

TN

-- 684

-- -- -- --

VA

-- 155,747

-- -- -- --

WV 272 28,898

-- -- -- --

PJM_WMAC PA 72,963 208,057

-- -- -- --

S_C_KY KY

-- 816,227

-- -- -- --

OH

-- 10,749

-- -- -- --

VA

-- 3

-- -- -- --

S_C_TVA AL

-- 591,879

-- -- -- --

GA

-- 71,479

-- -- -- --

KY

-- 736,215

-- -- -- --

MS

-- 1,174,913

-- -- -- --

NC

-- 7,785

-- -- -- --

TN

-- 2,239,778

-- -- -- --

VA

-- 5,167

-- -- -- --

S_D_AMSO LA

-- 271,634 8,334

-- -- --

S_D_N_AR AR

-- 1,619,623

-- -- -- --

MO

-- 807,954

-- -- -- --

S_D_REST AR

-- 716,350

-- -- -- --

LA

-- 1,131,420

-- -- -- --

MS

-- 1,951,363

-- -- -- --

S_D_WOTA LA

-- 214,557 252

-- -- --

TX

-- 627,283

-- -- -- --

S_SOU AL

-- 3,052,382 60,455

-- -- --

FL

-- 493,841 86,080

-- -- --

GA

-- 3,322,002 986,103

-- -- --

MS

-- 948,473 2,612

-- -- --

S_VACA GA

-- 23,380

-- -- -- --

NC

-- 2,773,996

-- -- -- --

SC

-- 2,110,013 123,622

-- -- --

SPP_N KS

-- 3,558,781 5,141,266 280

-- --

### 4

- 58 Model Region State Class

1 2 3 4 5 6

MO

-- 1,721,823

-- -- -- --

SPP_NEBR NE

-- 4,965,449 1,966,681

-- -- --

SPP_SE LA

-- 1,122,714 731

-- -- --

SPP_SPS NM

-- -- -- 109,326 1,514,976 698,712

OK

-- -- 452,416 198,521 4,648

--

TX

-- -- 1,263,287 1,705,702 653,658

--

SPP_WEST AR

-- 1,206,494

-- -- -- --

LA

-- 123,494

-- -- -- --

MO

-- 3,278

-- -- -- --

OK

-- 2,497,664 3,662,342

-- -- --

WEC_CALN CA 5 17,827 1,686,553 252,117 76,837 15,751

WEC_LADW CA

-- 190 1,721 2,111 11,541 125,266

WEC_SDGE CA

-- -- 3,150 4,613 2,169 48,580

WECC_AZ AZ

-- -- -- 4,413 173,124 6,915,162

WECC_CO CO

-- 71,601 4,752,161 608,847 210,420 171,178

WECC_ID ID

-- 531,421 2,050,967

-- -- --

WECC_IID CA

-- -- -- 1,822 294 351,868

WECC_MT MT 1,211 4,618,469

-- -- -- --

WECC_NM NM

-- -- 25,168 432,099 1,882,143 4,735,551

TX

-- -- -- -- 7,033 521,127

WECC_NNV NV

-- 343 2,079,313 896,221 587,765 1,587,307

WECC_PNW CA

-- 3,935 413,255

-- -- --

ID

-- 137,335

-- -- -- --

OR 90,067 700,240 2,032,709

-- -- --

WA 310,047 1,361,856 3,472

-- -- --

WECC_SCE CA

-- 1,638 191,598 276,918 61,789 1,474,690

WECC_SF CA

-- 2,481 114,703 999

-- --

WECC_SNV NV

-- -- -- -- -- 201,386

WECC_UT UT

-- 3,040 1,896,010 751,291 521,256 275,303

WECC_WY NE

-- -- 110,382

-- -- --

SD

-- 437,434 47,535

-- -- --

WY

-- 1,828,509 3,173,479

-- -- --

### 4

- 59 Table 4

- 27 Solar Thermal Regional Potential Capacity (MW) in EPA Base Case v.5.13

Model

Region State Class

1 2 3 4 5

FRCC FL 95,433

-- -- -- --

ERC_REST TX 2,115,870 740

-- -- --

ERC_WEST TX 2,659,629 1,949,748 4,854

-- --

MIS_MAPP MT 298,407

-- -- -- --

ND 23,728

-- -- -- --

SD 834,490

-- -- -- --

S_SOU AL 41

-- -- -- --

FL 1,740

-- -- -- --

S_VACA SC 322

-- -- -- --

SPP_N KS 3,918,845 74,124

-- -- --

SPP_NEBR NE 2,195,753

-- -- -- --

SPP_SPS NM

-- 1,448,536 957

-- --

OK 187,119 215,536

-- -- --

TX 411,642 1,842,654

-- -- --

SPP_WEST OK 2,119,578

-- -- -- --

WEC_CALN CA 1,040,697 223,718 24

-- --

WEC_LADW CA 566 8,894 9,262 12,719 48,426

WEC_SDGE CA 1,755 5,221 4,542 10,439 1,001

WECC_AZ AZ

-- 43,388 420,953 1,953,964 1,203,911

WECC_CO CO 1,634,219 1,546,981 110,412 99,734 2,319

WECC_ID ID 1,351,218 73,561

-- -- --

WECC_IID CA 1,292 24,647 42,366 121,604 34,391

WECC_MT MT 544,703

-- -- -- --

WECC_NM NM 26,098 991,156 1,051,211 1,072,218 325,541

TX

-- 64,109 225,279 3,306

--

WECC_NNV NV 213,922 1,493,428 209,881 208,912 448,126

WECC_PNW CA 89,511 135,681

-- -- --

ID 262

-- -- -- --

OR 1,097,342 46,192

-- -- --

WA 436,316

-- -- -- --

WECC_SCE CA 307,385 74,751 65,694 130,995 512,927

WECC_SF CA 80,914 450

-- -- --

WECC_SNV NV

-- 7,080 4,996 25,427 49,166

WECC_UT UT 571,368 1,056,308 114,414 64,245 1,491

WECC_WY NE 68,643

-- -- -- --

SD 191,888

-- -- -- --

WY 1,976,069 102,883

-- -- --



### 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.5.13 with

generation profiles which specify hourly generation patterns for t ypical winter and summer days in each

eligible region. The generation profiles were prepared with data from NREL which provided separate

winter and summer generation profiles for solar thermal and photovoltaic in each eligible IPM region. As

an illustrat ive example,

### 4

- 60

Excerpt of Table 4

- 28 shows the solar thermal and solar photovoltaic winter and summer generation

profiles in model region WECC_AZ

.

### 4

- 61 Excerpt of Table 4

- 28 Representative Solar Generation Profiles in EPA Base v. 5.13

Illustrative Hourly Solar Generation Profile (kWh of Generation per MW of Electricity)

The complete data set in spreadsheet format can be downloaded via the link found at

www.epa.gov/airmarkets/progsregs/epa

- ipm/BaseCasev513.html

Winter Hour Solar

Thermal Solar

Photovoltaic Summer Hour Solar

Thermal Solar

Photovoltaic

01 0 0 01 0 3

02 0 0 02 0 3

03 0 0 03 0 3

04 0 0 04 0 3

05 0 0 05 0 3

06 0 446 06 4 574

07 70 446 07 702 574

08 481 446 08 1348 574

09 869 446 09 1446 574

10 937 446 10 1468 574

11 856 446 11 1418 574

12 819 446 12 1383 574

13 832 552 13 1317 600

14 909 552 14 1295 600

15 987 552 15 1261 600

16 761 552 16 1212 600

17 245 64 17 962 155

18 2 64 18 273 155

19 0 64 19 0 155

20 0 64 20 0 155

21 0 64 21 0 155

22 0 0 22 0 3

23 0 0 23 0 3

24 0 0 24 0 3

Winter

Average 324 236 Summer

Average 587 301

Note:

Based on model region WECC_AZ.

This is an example of the solar data used in EPA Base Case v.5.13



### Reserve margin contribution :

### 4

- 62

Table 4

- 29 presents the annual average capacity factors (CFs) and reserve margin contributions by

model region for new solar thermal and photovoltaic units in EPA Base Case v.5.13. The state specific

capacity factors included in this table are from NREL and the associated reserve margin contribution

estimates are based on AEO 201 2 projections. NREL is the source for capacity factors; AEO 2012

Referenc e Case outputs are used to develop a ratio of capacity factors to reserve contribution.

### 4

- 63

Table 4

- 29 Solar Photovoltaic Reserve Margin Contribution and Average Capacity Factor by State

and Solar Thermal Reserve Margin Contribution and Average Capacity Factor by Class

State Solar Photovoltaic

Solar

Class Solar Thermal

Average Capacity

Factor Reserve Margin

Contribution Average Capacity

Factor Reserve Margin

Contribution

Alabama 20% 23% 1 32% 39%

Alaska 11% 12% 2 39% 49%

Arizona 26% 30% 3 43% 53%

Arkansas 21% 24% 4 43% 54%

California 25% 29% 5 45% 56%

Colorado 26% 30%

Connecticut 18% 21%

Delaware 19% 21%

Florida 21% 24%

Georgia 20% 23%

Hawaii 21% 24%

Idaho 22% 25%

Illinois 19% 21%

Indiana 18% 21%

Iowa 20% 23%

Kansas 24% 27%

Kentucky 19% 21%

Louisiana 20% 22%

Maine 19% 22%

Maryland 18% 20%

Massachusetts 18% 21%

Michigan 17% 20%

Minnesota 19% 22%

Mississippi 20% 22%

Missouri 19% 22%

Montana 21% 24%

Nebraska 22% 25%

Nevada 26% 30%

New Hampshire 18% 21%

New Jersey 20% 23%

New Mexico 26% 30%

New York 18% 21%

North Carolina 21% 23%

North Dakota 20% 23%

Ohio 17% 20%

Oklahoma 22% 25%

Oregon 23% 26%

Pennsylvania 18% 20%

Rhode Island 18% 20%

South Carolina 20% 23%

South Dakota 21% 24%

Tennessee 20% 23%

Texas 22% 25%

Utah 25% 28%

Vermont 18% 20%

Virginia 20% 23%

Washington 20% 23%

West Virginia 17% 20%

Wisconsin 18% 21%

Wyoming 23% 26%

### 4

- 64

Geothermal Generation

Geothermal Resource Potential : Ten model regions in EPA Base Case v. 5.13 have geothermal potential.

The potential capacity in each of these regions is shown in Table 4

- 30 . The values are based on AEO

2013 data.

Table 4

- 30 Regional Assumptions on Potential Geothermal Electric Capacity

IPM Model Region Capacity (M W)

WEC_CALN 191

WEC_LADW 83

WECC_AZ 70

WECC_IID 5,058

WECC_NM 292

WECC_NNV 820

WECC_PNW 1,069

WECC_SCE 621

WECC_SF 579

WECC_UT 127

Total 8,910

Notes:

This data is a summary of the geothermal data used in EPA Base Case v.5.13.



### Cost Calculation : EPA Base Case v. 5.13 does not contain a single capital cost, but multiple

geographically

- dependent capital costs for geothermal generation. The assumptions for geothermal were

developed using AEO 2013 cost and performance estimates for 100 known sites. Both dual flash and

binary cycle technologies 23 

### were represented. In EPA Base Case v. 5.13 the 100 sites were aggregated

into 62 different options based on geographic location and cost and performance characteristics of

geothermal sites in each of the ten eligible IPM regions where geothermal generation opportunities exist.

Table 4

- 31 shows the potential geothermal capacity and cost characteristics for applicable model regions.

Table 4

- 31 Potential Geothermal Capacity and Cost Characteristics by Model Region

IPM Region Capacity

(MW) Capital Cost

(2011$) FO&M

(2011$/kW

- yr)

WEC_CALN 5 24,731 822

6 20,629 920

6 29,144 791

9 20,017 572

11 14,841 493

13 17,615 487

16 5,051 221

16 10,073 352

19 11,692 348

29 4,495 161

29 7,613 315

32 9,122 282

WEC_LADW 10 10,361 324

73 7,200 196

23 

### 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 some 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 an d 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

- 65 IPM Region Capacity

(MW) Capital Cost

(2011$) FO&M

(2011$/kW

- yr)

WECC_AZ 26 29,114 1,001

44 27,769 652

WECC_IID 10 14,320 434

19 9,217 351

38 11,395 360

72 4,999 203

84 8,041 230

88 6,930 244

128 8,349 234

135 4,082 139

347 3,533 116

359 2,735 96

1,866 7,447 118

1,912 6,581 104

WECC_NM 9 23,780 756

11 28,310 714

24 18,793 481

62 6,998 197

186 4,016 103

WECC_NNV 66 3,366 142

78 2,602 119

93 4,080 139

152 4,387 194

431 5,247 187

WECC_PNW 9 24,402 986

18 24,198 653

19 17,474 535

36 15,350 490

38 20,609 620

81 9,215 252

101 7,760 237

113 3,481 119

124 2,654 110

264 4,408 126

266 4,074 93

WECC_SCE 7 19,885 705

8 23,338 643

11 16,931 553

32 19,802 586

274 3,091 119

289 2,196 113

WECC_SF 14 24,018 775

17 28,523 737

35 12,225 417

240 4,495 136

273 2,713 115

WECC_UT 52 2,684 132

75 4,049 147

### 4

- 66

Landfill Gas Electricity Generation

Landfill Gas Resource Potential : Estimates of potential electric capacity from landfill gas are based on

the AEO 2012 inventory. EPA Base Case v. 5.13 represents three categories of potential landfill gas

units; “hi gh ”, “low”, and “ very low”. The categories refer to the amount and rate of methane production

from the existing landfill site. Table 4

- 32 summarizes potential electric capacity from landfill gas used in

EPA Base Case v. 5.13

.

There are several things to note about Table 4

- 32

. The AEO 2012 NEMS region level estimates of t he

potential electric capacity from new landfill gas units are disaggregated to IPM regions based on

electricity demand. T he limits listed in Table 4

- 32 apply to the IPM regions indicated in column 1. In EPA

Base Case v. 5.13 the new landfill gas electric capacity in the corresponding IPM regions shown in column

1 cannot exceed the limi ts 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

- 32

represent an upper bo und on the amount of new landfill capacity that can be added in each of the

indicated model regions and states 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

### 4

- 6 7

Table 4

- 16

.

Table 4

- 32 Regional Assumptions on Potential Electric Capacity

from New Landfill Gas Units (MW)

IPM Region State Class

LGHI LGLo LGLVo

ERC_REST TX 12 19 296

ERC_WEST TX 1 1 23

FRCC FL 16 24 159

MAP_WAUE MN 0 0 3

MT 0 0 0

ND 0 1 5

SD 0 2 9

MIS_IA IA 0 3 16

MN 0 0 0

MIS_IL IL 12 18 99

MIS_INKY IN 9 14 103

KY 0 1 7

MIS_LMI MI 7 11 97

MIS_MAPP MT 0 0 0

ND 0 0 4

SD 0 0 2

MIS_MIDA IL 0 0 0

IA 0 5 23

MIS_MNWI MI 0 0 0

MN 1 13 59

SD 0 0 2

WI 0 2 9

MIS_MO IA 0 0 0

MO 10 15 83

MIS_WUMS MI 0 1 6

WI 10 17 99

NENG_CT CT 6 9 14

NENG_ME ME 2 3 4

NENGREST MA 11 17 25

NH 2 3 5

RI 1 2 4

VT 1 1 2

NY_Z_A&B NY 5 8 19

NY_Z_C&E NY 5 8 17

NY_Z_D NY 1 2 4

NY_Z_F NY 2 3 8

NY_Z_G

- I NY 4 6 14

NY_Z_J NY 13 20 43

NY_Z_K NY 5 8 17

PJM_AP MD 0 1 7

PA 3 4 33

VA 0 0 4

WV 1 1 12

PJM_ATSI OH 7 11 77

### 4

- 68 IPM Region State Class

LGHI LGLo LGLVo

PA 0 0 6

PJM_COMD IL 11 17 122

PJM_Dom NC 0 0 2

VA 3 5 46

PJM_EMAC DE 1 1 9

MD 0 1 7

NJ 12 18 92

PA 6 10 52

VA 0 0 0

PJM_PENE PA 2 3 19

PJM_SMAC MD 8 12 65

DC 1 1 8

PJM_West IN 3 5 37

KY 1 2 14

MI 0 0 6

OH 12 18 133

TN 0 0 0

VA 2 3 26

WV 2 3 23

PJM_WMAC PA 8 12 64

S_C_KY KY 2 3 27

OH 0 0 0

VA 0 0 0

S_C_TVA AL 1 1 13

GA 0 0 4

KY 0 0 8

MS 0 1 10

NC 0 0 2

TN 6 8 77

VA 0 0 0

S_D_AMSO LA 0 1 12

S_D_N_AR AR 0 1 11

MO 0 0 4

S_D_REST AR 0 0 1

LA 0 1 9

MS 0 1 9

S_D_WOTA LA 0 0 2

TX 0 1 9

S_SOU AL 2 3 30

FL 0 0 8

GA 6 8 77

MS 0 0 7

S_VACA GA 0 0 1

NC 5 8 73

SC 2 4 37

SPP_N KS 0 0 36

MO 0 0 28

SPP_NEBR NE 0 6 26

### 4

- 69 IPM Region State Class

LGHI LGLo LGLVo

SPP_SE LA 0 0 11

SPP_SPS NM 0 0 5

OK 0 0 0

TX 0 0 17

SPP_WEST AR 0 0 24

LA 0 0 4

MO 0 0 0

OK 1 1 59

TX 0 0 5

WEC_CALN CA 64 97 306

WEC_LADW CA 14 22 70

WEC_SDGE CA 11 17 55

WECC_AZ AZ 0 0 40

WECC_CO CO 0 0 27

WECC_ID ID 2 3 15

WECC_IID CA 0 0 0

WECC_MT MT 1 1 7

WECC_NM NM 0 0 6

TX 0 0 2

WECC_NNV NV 1 1 8

WECC_PNW CA 0 0 0

ID 0 0 3

OR 5 8 41

WA 10 15 73

WECC_SCE CA 61 92 291

WECC_SF CA 3 4 15

WECC_SNV NV 0 0 9

WECC_UT UT 3 5 26

WECC_WY NE 0 0 0

SD 0 0 1

WY 0 0 5



## 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 10 4 nuclear units in EPA Base Case v. 5.13 are represented by

separate model plants. As noted in Table 4

- 7 the 104 nuclear units include 100 currently operating units

plus Watts Bar Nuclear Plant (Unit 2), Vogtle (Units 3&4), and V C Summer (Units 2&3), which are

scheduled to come online during 20 15

– 2018 . All are listed in Table 4

- 34

. The population characteristics,

plant location, and unit configuration data in NEEDS v. 5.13 were obtained primarily from EIA F orm 860

and AEO 2013.

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. 5.13 uses capaci ty factor assumptions to define the

upper bound on generation from nuclear units. Nuclear capacity factor assumptions in EPA Base Case

### 4

- 70

v. 5.13 are based on an Annual Energy Outlook projection algorithm. The nuclear capacity factor

projection algorithm is d escribed 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 classifications (ol der and newer) are used.

For the older vintage (start before 1982) nuclear power plants, the performance peaks at 25 years:

Before 25 years: Performance increases by 0.5 percentage point per year;

25

- 60 years: Performance remains flat; and

For the newer v intage (start in or after 1982) nuclear power plants, the performance peaks at 30

years:

Before 30 years: Performance increases by 0.7 percentage points per year;

30

- 60 years: Performance remains flat; and

The maximum capacity factor is assumed to be 90 pe rcent. 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 that some units’ historical capac ity

factors are above 90 percent, the projected capacity factors range from 60 percent to 9 6 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 t here are no SO 2 

### , NO

x 

### , CO 2 

### , or mercury emissions

from nuclear units.

As with other generating resources , EPA Base Case v.5.13 uses variable operation and maintenance

(VOM) costs and fixed operation and maintenance ( FOM) costs to characterize the cost of op erating

nuclear units. The heat rate, FOM, and VOM values from AEO 2013 , which were used to characterize

the cost and performance of existing nuclear units in EPA Base Case v. 5.13 are shown in Table 4

- 34

.

EPA Base Case v. 5.13 also incorporates the planned nuclear capacity uprates sourced from AEO 2013

and EPA research . These are shown in Table 4

- 33

.

Table 4

- 33 Nuclear Upratings (MW) as Incorporated in EPA Base Case v. 5.13

Name Plant ID Unit ID Year Change in MWs

Fort Calhoun 2289 1 2017 75

McGuire 6038 1 2013 18.7

McGuire 6038 2 2013 18.7



### 4.5.2 Potential Nuclear Units

The cost and performance assumptions for nuclear potential units that the model has the option to build in

EPA Base Cas e v.5.13 are shown in Table 4

- 13 above. The cost assumptions are from AEO 2013.

Table 4

- 34 Characteristics of Existing Nuclear Units

Region State Plant Name Unique

ID On

Line

Year Capacity

(MW) Heat Rate

(Btu/kWh) FOM VOM

(2011$

/kW

- yr) (2011

mills/kWh)

ERC_REST Texas Comanche Peak 6145_1 1990 1,205 10,460 182.1 0.18

Comanche Peak 6145_2 1993 1,195 10,460 182.1 0.18

South Texas Project 6251_1 1988 1,280 10,460 199.2 0.18

South Texas Project 6251_2 1989 1,280 10,460 199.2 0.18

### 4

- 71 Region State Plant Name Unique

ID On

Line

Year Capacity

(MW) Heat Rate

(Btu/kWh) FOM VOM

(2011$

/kW

- yr) (2011

mills/kWh)

FRCC Florida St Lucie 6045_1 1976 961 10,460 160.8 0.15

St Lucie 6045_2 1983 949 10,460 160.8 0.15

Turkey Point 621_3 1972 802 10,460 227.2 0.21

Turkey Point 621_4 1973 802 10,460 227.2 0.21

MIS_IA Iowa Duane Arnold Energy Center 1060_1 1975 601 10,460 187.5 0.18

MIS_IL Illinois Clinton Power Station 204_1 1987 1,065 10,460 199.2 0.18

MIS_LMI Michigan Fermi 1729_2 1988 1,085 10,460 178.8 0.18

Palisades 1715_1 1972 803 10,460 200.3 0.18

MIS_MNWI Minnesota Monticello 1922_1 1971 633 10,460 251.6 0.25

Prairie Island 1925_1 1974 594 10,427 173.8 0.88

Prairie Island 1925_2 1974 592 10,427 173.8 0.89

MIS_MO Missouri Callaway 6153_1 1984 1,190 10,460 124.4 0.12

MIS_WUMS Wisconsin Point Beach Nuclear Plant 4046_1 1970 591 10,460 203.6 0.18

Po int Beach Nuclear Plant 4046_2 1972 593 10,460 203.6 0.18

NENG_CT Connecticut Millstone 566_2 1975 869 10,460 194.4 0.19

Millstone 566_3 1986 1,233 10,460 180.2 0.19

NENGREST Massachusetts Pilgrim Nuclear Power Station 1590_1 1972 685 10,460 225.7 0.18

New Hampshire Seabrook 6115_1 1990 1,246 10,460 199.2 0.19

NY_Z_A&B New York R E Ginna Nuclear Power Plant 6122_1 1970 581 10,460 216.8 0.18

NY_Z_C&E New York James A Fitzpatrick 6110_1 1976 828 10,460 216.1 0.18

Nine Mile Point Nuclear Station 2589_1 1969 630 10,460 204.2 0.18

Nine Mile Point Nuclear Station 2589_2 1987 1,143 10,460 199.2 0.18

NY_Z_G

- I New York Indian Point 2 2497_2 1973 1,006 10,460 207.2 0.18

Indian Point 3 8907_3 1976 1,031 10,460 194.9 0.18

PJM_ATSI Ohio Davis Besse 6149_1 1977 894 10,460 180.2 0.20

Perry 6020_1 1987 1,256 10,460 186.6 0.63

PJM_COMD Illinois Braidwood Generation Station 6022_1 1988 1,178 10,460 194.1 0.18

Braidwood Generation Station 6022_2 1988 1,152 10,460 194.1 0.18

Byron Generating Station 6023_1 1985 1,164 10,460 194.3 0.17

Byron Generating Station 6023_2 1987 1,136 10,460 194.3 0.17

Dresden Generating Station 869_2 1970 867 10,460 212.4 0.17

Dresden Generating Station 869_3 1971 867 10,460 212.4 0.18

LaSalle Generating Station 6026_1 1984 1,118 10,427 169.1 0.80

LaSalle Generating Station 6026_2 1984 1,120 10,427 169.1 0.82

Quad Cities Generating Station 880_1 1972 908 10,460 197.0 0.17

Quad Cities Generating Station 880_2 1972 911 10,460 197.0 0.18

PJM_Dom Virginia North Anna 6168_1 1978 943 10,460 114.1 0.10

North Anna 6168_2 1980 943 10,460 114.1 0.11

Surry 3806_1 1972 838 10,427 129.2 0.62

Surry 3806_2 1973 838 10,427 129.2 0.61

PJM_EMAC New Jersey Oyster Creek 2388_1 1969 614 10,460 225.4 0.19

PSEG Hope Creek Generating Station 6118_1 1986 1,173 10,460 180.2 0.18

PSEG Salem Generating Station 2410_1 1977 1,166 10,460 199.2 0.18

PSEG Salem Generating Station 2410_2 1981 1,160 10,460 199.2 0.18

Pennsylvania Limerick 6105_1 1986 1,146 10,460 199.9 0.17

Limerick 6105_2 1990 1,150 10,460 199.9 0.17

Peach Bottom 3166_2 1974 1,122 10,460 198.7 0.18

Peach Bottom 3166_3 1974 1,122 10,460 198.7 0.17

PJM_SMAC Maryland Calvert Cliffs Nuclear Power Plant 6011_1 1975 855 10,460 199.2 0.18

Calvert Cliffs Nuclear Power Plant 6011_2 1977 850 10,460 199.2 0.17

PJM_West Michigan Donald C Cook 6000_1 1975 1,009 10,460 150.6 0.24

Donald C Cook 6000_2 1978 1,060 10,460 150.6 0.14

Pennsylvania Beaver Valley 6040_1 1976 921 10,460 229.6 0.56

Beaver Valley 6040_2 1987 914 10,460 229.6 0.57

PJM_WMAC Pennsylvania PPL Susquehanna 6103_1 1983 1,260 10,460 186.3 0.20

### 4

- 72 Region State Plant Name Unique

ID On

Line

Year Capacity

(MW) Heat Rate

(Btu/kWh) FOM VOM

(2011$

/kW

- yr) (2011

mills/kWh)

PPL Susquehanna 6103_2 1985 1,260 10,460 186.3 0.18

Three Mile Island 8011_1 1974 805 10,460 194.3 0.18

S_C_TVA Alabama Browns Ferry 46_1 1974 1,101 10,460 199.2 0.19

Browns Ferry 46_2 1975 1,104 10,460 199.2 0.19

Browns Ferry 46_3 1977 1,105 10,460 199.2 0.20

Tennessee Sequoyah 6152_1 1981 1,152 10,460 210.3 0.18

Sequoyah 6152_2 1982 1,126 10,460 210.3 0.18

Watts Bar Nuclear Plant 7722_1 1996 1,123 10,460 198.0 0.18

Watts Bar Nuclear Plant 7722_2 2015 1,122 10,460 137.0 2.16

S_D_AMSO Louisiana Waterford 3 4270_3 1985 1,159 10,460 180.1 0.13

S_D_N_AR Arkansas Arkansas Nuclear One 8055_1 1974 834 10,460 161.7 0.13

Arkansas Nuclear One 8055_2 1980 989 10,460 161.7 0.12

S_D_REST Louisiana River Bend 6462_1 1986 974 10,460 163.2 0.17

Mississippi Grand Gulf 6072_1 1985 1,368 10,460 158.2 0.13

S_SOU Alabama Joseph M Farley 6001_1 1977 874 10,460 149.5 0.14

Joseph M Farley 6001_2 1981 860 10,460 149.5 0.14

Georgia Edwin I Hatch 6051_1 1975 876 10,460 133.2 0.14

Edwin I Hatch 6051_2 1979 883 10,460 133.2 0.14

Vogtle 649_1 1987 1,150 10,460 111.3 0.09

Vogtle 649_2 1989 1,152 10,460 111.3 0.09

Vogtle 649_3 2017 1,100 10,400 112.9 2.16

Vogtle 649_4 2018 1,100 10,400 112.9 2.16

S_VACA North Carolina Brunswick 6014_1 1977 938 10,460 155.7 0.14

Brunswick 6014_2 1975 932 10,460 155.7 0.14

Harris 6015_1 1987 900 10,460 186.9 0.16

McGuire 6038_1 1981 1,100 10,460 137.5 0.11

McGuire 6038_2 1984 1,100 10,460 137.5 0.11

South Carolina Catawba 6036_1 1985 1,129 10,460 137.7 0.13

Catawba 6036_2 1986 1,129 10,460 137.7 0.12

H B Robinson 3251_2 1971 724 10,460 142.2 0.16

Oconee 3265_1 1973 846 10,460 137.0 0.13

Oconee 3265_2 1974 846 10,460 137.0 0.12

Oconee 3265_3 1974 846 10,460 137.0 0.12

V C Summer 6127_1 1984 966 10,460 170.6 0.17

V C Summer 6127_2 2017 1,100 10,400 112.9 2.16

V C Summer 6127_3 2018 1,100 10,400 112.9 2.16

SPP_N Kansas Wolf Creek Generating Station 210_1 1985 1,175 10,460 159.6 0.16

SPP_NEBR Nebraska Cooper 8036_1 1974 766 10,460 199.2 0.18

Fort Calhoun 2289_1 1973 479 10,460 187.2 0.18

WEC_CALN California Diablo Canyon 6099_1 1985 1,122 10,460 169.8 0.18

Diablo Canyon 6099_2 1986 1,118 10,460 169.8 0.18

WECC_AZ Arizona Palo Verde 6008_1 1986 1,311 10,460 236.2 0.23

Palo Verde 6008_2 1986 1,314 10,460 236.2 0.23

Palo Verde 6008_3 1988 1,312 10,460 236.2 0.23

WECC_PNW Washington Columbia Generating Station 371_2 1984 1,097 10,460 202.3 0.19



### Excerpt from Table 4

- 35 Capacity Not Included Based on EIA Fo r m 860

- Existing Units

This is a small excerpt of the data in Excerpt from Table 4

- 35 . The complete data set in spreadsheet

format can be downloaded via the link found at http://www.epa.gov/airmarkets/progsregs/epa

ipm/BaseCasev513.html

.

Plant Name ORIS

Plant

Code Unit

ID Plant Type State Name Capacity

(MW) Notes

Hospira Inc 55788 GEN1 Combustion Turbine New York 1.1 Dropped

- Onsite Unit

### 4

- 73 Plant Name ORIS

Plant

Code Unit

ID Plant Type State Name Capacity

(MW) Notes

Hospira Inc 55788 GEN2 Combustion Turbine New York 1.1 Dropped

- Onsite Unit

AG Processing Inc 10223 E.C. Coal Steam Iowa 8.5 Dropped

- Onsite Unit

Oxford Cogeneration Facility 52093 GEN1 Combustion Turbine California 2.4 Dropped

- PLANNED_RETIREMENT_YEAR <=2015

Oxford Cogeneration Facility 52093 GEN2 Combustion Turbine California 2.4 Dropped

- PLANNED_RETIREMENT_YEAR <=2015

South Belridge Cogeneration Facility 50752 GEN1 Combustion Turbine California 19 Dropped

- Onsite Unit

South Belridge Cogeneration Facility 50752 GEN2 Combustion Turbine California 19 Dropped

- Onsite Unit

South Belridge Cogeneration Facility 50752 GEN3 Combustion Turbine California 19 Dropped

- Onsite Unit

Lost Hills Cogeneration Plant 52077 GEN4 Combustion Turbine California 2.7 Dropped

- Onsite Unit

Lost Hills Cogeneration Plant 52077 GEN5 Combustion Turbine California 2.7 Dropped

- Onsite Unit

Lost Hills Cogeneration Plant 52077 GEN6 Combustion Turbine California 2.7 Dropped

- Onsite Unit

AES Hawaii 10673 GEN1 Coal Steam Hawaii 180 Dropped

- in Alaska or in Hawaii

Agrium Kenai Nitrogen Operations 54452 744A Combustion Turbine Alaska 2.5 Dropped

- Onsite Unit

Agrium Kenai Nitrogen Operations 54452 744B Combustion Turbine Alaska 2.5 Dropped

- Onsite Unit

Agrium Kenai Nitrogen Operations 54452 744C Combustion Turbine Alaska 2.5 Dropped

- Onsite Unit

Agrium Kenai Nitrogen Operations 54452 744D Combustion Turbine Alaska 2.5 Dropped

- Onsite Unit

Agrium Kenai Nitrogen Operations 54452 744E Combustion Turbine Alaska 2.5 Dropped

- Onsite Unit

Southside Water Reclamation Plant 10339 GEN1 Non

- Fossil Waste New Mexico 2.1 Dropped

- Onsite Unit

Southside Water Reclamation Plant 10339 GEN2 Non

- Fossil Waste New Mexico 2.1 Dropped

- Onsite Unit

Southside Water Reclamation Plant 10339 GEN3 Non

- Fossil Waste New Mexico 1.1 Dropped

- Onsite Unit

Southside Water Reclamation Plant 10339 GEN4 Non

- Fossil Waste New Mexico 1.1 Dropped

- Onsite Unit

Martin Dam 16 1 Hydro Alabama 46.5 Dropped

- PLANNED_RETIREMENT_YEAR <=2015

Martin Dam 16 2 Hydro Alabama 46.5 Dropped

- PLANNED_RETIREMENT_YEAR <=2015



### Table 4

- 36 Capacity Not Included Due to Recent Announcements

Plant Name ORIS

Plant

Code Unit ID Plant Type State Name Capacity

(MW) Retirement

Year

5 in 1 Dam Hydroelectric 10171 GEN1 Hydro Iowa 0.7 2015

5 in 1 Dam Hydroelectric 10171 GEN2 Hydro Iowa 0.7 2015

5 in 1 Dam Hydroelectric 10171 GEN3 Hydro Iowa 0.7 2015

Abilene Energy Center Combustion Turbine 1251 GT1 Combustion Turbine Kansas 64 2012

ACE Cogeneration Facility 10002 CFB Coal Steam California 101 2015

AES Greenidge LLC 2527 6 Coal Steam New York 108 2012

AES Thames 10675 A Coal Steam Connecticut 90 2012

AES Thames 10675 B Coal Steam Connecticut 90 2012

AES Westover 2526 13 Coal Steam New York 84 2012

Albany 2113 3 Combustion Turbine Missouri 0.6 2015

Alliant SBD 9801 Aegon Martha's Way 56072 01 Combustion Turbine Iowa 1 2012

Alloy Steam Station 50012 BLR4 Coal Steam West Virginia 38 2007

Alma 4140 B1 Coal Steam Wisconsin 17.4 2013

Alma 4140 B2 Coal Steam Wisconsin 17.4 2013

Alma 4140 B3 Coal Steam Wisconsin 20.9 2013

Alma 4140 B4 Coal Steam Wisconsin 48 2015

Alma 4140 B5 Coal Steam Wisconsin 72 2015

Alvarado Hydro Facility 54242 AHF Hydro California 1.4 2015

Animas 2465 4 O/G Steam New Mexico 16 2015

Arapahoe 465 4 Coal Steam Colorado 109 2013

Astoria Generating Station 8906 20 O/G Steam New York 181 2012

B C Cobb 1695 1 O/G Steam Michigan 62 2015

B C Cobb 1695 2 O/G Steam Michigan 62 2015

B C Cobb 1695 3 O/G Steam Michigan 62 2015

B C Cobb 1695 4 Coal Steam Michigan 156 2015

B C Cobb 1695 5 Coal Steam Michigan 156 2015

B L England 2378 1 Coal Steam New Jersey 113 2013

B L England 2378 2 Coal Steam New Jersey 155 2015

B L England 2378 IC1 Combustion Turbine New Jersey 2 2015

B L England 2378 IC2 Combustion Turbine New Jersey 2 2015

B L England 2378 IC3 Combustion Turbine New Jersey 2 2015

B L England 2378 IC4 Combustion Turbine New Jersey 2 2015

Balefill LFG Project 55159 UNT1 Landfill Gas New Jersey 0.1 2010

Balefill LFG Project 55159 UNT2 Landfill Gas New Jersey 0.1 2010

### 4

- 74 Plant Name ORIS

Plant

Code Unit ID Plant Type State Name Capacity

(MW) Retirement

Year

Ben French 3325 1 Coal Steam South Dakota 21.6 2014

Berlin 6565 3A Combustion Turbine Maryland 1.8 2015

Berlin Gorham 54639 GOR1 Hydro New Hampshire 1.2 2015

Big Sandy 1353 BSU2 Coal Steam Kentucky 800 2014

Binghamton Cogen 55600 1 Combustion Turbine New York 42 2012

Biodyne Lyons 55060 001 Landfill Gas Illinois 0.9 2015

Biodyne Lyons 55060 002 Landfill Gas Illinois 0.9 2015

Biodyne Lyons 55060 004 Landfill Gas Illinois 0.9 2015

Biodyne Peoria 55057 001 Landfill Gas Illinois 0.8 2015

Biodyne Peoria 55057 002 Landfill Gas Illinois 0.8 2015

Biodyne Peoria 55057 004 Landfill Gas Illinois 0.8 2015

Biodyne Peoria 55057 005 Landfill Gas Illinois 0.8 2015

Biodyne Pontiac 55054 1 Landfill Gas Illinois 4.2 2015

Biodyne Pontiac 55054 3 Landfill Gas Illinois 4.2 2015

Biodyne Pontiac 55054 GEN2 Landfill Gas Illinois 4.2 2015

Biron 3971 6 Hydro Wisconsin 0.4 2015

Bluebonnet 55552 UNT2 Landfill Gas Texas 1 2015

Bountiful City 3665 2 Combustion Turbine Utah 1.2 2015

Bountiful City 3665 6 Combustion Turbine Utah 2.5 2015

Brunot Island 3096 1B Combustion Turbine Pennsylvania 15 2011

Brunot Island 3096 1C Combustion Turbine Pennsylvania 15 2011

Bryan 3561 3 O/G Steam Texas 12 2015

Bryan 3561 4 O/G Steam Texas 22 2015

Bryan 3561 5 O/G Steam Texas 25 2015

Bryan 3561 6 O/G Steam Texas 50 2015

Canadys Steam 3280 CAN1 Coal Steam South Carolina 105 2012

Canadys Steam 3280 CAN2 Coal Steam South Carolina 115 2013

Canadys Steam 3280 CAN3 Coal Steam South Carolina 180 2013

Cane Run 1363 4 Coal Steam Kentucky 155 2015

Cane Run 1363 5 Coal Steam Kentucky 168 2015

Cane Run 1363 6 Coal Steam Kentucky 240 2015

Cape Canaveral 609 PCC1 O/G Steam Florida 396 2010

Cape Canaveral 609 PCC2 O/G Steam Florida 396 2010

Cape Fear 2708 5 Coal Steam North Carolina 144 2012

Cape Fear 2708 6 Coal Steam North Carolina 172 2012

Cape Fear 2708 1B Combined Cycle North Carolina 11 2012

Carbon 3644 1 Coal Steam Utah 67 2015

Carbon 3644 2 Coal Steam Utah 105 2015

Cedar Station 2380 CED1 Combustion Turbine New Jersey 44 2015

Cedar Station 2380 CED2 Combustion Turbine New Jersey 22.3 2015

CES Placerita Power Plant 10677 UNT2 Combined Cycle California 46 2015

CES Placerita Power Plant 10677 UNT3 Combined Cycle California 23 2015

Chamois 2169 1 Coal Steam Missouri 16 2013

Chamois 2169 2 Coal Steam Missouri 47 2013

Cherokee 469 3 Coal Steam Colorado 152 2014

Chesapeake 3803 1 Coal Steam Virginia 111 2014

Chesapeake 3803 2 Coal Steam Virginia 111 2014

Chesapeake 3803 3 Coal Steam Virginia 156 2014

Chesapeake 3803 4 Coal Steam Virginia 217 2014

Chesapeake 3803 7 Combustion Turbine Virginia 16 2011

Chesapeake 3803 8 Combustion Turbine Virginia 16 2011

Chesapeake 3803 9 Combustion Turbine Virginia 16 2011

Chesapeake 3803 10 Combustion Turbine Virginia 16 2011

Clinch River 3775 3 Coal Steam Virginia 230 2015

Coal Canyon 226 1 Hydro California 0.9 2015

Conesville 2840 3 Coal Steam Ohio 165 2012

Conners Creek 1726 15 O/G Steam Michigan 58 2011

Conners Creek 1726 16 O/G Steam Michigan 58 2011

Conners Creek 1726 17 O/G Steam Michigan 58 2011

Conners Creek 1726 18 O/G Steam Michigan 58 2011

Crawford 867 7 Coal Steam Illinois 213 2012

### 4

- 75 Plant Name ORIS

Plant

Code Unit ID Plant Type State Name Capacity

(MW) Retirement

Year

Crawford 867 8 Coal Steam Illinois 319 2012

Crosscut 143 1 O/G Steam Arizona 7.5 2015

Crosscut 143 2 O/G Steam Arizona 7.5 2015

Crosscut 143 3 O/G Steam Arizona 7.5 2015

Crosscut 143 4 O/G Steam Arizona 2.5 2015

Crosscut 143 5 O/G Steam Arizona 2.5 2015

Crosscut 143 6 O/G Steam Arizona 2.5 2015

Crystal River 628 3 Nuclear Florida 1028 2013

CTV Power Purchase Contract Trust 54300 SX1S Wind California 0.1 2015

Cutler 610 PCU5 O/G Steam Florida 68 2012

Cutler 610 PCU6 O/G Steam Florida 137 2012

Cytec 1, 2 & 3 56257 CY 1 Combustion Turbine Connecticut 2 2011

Cytec 1, 2 & 3 56257 CY 2 Combustion Turbine Connecticut 2 2011

Cytec 1, 2 & 3 56257 CY 3 Combustion Turbine Connecticut 2 2011

Danskammer Generating Station 2480 1 O/G Steam New York 66 2013

Danskammer Generating Station 2480 2 O/G Steam New York 62 2013

Danskammer Generating Station 2480 3 Coal Steam New York 138 2013

Danskammer Generating Station 2480 4 Coal Steam New York 237 2013

Danskammer Generating Station 2480 5 Combustion Turbine New York 2.5 2013

Danskammer Generating Station 2480 6 Combustion Turbine New York 2.5 2013

DeCordova Power Company LLC 8063 1 O/G Steam Texas 818 2011

Deepwater 2384 1 O/G Steam New Jersey 78 2015

Deepwater 2384 8 Coal Steam New Jersey 81 2015

Dolphus M Grainger 3317 1 Coal Steam South Carolina 83 2013

Dolphus M Grainger 3317 2 Coal Steam South Carolina 83 2013

Dunbarton Energy Partners LP 55779 MA1 Landfill Gas New Hampshire 0.6 2012

Dunbarton Energy Partners LP 55779 MA2 Landfill Gas New Hampshire 0.6 2012

E F Barrett 2511 7 Combustion Turbine New York 16.6 2011

Eagle Mountain 3489 1 O/G Steam Texas 115 2015

Eagle Mountain 3489 2 O/G Steam Texas 175 2015

Eagle Mountain 3489 3 O/G Steam Texas 375 2015

Eagle Valley 991 3 Coal Steam Indiana 40 2015

Eagle Valley 991 4 Coal Steam Indiana 56 2015

Eagle Valley 991 5 Coal Steam Indiana 62 2015

East Third Street Power Plant 10367 CB1302 Coal Steam California 18.7 2012

Edgewater 4050 3 Coal Steam Wisconsin 70 2015

El Segundo Power 330 3 O/G Steam California 325 2013

Elrama Power Plant 3098 1 Coal Steam Pennsylvania 93 2012

Elrama Power Plant 3098 2 Coal Steam Pennsylvania 93 2012

Elrama Power Plant 3098 3 Coal Steam Pennsylvania 103 2012

Elrama Power Plant 3098 4 Coal Steam Pennsylvania 171 2012

FirstEnergy Albright 3942 1 Coal Steam West Virginia 73 2012

FirstEnergy Albright 3942 2 Coal Steam West Virginia 73 2012

FirstEnergy Albright 3942 3 Coal Steam West Virginia 137 2012

FirstEnergy Armstrong Power Station 3178 1 Coal Steam Pennsylvania 172 2012

FirstEnergy Armstrong Power Station 3178 2 Coal Steam Pennsylvania 172 2012

FirstEnergy Ashtabula 2835 7 Coal Steam Ohio 244 2015

FirstEnergy Bay Shore 2878 2 Coal Steam Ohio 138 2012

FirstEnergy Bay Shore 2878 3 Coal Steam Ohio 142 2012

FirstEnergy Bay Shore 2878 4 Coal Steam Ohio 215 2012

FirstEnergy Eastlake 2837 1 Coal Steam Ohio 132 2015

FirstEnergy Eastlake 2837 2 Coal Steam Ohio 132 2015

FirstEnergy Eastlake 2837 3 Coal Steam Ohio 132 2015

FirstEnergy Eastlake 2837 4 Coal Steam Ohio 240 2012

FirstEnergy Eastlake 2837 5 Coal Steam Ohio 597 2012

FirstEnergy Lake Shore 2838 18 Coal Steam Ohio 245 2015

FirstEnergy Mitchell Power Station 3181 1 O/G Steam Pennsylvania 27 2013

FirstEnergy Mitchell Power Station 3181 2 O/G Steam Pennsylvania 27 2013

FirstEnergy Mitchell Power Station 3181 3 O/G Steam Pennsylvania 27 2013

FirstEnergy Mitchell Power Station 3181 33 Coal Steam Pennsylvania 278 2013

FirstEnergy R E Burger 2864 5 Coal Steam Ohio 47 2011

### 4

- 76 Plant Name ORIS

Plant

Code Unit ID Plant Type State Name Capacity

(MW) Retirement

Year

FirstEnergy R E Burger 2864 6 Coal Steam Ohio 47 2011

FirstEnergy R Paul Smith Power Station 1570 9 Coal Steam Maryland 28 2012

FirstEnergy R Paul Smith Power Station 1570 11 Coal Steam Maryland 87 2012

FirstEnergy Rivesville 3945 7 Coal Steam West Virginia 37 2012

FirstEnergy Rivesville 3945 8 Coal Steam West Virginia 88 2012

FirstEnergy Willow Island 3946 1 Coal Steam West Virginia 54 2012

FirstEnergy Willow Island 3946 2 Coal Steam West Virginia 181 2012

Fisk Street 886 19 Coal Steam Illinois 326 2012

Frank E Ratts 1043 1SG1 Coal Steam Indiana 120 2015

Frank E Ratts 1043 2SG1 Coal Steam Indiana 121 2015

G W Ivey 665 18 Combustion Turbine Florida 8 2015

Gaylord 1706 5 Combustion Turbine Michigan 14 2015

George Neal North 1091 1 Coal Steam Iowa 137 2015

George Neal North 1091 2 Coal Steam Iowa 301 2015

Geysers Unit 5

- 20 286 U10 Geothermal California 30 2015

Geysers Unit 5

- 20 286 U9 Geothermal California 30 2015

Gilbert 2393 8 Combined Cycle New Jersey 90 2015

Gilbert 2393 C1 Combustion Turbine New Jersey 23 2015

Gilbert 2393 C2 Combustion Turbine New Jersey 25 2015

Gilbert 2393 C3 Combustion Turbine New Jersey 25 2015

Gilbert 2393 C4 Combustion Turbine New Jersey 25 2015

Glen Gardner 8227 1 Combustion Turbine New Jersey 20 2015

Glen Gardner 8227 2 Combustion Turbine New Jersey 20 2015

Glen Gardner 8227 3 Combustion Turbine New Jersey 20 2015

Glen Gardner 8227 4 Combustion Turbine New Jersey 20 2015

Glen Gardner 8227 5 Combustion Turbine New Jersey 20 2015

Glen Gardner 8227 6 Combustion Turbine New Jersey 20 2015

Glen Gardner 8227 7 Combustion Turbine New Jersey 20 2015

Glen Gardner 8227 8 Combustion Turbine New Jersey 20 2015

Glen Lyn 3776 6 Coal Steam Virginia 235 2015

Glen Lyn 3776 51 Coal Steam Virginia 45 2015

Glen Lyn 3776 52 Coal Steam Virginia 45 2015

Green River 1357 4 Coal Steam Kentucky 68 2015

Green River 1357 5 Coal Steam Kentucky 95 2015

Greenport 2681 2 Combustion Turbine New York 1.5 2015

Greenport 2681 7 Combustion Turbine New York 1.6 2015

Groveton Paper Board 56140 TUR1 Combustion Turbine New Hampshire 4 2015

Groveton Paper Board 56140 TUR2 Combustion Turbine New Hampshire 4 2015

H B Robinson 3251 1 Coal Steam South Carolina 177 2012

Hanford 10373 CB1302 Coal Steam California 25 2012

Hansel 672 21 Combined Cycle Florida 30 2012

Hansel 672 22 Combined Cycle Florida 8 2012

Hansel 672 23 Combined Cycle Florida 8 2012

Harbor Beach 1731 1 Coal Steam Michigan 95 2015

Harllee Branch 709 3 Coal Steam Georgia 509 2015

Harllee Branch 709 4 Coal Steam Georgia 507 2015

Harvey Couch 169 1 O/G Steam Arkansas 12 2015

Hatfields Ferry Power Station 3179 1 Coal Steam Pennsylvania 506 2013

Hatfields Ferry Power Station 3179 2 Coal Steam Pennsylvania 506 2013

Hatfields Ferry Power Station 3179 3 Coal Steam Pennsylvania 506 2013

Herington 1283 1 Combustion Turbine Kansas 1.6 2012

Herington 1283 2 Combustion Turbine Kansas 1 2012

Herington 1283 3 Combustion Turbine Kansas 3.1 2012

Herington 1283 5 Combustion Turbine Kansas 0.9 2012

Herkimer 52057 01 Hydro New York 0.1 2015

Herkimer 52057 02 Hydro New York 0.1 2015

Herkimer 52057 03 Hydro New York 0.1 2015

Herkimer 52057 04 Hydro New York 0.1 2015

High Street Station 1670 3 Combustion Turbine Massachusetts 0.7 2015

HMDC Kingsland Landfill 55604 UNT1 Landfill Gas New Jersey 0.1 2010

HMDC Kingsland Landfill 55604 UNT2 Landfill Gas New Jersey 0.1 2010

### 4

- 77 Plant Name ORIS

Plant

Code Unit ID Plant Type State Name Capacity

(MW) Retirement

Year

HMDC Kingsland Landfill 55604 UNT3 Landfill Gas New Jersey 0.1 2010

Holcomb Rock 56314 HG2 Hydro Virginia 0.2 2015

Howard Down 2434 10 O/G Steam New Jersey 23 2010

Hutsonville 863 05 Coal Steam Illinois 75 2011

Hutsonville 863 06 Coal Steam Illinois 76 2011

Indian River Generating Station 594 1 Coal Steam Delaware 89 2011

Indian River Generating Station 594 2 Coal Steam Delaware 89 2010

Ivy River Hydro 50890 GEN1 Hydro North Carolina 0.2 2015

Ivy River Hydro 50890 GEN2 Hydro North Carolina 0.2 2015

Ivy River Hydro 50890 GEN3 Hydro North Carolina 0.2 2015

Ivy River Hydro 50890 GEN4 Hydro North Carolina 0.2 2015

Ivy River Hydro 50890 GEN5 Hydro North Carolina 0.2 2015

Ivy River Hydro 50890 GEN6 Hydro North Carolina 0.2 2015

J C Weadock 1720 7 Coal Steam Michigan 155 2015

J C Weadock 1720 8 Coal Steam Michigan 151 2015

J R Whiting 1723 1 Coal Steam Michigan 97 2015

J R Whiting 1723 2 Coal Steam Michigan 101 2015

J R Whiting 1723 3 Coal Steam Michigan 124 2015

Jefferies 3319 3 Coal Steam South Carolina 152 2012

Jefferies 3319 4 Coal Steam South Carolina 150 2012

John Sevier 3405 3 Coal Steam Tennessee 176 2015

John Sevier 3405 4 Coal Steam Tennessee 176 2015

Johnsonville 3406 1 Coal Steam Tennessee 107 2015

Johnsonville 3406 2 Coal Steam Tennessee 107 2015

Johnsonville 3406 3 Coal Steam Tennessee 107 2015

Johnsonville 3406 4 Coal Steam Tennessee 107 2015

Kammer 3947 1 Coal Steam West Virginia 200 2015

Kammer 3947 2 Coal Steam West Virginia 200 2015

Kammer 3947 3 Coal Steam West Virginia 200 2015

Kanawha River 3936 1 Coal Steam West Virginia 200 2015

Kanawha River 3936 2 Coal Steam West Virginia 200 2015

Kaw 1294 1 O/G Steam Kansas 42 2013

Kaw 1294 2 O/G Steam Kansas 42 2013

Kaw 1294 3 O/G Steam Kansas 56 2013

Kewaunee 8024 1 Nuclear Wisconsin 566 2013

Kitty Hawk 2757 GT1 Combustion Turbine North Carolina 16 2011

Kitty Hawk 2757 GT2 Combustion Turbine North Carolina 15 2011

Kraft 733 3 Coal Steam Georgia 101 2015

L V Sutton 2713 1 Coal Steam North Carolina 97 2013

L V Sutton 2713 2 Coal Steam North Carolina 104 2013

L V Sutton 2713 3 Coal Steam North Carolina 389 2013

Lake Creek 3502 D1 Combustion Turbine Texas 2 2009

Lake Creek 3502 D2 Combustion Turbine Texas 2 2009

Lansing 1047 2 Coal Steam Iowa 8.4 2012

Lansing 1047 3 Coal Steam Iowa 21 2014

Lee 2709 GT1 Combustion Turbine North Carolina 12 2012

Lee 2709 GT2 Combustion Turbine North Carolina 21 2012

Lee 2709 GT3 Combustion Turbine North Carolina 21 2012

Lee 2709 GT4 Combustion Turbine North Carolina 21 2012

Lilliwaup Falls Generating 50700 4735 Hydro Washington 0.2 2015

Lilliwaup Falls Generating 50700 4736 Hydro Washington 0.2 2015

Lilliwaup Falls Generating 50700 4737 Hydro Washington 0.2 2015

Lilliwaup Falls Generating 50700 4738 Hydro Washington 0.2 2015

Lilliwaup Falls Generating 50700 4739 Hydro Washington 0.2 2015

Lilliwaup Falls Generating 50700 4740 Hydro Washington 0.2 2015

Lilliwaup Falls Generating 50700 4741 Hydro Washington 0.2 2015

Loveridge Road Power Plant 10368 CB1302 Coal Steam California 18 2012

Maine Energy Recovery 10338 ABLR Municipal Solid Waste Maine 9 2012

Maine Energy Recovery 10338 BBLR Municipal Solid Waste Maine 9 2012

Marysville 1732 9 Coal Steam Michigan 42 2011

Marysville 1732 10 Coal Steam Michigan 42 2011

### 4

- 78 Plant Name ORIS

Plant

Code Unit ID Plant Type State Name Capacity

(MW) Retirement

Year

Marysville 1732 11 Coal Steam Michigan 42 2011

Marysville 1732 12 Coal Steam Michigan 42 2011

McIntosh 6124 1 Coal Steam Georgia 156 2015

Meredosia 864 01 Coal Steam Illinois 26 2011

Meredosia 864 02 Coal Steam Illinois 26 2011

Meredosia 864 03 Coal Steam Illinois 26 2011

Meredosia 864 04 Coal Steam Illinois 26 2011

Meredosia 864 05 Coal Steam Illinois 203 2011

Meredosia 864 06 O/G Steam Illinois 166 2011

Miami Fort 2832 6 Coal Steam Ohio 163 2015

Middle Station 2382 MID1 Combustion Turbine New Jersey 19.1 2015

Middle Station 2382 MID2 Combustion Turbine New Jersey 19.5 2015

Middle Station 2382 MID3 Combustion Turbine New Jersey 36 2015

Missouri Avenue 2383 MISB Combustion Turbine New Jersey 20.5 2015

Missouri Avenue 2383 MISC Combustion Turbine New Jersey 20.5 2015

Missouri Avenue 2383 MISD Combustion Turbine New Jersey 20.6 2015

Montgomery 8025 1 Combustion Turbine Minnesota 20.6 2012

Morgan City 1449 1 O/G Steam Louisiana 5.8 2015

Morgan City 1449 2 O/G Steam Louisiana 5.8 2015

Morgan Creek 3492 5 O/G Steam Texas 175 2015

Morgan Creek 3492 6 O/G Steam Texas 511 2015

Morris Sheppard 3557 1 Hydro Texas 12 2015

Morris Sheppard 3557 2 Hydro Texas 12 2015

Muskingum River 2872 1 Coal Steam Ohio 190 2015

Muskingum River 2872 2 Coal Steam Ohio 190 2015

Muskingum River 2872 3 Coal Steam Ohio 205 2015

Muskingum River 2872 4 Coal Steam Ohio 205 2015

Muskingum River 2872 5 Coal Steam Ohio 585 2014

Neil Simpson 4150 5 Coal Steam Wyoming 14.6 2014

Nelson Dewey 4054 1 Coal Steam Wisconsin 115 2015

Nelson Dewey 4054 2 Coal Steam Wisconsin 111 2015

Neosho 1243 7 O/G Steam Kansas 67 2012

New Albany Energy Facility 55080 1 Combustion Turbine Mississippi 60 2015

New Albany Energy Facility 55080 2 Combustion Turbine Mississippi 60 2015

New Albany Energy Facility 55080 3 Combustion Turbine Mississippi 60 2015

New Albany Energy Facility 55080 4 Combustion Turbine Mississippi 60 2015

New Albany Energy Facility 55080 5 Combustion Turbine Mississippi 60 2015

New Albany Energy Facility 55080 6 Combustion Turbine Mississippi 60 2015

Nichols Road Power Plant 10371 CB1302 Coal Steam California 17.8 2012

Niles 2861 1 Coal Steam Ohio 108 2012

Niles 2861 2 Coal Steam Ohio 108 2012

Nine Mile 3869 1 Hydro Washington 8.9 2015

North Branch 7537 A Coal Steam West Virginia 37 2014

North Branch 7537 B Coal Steam West Virginia 37 2014

Norton 1310 1 Combustion Turbine Kansas 0.9 2011

Norton 1310 2 Combustion Turbine Kansas 1.3 2011

Norton 1310 3 Combustion Turbine Kansas 2.4 2011

Norton 1310 4 Combustion Turbine Kansas 3.1 2011

Norton 1310 5 Combustion Turbine Kansas 2.2 2011

O H Hutchings 2848 H

- 1 Coal Steam Ohio 58 2015

O H Hutchings 2848 H

- 2 Coal Steam Ohio 55 2015

O H Hutchings 2848 H

- 3 Coal Steam Ohio 63 2015

O H Hutchings 2848 H

- 4 Coal Steam Ohio 63 2013

O H Hutchings 2848 H

- 5 Coal Steam Ohio 63 2015

O H Hutchings 2848 H

- 6 Coal Steam Ohio 63 2015

Oakely 1311 1 Combustion Turbine Kansas 1.2 2012

Oakely 1311 2 Combustion Turbine Kansas 0.3 2012

Oakely 1311 4 Combustion Turbine Kansas 0.8 2012

Oakely 1311 6 Combustion Turbine Kansas 3.2 2012

Oakland Dam Hydroelectric 10433 1 Hydro Pennsylvania 0.5 2015

Oakland Dam Hydroelectric 10433 2 Hydro Pennsylvania 0.5 2015

### 4

- 79 Plant Name ORIS

Plant

Code Unit ID Plant Type State Name Capacity

(MW) Retirement

Year

Osage 4151 1 Coal Steam Wyoming 10.1 2010

Osage 4151 2 Coal Steam Wyoming 10.1 2010

Osage 4151 3 Coal Steam Wyoming 10.1 2010

Pearl Station 6238 1A Coal Steam Illinois 22.2 2012

Pella 1175 6 Coal Steam Iowa 11.5 2012

Pella 1175 7 Coal Steam Iowa 11.5 2012

Pella 1175 8 Coal Steam Iowa 11.5 2012

Permian Basin 3494 5 O/G Steam Texas 115 2011

Philip Sporn 3938 11 Coal Steam West Virginia 145 2015

Philip Sporn 3938 21 Coal Steam West Virginia 145 2015

Philip Sporn 3938 31 Coal Steam West Virginia 145 2015

Philip Sporn 3938 41 Coal Steam West Virginia 145 2015

Philip Sporn 3938 51 Coal Steam West Virginia 440 2012

Picway 2843 9 Coal Steam Ohio 95 2015

Port Everglades 617 PPE1 O/G Steam Florida 213 2013

Port Everglades 617 PPE2 O/G Steam Florida 213 2013

Port Everglades 617 PPE3 O/G Steam Florida 387 2013

Port Everglades 617 PPE4 O/G Steam Florida 392 2013

Porterdale Hydro 50242 TB

- 1 Hydro Georgia 0.7 2015

Porterdale Hydro 50242 TB

- 2 Hydro Georgia 0.7 2015

Portland 3113 1 Coal Steam Pennsylvania 158 2015

Portland 3113 2 Coal Steam Pennsylvania 243 2015

Powerdale 3031 1 Hydro Oregon 6 2015

Prairie Creek 1073 2 Coal Steam Iowa 2.1 2010

Prairie River 378 1 Hydro Minnesota 0.3 2015

Prairie River 378 2 Hydro Minnesota 0.3 2015

PSEG Burlington Generating Station 2399 91 Combustion Turbine New Jersey 46 2014

PSEG Burlington Generating Station 2399 92 Combustion Turbine New Jersey 46 2014

PSEG Burlington Generating Station 2399 93 Combustion Turbine New Jersey 46 2014

PSEG Burlington Generating Station 2399 94 Combustion Turbine New Jersey 46 2014

PSEG Burlington Generating Station 2399 111 Combustion Turbine New Jersey 46 2015

PSEG Burlington Generating Station 2399 112 Combustion Turbine New Jersey 46 2015

PSEG Burlington Generating Station 2399 113 Combustion Turbine New Jersey 46 2015

PSEG Burlington Generating Station 2399 114 Combustion Turbine New Jersey 46 2015

PSEG Edison Generating Station 2400 11 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 12 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 13 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 14 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 21 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 22 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 23 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 24 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 31 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 32 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 33 Combustion Turbine New Jersey 42 2015

PSEG Edison Generating Station 2400 34 Combustion Turbine New Jersey 42 2015

PSEG Essex Generating Station 2401 101 Combustion Turbine New Jersey 42 2015

PSEG Essex Generating Station 2401 102 Combustion Turbine New Jersey 42 2015

PSEG Essex Generating Station 2401 103 Combustion Turbine New Jersey 42 2015

PSEG Essex Generating Station 2401 104 Combustion Turbine New Jersey 42 2015

PSEG Essex Generating Station 2401 111 Combustion Turbine New Jersey 46 2015

PSEG Essex Generating Station 2401 112 Combustion Turbine New Jersey 46 2015

PSEG Essex Generating Station 2401 113 Combustion Turbine New Jersey 46 2015

PSEG Essex Generating Station 2401 114 Combustion Turbine New Jersey 46 2015

PSEG Essex Generating Station 2401 121 Combustion Turbine New Jersey 46 2015

PSEG Essex Generating Station 2401 122 Combustion Turbine New Jersey 46 2015

PSEG Essex Generating Station 2401 123 Combustion Turbine New Jersey 46 2015

PSEG Essex Generating Station 2401 124 Combustion Turbine New Jersey 46 2015

PSEG Sewaren Generating Station 2411 1 O/G Steam New Jersey 104 2015

PSEG Sewaren Generating Station 2411 2 O/G Steam New Jersey 118 2015

PSEG Sewaren Generating Station 2411 3 O/G Steam New Jersey 107 2015

### 4

- 80 Plant Name ORIS

Plant

Code Unit ID Plant Type State Name Capacity

(MW) Retirement

Year

PSEG Sewaren Generating Station 2411 4 O/G Steam New Jersey 124 2015

Pulliam 4072 5 Coal Steam Wisconsin 52 2015

Pulliam 4072 6 Coal Steam Wisconsin 71 2015

R Gallagher 1008 1 Coal Steam Indiana 140 2012

R Gallagher 1008 3 Coal Steam Indiana 140 2012

Ravenswood 2500 GT8 Combustion Turbine New York 20 2015

Reid Gardner 2324 1 Coal Steam Nevada 100 2014

Reid Gardner 2324 2 Coal Steam Nevada 100 2014

Reid Gardner 2324 3 Coal Steam Nevada 98 2014

Riverside 1559 GT6 Combustion Turbine Maryland 115 2014

Riverton 1239 39 Coal Steam Kansas 38 2015

Riverton 1239 40 Coal Steam Kansas 54 2015

Riviera 619 PRV3 O/G Steam Florida 277 2011

Riviera 619 PRV4 O/G Steam Florida 288 2011

Rochester 5 2641 2 Hydro New York 12.9 2015

Rochester 5 2641 HY1 Hydro New York 12.9 2015

Rochester 5 2641 HY3 Hydro New York 18 2015

Sabetha Power Plant 1320 4 Combustion Turbine Kansas 0.7 2012

Sabetha Power Plant 1320 8 Combustion Turbine Kansas 2.1 2012

San Francisquito 2 6480 1 Hydro California 14.5 2015

San Onofre Nuclear Generating Station 360 2 Nuclear California 1094 2013

San Onofre Nuclear Generating Station 360 3 Nuclear California 1080 2013

Schuylkill Generating Station 3169 1 O/G Steam Pennsylvania 166 2013

Schuylkill Generating Station 3169 IC1 Combustion Turbine Pennsylvania 2.7 2013

Shawville 3131 1 Coal Steam Pennsylvania 122 2015

Shawville 3131 2 Coal Steam Pennsylvania 125 2015

Shawville 3131 3 Coal Steam Pennsylvania 175 2015

Shawville 3131 4 Coal Steam Pennsylvania 175 2015

Shelby Municipal Light Plant 2943 1 Coal Steam Ohio 12 2012

Shelby Municipal Light Plant 2943 2 Coal Steam Ohio 12 2012

Small Hydro of Texas 55000 01 Hydro Texas 0.4 2015

Small Hydro of Texas 55000 02 Hydro Texas 0.4 2015

Small Hydro of Texas 55000 03 Hydro Texas 0.4 2015

Smart Papers LLC 50247 B010 Coal Steam Ohio 26 2012

Smart Papers LLC 50247 B020 Coal Steam Ohio 15.1 2012

Smart Papers LLC 50247 B022 Coal Steam Ohio 4.5 2012

Somerset Station 1613 6 Coal Steam Massachusetts 109 2011

Steamboat 1 50763 OE11 Geothermal Nevada 0.9 2015

Steamboat 1 50763 OE12 Geothermal Nevada 0.9 2015

Steamboat 1 50763 OE13 Geothermal Nevada 0.9 2015

Steamboat 1 50763 OE14 Geothermal Nevada 0.9 2015

Steamboat 1 50763 OE21 Geothermal Nevada 0.9 2015

Steamboat 1 50763 OE22 Geothermal Nevada 0.9 2015

Steamboat 1 50763 OE23 Geothermal Nevada 0.9 2015

Steamboat 1A Power Plant 52138 DE32 Geothermal Nevada 0.9 2015

Swift 2 6265 21 Hydro Washington 34 2015

Taconite Harbor Energy Center 10075 3 Coal Steam Minnesota 76 2015

Tangier 6390 3 Combustion Turbine Virginia 0.6 2015

Tangier 6390 4 Combustion Turbine Virginia 0.8 2015

Tanners Creek 988 U1 Coal Steam Indiana 145 2015

Tanners Creek 988 U2 Coal Steam Indiana 145 2015

Tanners Creek 988 U3 Coal Steam Indiana 200 2015

Tanners Creek 988 U4 Coal Steam Indiana 500 2014

Teche 1400 2 O/G Steam Louisiana 33 2011

Tecumseh Energy Center 1252 1 Combustion Turbine Kansas 18 2012

Tecumseh Energy Center 1252 2 Combustion Turbine Kansas 19 2012

Thomas C Ferguson 4937 1 O/G Steam Texas 420 2013

Thousand Springs 820 1 Hydro Idaho 0.8 2015

Thousand Springs 820 2 Hydro Idaho 0.8 2015

Tillotson Rubber 50095 IC1 Combustion Turbine New Hampshire 0.4 2012

Tillotson Rubber 50095 IC2 Combustion Turbine New Hampshire 0.6 2012

### 4

- 81 Plant Name ORIS

Plant

Code Unit ID Plant Type State Name Capacity

(MW) Retirement

Year

Tillotson Rubber 50095 TG2 Biomass New Hampshire 0.6 2012

Tillotson Rubber 50095 TGI Biomass New Hampshire 0.7 2012

Titus 3115 1 Coal Steam Pennsylvania 81 2015

Titus 3115 2 Coal Steam Pennsylvania 81 2015

Titus 3115 3 Coal Steam Pennsylvania 81 2015

Tradinghouse Power Company LLC 3506 2 O/G Steam Texas 818 2011

Trigen Syracuse Energy 50651 2 Coal Steam New York 24.6 2013

Trigen Syracuse Energy 50651 3 Coal Steam New York 24.6 2013

Trigen Syracuse Energy 50651 4 Coal Steam New York 12.3 2013

Trigen Syracuse Energy 50651 5 Coal Steam New York 12.3 2013

Tulsa 2965 1403 O/G Steam Oklahoma 65 2015

Turkey Point 621 PTP2 O/G Steam Florida 392 2013

TXU Sweetwater Generating Plant 50615 GT01 Combined Cycle Texas 41 2009

TXU Sweetwater Generating Plant 50615 GT02 Combined Cycle Texas 86 2009

TXU Sweetwater Generating Plant 50615 GT03 Combined Cycle Texas 86 2009

Tyrone 1361 5 Coal Steam Kentucky 71 2013

Union Carbide Seadrift Cogen 50150 IGT Combined Cycle Texas 12 2015

Upper Androscoggin 54202 2 Hydro Maine 0.5 2015

Venice 913 GT1 Combustion Turbine Illinois 26 2015

Vermilion 897 1 Coal Steam Illinois 62 2011

Vermilion 897 2 Coal Steam Illinois 99 2011

Vermilion 897 3 Combustion Turbine Illinois 10 2011

Vermont Yankee 3751 1 Nuclear Vermont 620.3 2014

Viking Energy of Northumberland 50771 B1 Biomass Pennsylvania 16.2 2012

W N Clark 462 55 Coal Steam Colorado 17.6 2013

W N Clark 462 59 Coal Steam Colorado 24.9 2013

W S Lee 3264 1 Coal Steam South Carolina 100 2015

W S Lee 3264 2 Coal Steam South Carolina 100 2015

W S Lee 3264 3 Coal Steam South Carolina 170 2015

Wabash River 1010 2 Coal Steam Indiana 85 2014

Wabash River 1010 3 Coal Steam Indiana 85 2014

Wabash River 1010 4 Coal Steam Indiana 85 2014

Wabash River 1010 5 Coal Steam Indiana 95 2014

Walter C Beckjord 2830 1 Coal Steam Ohio 94 2012

Walter C Beckjord 2830 2 Coal Steam Ohio 94 2015

Walter C Beckjord 2830 3 Coal Steam Ohio 128 2015

Walter C Beckjord 2830 4 Coal Steam Ohio 150 2015

Walter C Beckjord 2830 5 Coal Steam Ohio 238 2015

Walter C Beckjord 2830 6 Coal Steam Ohio 414 2015

Walter Scott Jr Energy Center 1082 1 Coal Steam Iowa 43 2015

Walter Scott Jr Energy Center 1082 2 Coal Steam Iowa 88 2015

Wanapum 3888 2 Hydro Washington 97 2012

Washington Parish Energy Center 55486 CTG1 Combined Cycle Louisiana 172 2015

Washington Parish Energy Center 55486 CTG2 Combined Cycle Louisiana 172 2015

Washington Parish Energy Center 55486 ST1 Combined Cycle Louisiana 215 2015

Watts Bar Fossil 3419 A Coal Steam Tennessee 56 2011

Watts Bar Fossil 3419 B Coal Steam Tennessee 56 2011

Watts Bar Fossil 3419 C Coal Steam Tennessee 56 2011

Watts Bar Fossil 3419 D Coal Steam Tennessee 56 2011

Webbers Falls 2987 3 Hydro Oklahoma 23 2015

Welsh 6139 2 Coal Steam Texas 528 2014

Werner 2385 GT1 Combustion Turbine New Jersey 53 2015

Werner 2385 GT2 Combustion Turbine New Jersey 53 2015

Werner 2385 GT3 Combustion Turbine New Jersey 53 2015

Werner 2385 GT4 Combustion Turbine New Jersey 53 2015

Western Renewable Energy 56358 1 Biomass Arizona 2.5 2015

Weston 4078 1 Coal Steam Wisconsin 58 2015

Weston 4078 2 Coal Steam Wisconsin 81 2015

Wilbur East Power Plant 10370 CB1302 Coal Steam California 18.1 2012

Wilbur West Power Plant 10369 CB1302 Coal Steam California 18.2 2012

Williston 2791 2 Combustion Turbine North Dakota 4.7 2012

### 4

- 82 Plant Name ORIS

Plant

Code Unit ID Plant Type State Name Capacity

(MW) Retirement

Year

Wisconsin Rapids 3974 6 Hydro Wisconsin 0.3 2015

Wisconsin Rapids 3974 8 Hydro Wisconsin 0.3 2015

Wiscoy 170 2646 1 Hydro New York 0.6 2015

Wiscoy 170 2646 2 Hydro New York 0.4 2015

Worcester Energy 10165 1 Biomass Maine 5.7 2015

Worcester Energy 10165 2 Biomass Maine 5.7 2015

Worcester Energy 10165 3 Biomass Maine 5.7 2015

Wythe Park Power Petersburg Plant 54045 1 Fossil Waste Virginia 3 2013

Yorktown 3809 1 Coal Steam Virginia 159 2014

Yuma 524 3 Combustion Turbine Colorado 0.2 2015

### 5

- 1



# 5. Emission Control Technologies

EPA Base Case v.5.13 includes an update of emission control technology assumptions. EPA contracted

with engineering firm Sargent and Lundy to update and add to the retrofit emission control models

previously developed for EPA and used in EPA Base Case v.4 .10. EPA Base Case v.5.13 thus includes

updated assumptions regarding control options for sulfur dioxide ( SO 2 

### ), nitrogen oxides ( NO

x 

### ), mercury

(Hg), and particulate matter (PM). These emission control options are listed in Table 5

- 1 . They are

available in EPA Base Case v.5.13 for meeting existing and potential federal, regional, and state emission

limits. It is important to note that, besides the emission control opt ions shown in Table 5

- 1 and described

in this chapter, EPA Base Case v.5.13 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.5.13

SO 2 

### and HCl

Control

Tech nology

Options NO

x 

### Control

Technology Options Mercury Control

Technology Options Particulate Matter

Control Technology

Options CO 2 

### Control

Technology

Options

Limestone Forced

Oxidation (LSFO)

Scrubber Selective Catalytic

Reduction (SCR)

System Activated Carbon

Injection (ACI) System Pulse

- Jet Fabric Filter

(FF) CO 2 

### Capture and

Sequestration

Lime Spray Dryer

(LSD) Scrubber Selective Non

Catalytic Reduction

(SNCR) System SO 2 

### and NO

x 

### Control

Technology Removal

Co

- benefits Electrostatic

Precipitator (ESP)

Upgrade Adjustment Coal

- to

- Gas

Conversion

Dry Sorbent

Injection (DSI) Combustion Controls Heat Rate

Improvement

FGD Upgrade

Adjustment



### Detailed reports and example calculation worksheets for Sargent & Lundy retrofit emission control models

used by EPA are available in Attachments 5

- 1 through 5

- 7 at: www.epa.gov/airmarkets/progsregs/epa

ipm/BaseCasev513.html

.



## 5.1 Sulfur Dioxide Control Technologies

- Scrubbers

Two commercially available Flue Gas Desulfurization (FGD) “scrubber” technology options for removing

the SO 2 

### produced by coal

- fired power plants are offered in EPA Base Case v.5.13: Limestone Forced

Oxidation (LSFO)

— a wet FGD technology and Lime Spray D ryer (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 exce eds 3 lb s 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.5.13 when a unit retrofits with an LSD SO 2 

### scrubber, it loses the option of burni ng

certain high sulfur content coals (see Table 5

- 2 ).

In EPA Base Case v.5.13 the LSFO and LSD SO 2 

### emission control technologies are available to existing

"unscrubbed" units. They are also available 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 databas e of 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 unit s retrofitting with a scrubber.

### 5

- 2

Default SO 2 

### removal rates for wet and dry FGD were based on data reported in EIA 860 (2010). These

default removal rates were the average of all SO 2 

### removal rates for a dry or wet FGD as reported in EIA

860 (2010) for the FGD installation year.

To reduce the inciden ce of implausibly high, outlier removal rates, units whose reported EIA Form 860

(2010) SO 2 

### removal rates are higher than the average of the upper quartile of SO 2 

### removal rates across

all scrubbed units are instead assigned the upper quartile average unles s the reported EIA 860 rate was

recently confirmed by utility comments. One upper quartile removal rate is calculated across all

installation years and replaces any reported removal rate that exceeds it no matter the installation year.

Existing units not reporting FGD removal rates in form EIA 860 (2010) will be assigned the default SO 2 

### removal rate for a dry or wet FGD for that installation year.

As shown in Table 5

- 2 , for FGD retrofits installed by the model, the assumed SO 2 

### removal rates will be

96% for wet FGD and 92% for dry FGD. These are the average of the SO 2 

### removal efficiencies reported

in EIA 860 (2008) for dry and wet FGD installed in 2008 or later. These rates have been subjected to

numerous reviews from utilities and other stakeholders recently, so they remain unchanged and continue

to be used in EPA Base Case v.5.13.

The procedures used to der ive 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 in Base Case v.5.13

Performance

Assumptions Lime stone Forced Oxidation (LSFO) Lime Spray Dryer (LSD)

Percent Removal 96%

with a floor of 0.06 lbs/MMBtu 92%

with a floor of 0.08 lbs/MMBtu

Capacity Penalty

Calculated based on characteristics of the unit:

See 

### Table 5

- 3 Calculated based on characteristics

of the unit:

See 

### Table 5

- 3 Heat Rate Penalty

Cost (2011$)

Applicability Units ≥ 25 MW Units ≥ 25 MW

Sulfur Content

Applicability Coals ≤ 3 lbs SO 2 

### /MMBtu 1



### Applicable Coal Types BA, BB, BD, BE, BG, BH, SA, SB, SD, SE, LD, LE,

LG, LH, PK and WC BA, BB, BD, BE, SA, SB, SD, SE,

LD, and LE

1 

### FBC units burning WC and PK fuels are provided with LSD retrofit options

Potential (new) coal

- fired units built by the model are also assumed to be constructed with a scrubber

achieving a removal efficiency of 96%. In EPA Base Case v.5.13 the costs of potential new coal units

include the cost of scrubbers.

5.1.1 Methodology for Obtaining SO 2 

### Controls Costs

Sargent and Lu ndy’s updated performance and cost models for wet and dry SO 2 

### scrubbers are

implemented in EPA Base Case v.5.13 to develop the capital, fixed O&M (FOM), and variable O&M

(VOM) components of cost. See Attachments 5

- 1 and 5

- 2 ( www.epa.go v/airmarkets/progsregs/epa

ipm/BaseCasev513.html ).

Capacity and Heat Rate Penalty : In IPM the amount of electrical power required to operate a retrofit

emission control device is represented through a reduction in the amount of electricity that is availab le 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 g eneration both for sale to the grid and for internal load (i.e., for operating

### 5

- 3

the control 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 24 

### . 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). In EPA Base

Case v.5.13 specific LSFO and LSD heat rate and capacity penalties are calculated for each installation

based on equations from the Sargent and Lundy models that take into account the rank of coal burned, its

uncontrolled SO 2 

### rate, and the heat rate of the mod el plant.

Table 5

- 3 presents the capital, VOM, and FOM costs as well as the capacity and heat rate penalty for two

SO 2 

### emission control technologies (LSFO and LSD) inc luded in EPA Base Case v.5.13 for an illustrative

set of generating units with a representative range of capacities and heat rates.

24 

### Mathematically, the relationship of the heat rate an d capacity penalties (both expressed as positive percentage

values) can be represented as follows:

### 5

- 4

Table 5

- 3 Illustrative Scrubber Costs (2011$) for Representative Sizes and Heat Rates under the Assumptions in EPA Base Case v.5.13

Scrubber

Type Heat Rate

(Btu/kWh) Capacity

Penalty

(%) Heat Rate

Penalty

(%) Variable O&M

(mills/kWh) Capacity (MW)

50 100 300 500 700 1000

Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr)

LSFO 9,000

- 1.50 1.53 2.03 819 23.7 819 23.7 600 11.2 519 8.3 471 7.7 426 6.4

10,000

- 1.67 1.70 2.26 860 24.2 860 24.2 629 11.5 544 8.6 495 8.0 447 6.6

11,000

- 1.84 1.87 2.49 899 24.6 899 24.6 658 11.8 569 8.9 517 8.2 467 6.8

LSD 9,000

- 1.18 1.20 2.51 854 29.1 701 17.3 513 8.6 444 6.5 422 5.7 422 5.3

10,000

- 1.32 1.33 2.79 894 29.6 734 17.7 538 8.9 465 6.8 442 5.9 442 5.5

11,000

- 1.45 1.47 3.07 933 30.0 766 18.0 561 9.1 485 7.0 461 6.1 461 5.7

Note: The above cost estimates assume a boiler burning 3 lb/MMBtu SO 2 Content Bituminous Coal for LSFO and 2 lb/MMBtu SO 2 Content Bituminous Coal for LSD.

### 5

- 5



## 5.2 Nitrogen Oxides Control Technology

The EPA Base Case v.5.13 includes two categorie s of NO

x 

### reduction technologies: combustion and post

combustion controls. Combustion controls reduce NO

x 

### emissions during the combustion process by

regulating flame characteristics such as temperature and fuel

- air mixing. Post

- combustion 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.5.13 are

commercially available and currently in use in numerous power plants.

5.2.1 Combu stion Controls

The EPA Base Case v.5.13 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 ap plied 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 A ttachment 3

- 1. The EPA Base Case v.5.13 cost assumptions for NO

x 

### Combustion

Controls are summarized in Table 5

- 4

. Table 3

- 11 provides a mapping of existing coal unit configurations

and incremental combustion contro ls applied in EPA Base Case v.5.13 when units under certain

conditions are assumed to achieve a state

- of

- the

- art combustion control configuration.

Table 5

- 4 Cost (2011$) of NO

x 

### Combustion Controls for Coal Boi lers (300 MW Size)

Boiler Type Technology Capital

($/kW) Fixed

O&M

($/kW

- yr) Variable

O&M

(mills/kWh)

Dry Bottom Wall

Fired Low NO

x 

### Burner without Overfire Air (LNB without OFA) 48 0.3 0.07

Low NO

x 

### Burner with Overfire Air (LNB with OFA) 65 0.5 0.09

Tangentially

- Fired Low NO

x 

### Coal

- and

- Air Nozzles with Close

- Coupled

Overfire Air (LNC1) 26 0.2 0.00

Low NO

x 

### Coal

- and

- Air Nozzles with Separated Overfire

Air (LNC2) 35 0.2 0.03

Low NO

x 

### Coal

- and

- Air Nozzles with Close

- Coupled and

Separated Overfire Air (LNC3) 41 0.3 0.03

Vertically

- Fired NO

x 

### Combustion Control 31 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 = ($/kW for X MW Unit) = ($/kW for 300 MW Unit) x (300/ X ) 0.359



### LNC1, LNC2, and LNC3 = ($/kW for X MW Unit) = ($/kW for 300 MW Unit) x (300/ X ) 0.359



### Vertically

- Fired = ($/kW for X MW Unit) = ($/kW for 300 MW Unit) x (300/ X ) 0.553



### where ($/kW for 300 MW Unit) is a value from the above table and X is the capacity (in MW) of the unit taking on

combustion controls.



### 5.2.2 Post

- combustion NO

x 

### Controls

The EPA Base Case v.5.13 includes two post

- combustion retrofit NO

x 

### control technologies for existing

coal units: Selective Catalytic Reduction (SCR) and Selective Non

- Catalytic Reduction (SNCR). In EPA

Base Case v.5.13 oil/gas steam units are eligible for SCR only. NO

x 

### reduction in a SCR system takes

place by injecting ammonia (NH 3 

### ) vapor into the flue gas stream where the NO

x 

### is reduced to nitrogen

(N 2 

### ) 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 w ithout a catalyst. In SNCR

a nitrogenous reducing agent (reagent), typically urea or ammonia, 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

### 5

- 6

to the presence o f a catalyst, SCR can achieve greater NO

x 

### reductions than SNCR. However, SCR costs

are higher than SNCR costs

.

Table 5

- 5 summarizes the performance and applicability assumptions in EPA Base Case v.5.13 for each

post

- combustion NO

x 

### control technology and provides a cross

- reference to information on cost

assumptions.

Table 5

- 5 Summary of Retrofit NO

x 

### Emission Control Perform ance Assumptions

Control Performance

Assumptions Selective Catalytic Reduction

(SCR) Selective Non

- Catalytic

Reduction

(SNCR)

Unit Type Coal Oil/Gas Coal

Percent Removal 90% 80% Pulverized Coal: 25%

Fluidized Bed: 50%

Rate Floor Bituminous: 0.07 lb/MMBtu

-- Pulverized Coal: 0.1 lb/MMBtu

Subbituminous and Lignite: 0.05

lb/MMBtu Fluidized Bed: 0.08 lb/MMBtu

Size Applicability Units ≥ 25 MW Units ≥ 25

MW Pulverized Coal: Units ≥ 25 MW

and ≤ 100 MW

Fluidized Bed: Units ≥ 25 MW

Costs (2011$) See Table 5

- 6 

### Illustrative Post

combustion NO x Control

Costs (2011$) for Coal Plants

for Representative Sizes and

Heat Rates under the

Assumptions in EPA Base

Case v.5.13 See 

### Table

5

- 7 See Table 5

- 6 

### Illustrative Post

combustion NO x Control Costs

(2011$) for Coal Plants for

Representative Sizes and Heat

Rates under the Assumptions

in EPA Base Case v.5.13

5.2.3 Methodology for Obtaining SCR Costs for Coal

Sargent and Lundy’s updated performance/cost models for SCR and SNCR technologies are

implemented in EPA Base Case v.5.13 to develop the capital, fixed O&M (FOM), and variable O&M

(VOM) components of cost. See Attachments 5

- 3 and 5

- 4 ( www.epa.gov/airmarkets/progsregs/epa

ipm/BaseCasev513.html ).

Table 5

- 6 presents the SCR and SNCR capital, VOM, and F OM costs and capacity and heat rate

penalties for an illustrative set of coal generating units with a representative range of capacities and heat

rates

.

### 5

- 7

Table 5

- 6 Illustrative Post

- combustion NO

x 

### Control Costs (2011$) for Coal Plants for Representative Sizes and Heat Rates under the

Assumptions in EPA Base Case v.5.13

Control

Type Heat Rate

(Btu/kWh) Capacity

Penalty

(%) Heat Rate

Penalty

(%) Variable O&M

(mills/kWh) Capacity (MW)

100 300 500 700 1000

Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr)

SCR 9,000

- 0.54 0.54 1.23 321 1.76 263 0.76 243 0.64 232 0.58 222 0.53

10,000

- 0.56 0 .56 1.32 349 1.86 287 0.81 266 0.69 255 0.63 244 0.57

11,000

- 0.58 0.59 1.41 377 1.96 311 0.87 289 0.73 277 0.67 265 0.62

SNCR

- Tangential 9,000

- 0.05 0.7 8 1.04 55 0.48 30 0.26 22 0.20 18 0.16 15 0.13

10,000 1.15 56 0.50 30 0.27 23 0.20 19 0.17 15 0.14

11,000 1.27 57 0.51 31 0.27 23 0.21 19 0.17 16 0.14

SNCR

-

Fluidized

Bed 9,000

- 0.05 0.78 1.04 41 0.36 22 0.20 17 0.15 14 0.12 11 0.10

10,000 1.15 42 0.37 23 0.20 17 0.15 14 0.12 12 0.10

11,000 1.27 43 0.38 23 0.21 17 0.15 14 0.13 12 0.10

Note: Assumes a boiler burning bituminous coal with an input NO

x rate of 0.5 lbs/MMBtu. The technology is applied to boilers larger than 25 MW.

### 5

- 8

5.2.4 Methodology for Obtaining SCR Costs for Oil/Gas Steam U nits

The cost calculations for SCR described in section 5.2.2 apply to coal units. For SCR on oil/gas steam

units the cost calculation procedure shown in Table 5

- 7 is used in EPA Base Case v.5.13. The scaling

factor for capital and fixed O&M costs, described in footnote a , applies to all size units from 25 MW and

up.

Table 5

- 7 Post

- Combustion NO

x 

### Controls for Oil/Gas Steam Unit s in EPA Base Case v.5.13

Post

- Combustion

Control Technology Capital

($/kW) Fixed O&M

($/kW

- yr) Variable O&M

($/MWh) Percent

Removal

SCR a 

### 80 1.16 0.13 80%

Notes:

The “Coefficients” in the table above are multiplied by the terms below to determine costs.

“MW” in the terms below is the unit’s capacity in megawatts.

Cost data are adjusted to 2011$ by EPA.

a 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) = 80 * (200/275) 0.35 ≈ 71.64 $/kW

SCR FOM Cost ($/kW

- yr) = 1.16 * (200/275) 0.35 ≈ 1.04 $/kW

- yr

SCR VOM Cost ($/MWh) = 0.13 $/MWh



### 5.2.5 Methodology for Obtaining SNCR C osts

In the Sargent and Lundy ’s 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) units are distinguished from the c orresponding costs for other boiler types (e.g.

cyclone, and wall fired). As with SCR an air heater modification cost applies for plants that burn

bituminous coal whose SO 2 

### content is 3 lbs/MMBtu or greater.

5.2.6 SO 2 

### and NO

x 

### Controls for Units with Capacitie s from 25 MW to 100 MW (25 M W ≤ capacity

< 100 MW)

In EPA Base Case v.5.13 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 for SCR, 50 MW for Dry

FGD, and 100 MW for Wet FGD

. These assumption s are based on s everal 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 minimum capacity cost equivalency assumption, though generic, would be

technically plausible. S econd, 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. 25



### 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 higher for units below 100 MW than

for a 100 MW unit, but when combined in the Multi

- Pollutant Technologies (MPTs) their costs would be

roughly equivale nt to the cost of individual controls on a 100 MW unit. While MPTs are not explicitly

represented in EPA Base Case v.5.13, 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 retr ofit ted with an MPT.

25 

### See, for example, the Greenidge Multi

- Pollutant Control Project, which was part of the U.S. Department of Energy,

National Energy Technology Lab’s Pow er Plant Improvement Initiative. A joint effort of CONSOL Energy Inc. AES

Greenidge LLC, and Babcock Power Environmental, Inc., the project is described in greater detail at

www.netl.doe.gov/technologies/coalpower/cctc/PPII/bibliography/demonstration/environmental/bib_greenidge.html

.

### 5

- 9

Illustrative SCR costs for 25

- 100 MW coal units with a range of heat rates can be found by referring to the

100 MW “Capital Costs ($/kW)” and “Fixed O&M” columns in Table 5

- 6 and i llustrative scrubber costs for

25

- 100 MW coal units with a range of heat rates can be found by referring to the LSFO 100 MW and LSD

50MW “Capital Costs ($/kW)” and “Fixed O&M” columns in Table 5

- 3 . 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

Biomass co

- firing is provided as a fuel choice for all coal

- fired power plants in EPA Base Case v.5.13.

However, logistics and boiler engineering considerations place limits on the extent of biomass that can be

fired. The logistic considerations arise because it is only economic to transport biomass a limited

distance from where it is grown given the low energy density of the fuel . 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 engin eering considerations, largely 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.5.13 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

m odeling projections . In EPA Base Case v.5.13 “facility level” is defined as the set of generating units

which share the same ORIS code 26 

### in NEEDS v.5.13.

The capital and FOM cost assumptions informing EPA Base Case v.5.13 regarding biomass co

- firing are

summarized in Table 5

- 8

, developed by EPA’s power sector engineering staff and updated to 2011$. 27



### Table 5

- 8 Biomass Co

- f iring for Coal Plants

Output From Biomass (MW) 5 10 15 20 25 30 35 40 45 50

Capital Cost (2011$/kW From Biomass) 521 439 396 368 349 333 320 309 301 293

Fixed O&M (2011$/kW

- yr) 25.8 17.3 12.5 10.0 8.5 11.8 10.6 9.5 8.6 8.0



### In order to economize on model space, instead of designing a biomass co

- firing “retrofit” modification for

units that would include direct representations of the capital and FOM costs shown in Table 5

- 8

. The

26 

### The ORIS plant locator code is a unique identifying number (originally assigned by the Office of Regulatory

Information Systems from which the acronym derived). The ORIS code is given to power plants by EIA and remains

unchanged ownership changes.

27 

### 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 , presen ted at 24th International Technical Conference on

Coal & Fuel Systems, Clearwater, FL., March 8

– 11, 1999.

(c) EPRI, Biomass Co

- firing : Field Test Results: Summary of Results of the Bailly and Seward

Demonstrations , Palo Alto, CA, supported by U.S. Department of Energy Division of Energy Efficiency and

Renewable Energy, Washington 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., Johnsto wn, PA: 1999. TR

- 113903.

(d) Laux S., J. Grusha, and D. Tillman, Co

- firing of Biomass and Opportunity Fuels in Low NOx Burners,

PowerGen 2000

- Orlando, FL, www.fwc.com/publicat ions/tech_papers/powgen/pdfs/clrw_bio.pdf

. (e) Tillman, D. A., Co

- firing Biomass for Greenhouse Gas Mitigation , presented at Power

- Gen 99, New

Orleans, LA, November 30

– December 1, 1999.

(f) Tillman, D. A. and P. Hus, Blending Opportunity Fuels with Coal for Eff iciency and Environmental Benefit

, presented at 25th International Technical Conference on Coal Utilization & Fuel Systems, Clearwater, FL.,

March 6

– 9, 2000

### 5

- 10

capital and FOM costs were implemented by in EPA Base Case v.5.13 as a $/MMBtu biomass fuel cost

adder. T he discrete costs shown in Table 5

- 8 are first represented as continuous exponential cost

functions showing the FOM and capital costs for all biomass outputs between 0 and 50 MW in size. Then,

for every coal generating unit represented in EPA Base Case 5.13, the annual paym ent to capital for the

biomass co

- firing capability was derived by multiplying the total capital cost obtained from the capital cost

exponential function by a 1 2.1 % capital charge rate for utility

- owned unit s and a 16.47% capital charge

rate for merchant units. 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 fo r 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 fu el required

by the generating unit in MMBtus. Dividing this number into the previously calculated total annual cost for

biomass co

- firing capability 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 unit co

- fires biomass. In this

manner, the model’s decision process for determining biomass consumption takes into account not just

the cost of the biomass fuel, but also the capital and FOM costs associated with bio mass co

- firing at the

units in question.

Chapter 11 discusses factors related to the delivered cost of biomass fuel in EPA Base Case v.5.13.



## 5.4 Mercury Control Technologies

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

activated carbon injection (ACI) , mercury emission reductions below the mercury content of the fuel are

strictly due to characterist ics of the combustion process and incidental removal resulting from other

pollution control technologies, e

. g ., the SO 2 

### , NO

x 

### , and particulate matter controls. The following

discussion is divided into three parts. Sections 5.4.1 and 5.4.2 explain the two factors that determine

mercury emissi ons that result from unit configurations lacking ACI under EPA Base Case v.5.13. Section

5.4.1 discusses how mercury content of fuel is modeled in EPA Base Case v.5.13. Section 5.4.2 looks at

the procedure used to capture the mercury reduct ions resulting from different unit and (non

- mercury)

control configurations. Section 5.4.4 explains the mercury emission control options that are available

under EPA Base Case v.5.13. Each section indicates the data sources and methodology used.

5.4.1 Mercury Content of Fuels

Coal : The assumptions in EPA Base Case v.5.13 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 Co llection

Effort” (ICR). 28 

### 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 coo peratives, 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 electr ic [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.

28 

### Data from the ICR can be found at http://www.epa.gov/ttn/atw/combust/utiltox/mercury.html

. In 2009, EPA collected some additional information regarding mecury through the Collection Effort for New and

Existing Coal

- and Oil

- Fired Electricty Utility Steam Generating Units (EPA ICR No.2362.01 (OMB Control Number

2060

- 0631), however the information collected was not similarly comprehensive and was thus not used to update

mercury assumptions in this EPA base case.

### 5

- 11

The ICR resulted in more than 40,000 data points indicating the coal type, sulfur content, mercury content

and other characteristics of coal burned at coal

- fired utility units greater than 25 MW. To make this data

usable, these data points were first grouped by IPM coal types and IPM coal supply regions. IPM coal

types divide bituminous, subbituminous, and lignite coal into different grades based on sulfur content.

Oil, natural gas, and waste fuels : The EPA Base Case v.5.13 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. 29 

### Table 5

- 9 provides a summary of the assumptions on the

mercury content for oil, gas and waste fuels included in EPA Base Case v.5.13.

Table 5

- 9 Assumptions on Mercury Concentration in Non

- Coal Fuel in EPA Base Case v.5.13

Fuel Type Mercury Concentration (lbs/TBtu)

Oil 0.48

Natural Gas 0.00 a



### Petroleum Coke 2.66 b



### Biomass 0.57

Municipal Solid Waste 71.85

Geothermal Resource 2.97

- 3.7

Note:

a 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 0.000138 lbs/TBtu. Values for geothermal resources represent a range.

b A previous computational error in the mercury emission factor for petroleum coke as presented in Table 6

- 3 of the EPA report

titled Control of Mercury Emissions from Coal

- fired Electric Utility Boilers: Interim Report Including Errata, 3

- 21

- 02 was corrected

(from 23.18 lbs/TBtu to 2.66 lb/TBtu) based on re

- examination of the 1999 ICR data for petroleu m coke and implementation of a

procedure for flagging and excluding outlier values above the 95 percentile value.



### 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 conc entration 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 the inlet) during combustion and flue

- gas

treatment process is (1

- EMF). The EMF varies by the type of coal (bituminous, subbituminous, and

lignite) used during the combustion process.

Deriving EMFs involves obtaining mercury inlet data by coal sampling and mercury emission data by

sta ck testing at a representative 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 30 

### has provided a better understanding of merc ury 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 subbituminous and lignite coal

- fired plants, and

s ignificant capture 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 u nderstanding of mercury capture with SCRs is

incorporated in EPA Base Case v.5.13 mercury EMFs for unit configurations with SCR and wet

scrubbers.

29 

### Analysis of Emission Reduction Options for the Electric Power Indust ry,” Office of Air and Radiation, US EPA,

March 1999.

30 

### For a detailed summary of emissions test data see Control of Emissions from Coal

- Fired Electric Utility Boilers: An

Update, EPA/Office of Research and Development, February 2005. This report can be f ound at

www.epa.gov/ttnatw01/utility/hgwhitepaperfinal.pdf

.

### 5

- 12

Table 5

- 10 below provides a summary of EMFs used in EPA Base Case v.5.13. Table 5

- 11 provides

definitions of acronyms for existing controls that appear in

### 5

- 13

Table 5

- 16

. Table 5

- 12 provides a key to the burner type designations appearing in

### 5

- 14

Table 5

- 16

.

Table 5

- 10 Mercury Emission Modification Factors Used in EPA Base Case v.5.13

Burner

Type Particulate Control Post

- combustion

Control

- NO

x Post

- combustion

Control

- SO 2 Bituminous

EMF Subbituminous

EMF Lignite

EMF

Cyclone Cold Side ESP SCR None 0.64 0.97 1

Cyclone Cold Side ESP SCR Wet FGD 0.1 0.84 0.56

Cyclone Cold Side ESP SCR Dry FGD 0.64 0.65 1

Cyclone Cold Side ESP No SCR None 0.64 0.97 1

Cyclone Cold Side ESP No SCR Wet FGD 0.34 0.84 0.56

Cyclone Cold Side ESP No SCR Dry FGD 0.64 0.65 1

Cyclone Cold Side ESP + FF SCR None 0.2 0.75 1

Cyclone Cold Side ESP + FF SCR Wet FGD 0.1 0.3 0.56

Cyclone Cold Side ESP + FF SCR Dry FGD 0.05 0.75 1

Cyclone Cold Side ESP + FF No SCR None 0.2 0.75 1

Cyclone Cold Side ESP + FF No SCR Wet FGD 0.3 0.3 0.56

Cyclone Cold Side ESP + FF No SCR Dry FGD 0.05 0.75 1

Cyclone Cold Side ESP +

FGC SCR None 0.64 0.97 1

Cyclone Cold Side ESP +

FGC SCR Wet FGD 0.1 0.84 0.56

Cyclone Cold Side ESP +

FGC SCR Dry FGD 0.64 0.65 1

Cyclone Cold Side ESP +

FGC No SCR None 0.64 0.97 1

Cyclone Cold Side ESP +

FGC No SCR Wet FGD 0.34 0.84 0.56

Cyclone Cold Side ESP +

FGC No SCR Dry FGD 0.64 0.65 1

Cyclone Cold Side ESP +

FGC + FF SCR None 0.2 0.75 1

Cyclone Cold Side ESP +

FGC + FF SCR Wet FGD 0.1 0.3 0.56

Cyclone Cold Side ESP +

FGC + FF SCR Dry FGD 0.1 0.3 0.56

Cyclone Cold Side ESP +

FGC + FF No SCR None 0.2 0.75 1

Cyclone Cold Side ESP +

FGC + FF No SCR Wet FGD 0.1 0.3 0.56

Cyclone Cold Side ESP +

FGC + FF No SCR Dry FGD 0.1 0.3 0.56

Cyclone Fabric Filter SCR None 0.11 0.27 1

Cyclone Fabric Filter SCR Wet FGD 0.1 0.27 0.56

Cyclone Fabric Filter SCR Dry FGD 0.05 0.75 1

Cyclone Fabric Filter No SCR None 0.11 0.27 1

Cyclone Fabric Filter No SCR Wet FGD 0.1 0.27 0.56

Cyclone Fabric Filter No SCR Dry FGD 0.05 0.75 1

Cyclone Hot Side ESP SCR None 0.9 0.9 1

Cyclone Hot Side ESP SCR Wet FGD 0.1 0.8 1

Cyclone Hot Side ESP SCR Dry FGD 0.6 0.85 1

Cyclone Hot Side ESP No SCR None 0.9 0.94 1

Cyclone Hot Side ESP No SCR Wet FGD 0.58 0.8 1

Cyclone Hot Side ESP No SCR Dry FGD 0.6 0.85 1

Cyclone Hot Side ESP + FF SCR None 0.11 0.27 1

Cyclone Hot Side ESP + FF SCR Wet FGD 0.1 0.15 0.56

Cyclone Hot Side ESP + FF SCR Dry FGD 0.05 0.75 1

Cyclone Hot Side ESP + FF No SCR None 0.11 0.27 1

Cyclone Hot Side ESP + FF No SCR Wet FGD 0.03 0.27 0.56

Cyclone Hot Side ESP + FF No SCR Dry FGD 0.05 0.75 1

Cyclone Hot Side ESP + FGC SCR None 0.9 0.9 1

Cyclone Hot Side ESP + FGC SCR Wet FGD 0.1 0.8 1

Cyclone Hot Side ESP + FGC SCR Dry FGD 0.6 0.85 1

Cyclone Hot Side ESP + FGC No SCR None 0.9 0.94 1

Cyclone Hot Side ESP + FGC No SCR Wet FGD 0.58 0.8 1

Cyclone Hot Side ESP + FGC No SCR Dry FGD 0.6 0.85 1

### 5

- 15 Burner

Type Particulate Control Post

- combustion

Control

- NO

x Post

- combustion

Control

- SO 2 Bituminous

EMF Subbituminous

EMF Lignite

EMF

Cyclone No Control SCR Wet FGD 0.1 0.7 1

Cyclone No Control No SCR Wet FGD 0.1 0.7 1

FBC Cold Side ESP No SCR None 0.65 0.65 0.62

FBC Cold Side ESP No SCR Dry FGD 0.64 0.65 1

FBC Cold Side ESP + FF No SCR None 0.05 0.43 0.43

FBC Cold Side ESP + FF No SCR Dry FGD 0.05 0.75 1

FBC Fabric Filter SCR None 0.11 0.27 1

FBC Fabric Filter SCR Dry FGD 0.05 0.75 1

FBC Fabric Filter No SCR None 0.05 0.43 0.43

FBC Fabric Filter No SCR Dry FGD 0.05 0.43 0.43

FBC Hot Side ESP + FGC No SCR None 1 1 1

FBC Hot Side ESP + FGC No SCR Dry FGD 0.6 0.85 1

FBC No Control No SCR None 1 1 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 No SCR None 0.64 0.97 1

PC Cold Side ESP No SCR Wet FGD 0.34 0.84 0.56

PC Cold Side ESP No SCR Dry FGD 0.64 0.65 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 No SCR None 0.2 0.75 1

PC Cold Side ESP + FF No SCR Wet FGD 0.3 0.3 0.56

PC Cold Side ESP + FF No SCR Dry FGD 0.05 0.75 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 No SCR None 0.64 0.97 1

PC Cold Side ESP +

FGC No SCR Wet FGD 0.34 0.84 0.56

PC Cold Side ESP +

FGC No SCR Dry FGD 0.64 0.65 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 No SCR None 0.2 0.75 1

PC Cold Side ESP +

FGC + FF No SCR Wet FGD 0.3 0.3 0.56

PC Cold Side ESP +

FGC + FF No SCR 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 No SCR None 0.11 0.27 1

PC Fabric Filter No SCR Wet FGD 0.1 0.27 0.56

PC Fabric Filter No SCR Dry FGD 0.05 0.75 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 No SCR None 0.9 0.94 1

PC Hot Side ESP No SCR Wet FGD 0.58 0.8 1

PC Hot Side ESP No SCR Dry FGD 0.6 0.85 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

### 5

- 16 Burner

Type Particulate Control Post

- combustion

Control

- NO

x Post

- combustion

Control

- SO 2 Bituminous

EMF Subbituminous

EMF Lignite

EMF

PC Hot Side ESP + FF No SCR None 0.11 0.27 1

PC Hot Side ESP + FF No SCR Wet FGD 0.03 0.27 0.56

PC Hot Side ESP + FF No SCR Dry FGD 0.05 0.75 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 No SCR None 0.9 0.94 1

PC Hot Side ESP + FGC No SCR Wet FGD 0.58 0.8 1

PC Hot Side ESP + FGC No SCR Dry FGD 0.6 0.85 1

PC Hot Side ESP + FGC

+ FF SCR Dry FGD 0.05 0.75 1

PC Hot Side ESP + FGC

+ FF No SCR None 0.11 0.27 1

PC Hot Side ESP + FGC

+ FF No SCR Dry FGD 0.05 0.75 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 No SCR None 1 1 1

PC No Control No SCR Wet FGD 0.58 0.7 1

PC No Control No SCR Dry FGD 0.6 0.85 1

PC PM Scrubber SCR None 0.9 1 1

PC PM Scrubber SCR Wet FGD 0.1 0.7 1

PC PM Scrubber SCR Dry FGD 0.6 0.85 1

PC PM Scrubber No SCR None 0.9 0.91 1

PC PM Scrubber No SCR Wet FGD 0.58 0.7 1

PC PM Scrubber No SCR Dry FGD 0.6 0.85 1



### Table 5

- 11 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

- 12 Key to Burner Type Designations in Table 5

- 1 0

“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 co al particles to be captured on the cyclone barrel wall where they are burned in molten slag.

“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.

### 5

- 17

5.4.3 Mercury Control Capabilities

EPA Base Case v.5.13 offers two options f or mercury pollution control : (1) combinations of SO 2 

### , NO

x 

### , and

par t iculate cont r ols which d e liver mercury reductions as a co

- benefit and ( 2) Activated Carbon Injection

(ACI), a retrofit option speci f ically designed for mercury co n trol. These t wo options are discussed below.

Mercury C o ntrol thro u gh SO 2 

### and NO

x 

### Retrofits

In EPA Base Case v.5.13

, units that install SO 2 

### , NO

x 

### , and p a rticulate co n trols, reduce mercury emissions

a s a byproduct of these r etrofits. Sec t ion 5.4.2 d e scribed how EMFs are used in the base case t o capture

mercury e m issions d e pending on t he rank of coal burned, the generating unit’s c o mbustion c h aracteristics,

and the s p ecific confi g uration of SO 2 

### , NO

x 

### , and particulate controls (i.e., hot and cold

- side electrostatic

precipitators (ESPs), fabric f il t ers (also called “bag h ouses”) and particulate matter (PM) scrubbers).

Activated Carbon Injection (ACI)

The technology used for mercury control in EPA Base Case v.5.1 3 is Activated Carbon Injection (ACI)

downstream of the combustion proc e ss in c o al fi r ed units. Sargent & Lundy ’s updated cost and

performance assum p tions for ACI are used.

Three altern a tive ACI options are represented as capable of providing 90% mercury removal for all possible

c o nfigurations of boiler, emission contr o ls, and coal ty pes used in the U.S. electric power sector. The

three ACI options diff e r, based o n whether they are used in conjunction with an electrostatic precipitator

(ESP) or a fabric filter (also called a “ baghouse” ). The three ACI options are:

ACI with Ex i s ting ESP

ACI with Ex i s ting Bagh o use

ACI with an Additional Baghouse (also refe r red to as T o xecon)

I n the third option lis t ed above the additional baghouse is installed d o wnstream of the pre

- existing

partic u late matter device and the activated carbon is injected after t he existing controls. This configura t ion

allows t h e fly ash to be removed before it is contaminated by the mercury.

For modeling purposes, EPA currently assumes that all three co n figurations use b r ominated ACI, where a

small amount of bromine is chemically bonded to the powdered carbon which is inj e cted into the flue gas

stream. EPA recognizes that amended silicates and possibly other non

- carbon, non

- brominated

substances are in development and may become available as alternatives to brominated carbon as a

mercury sorbent.

The a pplic a ble ACI option depends on the coal ty pe burned, its SO 2 

### con t ent, the boiler and particulate

control type and, in some instanc e s

, consideration of whether an SO 2 

### scrubber (FGD) syst e m and SCR

N O

x 

### post

- combustion c o ntrol are pr e s ent.

### 5

- 18

Table 5

- 13 shows the ACI assignment scheme used in EPA B a se Case v.5.13 to achieve 90% mercury

r emoval.

### 5

- 19

Table 5

- 13 Assignment Scheme for Mercury Emissions Control Using Activated Carbon Injection

(ACI) in EPA Base Case v.5.13

Air pollution controls Bituminous Coal Subbituminous Coal Lignite Coal

Burner

Type Particulate Control Type SCR

System FGD

System ACI

Required? Toxecon

Required? Sorbent Inj

Rate

ACI

Required? Toxecon

Required? Sorbent Inj

Rate

ACI

Required? Toxecon

Required? Sorbent Inj

Rate

(lb/million

acf) (lb/million

acf) (lb/million

acf)

FBC Cold Side ESP + Fabric Filter without FGC

-- -- Yes No 2 Yes No 2 Yes No 2

FBC Cold Side ESP without FGC

-- -- Yes No 5 Yes No 5 Yes No 5

FBC Fabric Filter

-- Dry FGD No No 0 Yes No 2 Yes No 2

FBC Fabric Filter

-- -- Yes No 2 Yes No 2 Yes No 2

FBC Hot Side ESP with FGC

-- -- Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Cold Side ESP + Fabric Filter with FGC

-- Dry FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter with FGC

-- -- Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter with FGC

-- Wet FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter with FGC SCR

-- Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter with FGC SCR Dry FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter with FGC SCR Wet FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter without FGC

-- Dry FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter without FGC

-- -- Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter without FGC

-- Wet FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter without FGC SCR

-- Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter without FGC SCR Dry FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP + Fabric Filter without FGC SCR Wet FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Cold Side ESP with FGC

-- Dry FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Cold Side ESP with FGC

-- -- Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Cold Side ESP with FGC

-- Wet FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Cold Side ESP with FGC SCR

-- Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Cold Side ESP with FGC SCR Dry FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Cold Side ESP with FGC SCR Wet FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Cold Side ESP without FGC

-- Dry FGD Yes No 5 Yes No 5 Yes No 5

Non

- FBC Cold Side ESP without FGC

-- -- Yes No 5 Yes No 5 Yes No 5

Non

- FBC Cold Side ESP without FGC

-- Wet FGD Yes No 5 Yes No 5 Yes No 5

Non

- FBC Cold Side ESP without FGC SCR

-- Yes No 5 Yes No 5 Yes No 5

Non

- FBC Cold Side ESP without FGC SCR Dry FGD Yes No 5 Yes No 5 Yes No 5

Non

- FBC Cold Side ESP without FGC SCR Wet FGD Yes No 5 Yes No 5 Yes No 5

Non

- FBC Fabric Filter

-- Dry FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Fabric Filter

-- -- Yes No 2 Yes No 2 Yes No 2

Non

- FBC Fabric Filter

-- Wet FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Fabric Filter SCR Dry FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Fabric Filter SCR

-- Yes No 2 Yes No 2 Yes No 2

Non

- FBC Fabric Filter SCR Wet FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Hot Side ESP + Fabric Filter with FGC

-- -- Yes No 2 Yes No 2 Yes No 2

Non

- FBC Hot Side ESP + Fabric Filter with FGC

-- Wet FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Hot Side ESP + Fabric Filter with FGC

-- Dry FGD No No 0 Yes No 2 Y(b) No 2

Non

- FBC Hot Side ESP + Fabric Filter with FGC SCR Wet FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Hot Side ESP + Fabric Filter with FGC SCR Dry FGD No No 0 Yes No 2 Y(b) No 2

Non

- FBC Hot Side ESP + Fabric Filter with FGC SCR

-- Yes No 2 Yes No 2 Y(b) No 2

Non

- FBC Hot Side ESP + Fabric Filter without FGC

-- Dry FGD No No 0 Yes No 2 Yes No 2

Non

- FBC Hot Side ESP + Fabric Filter without FGC

-- -- Yes No 2 Yes No 2 Yes No 2

Non

- FBC Hot Side ESP + Fabric Filter without FGC

-- Wet FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Hot Side ESP + Fabric Filter without FGC SCR Dry FGD No No 0 Yes No 2 Yes No 2

Non

- FBC Hot Side ESP + Fabric Filter without FGC SCR

-- Yes No 2 Yes No 2 Yes No 2

Non

- FBC Hot Side ESP + Fabric Filter without FGC SCR Wet FGD Yes No 2 Yes No 2 Yes No 2

Non

- FBC Hot Side ESP with FGC

-- Dry FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Hot Side ESP with FGC

-- -- Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Hot Side ESP with FGC

-- Wet FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Hot Side ESP with FGC SCR Dry FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Hot Side ESP with FGC SCR

-- Yes Yes 2 Yes Yes 2 Yes Yes 2

### 5

- 20 Air pollution controls Bituminous Coal Subbituminous Coal Lignite Coal

Burner

Type Particulate Control Type SCR

System FGD

System ACI

Required? Toxecon

Required? Sorbent Inj

Rate

ACI

Required? Toxecon

Required? Sorbent Inj

Rate

ACI

Required? Toxecon

Required? Sorbent Inj

Rate

(lb/million

acf) (lb/million

acf) (lb/million

acf)

Non

- FBC Hot Side ESP with FGC SCR Wet FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Hot Side ESP without FGC

-- Dry FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Hot Side ESP without FGC

-- -- Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Hot Side ESP without FGC

-- Wet FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Hot Side ESP without FGC SCR Dry FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Hot Side ESP without FGC SCR

-- Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC Hot Side ESP without FGC SCR Wet FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC No Control

-- Dry FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC No Control

-- -- Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC No Control

-- Wet FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC No Control SCR Dry FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC No Control SCR

-- Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC No Control SCR Wet FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC PM Scrubber

-- Dry FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC PM Scrubber

-- -- Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC PM Scrubber

-- Wet FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC PM Scrubber SCR Dry FGD Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC PM Scrubber SCR

-- Yes Yes 2 Yes Yes 2 Yes Yes 2

Non

- FBC PM Scrubber SCR Wet FGD Yes Yes 2 Yes Yes 2 Yes Yes 2



### 5.4.4 Methodology for Obtaining ACI Control Costs

Sargent & Lundy’s ACI model assumes that the carbon feed rate dictates the size of t he equipment and

resul t ing cos t s. T he feed rate in turn is a f unction of t h e required removal (in t his case 9 0 %) and the type

of particulate control device. Sargent & Lundy established that a carbon feed rate of 5 po u nds of carb o n

injected f o r every 1,0 0 0,000 actual cu b ic feet per minute (acfm) of flue gas w ould provide the stipula t ed

90% mercury removal rate for units shown in Table 5

- 14 as q u alifying for ACI systems with existing ESP.

For generating u nits with fa b ric filte r s a lower injection rate of 2 pound per million acfm is required.

Alternative sets of costs were developed for each of t h e three ACI options: ACI systems for units with

existing ESPs, ACI f or units with existing fabric filte r s (baghouses), and the combined cost of ACI plus an

addition a l baghouse f or units that either have no existing particulate control or th a t require ACI plus a

baghouse in addition to t heir existing particulate control. Th e re are various reasons that a combined ACI

plus addition a l baghouse would be req u ired. These include situ a tions where the existing ESP cannot

handle the additional particulate load associate with t he ACI or where SO

3 

### i njection is currently in use to

condi ti on the flue g as for the E SP. Another cause for combined ACI and baghouse is u s e of PRB coal

whose combustion pro d uces mostly elemental mercury, not ionic merc u ry, due to this coal’s low chlorine

co n tent.

For the combined ACI and fabric fil t er option a f ull size bag h ouse with a n air

- to

- cloth (A/C) ratio of 4.0 is

assumed, as opposed to a polishing ba ghouse with a 6.0 A/C rati o 31



### .

Table 5

- 14 presents the capital, VOM, and FOM costs as well as the capacity and heat rate penalties for

the three ACI options represented in EPA Base Case v.5.13 . For each ACI option valu es are shown for

an illustrative set of generating units with a representative range of capacities and heat rates. See

Attachment 5

- 6 ( www.epa.gov/airmarkets/progsregs/epa

- ip m/BaseCasev513.html ) for details on the

Sargent & Lundy model of ACI for Hg control.

31 The “air

- to

- c l oth” (A/C) rat i o is the vol u metric fl o w, (typically expr e s sed in Actu a l Cub i c Feet per Mi n ute, ACFM) of fl u e gas

e nteri n g the bag h o u se divid e d by the a r e a s (ty p ically in s q uare feet) of fabric filter cloth in the bag h o u se. The l o w e r the A/C rat i o, e.g., A/C = 4.0 comp a r e d to A/C = 6.0, the great e r area of the cloth r e qu i red and the hig h er the cost for a given v olumetric fl o w

### 5

- 21



## 5.5 Hydrogen Chloride (HCl) Control Technologies

T he following sub

- sections describe how HCl emissio n s from coal are repres e nted in IPM for EPA Base

Case v.5.13, the emissi o n control technologi e s available f or HCl removal, and the cost and p erformance

characteris t ics of these technologi e s

.

5.5.1 Chlorine Content of Fuels

HCl emissions from the power sector result from the chlorine content of the coal that is combusted by

electric generating units. Data on chlorine content of coals had been collected as part EPA’s 1999

“Information Collection Request for Electric Utility Steam Generating Unit Mercury Emissions Information

Collection Effort” (ICR 1999) described above in section 5.4.1 This data is incorporated into the model in

order to provide the capability for EPA Base Case v.5.13 to project HCl emissions. The procedure s used

for this are presented below.

Western subbituminous coal (such as that mined in the Powder River Basin) and lignite coal contain

natural alkalinity in the form of non

- glassy calcium oxide (CaO) and other alkaline and alkaline earth

oxides. This fl y ash (classified as ‘Class C’ fly ash) has a natural pH of 9 and higher and the natural

alkalinity can effectively neutralize much of the HCl in the flue gas stream prior to the primary control

device.

Eastern bituminous coals, by contrast, tend to produ ce fly ash with lower natural alkalinity. Though

bituminous fly ash (classified as ‘Class F’ fly ash) may contain calcium, it tends to be present in a glassy

matrix and unavailable for acid

- base neutralization reactions.

In order to assess the extent of ex pected natural neutralization, the 2010 ICR 32 

### data was examined.

According to that data, units burning some of the subbituminous coals without operating acid gas control

technology emitted substantially lower HCl emissions than would otherwise be expected f rom the chlorine

content of those coals The data also showed that some other units burning subbituminous or lignite coals

with higher levels of Cl were achieving 50

- 85 % HCl control with only cold

- side ESP (i.e., with no flue gas

desulfurization or other a cid gas control technology). Comparing the Cl content of the subbituminous

coals modeled in IPM with the ICR results supports an assumption that combustion of those coals can

expect to experience at least 75% natural HCl neutralization from the alkaline fl y ash. Therefore, the HCl

emissions from combustion of lignite and subbituminous coals are reduced by 75% in EPA Base Case

v.5.13.

32 

### Collection Effort for New and Existing Coal

- and Oil

- Fired Electricty Utility Steam Generating Units (EPA ICR

No.2362.01 (OMB Control Number 2060

- 0631)

### 5

- 22 Table 5

- 14 Illustrative Activated Carbon Injection (ACI) Costs (2011$) for Representative Sizes and Heat Rates

under the Assumptions in EPA Base Case v.5.13

Control Type Heat Rate

(Btu/kWh) Capacity

Penalty (%) Heat Rate

Penalty (%) Variable O&M

cost (mills/kWh) Capacity (MW)

100 300 500 700 1000

Capital

Cost

($/kW) Fixed

O&M

($/kW

- yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

- yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

- yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

- yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

- yr)

ACI System with an

Existing ESP 9,000

- 0.10 0.10 2.19 37.89 0.32 14.90 0.13 9.65 0.08 7.25 0.06 5.35 0.04

10,000

- 0.11 0.11 2.43 38.51 0.32 15.14 0.13 9.81 0.08 7.36 0.06 5.44 0.05

Sorbent Injection

Rate of 5 lbs/million

acfm 11,000

- 0.12 0.12 2.68 39.07 0.33 15.35 0.13 9.95 0.08 7.47 0.06 5.52 0.05

ACI System with an

Existing Baghouse 9,000

- 0.04 0.04 1.57 33.03 0.28 12.98 0.11 8.41 0.07 6.32 0.05 4.66 0.04

10,000

- 0.04 0.04 1.75 33.54 0.28 13.18 0.11 8.54 0.07 6.42 0.05 4.74 0.04

Sorbent Injection

Rate of 2 lbs/million

acfm 11,000

- 0.05 0.05 1.92 34.02 0.29 13.38 0.11 8.66 0.07 6.51 0.06 4.81 0.04

ACI System with an

Additional

Baghouse 9,000

- 0.64 0.64 0.47 291.26 1.02 219.74 0.77 195.35 0.68 181.36 0.63 167.98 0.59

10,000

- 0.64 0.65 0.52 314.32 1.10 238.18 0.83 212.02 0.74 196.97 0.69 182.55 0.64

Sorbent Injection

Rate of 2 lbs/million

acfm 11,000

- 0.65 0.65 0.57 336.91 1.18 256.26 0.90 228.37 0.80 212.28 0.74 196.83 0.69

Note: The above cost estimates assume bituminous coal consumption.

### 5

- 23

5.5.2 HCl Removal Rate Assumptions for Existing and Potential Units

SO 2 

### emission controls o n existing a nd new (potential) uni t s provide the HCl reductions indic a ted in Table

5

- 15

. New supercritic a l pulverized coal units (col um n 3) that the model builds i n clude FGD (wet or dry)

which is assumed t o provide a 99% removal rate for HCl. For ex i s ting conventional pulverized coal uni t s

with pre

- exist i ng FGD (column 5), the HCl rem o val rate is assumed to be 5% higher than t he reported SO 2



### rem o v a l rate up to a max i mum o f 99% removal. In addition, for fluidized be d combusti o n units (c o l um n 4)

with no FGD and no fabric fil t er, the HCl removal rate is assumed to be the same as the SO 2 

### removal rate

up to a max i mum of 9 5 %. FBCs with fabric filt e rs are assumed to have an HCl rem o val rate of 95%.

Table 5

- 15 HCl Removal Rate Assumptions for Potential (New) and Existing Units

in EPA Base Case v.5.13

Potential (New) Existing Units with FGD

Gas Controls Supercritical

Pulverized Coal with

Wet or Dry FGD Fluidized Bed

Combustion (FBC) Conventional

Pulverized Coal (CPC)

with Wet or Dry FGD Existing Coal Steam Units with

FGD Upgrade Adjustment

HCl Removal

Rate 99% Without fabric filter:

Same as reported SO 2 removal rate up to a

maximum of

95%

−−−

With fabric filter: 95% Reported SO 2

removal rate +

5% up to a

maximum of

99% If reported SO 2 removal < 90%, unit

incurs cost to upgrade FGD, so that

SO 2 removal is 90%. Then, the

resulting HCl removal rate is 99%

−−− If reported SO 2 removal is ≥ 90% and

< 94%, then the unit incurs a cost to

upgrade FGD and the HCl removal

rate is 99%. (The SO 2 removal rate

remains as reported.)

−−− If the reported SO 2 removal rate is ≥

94%, the unit incurs no upgrade cost

and the HCl removal rate is 99%.



### In EPA Base Case v.5.13, coal steam units with existing FGD that do not achieve an SO 2 

### removal rate of

at least 90% are assumed to upgrade their FGDs in order to obtain at least 90% SO 2 

### removal and 99%

HCl removal. The cost of this “FGD Upgrade Adjustment” is assumed to be $100/kW and is considered

a sunk cost for modeling purposes.

5.5.3 HCl Retrofit Emission Control Options

The retro f it options for HCl emission control a r e discussed in detail in the following sub

- sectio n s and

summarized in

### 5

- 24

Table 5

- 16 . The scrubber upgrade adjustment was disc u ssed above in 5.5.2.

Wet and Dry F GD

In addition t o providing SO 2 

### reductions, wet sc r ubbers (Limestone Forced Oxidation, LSFO) a n d dry

scrubbers (Lime Spray Dryer, LSD) reduce HCl as well. For both LSFO and LSD the HCl removal rate is

assumed to be 99% with a floor of 0.0001 l b s/MMBtu. T his is summarized in columns 2

- 5 of

### 5

- 25

Table 5

- 16

.

### 5

- 26 Table 5

- 16 Summary of Retrofit HCl (and SO 2 

### ) Emission Control Performance

Assumptions in v.5.13

Performance

Assumptions Limestone Forced Oxid ation (LSFO) Lime Spray Dryer (LSD) Dry Sorbent Injection (DSI)

SO 2 HCl SO 2 HCl SO 2 HCl

Percent

Removal 96%

with a floor of

0.06 lbs/MMBtu 99%

with a floor of

0.0001 lbs/MMBtu 92%

with a floor of

0.08 lbs/MMBtu 99%

with a floor of

0.0001

lbs/MMBtu 70% 90%

with a floor of 0.0001

lbs/MMBtu

Capacity

Penalty Calculated based on characteristics of

the unit:

See Table 5

- 3 Calculated based on characteristics

of the unit:

See Table 5

- 3 Calculated based on

cha racteristics of the unit:

See

Excerpt from Table 5

- 22 Heat Rate

Penalty

Cost (2011$)

Applicability Units ≥ 25 MW Units ≥ 25 MW Units ≥ 25 MW

Sulfur Content

Applicability Coals ≤ 3.0 lbs of SO 2 /MMBtu Coals ≤ 2.0 lbs of

SO 2 /MMBtu

Applicable

Coal

Types BA, BB, BD, BE, BG, BH, SA, SB, SD,

SE, LD, LE, LG, LH, PK and WC BA, BB, BD, BE, SA, SB, SD, SE, LD,

and LE BA, BB, BD, SA, SB, SD,

and LD



### Dry Sorbent Injection

EPA Base Case v.5.13 includes dry sorbent injection ( D SI) a s a retrofit option for achi ev ing (in

combination with a particulate cont r ol device) b oth SO 2 

### and HCl rem o v a l. In DSI for HCl reduction, a dry

sorbent is inject e d in t o the flue g a s duct whe r e it reacts with the HCl and SO 2 

### in the flue gas to form

compounds that are t h en captured in a downstream fabric filter or electrosta t ic precipitator (ESP) and

d isposed of as waste. (A sorbent is a material that takes up another su b s tance by either adsorption on its

su r fa c e or abs o rption inter n ally or in solution. A sorbent may also chemically react with another

s u bstance.) T he sorbent a ssumed in t he cost and performance characterization di scussed in t his sec t ion

is Trona (sodium sesquicarbonate), a s o dium

- rich material with major underground deposits found in

Sweetwa t er C ounty, Wyoming. Trona is typically delivered with an average particle size of 30 μm

d iameter, but can be red u ced to about 15 μm through ons i te in

- line mil l ing to incre a se its surface area and

capture cap a bility.

Rem o val rate assumpti o n s : The removal rate assumptions f or DSI are summarized in

### 5

- 27

Table 5

- 16 . The assumptions shown in the last two col u mns of

### 5

- 28

Table 5

- 16 were derived from assessmen t s by EPA e n gineering s t aff in consultation with Sargent &

Lundy. As indicated in this table, the assumed SO 2 

### rem o val rate for DSI + fabric filter is 70%. The ret r ofit

DSI option on an existing uni t with existing ESP is always provided in combination with a fabric filter

(Toxecon configuration) in EPA Base Case v.5.13.

Methodology for Obtaining DSI Con t rol Costs : Sargent & Lundy’s updated performance/cost model for

DSI is used in EPA Base Case v.5.13 to derive the cost o f DSI retrofits with two alternative, associa t ed

particulate control de v ices, i.e., ESP and fabric filter “baghouse”. Their analysis of DSI noted that t h e cost

drive r s of DSI are quite differ e nt from those of wet or dry FGD. Whereas p lant size a n d coal s u lfur rates

are k e y underlying determinants of FGD cost, sorbent f eed rate and fly ash waste handling are the main

drivers of the capital cost of DSI with plant size and coal s u lfur rates pl ay ing a sec o ndary role.

In EPA Base Case v.5.13 the DSI sorbent feed rate a n d variable O&M costs are based on assumptio n s

that a fabr i c filter and i n

- line trona milling are u s ed, and th a t the SO 2 

### removal rate is 70%. The

corresponding HCl removal effect is assumed to be 90%, b a sed on info rm ation from Solv a y Chemicals (H.

Davidso n

, Dry Sorbent Injection f or Multi

- pollutant Control Case Stud y

, CIBO IECT VIII, August, 2010 )

.

The cost of f ly ash waste handling, t h e other key contributor t o DSI cost, is a function of the type of

particula t e capture d e vice and the flue gas SO 2



### .

Total waste production involves the production o f both react e d and unreacted sorbent and fly ash.

Sorbent waste is a function of t he sorbent f eed rate with an adjus tm ent for ex c ess sorbent feed. Use of

sodium

- based DSI may make the fly ash unsalable, w hich would mean that any fly ash produc e d must be

landfilled a l o ng with the reacted and unreacted sorbent waste. Typical a s h contents for each fuel are us e d

to calc u la t e a total fly ash production rate. T h e fly ash production is a dded to the sorbent waste to accou n t

for the total waste stre a m for the VOM ana l ysis.

For purpos e s of modeli n g, the total VOM includes the first t wo component costs n o ted in the previous

paragraph, i.e., the costs f or sorbent us a ge and the costs associ a ted with waste production a nd dispos a l.

Table 5

- 17 presents the capital, VOM, and FOM costs a s well as the ca p acity and heat rate penalties of a

DSI retrofit for an ill u s t rative and representative set of gene r ating units w ith the capaciti e s a nd heat rat e s

indica t ed. See Attachment 5

- 5 ( www.epa.gov/airmarkets/progsregs/epa

- ipm/BaseCasev513.html ) for

details on the Sargent & Lundy DSI model.



## 5.6 Fabric Filter (Baghouse) Cost Development

Fabric filters are not endogenously modeled as a separate retrofit option. In EPA Base Case v.5.13 , an

existing or new fabric filter particulate control device is a pre

- condition for installing a DSI retrofit, and the

cost of t hese retrofits at plants without an existing fabric filter include the cost of installing a new fabric

filter. This cost was added to the DSI costs discussed in section 5.5.3.2. The costs associated with a new

fabric filter retrofit are derived from Sarge nt & Lundy’s performance/cost model. Similarly, dry scrubber

retrofit costs also include the cost of a fabric filter.

The engineering cost analysis is based on a pulse

- jet fabric filter which collects particulate matter on a

fabric bag and uses air pulses to dislodge the particulate from the bag surface and collect it in hoppers for

removal via an ash handling system to a silo. This is a mature technology that has been operating

commercially for more than 25 years. “Baghouse” and “fabric filters” are used interchangeably to refer to

such installations.

Capital Cost : The major driver of fabric filter capital cost is the “air

- to

- cloth” (A/C) ratio. The A/C ratio is

defined as the volumetric flow, (typically expressed in Actual Cubic Feet per Minute, ACFM) of flue gas

entering the baghouse divided by the areas (typically in square feet) of fabric filter cloth in the baghouse.

The lower the A/C ratio, e.g., A/C = 4.0 compared to A/C = 6.0, the greater the area of the cloth required

### 5

- 29

and the higher the cost for a given volumetric flow. An air

- to

- cloth ratio of 4.0 is used in EPA Base Case

v.5.13, and it is assumed that the existing ESP remains in place and a c tive.

Table 5

- 18 presents the capital, VOM, and FOM costs for fabric filte r s as represented in EPA Base Case

v.5.13 for an ill u st r ative set of generating u nits with a r epresentative range of capaciti e s a nd heat rat e s.

See Attachment 5

- 7 ( www.epa.gov/airmarkets/progsregs/epa

- ipm/BaseCasev513.html ) for details of the

Sargent & Lundy fabric filter PM control model.

### 5

- 30 Table 5

- 17 Illustrative Dry Sorbent Injection (DSI) Costs for Representative Sizes and Heat Rates

u nder Assumptions in EPA Base Case v.5.13

Control Type Heat Rate

(Btu/kWh ) SO 2

Rate

(lb/

MMBtu) Capacity

Penalty (%) Heat Rate

Penalty

(%) Variable O&M

(mills/kWh) Capacity (MW)

100 300 500 700 1000

Capital

Cost

($/kW) Fixed

O&M

($/kW

- yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

- yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

- yr) Cap ital

Cost

($/kW) Fixed

O&M

($/kW

- yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

- yr)

DSI 9,000 2.0

- 0.64 0.65 8.49 138.5 3.71 63.1 1. 38 43.7 0.88 34.4 0.65 31.6 0.52

Assuming

Bituminous

Coal 10,000 2.0

- 0.71 0.72 9.44 142.8 3.75 65.0 1.40 45.1 0.89 35.1 0.66 35.1 0.55

11,000 2.0

- 0.79 0.79 10.39 146.8 3.78 66.9 1.41 46.4 0.90 38.6 0.69 38.6 0.58



### Table 5

- 18 Illustrative Particulate Controls for Costs (2011$) for Representative Sizes and Heat Rates

under the Assumptions in EPA Base Case v.5.13

Coal Type Heat Rate

(Btu/kWh) Capacity

Penalty (%) Heat Rate

Penalty (%) Variable

O&M

(mills/kWh) Capacity (MW)

100 300 500 700 1000

Capital

Cost

($/kW) Fixed O&M

($/kW

- yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr ) Capital

Cost

($/kW) Fixed

O&M

($/kW

yr)

Bituminous 9,000

- 0.60 0.60 0.05 251 0.9 204 0.7 185 0.6 174 0.6 162 0.6

10,000 0.06 274 1.0 222 0.8 202 0.7 189 0.7 177 0.6

11,000 0.07 296 1.0 240 0.8 218 0.8 204 0.7 191 0.7

### 5

- 31

5.6.1 MATS Filterable Particulate Matter (PM) Compliance

EPA Base Case v.5.13 assumes that all coal

- fired generating units with a capacity greater than 25 MW

will comply with the MATS filterable PM requirements through the operation of either el ectrostatic

precipitator (ESP) or fabric filter (FF) particulate controls. The control mechanism is not modeled

endogenously but supplied as an input when setting up the run as specified below.

Units with existing fabric filters are assumed to be able to meet the filterable PM compliance requirement.

For units with existing ESPs the following procedure is used to determine if they already meet the

filterable PM requirement, can meet it by one of three possible ESP upgrades, or can only meet it by

install ing a FF.

First, PM emission rate data derived either from 2005 EIA Form 767 or (where available) from EPA’s

2010 Information Collection Request 33 

### are compared to the applicable filterable PM compliance

requirement. If the unit’s emission rate is equal to or less than the compliance requirement, adequate

controls are assumed already to be in place and no additional upgrade costs are imposed. For units that

do not meet the filterable PM compliance requirement, the incremental reduction needed (in lbs/mmBtu )

is calculated by subtracting the filterable PM compliance standard from the reported emission rate.

Depending on the magnitude of the incremental reduction needed, the unit is assigned one of three ESP

upgrade costs (designated ESP1, ESP2, and ESP3) or the cost of a FF installation (designated ESP4), if

the required incremental reduction cannot be achieved by an ESP upgrade. Table 5

- 19 shows the four

levels of ESP upgrades (column 1), the key technologies included in each upgrade (column 2), trigger

points for the upgrades (column 3), the capital cost of each upgrade (column 4), and the percent increase

in collection efficiency provided by the upgrade, differentiate d according to the rank (subbituminous,

bituminous, or lignite) of coal burned.

The percentage improvements in collection efficiency shown in column 5 in Table 5

- 19 are additive in the

sense that the values shown in this column are added to the pre

- upgrade collection efficiency to obtain

the after

- upgrade collection efficiency.

Table 5

- 19 Electrostatic Precipitator (ES P) Upgrades as Implemented in EPA Base Case v.5.13

--- Characteristics, Trigger Points, Associated Costs, and Performance Improvements

Upgrade

Level Key Technologies

Employed in

Upgrade Trigger Points for ESP

Upgrade

(Expressed in terms of

incremental reduction

needed (lbs/mmBtu) to

meet the filterable PM

Compliance Standard) Capital Cost Additive Percent Improvement e in

Collection Efficiency as a Result of

the Upgrade (differentiated by the

rank of coal combusted)

1 High Frequency transformer

rectifie r

(TR) sets > 0.0 to ≤ 0.005 $55/kW a 0.12 for subbituminous

0.05 for bituminous

0.01 for lignite

2 High frequency transformer

rectifier

(TR) sets + New internals

(rigid electrodes, increased

plate spacing, increased

plate height) > 0.005 to ≤ 0.01 $80/kW b 0.25 for subbituminous

0.10 for bituminous

0.02 for lignite

33 

### 2005 EIA Form 767 is the last year where the data was reported in the format of lb/ MMBtu, which is compatible

with this analysis. Since any changes to facilities since 2005 would likely have improved (reduced) emissions, the

use of this data is conservative. More recent 2010 ICR test data is used where available. (Collection Effort fo r New

and Existing Coal

- and Oil

- Fired Electricty Utility Steam Generating Units (EPA ICR No.2362.01 (OMB Control

Number 2060

- 0631).

### 5

- 32

Upgrade

Level Key Technologies

Employed in

Upgrade Trigger Points for ESP

Upgrade

(Expressed in terms of

incremental reduction

needed (lbs/mmBtu) to

meet the filterable PM

Compliance Standard) Capital Cost Additive Percent Improvement e in

Collection Efficiency as a Result of

the Upgrade (differentiated by the

rank of coal combusted)

3 High frequency transformer

rectifier

(TR) sets + New internals

(rigid electrodes, increased

plate spacing, increased

plate height) + Additional field > 0.01 to ≤ 0.02 $100/kW c 0.50 for subbituminous

0.20 for bituminous

0.05 for lignite

4 Replacement with fabric filter

(baghouse) > 0.02 Use capital cost

equations for a

fabric filter d (Not Applicable)

a Assumes upgrading the specific collection area (SCA) to 250 square

- feet/1000 afm (actual feet per minute).

b Assumes upgrading the specific collection area (SCA) to 300 square

- feet/1000 afm (actual feet per minute).

c Assumes upgrading the existing specific collection area (SCA) by 100 square

- feet/1000 afm (actual feet per minute), a 2 0% height increase, and

additional field.

d The cost equations for fabric filters are described in Section 5.5.4

e The percentage improvement due to the ESP upgrade as shown in this column is added to the pre

- upgrade collection efficiency to obtain the after

upgrade collection removal efficiency.



### Excerpt from Table 5

- 20 contains a complete listing of coal generating units with either cold

- or hot

- side

ESPs but no fabric filters. For each generating unit in Excerpt from Table 5

- 20 shows the incremental

reductions needed to meet the PM filterable compliance requirement and the corresponding ESP upgrade

(if any) assigned to the unit to enable it to meet that requirement. A filterable PM limit of 0.279 lb/mmBtu

was used in this analysis. This value is roughly 10% below the limit in the final MATS rule, therefore

resulting in a conservative estimate of the need to upgrade existing ESPs.



## 5.7 Coal

- to

- Gas Conversions 34



### In EPA Base C ase v.5.13 exi s ting coal plants are given the option to burn natural gas in addition to coal by

investing in a co al

- to

- gas retr o fit. There a r e two components of c o st in this option: Boiler modification

costs a n d the cost of extending natural gas l a teral pipeli n e spurs from the boiler to a natural gas main

pipeline

. These two components of c o st and their associa t ed performance implicatio n s are dis c ussed in

the f o llowing sec t ions.

5.7.1 Boiler Modifications For Coal

- To

- Gas Conversions

Enabling na t ural gas firi n g in a coal b oiler typically involves i n stallation of new gas burners and

modificatio n s to the duc t ing, windbox (i.e., the c h amber surrounding a b u rner through which pressurized

air is sup p li e d for fuel c o mbustion), and possibly t o the heati n g surfaces u sed to transfer en e rgy from the

exiting hot flue gas to s t eam (referr e d to as the “ convection pass”). It may also involve modification of

environmental equipment. Engineering studies are performed to assess o perating ch a racteristics like

furnace h eat absorption and exit gas temperature; material changes affecting piping a n d components like

sup e rheaters, reheaters, economizers, and reci r c ulating fans; and operational chang e s to sootbl o w ers,

spray flows, air heaters, and emission c o ntrols.

34 

### As disc u ssed here coa l

- to

- gas conv e rs i on ref e rs to the modificatio n o f a n exis t in g boil e r t o allo w i t t o fire natura l g a s

. I t doe s no t refe r t o th e addi t io n o f a ga s t urbi n e to an e x isting boil e r cycle, the re p l ace m ent of a coal boi l er with a n e w

natural g a s combin e d cyc l e plant, or to the g a sificati o n of coal for u se in a natural g as comb u stion turbine

### 5

- 33

Excerpt from Table 5

- 20 ESP Upgrade Provided to Existing Units without Fabric Filters so that

They Meet Their Filterable PM Compliance Requirement

This is a small excerp t of the data in Excerpt from Table 5

- 20 . The complete data set in spreadsheet

format can be downloaded via the link found at www.epa.gov/airmarkets/pro gsregs/epa

ipm/BaseCasev513.html )

Plant Name Unit ID State

Name Unique ID Capacity

(MW) Level of

ESP

Upgrade

Required

to Meet

Filterable

PM

Requirement

A B Brown 2 Indiana 6137_B_2 245

---

AES Beaver Valley Partners Beaver Valley 2 Pennsylvania 10676_B_2 43 ESP

- 4

AES Beaver Valley Partners Beaver Valley 3 Pennsylvania 10676_B_3 43 ESP

- 4

AES Beaver Valley Partners Beaver Valley 4 Pennsylvania 10676_B_4 43 ESP

- 1

AES Cayuga 1 New York 2535_B_1 150

---

AES Cayuga 2 New York 2535_B_2 151

---

AES Deepwater AAB001 Texas 10670_B_AAB001 139

---

AES Somerset LLC 1 New York 6082_B_1 681

---

Allen Steam Plant 1 Tennessee 3393_B_1 245

---

Allen Steam Plant 2 Tennessee 3393_B_2 245

---

Allen Steam Plant 3 Tennessee 3393_B_3 245

---

Alma B4 Wisconsin 4140_B_B4 51

---

Alma B5 Wisconsin 4140_B_B5 77 ESP

- 4

Ames Electric Services Power Plant 7 Iowa 1122_B_7 33

---

Ames Electric Services Power Plant 8 Iowa 1122_B_8 70

---

Apache Station 2 Arizona 160_B_2 175

---

Apache Station 3 Arizona 160_B_3 175

---

Asbury 1 Missouri 2076_B_1 213 ESP

- 4

Asheville 1 North Carolina 2706_B_1 191

---

Asheville 2 North Carolina 2706_B_2 185

---



### The followi n g table summarizes the cost and pe r formance assumptions f or coal

- to

- gas boiler modificatio n s

as incorp o rated in EPA Base Case v.5.13

. T h e values in t he table were developed by EPA’s en g ineering

sta f f based on t echnical papers 35 

### and discussions wi t h industry engineers f a miliar with such projec t s

. They

were designed to be applica b le across t h e existing coal fleet.

Table 5

- 21 Cost and Performance Assumptions for Coal

- to

- Gas Retrofits

Factor Description Notes

Applicabilit y : Existing pulverized coal (PC) fired and

cyclone boiler units of a size gr e ater

than 25 MW: Not applica b le for fluidized bed

combustion (FBC) and stoker boilers.

Capacity Penalty: None The furnace of a boiler d esigned to burn coal is

oversized f o r natural gas, and c o al boilers

include equipment, such as coal mills, that are

not needed for gas. As a result, burning gas

should have no impact on net power output.

35 

### For an exa m ple s e e Babcock and Wil c ox’ s Whit e P a pe r MS

- 1 4 “Natura l G a s Conversio n s o f Exiting Coa l

- Fired

B oilers” 2 010 ( www.babcock.c o m/library/tech

- utility.html#1 4 ).

### 5

- 34 Factor Description Notes

Heat Rate

Penalt y : + 5% When gas is combusted instead of coal, the

st a ck temperature is lower and the moisture

loss to stack is higher. This reduces

efficiency, which is reflected in an increase in

the heat ra t e.

Incremental Capital

Cost: PC units: $/kW

= 267 *(75/M W )^0.35

Cyclone units: $/kW

=

374 *(75/M W )^0.35 The cost fu n ction covers new gas burners and

piping, win d box modificatio n s, air heater

upgrades, gas recirc u l ating fans, a nd control

s y stem m odificatio n s.

Ex a m ple for 50 MW PC unit:

$/kW

= 267 *(75/50)^0.35

= 308

Incremental

Fixed O&M: - 33% of the FOM cost of the existing

coal unit Due to reduced needs f or operators,

maintenance materials, and maintenance staff

when natural gas combusted, FOM costs

decrease by 33%.

Incremental

Variable O&M: - 25% of the VOM cost of the existing

coal unit Due to reduced waste disposal and

miscellane o us other costs, VOM costs

d e cr e ase by 25%.

Fuel Cost: Natural gas To obtain n a tural gas the unit incu r s the cost of

ex tending lateral pipeline spurs from the boiler

location to the natural gas transmission

pipeline

. See section 5.7.2.

NO

x 

### emiss i on rate: 50% of existing coal u n it NO

x 

### emission ra t e, with a fl o or of

0.05 lbs/MMBtu The 0.05 lbs/MMBtu floor is the same as the

NO

x 

### rate floor for new retrofit SCR on units

bu r ning subbituminous coal

SO 2 

### emiss i ons: Zero



### 5.7.2 Natural Gas Pipeline Requirements For Coal

- To

- Gas Conversions

For every i n dividual coal boiler in the U.S., EPA t asked ICF to determine the miles and associa t ed cost of

extending pipeli n e laterals fr o m each boiler to the interstate n a tural gas pi p eline system.

To develop these cos t s t he following principl e s were applied:

For each boiler, gas vol u me was estimated based on size a n d heat rate.

Direct dista n ce to the cl o sest pip e line was calcul a ted. (The a nalysis only considered mainlines with

diameters that were 16 inches or greater. The lateral dis t ance represented the shortest distance

– “ as

the crow flies”

– bet w een the boiler and the mainline.)

Gas volume (per day) of the initi a l la t eral was not allowed to exceed more than 10 percent of t h e

estimated capacity of t he mainline.

The mainline capaci t ies were estimated from the pipe’s dia m eter using t h e Weymou t h equatio n 36



### .

If the gas r e quirement exceeded 10 percent of t h e estimated capacity of t he mainline, the cost of a

second lat e ral to conn e ct to the next closest mainline was c a lculated.

This proce d ure was repeated until t he entire c a pacity required for the b oiler was reached.

Diameters of each later a l were then calculated u sing the W e ymouth eq u ation based on their requir e d

capaci t ies.

36 

### Th e W e ymou t h equatio n i n classica l flui d dy na mi cs i s us e d i n calculatin g compressibl e g a s flo w as a f u n ct io n of

pipelin e diam e t e r an d fricti o n factors

. I t i s use d fo r pipe sizing.

### 5

- 35

The cost of all the later a ls was calc u lated based on the pipeline diameter and mileage required. T hus,

the fin a l pipeline c o st for each b oiler was b a sed on the t otal miles of laterals req u ired.

Figure 5

- 1 shows the calculations p e rformed.

Figure 5

- 1 Calculations Performed in Costing Lateral Pipeline Requirement

There are several points to not e a b out the above app r oach. First, for relatively large boile r s or in cases

whe r e the clos e s t mainline h as a relativ e ly small diameter, mult i ple laterals are required to connect t he

boiler to t he interstate gas transmission grid. This assur e s that each individual boiler will n o t become a

relatively large portion of a pipelines’ t ransmission capacity. It also reflects real

- w orld practices where

larger gas

- f i r ed power plants typically have multiple laterals connecting t hem to different mainlines. This

inc r eases the r e liability of their gas sup p ly and provides m u ltiple opti o ns for gas purchase all o wi n g them to

capture favorable prices from multiple so u rces of gas supply at di f ferent points in time.

Second, expansion of mainlines was not includ e d in the boiler specific pipeline cost, because the

integrat e d gas model within IPM already inc l udes corrid o r expansion capabili t ies. However, if in future IP M

runs, multiple conv e rted boilers are concent r ated on a single pipeli n e along a corridor that includes

multiple pip e lines, a furth e r assessm e nt may be r e quired to make sure that the mainline expansion is n ot

b eing unders t ated due to modeled efficienci e s that may not actually be available in t he field.

Figures 5

- 2 through 5

- 7 summarize the results of the pipeline costing pr o cedure described above. They

provide histograms of the number of laterals r e quired per b oiler (

### 5

- 36

Figure 5

- 2 ), miles of pipeline r equired per boiler ( Figure 5

- 3 ), diameters of the laterals in i n ches (

### 5

- 37

Figure 5

- 4 ), total inch

- mi les o f laterals re q uired per bo i ler ( Figure 5

- 5 ), total cost to each bo i ler in millio n $

( Figure 5

- 6 ), and c o st (in $) per kW of boiler capacity ( Figu re 5

- 7 ). Excerpt from Table 5

- 22 sh ow s the

pipeline costing r esults for e a ch qualifying existing co a l fired unit r e presented in EPA Base Case v.5.13.

### 5

- 38 Figure 5

- 2 Number of Laterals Required per Boiler

Figure 5

- 3 Miles of Pipeline Required per Boiler

### 5

- 39 Figure 5

- 4 Diameter of Laterals

Figure 5

- 5 Total Inch

- Miles of Laterals Required per Boiler

### 5

- 40 Figure 5

- 6 Total Cost to Each Boiler

Figure 5

- 7 Cost per kW of Boiler Capacity

### 5

- 41 Excerpt from Table 5

- 22 Cost of Building Pipelines to Coal Plants

This is a small excerpt of the data in Table

- 22. The complete data set in spreadsheet format can be

downloaded via the link found at http://www.epa.gov/airmarkets/progsregs/epa

- ipm/BaseCasev513.html

.

Unique ID Plant Name Sta te Name Coal Boiler

Capacity

(MW) Number

of

Laterals

Required Miles of

New Pipeline

Required to

Hook Up Unit

(miles) Cost of New

Pipeline

(2011$) Cost of New

Pipeline

per KW

of Coal

Capacity

(2011$/kW)

3_B_1 Barry Alabama 138 2 8.5 2324786 16.85

3_B_2 Barry Alabama 137 2 8.5 2136794 15.60

3_B_3 Barry Alabama 249 2 8.5 7209727 28.95

3_B_4 Barry Alabama 362 2 8.5 8979092 24.80

3_B_5 Barry Alabama 726 2 8.5 12412831 17.10

7_G_1 Gadsden Alabama 64 1 28.7 22383509 349.74

7_G_2 Gadsden Alabama 66 1 28.7 22617875 342.70

8_B_10 Gorgas Alabama 703 2 68.4 87979597 125.15

8_B_6 Gorgas Alabama 103 1 7.6 6250679 60.69

8_B_7 Gorgas Alabama 104 1 7.6 6269532 60.28

8_B_8 Gorgas Alabama 161 1 7.6 7407093 46.01

8_B_9 Gorgas Alabama 170 1 7.6 7533473 44.31

10_B_1 Greene County Alabama 254 1 6.9 7898586 31.10

10_B_2 Greene County Alabama 243 1 6.9 7776757 32.00

26_B_1 E C Gaston Alabama 254 1 23.0 26126943 102.86

26_B_2 E C Gaston Alabama 256 1 23.0 26294370 102.71

26_B_3 E C Gaston Alabama 254 1 23.0 26143766 102.93

26_B_4 E C Gaston Alabama 256 1 23.0 26143766 102.12

26_B_5 E C Gaston Alabama 842 3 162.4 201898208 239.78

47_B_1 Colbert Alabama 178 1 0.7 725276 4.07

47_B_2 Colbert Alabama 178 1 0.7 722785 4.06

47_B_3 Colbert Alabama 178 1 0.7 722785 4.06

47_B_4 Colbert Alabama 178 1 0.7 723409 4.06

47_B_5 Colbert Alabama 472 2 4.6 5183155 10.98

50_B_7 Widows Creek Alabama 473 3 253.0 231385577 489.19

50_B_8 Widows Creek Alabama 465 3 253.0 227553333 489.36

51_B_1 Dolet Hills Louisiana 638 4 28.3 28812871 45.16

56_B_1 Charles R Lowman Alabama 80 1 17.3 13132673 164.16

56_B_2 Charles R Lowman Alabama 235 2 43.8 38349442 163.19

56_B_3 Charles R Lowman Alabama 235 2 43.8 38128365 162.25

59_B_1 Platte Nebraska 100 1 25.8 21561000 215.61

60_B_1 Whelan Energy Center Nebraska 77 1 8.1 6169545 80.12

60_B_2 Whelan Energy Center Nebraska 220 1 8.1 9036600 41.08

87_B_1 Escalante New Mexico 247 2 11.4 7831404 31.71

108_B_SGU1 Holcomb Kansas 362 5 77.1 43429164 119.97

113_B_1 Cholla Arizona 116 1 27.5 23648324 203.86

113_B_2 Cholla Arizona 260 1 27.5 32391059 124.58

113_B_3 Cholla Arizona 271 1 27.5 32691880 120.63

### 5

- 42



## 5.8 Natural Gas Co

- firing

Existing coal plants with existing natural gas pipelines have an option of co

- firing with natural gas. Gas

co

- firing at these units is limited to 10% of the unit’s power output.

The option of co

- firing with gas at an existing coal boiler is only offered if one of the following two criteria

based on 2012 EIA 860

, 2012 EIA Form 923 and NEEDS v.5.13 is met: (1) the unit reported the use of

gas as a startup fuel, or (2) an existing gas

- fired unit (e.g., NGCC) is located at the same facility (with the

same ORIS) as the coal

- fired unit. EPA assumes that in either of these cases, sufficient pipeline capacity

exists to supply up to 10% of total power output of the coal steam boiler located at these sites. These

units are detailed below in Excerpt from Table 5

- 23

.

Similar to the coal

- to

- gas retrofit option, there is a 5% increase in heat rate for the share of generation

fueled by natural gas (accounting for the increased flue gas moisture and stack heat loss). On a $/kWh

basis, any change in capital or operating costs of co

- firing with natural gas at low levels is very small.

Hence, EPA do not include additional capital or operating costs for this option.

Excerpt from Table 5

- 23 List of Coal S team U nits with Natural Gas Co

- firing option

This is a small excerpt of the data in Excerpt from Table 5

- 23

. The complete data set in spreadsheet

format can be downloaded via the link found at http://www.epa.gov/airmarkets/progsregs/epa

ipm/BaseCasev513.html

UniqueID Plant Name ORIS Code State N ame Capacity ( MW )

10684_G_TG5 Argus Cogen Plant 10684 California 7

1077_G_3 Sutherland 1077 Iowa 78

1554_G_2 Herbert A Wagner 1554 Maryland 135

2943_G_3 Shelby Municipal Light Plant 2943 Ohio 5

511_G_1 Trinidad 511 Colorado 3.8

54407_G_1 Waupun Correctional Central Heating Plt 54407 Wisconsin 0.2

54407_G_2 Waupun Correctional Central Heating Plt 54407 Wisconsin 0.5

56564_G_1 John W Turk Jr Power Plant 56564 Arkansas 609

56785_G_WG01 Virginia Tech Power Plant 56785 Virginia 2.5

7_G_1 Gadsden 7 Alabama 64

7_G_2 Gadsden 7 Alabama 66

728_G_4 Yates 728 Georgia 133

728_G_5 Yates 728 Georgia 135

10_B_1 Greene County 10 Alabama 254

10_B_2 Greene County 10 Alabama 243

### 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.5.13 are advanced coal

fired units with CO 2 

### capture (carbon capture). 37 

### The cost and performance characteristics of these units

are shown in Table 4

- 13 and are discussed in Chapter 4.

In addition to offering carbon capture capabilities on potential units th at the model builds as new capacity,

EPA Base Case v.5.13 provides carbon capt ure 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 Capt ure

Retrofits on Pulverized Coal Plants

Applicability (Original MW Size) > 400 MW

Incremental a 

### Capital Cost (2011 $/kW) 1,794

Incremental a 

### FOM (2011 $/kW

- yr) 27.2

Incremental a 

### VOM (2011 (mills/kWh) 3.2

Capacity Penalty (%)

- 25%

Heat Rate Penalty (%) 33%

CO 2 

### Removal (%) 90%

Note:

a 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 performed for

the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) by a t eam

consisting of Alstom Power, Inc., American Electric Power (AEP), ABB Global, and the Ohio Coal

Development Office

. 38 

### 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 subcriticalpulverized

bituminous coal plant with a lime based FGD, and an electrostatic precipitator for particulate control. 39



### The ca rbon 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 

### concentr ation 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

remove d. For use in EPA Base Case v.5.13 the capital cost was converted to constant 2011 $ from the

2006$ costs reported in the NETL stud y.

A capacity derating penalty of 25% was assumed, based on reported research and fiel d experience as of

2010. The corresponding heat rate penalty was 33%. (For an explanation of the capacity and heat rate

penalties and how they are calculated, see the d iscussion under VOM in section 5.1.1 .)

37 

### 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 capture. For purposes of characterizing the cost and performanc e characteristics of advanced coal with

carbon capture, supercritical steam generators with carbon capture was used in Table 4

- 13

.

38 

### Carbon Dioxide Capture from Existi ng Coal

- Fired Power Plants” DOE/NETL

- 401/110907. Final Report (Original

Issue Date, December 2006) Revision Date, November 2007 ( http://www.netl.doe.gov/energy

analyses/pubs/CO2%20Retrofit%20From%20Existing%20Plants%20Revised%20November%202007.pdf . A

summary of costs for each of the cases appears in Table 3

- 65 (p. 139).

39 

### Subcritical” refers to thermal power plants that opera te below the “ critical temperature” and “critical pr essure” (220

bar) where boiling ( i.e., the formation of stea m bubbles in water) no longer occurs. Such units are less efficient than

“supercritical” and “ultra supercritical” steam generators.

### 6

- 2

Since the fixed (FOM) and variable operating and maintenance (VOM) costs from the Conesville study

were give n without documen tation, EPA relied on a NETL study that fully documented these costs

coupled with the expert judgment of EPA’s engineering staff to obtain the FOM and VOM values shown in

Table 6

- 1

. 40



## 6.2 CO 2 

## Storage

The capacity and cost assumptions for CO 2 

### storage in EPA Base Case v.5.13 are based on GeoCAT

(Geosequestration Cost Analysis Tool), a spreadsheet model developed for EPA by ICF in support of

EP A’s draft Federal Requirements u nder the Underground Injection Control (UIC) Program for Carbon

Dioxide Geologic Storage Wells

. 41 

### The GeoCAT model combines detailed characteristics of

sequestration capacity by state and geologic setting for the U.S. with costing algorithms for individua l

components of geologic sequestration of CO 2 

### . The outputs of the model are regional sequestration cost

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 c har acterization

Monitoring the movement of CO 2 

### in the subsurface

Injection well c onstruction

Area of review and corrective a ction (including fluid flow and reservoir modeling during and after

injection and identification, evaluation, and remediation of exist ing wells within the area of review)

Well o peration

Mechanical integrity t esting

Financial r esponsibility (to maintain sufficient resources for activities related to closing and

remediation of the site)

General and a dministrative

Of the ten cost categorie s 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 GeoC AT 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

E nha nced coal bed methane recovery

40 

### 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/energy

- analyses/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).

41 

### Feder al Requirements u nder 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/Day

- 25/w16626.htm and www.epa.gov/safewater/u ic/

wells_sequestration.html#regdevelopment

.

### 6

- 3

Enhanced shale gas

Basalt storage

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 which 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

, ”

enhanced by ICF to include assessments of the Gulf of Mexico, shale gas sequestration 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. 42 

### The geologic and regional cost curve module also has a characterization of

regionalized costs, drilling depths, and other factors that go into the regional cost curves

. 43



### For EPA Base Case v.5.13, GeoCAT identified storage opportunities in 33 of the lower 48 continental

states and storag e cost curves were developed for each of them

. 44 

### 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 cost curves. The result is a

total of 37 storage cost curves which are shown in Excerpt from Table 6

- 2

. 45



### The cost curves shown in Excerpt from Table 6

- 2 are in the form of step functions. This implies tha t in any

given year a specified amount of storage is available 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 reac hed, 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.

CO 2 

### storage opportunities are rel evant not just to power sector sources, but also to sources in other

industrial sectors . Therefore, before being inc orporated as a supply representation into EPA Base Case

v.5.13 , the original CO 2 

### storage capacity in each storage region was reduced by an estimate of the

storage that would be occupied by CO 2 

### generated by other industrial sector sources at the relevant level

of cost effectiveness (represented by $/ton CO

2 

### storage cost) . To do this, ICF first estimated the level of

industrial demand for CO 2 

### storage in each CO 2 

### storage region in a scenario where the value of abating

CO 2 

### emissions is assumed to be $150 per ton (this abatement value is relevant not only to willingness to

42 

### Carbon Sequestration Atlas of the United States and Canada”, U.S. Department of Energy, National Energy

Technology Laboratory, Morgantown, WV, March, 2007.

43 

### Detailed discussions of the GeoCAT model and its ap plication 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/

support_uic_ co 2_technologyandcostanalysis.pdf and Harry Vidas, Robert Hugman and Christa Clapp, “Analysis of

Geologic Sequestration Costs for the United States and Impli cations for Climate Change Mitigation,” Science Digest,

Energy Procedia, Volume 1, Issue 1, February 2009, Pages 4281

- 4288. Available online at www.sciencedirect.com

.

44 

### The states without identified storage opport unities in EPA Base Case v.5.13 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 st orage 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.

45 

### For consistency across the emission costs represented in v.5.13, the costs shown in Tables 9

- 23 and 9

- 2 4 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 t he modeling

framework, the international convention is to use metric tons. To obtain the $/tonne equivalent multiply the $/ton

values shown In Tables 9

- 34 and 9

- 24 by 1.1023

.

### 6

- 4

pay for storage but also for the cost of capture and transportation of the abated CO 2 

### ). 46 

### Then, for each

region ICF calculated the ratio of the industrial demand to total storage capacity available for a storage

price of less than $10/ton. (An upper limit of $10/ton was chosen because the considerable amount of

storage available up to that price could be expected to exhaust the industrial demand.) Converting this to

a percent value and subtr acting from 100%, ICF obtained the percent of storage capacity availab le to the

electricity sector at less 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 47 

### in the cost

curves for the region to obtain the reduced storage capacity that went into the storage cost curves for the

elec tric sector in EPA Base Case v.5.13 . Thus, the values shown in Excerpt from Table 6

- 2 represent the

storage available specifically to the electric se ctor.

The p rice steps in the Excerpt from Table 6

- 2 are the same from region to region. (That is, STEP5

[ column 2] has a step cost value of $4.84/Ton [column 3] acros s all 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 (lab eled “Total Storage Capacity (MMTons)” in column 5) vary from region to region. I n any given

region, the cost curves are the same for every run year. Thi s feature implies that over the modeli ng time

horizon no new storage will be added to augmen t the cur rent storage inventory. This assumption is not

meant to imply that ad ditional storage is unavailable and may be revisited if model runs exhaust key

components in the storage inventory.

Excerpt from Table 6

- 2 CO 2 

### Storage Cost Curves in EPA Base Case v.5.13

This is a small excerpt of the data in Excerpt from Table 6

- 2 . The complete data set in spreadsheet format

can be downl oaded via the link found at http://www.epa.gov/airmarkets/progsregs/epa

ipm/BaseCasev513.html

.

CO 2 

### Storage Region Step Name CO 2 

### Storage

Step Cost

(2011$/Ton) Annual Step

Bound

(MMTons) Total Storage

Capacity

(MMTons)

Alabama STEP1

- 14.52 1 45

STEP2

- 9.68 0 0

STEP3

- 4.84 0 0

STEP4 0.00 0 6

STEP5 4.84 31 1,568

STEP6 9.68 39 1,967

STEP7 14.52 38 1,895

STEP8 19.36 0 9

STEP9 24.20 4 186

STEP10 29.04 13 639

STEP11 33.88 0 7

STEP12 38.72 0 14

STEP13 43.56 0 0

STEP14 48.41 1 68

STEP15 53.25 0 0

STEP16 58.09 0 14

STEP17 62.93 0 0

46 

### 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.

47 

### Zero and negative cost steps represent storage available from enhanced oil recovery (EOR) where oil producers

either pay or offer f ree storage for CO 2 

### that is inject ed into mature oil wells to enhance the amount of oil recovered.

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.

### 6

- 5 CO 2 

### Storage Region Step Name CO 2 

### Storage

Step Cost

(2011$/Ton) Annual Step

Bound

(MMTons) Total Storage

Capacity

(MMTons)

STEP18 67.77 0 0

STEP19 72.61 0 0

Arizona STEP1

- 14.52 0 0

STEP2

- 9.68 0 0

STEP3

- 4.84 0 0

STEP4 0.00 0 0

STEP5 4.84 121 6,026

STEP6 9.68 145 7,275

STEP7 14.52 113 5,659

STEP8 19.36 0 0

STEP9 24.20 38 1,887

STEP10 29.04 0 1

STEP11 33.88 0 0

STEP12 38.72 0 0

STEP13 43.56 0 0

STEP14 48.41 0 0

STEP15 53.25 0 0

STEP16 58.09 0 0

STEP17 62.93 0 0

STEP18 67.77 0 0

STEP19 72.61 0 0

Note: The curves for each region are applicable in each model run year 2016

- 2050.



## 6.3 CO 2 

## Transport

Each of the 64 IPM model regions can send CO 2 

### to the 37 regions represented by the storage cost

curves in Excerpt from Table 6

- 2 . The ass ociated transport costs (in 2011 $/Ton) are shown in Excerpt

from Table 6

- 3

.

These costs were derived by first calculating the pipeline distance from each of the CO 2 

### Production

Regions to each of the CO 2 

### Storage Re gions listed in Excerpt from Table 6

- 3

. Since there are large

economies of scale for pipelines, CO 2 

### transportation costs depend on how many power plants and

industrial CO 2 

### sources could share a pipeline over a given distance. Consequently, the method assumes

that the longer the distance from the source of the CO 2 

### to the sink for the CO 2 

### the greater the chance for

other sources to sh are in the transportation costs, including pipeline costs (in $/inch

- mile) and cost of

service (in $/ton per 75 mi les) . These cost components 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.

### 6

- 6 Excerpt from Table 6

- 3 CO 2 

### Transportation Matrix in EPA Base Case v.5.13

This is a small excerpt of the data in Table 6

- 3. The complete data set in spreadsheet format can be

downloaded via the link found at http://www.epa.gov/airmarkets/progsregs/epa

- ipm/BaseCasev513.html

.

CO 2 

### Production

Region CO 2 

### Storage Region Cost

(2011$/Ton)

ERC_REST Alabama 13.20

Arizona 18.75

Arkansas 8.27

Atlantic Offshore 24.44

California 30.21

Colorado 17.79

Florida 20.86

Georgia 19.97

Illinois 17.01

Indiana 18.43

Kansas 12.54

Kentucky 20.25

Louisiana 8.48

Louisiana Offshore 8.61

Michigan 23.76

Mississippi 9.94

Montana 26.83

Nebraska 17.88

Nevada 25.95

New Mexico 16.77

New York 28.40

North Dakota 26.53

Ohio 23.70

Oklahoma 9.35

Oregon 37.00

Pacific Offshore 27.83

Pennsylvania 26.33

South Carolina 20.72

South Dakota 23.53

Tennessee 17.12

Texas 4.48

Texas Offshore 6.64

Utah 21.96

Virginia 23.60

Washington 967.14

West Virginia 22.32

Wyoming 22.76

Notes:

Production Regions are equal to IPM model regions

### 7

- 1



# 7. Set

- up Parameters and Rules

The EPA Base Case v.5.13 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 capa ble of representing every individual 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 c aptured by mapping run years to the

individual years they represent.

The analysis time horizon for EPA Base Case v.5.13 extends from 2016 through 2054, with IPM seeking

the least cost solution that meets all constraints and minimizes the net present value of system cost. The

seven 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 Mappi ng Used in the EPA Base Case v.5.13

Run Year Years Represented

2016 2016

- 2017

2018 2018

2020 2019

- 2022

2025 2023

- 2027

2030 2028

- 2033

2040 2034

- 2045

2050 2046

- 2054



## 7.2 Retrofit Assignments

In IPM, model plants that represent existing generating units have the option of maintaining their current

system configuration, retrofitting with pollution controls, or retiring. The decision to retrofit or retire is

endogenous to IPM and bas ed on the least cost approach to meeting demand subject to modeled system

and operational constraints. IPM is capable of modeling retrofits and retirements at each applicable

model unit at three different points in time, referred to as three stages. At e ach stage a retrofit set may

consist of a single retrofit (e.g. LSFO Scrubber) or pre

- specified combinations of retrofits (e.g., ACI +

LSFO Scrubber +SCR). In EPA Base Case v.5.13 first stage retrofit options are provided to existing coal

steam and oil/ ga s steam plants. These plants

- as well as combined cycle plants, combusti on turbines,

and nuclear plants

- are also given re tirement as an option in stage one

. Third sta ge retrofit options are

offered to coal

- steam plants only

.

Table 7

- 2 presents the first stage retrofit options available by plant type; Table 7

- 3 presents the seco nd

and third stage retrofit options available to coal

- steam plants. The cost of multiple retrofits on the same

model plant, whether installed in one or multiple stages, are additive. In linear programming models such

as IPM, projections of pollution cont rol equipment capacity and retirements are limited to the pre

- specified

combinations listed in Table 7

- 2 and Table 7

- 3 below.

### 7

- 2 Table 7

- 2 First Stage Retrofit Assign ment Scheme in EPA Base Case v.5.13

Plant Type Retrofit Option 1 st 

### Stage Criteria

Coal Steam

Coal 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

– Non FBC

Boilers All 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 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

(with and without 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

Combination options

– Individual technology level restrictions

apply 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

ACI + LSFO Scrubber + SNCR

DSI All unscrubbed and non FBC coal steam boilers 25 MW or larger

with Fabric Filter and burning less than 2 lbs/MMBtu SO 2 

### coal.

DSI + Fabric Filter All unscrubbed and non FBC coal steam boilers 25 MW or larger

without Fabric Filter and with CESP or HESP and burning less

than 2 lbs/MMBtu SO 2 

### coal.

DSI + SCR

Combination options

– Individual technology level restrictions

apply DSI + SNCR

ACI + DSI

ACI + DSI + SCR

ACI + DSI + SNCR

Heat Rate Improvement All coal steam boilers with a heat rate larger than 9,500 Btu/kWh

Coal

- to

- Gas All coal steam boilers that are 25 MW or larger

Integrated Gasification Combined Cycle

IGCC Retirement All integrated gasification combined cycle units

Combined Cycle

CC Retirement All combined cycle units

Combustion Turbine

CT Retirement All combustion turbine units

Nuclear

Nuclear Retirement All nuclear power units

### 7

- 3

Plant Type Retrofit Option 1 st 

### Stage Criteria

Oil and Gas Steam

Oil/Gas Retirement All O/G steam boilers

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



### Table 7

- 3 Second and Third Stage Retrofit Assign ment Scheme in EPA Base Case v.5.13

Plant Type Retrofit Option 1 st 

### Stage Retrofit Option 2 nd 

### Stage Retrofit Option 3 rd



### Stage

Coal Steam

NO

x 

### Control Option a 

### SO 2 

### Control Option Heat Rate Improvement

HCl Control Option Heat Rate Improvement

CO 2 

### Control Option None

Heat Rate Improvement CO 2 

### Control Option

Coal Retirement None

SO 2 

### Control Option b 

### NO

x 

### Control Option Heat Rate Improvement

CO 2 

### Control Option None

Heat Rate Improvement CO 2 

### Control Option

Coal Retirement None

Hg Control Option c 

### NO

x 

### Control Option Heat Rate Improvement

SO 2 

### Control Option Heat Rate Improvement

HCl Control Option Heat Rate Improvement

CO 2 

### Control Option None

Heat Rate Improvement CO 2 

### Control Option

Coal Retirement None

CO 2 

### Control Option d 

### None None

NO

x 

### Control Option a 

### + SO 2 

### Control

Option b 

### CO 2 

### Control Option None

Heat Rate Improvement CO 2 

### Control Option

Coal Retirement None

NO

x 

### Control Option a 

### + Hg Control

Option 3 

### SO 2 

### Control Option Heat Rate Improvement

HCl Control Option Heat Rate Improvement

CO 2 

### Control Option None

Heat Rate Improvement CO 2 

### Control Option

Coal Retirement None

SO 2 

### Control Option b 

### + Hg Control

Option 3 

### NO

x 

### Control Option Heat Rate Improvement

CO 2 

### Control Option None

Heat Rate Improvement CO 2 

### Control Option

Coal Retirement None

NO

x 

### Control Option a 

### + SO 2 

### Control

Option b 

### + Hg Control Option c 

### CO 2 

### Control Option None

Heat Rate Improvement CO 2 

### Control Option

Coal Retirement None

HCl Control Option e 

### NO

x 

### Control Option Heat Rate Improvement

SO 2 

### Control Option Heat Rate Improvement

Heat Rate Improvement None

Coal Retirement None

NO

x 

### Control Option a 

### + HCl Control

Option e 

### SO 2 

### Control Option Heat Rate Improvement

Heat Rate Improvement None

Coal Retirement None

Hg Control Option c 

### + HCl Control NO

x 

### Control Option Heat Rate Improvement

### 7

- 4

Plant Type Retrofit Option 1 st 

### Stage Retrofit Option 2 nd 

### Stage Retrofit Option 3 rd



### Stage

Option e 

### SO 2 

### Control Option Heat Rate Improvement

Heat Rate Improvement None

Coal Retirement None

NO

x 

### Control Option a 

### + HCl Control

Option e 

### + Hg Control Option c 

### SO 2 

### Control Option Heat Rate Improvement

Heat Rate Improvement None

Coal Retirement None

Heat Rate Improvement NO

x 

### Control Option None

SO 2 

### Control Option None

HCl Control Option None

CO 2 

### Control Option None

Coal Retirement None

Coal

- to

- Gas NO

x 

### Control Option None

Oil/Gas Retirement None

Coal Retirement None None

Oil and Gas Steam

NO

x 

### Control Option a 

### Oil/Gas Retirement None

Oil/Gas Retirement None None

Notes:

a "NO

x Control Option" implies that a model plant may be retrofitted with one of the following NO

x control technologies: SCR,

SNCR

- non

- FBC, or SNCR

- FBC

b " 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

c "Hg Control Option" implies that a model plant may be retrofitted with one of the following activated carbon injection techno logy

options for reduction of mercury emissions: ACI or ACI + Toxecon

d " CO 2 Control Option" implies that a model plant may be retrofitted with carbon capture and storage technology

e "HCl Control Option" implies that a model plant may be retrofitted with a DSI (with milled Trona)



## 7.3 Emissions Trading and Banking

Five environmental air regulations included in EPA Base Case v.5.13 involve regional trading and

banking of emission allowances 48 

### : The three programs of the Clean Air Interstate Rule (CAIR)

– Annual

SO 2 

### , Annual NO

x, 

### and Ozone Season NO

x 

### ; the Regional Greenhouse Gas Initiative (R GGI) for CO 2 

### ; and

the West Region Air Partnership’s (WRAP) program regulating SO 2 

### (adopted in response to the federal

Regional Haze Rule). Table 7

- 4 below summarizes the key parameters of these five trading and banking

programs as incorporated in EPA Base Case v.5.13. EPA Base Case v.5.13 does not include any explicit

assumptions on the allocation of emission allowances among model plants under any of the programs.

T he NO

x 

### SIP Call requirements for ozone season NO

x 

### for the state of Rhode Island are also included in

EPA Base Case v.5.13. 49



### Intertemporal Allowance Price Calculation

Under a perfectly competitive cap

- and

- trade program that allows banking (with a single

, fi xed future cap

and full “banking” allowed), 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 pr events sources from profiting by arbitraging allowances between two

periods.

48 

### For a detailed discus sion of the assumptions modeled for all environmental air regulations in the EPA Base Case

v.5.13, refer to C hapter 3.

49 

### For more information on individual state emission caps and constraints, see the All Constraints worksheet in the

SSR f ile.

### 7

- 5

The EPA Base Case v.5.13 uses the same discount rate assumption ( 4.77% ) that governs all

intertemporal economic decision

- making in the model in order to comput e the increase in al lowance price

for cap

- and

- trade programs when banking is engaged as a compliance strategy. This approach is based

on the assumption that allowance trading is a standard ac tivity engaged in by generation asset owners

and that their intertemporal investment decisions as related to allowance trading will not fundamentally

differ from other investment decisions. For more information on how this discount rate was calculated,

please see Section 8.2

.

Table 7

- 4 Trading and Banking Rules in EPA Base Case v.5.13

Coverage CAIR Annual SO 2 CAIR Annual N O

x CAIR

- Ozone

Season NO

x WRAP

- SO 2 RGGI

- CO 2

All fossil units > 25

MW a All fossil units > 25

MW 1 All fossil units > 25

MW b All fossil units > 25

MW d All fossil units > 25

MW e

Timing Annual Annual Ozone Season (May

- September) Annual Annual

Size of Initial

Bank (MTons) pre 2010:

5,985.768 2010

- 2014: 22,298.08

2015

- 2015: 2 ,333.776 2016: 1,514.702 2016: 740.665 The bank starting in

2018 is assumed to be

zero 2016: 107,743

Rules

Total Allowances

(MTons) 2016

- 2054: 8,950 2016

- 2054: 1,242 2016

- 2054: 484.5 2018

- 2054: 89.6 2016: 68,459

2017: 66,297

2018: 64,188

2019: 62,132

2020: 60,128 2021

- 2054: 78,175

Total Allowances

Less NSR

(MTons) 2016

- 2017: 8,808

2018: 8,740

2019: 8,682

2020

- 2054: 8,662 2016

- 2054: 1,242 2016

- 2054: 484.5 NA NA

Retirement Ratio 2016

- 2054: 2 .86 2016

- 2054: 1.0 2016

- 2054: 1.0 2016

- 2054: 1.0 2016

- 2054: 1.0

Notes:

a Alabama, Delaware, District of Columbia, Florida, Georgia, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maryland, Michigan,

Mississippi, Missouri, New Jersey, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Texas, Virginia,

West Virginia, and Wisconsin.

b Alabama, Arkansas, Connecticut, Delaware, District of Columbia, Florida, Illinois, Indiana, Iowa, K entucky, Louisiana, Maryland,

Massachusetts, Michigan, Mississippi, Missouri, New Jersey, New York, North Carolina, Ohio, Pennsylvania, South Carolina,

Tennessee, Virginia, West Virginia, and Wisconsin.

c Rhode Island is the only NO

x SIP Call state not covered by the CAIR Ozone Season NO

x program.

d New Mexico, Utah, Wyoming

e Connecticut, Delaware, Maine, New Hampshire, New York, Vermont, Rhode Island, Massachusetts, Maryland

### 8

- 6



# 8. Financial Assumptions

This chapter presents the financial assumptions used in the EPA Base Case v.5.13 along with an in

- depth

explanation of the theoretical unde r pinnings and methods used to develop the two most important financial

parameters

– the discount rate and capital charge rate. Investment options in IPM are selected by the

model given the cost and performance characteristics of available options, forecasts of customer demand

for electricity, reliability criteria and environmental regulations. The investment decisions are made bas ed

on minimizing the net present value of capital plus operating costs over the full planning horizon. The

pattern of capital costs over time is determined using capital charge rates to represent the financing of

capital investments. The net present value of all future capital and operating costs is determined with the

use of a discount rate.

EPA Base Case v.5.13 uses real 2011 dollars (2011$) as its real dollar baseline. 50



## 8.1 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 a greater share of U.S. generation capacity

being deregulated IPP (Independent Power Producer) capacity. 51 

### For example, merchant IPPs selling in to

spot market have more market risk than regulated plants or IPPs having long

- term, known

- price contracts

with credit worthy counter parties. There are also technology risks and financing structure risks (corporate

vs. project financings). Lastly, there i s financial risk related to the extent of leverage.

The risk, especially to the extent it is correlated with overall market conditions, is an important driver of

financing costs. Other risks are handled in the cash flows and are treated as non

- correlated w ith the

market. This emphasis on correlated market risk is based on the Capital Asset Pricing Model (CAPM) and

associated financial theory. This analysis takes into account differences in technology and market

structure risks.

Differences between corporate and project financings are highlighted but no specific adjustment has been

made for them.

8.1.1 Market Structure Risks

The power sector in North America can be divided into the traditional regulated sector (also known as

“cost of service” sector) and deregulate d merchant sector (also known as “competitive” sector).

Traditional Regulated

The traditional regulated market structure is typical of the vertically integrated utilities where generation

(and transmission and distribution, abbreviated T&D) investments ar e approved through a regulatory

process and the investment is provided a regulated rate of return. In theory, 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

regulated investments are less exposed to the market than deregulated investments, all else held equal.

In this report, the term “utility financing” refers to th is 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 contra ct period. In

such an arrangement, the risks are more credit

- and performance

- related and much less market

- related.

50 

### Unless otherwise indicated, all rates presented in this document are provided in real terms.

51 

### SNL classifies power plants as merchant and unregulated if a plant in question was not part of any rate case.

Based on this classification criterion, in 2012, about 52% of all operating capacity is merchant and unregulated

capacity.

### 8

- 7

Deregulated Merchant

In a deregulated merchant market structure, investments bear the full or a very high degree of market risk

as the pric e at which that they can sell electricity 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 prices, as well as on capital in vestment 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 uncertainty in the sector that can be highly correlated with

over all macro

- economic trends. The operating cash flows from investments in this sector are more volatile

as compared to the traditional regulated sector and hence carry more business or market risk. In this

documentation , the term “merchant financing” refers to this type of market structure.

8.1.2 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

tur bine 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.

8.1.3 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

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. Also, a common assumption is that debt is refinanced rather

than repaid such that overall deb t is eliminated.

Project finance

Project finance 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 the developer defaults on the loan, creditors have recourse only to the

project itself and not against the larger holdings of the project developer. This approach can be more risky

for investors than corporate finance, all else being eq ual, because there is less diversification of assets

than the assets held by a 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. In this situation, debt pri ncipal is commonly assumed to be repaid at the end of the asset's

useful life.

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 e ven 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 face

. 52

52 

### 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

.

### 8

- 8

However, as noted above, this analysis does not make an effort to quan tify the relative costs and benefits

of one structure over the other. Rather, the approach 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 f inancing is more observable than project financing

, 53 

### 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 for the purposes of deriving the financ ial assumptions used in EPA Base Case v.5.13

were based on IPP and utility corporate financing.



## 8.2 Calculation of the Financial Discount Rate

8.2.1 Introduction to Discount Rate Calculations

The real discount rate for expenditures 54 

### (e.g., capital, fuel, variable op erations and maintenance, and

fixed operations and maintenance costs) in the EPA Base Case v.5.13 is 4.77%. This serves as the

default discount rate for all expenditures.

A discount rate is used to translate future cash flows into current dollars by taking into account factors

(such as expected inflation and the ability to earn interest), which make one dollar tomorrow worth less

than one dollar today. The discount rate allo ws intertemporal trade

- offs and represents the risk adjusted

time value of money.

8.2.2 Choosing a Discount Rate

Th e choice of discount rate often has a major effect on analytical results. The discount rate adopted for

modeling investment behavior should reflec t the time preference of money or the rate at which investors

are willing to sacrifice present consumption for future consumption. The return on private investment

represents the opportunity cost of money and is commonly used as an appropriate approximatio n of a

discount rate.

8.2.3 Discount Rate Components

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 equ ity (common stock) because preferred stock is generally a small share

of capital structures. Its intermediate status between debt and equity in terms of access to cash flow also

tends not to change the weighted average.

53 

### Pr oject financing data is less observable as the securities, debt and equity, are usually not explicitly traded. Also,

often key financing parameters are unavailable due to confidentiality reasons. Thus, the analysis is implicitly

assuming that the corpora te risks and financing costs are equal to the project risks. This is especially reasonable

when the corporate activities are aggregations of projects.

54 

### This rate is equivalent to the real discount rate for a combine cycle plant under hybrid 75:25 utility to merchant ratio

assumption. It represents a most common type of investment.

### 8

- 9

8.2.4 Market Structure: Utility

- Merchant Financing Ratio

The first step in calculating the discount rate was to determine proper utility

- merchant financing ratio. In

EPA Base Case v.5.13 , a hybrid financing model is used that assumes future new unit development

activity would be split 75:25 betw een 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 contracts

. 55

, 56 

### This approach assumes t hat new units are

financed as a weighted average of utility and merchant financing parameters. For new units the

assumption is that utility and merchant components get the 75:25 weights. However, since existing coal

units can be classified as belonging to a merchant or regulated structure, for retrofit investments the EPA

Base Case v.5.13 assumption is that plants owned by a utility get pure utility financing parameters,

whereas plants owned by merchant companies get pure merchant financing parameters.

Exam ple 1 : The debt to equity capital structure of a combustion turbine is 55/45 under

utility financing and 40/60 under merchant financing. Under the assumption that utility

and merchant components get 75:25 weights, the debt

- to

- equity ratio under hybrid

fina ncing is D = (0.75*55 + 0.25*40) = 51 / E = (0.75*45 + 0.25*60) = 49.

Example 2 : The debt to equity capital structure of a retrofit is 55/45 under both utility and

merchant financing. Under the assumption that utility owned plants are financed through

pure utility financing parameters, and merchant owned plants are financed through pure

merchant financing parameters, the debt to equity ratio remains unchanged regardless of

the ownership type. A full summary for all technologies appears in Table 8

- 1 below.

Capital Structure: Debt

- Equity Share

The second step in calculating the discount rate is the determination of the capital structures (D/E) 57



### shares for the various technology types using an appropriate utility

- merchant financing ratio. The utility

debt capacity (and returns) is assumed to be independent of technology type based on the theoretical

assumption that regulation will provide an average return to the entire rate base. This assumption is

supported by empirical evidence which suggests that utility rate of return is based on an average return to

the entire rate base. 58 

### The merchant debt capacity is based on market risk where a base load plant is

55 

### 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 lowe r capital charge rates in the

absence of regulatory prohibitions o f 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 lim its, and

because fully characterizing these limits are extremely complex, the EPA Base Case v.5.13 uses a hybrid approach.

For example, recent proposals in PJM explicitly limit capacity expansion by some entities to be such that the total

capacity does no t exceed internal requirements. (Source: Current MOPR modification proposal)

.

56 

### Based on ICF research, current operating capacity in U.S. is approximately evenly split between IPP and utility

owned generation. However, in the last five years (2008

- 2011), 62% of all large fossil plants were built by regulated

companies. In addition, another 12% of all new entrants secured long

- term PPA agreements in which the risk is

expected to be similar to that of utilities generally. Thus, future capacity expansion ha s a lower merchant component

than the existing mix which is closer to 52%.

57 

### 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. The debt is the sum of all intere st bearing short term and long term liabilities while equity is

the amount that the project sponsors inject as equity capital.

58 

### The U.S. wide average authorized rate of return on equity, authorized return on rate base, and authorized equity

ratio during l ast 5 years (2008

- 2012) for all 108 companies was 10.26%, 8.00%, and 48.32% respectively. For the

subset of 50 utilities that completed new rate base cases without financing new generation capacity, those averages

were only slightly lower with average aut horized rate of return on equity, authorized return on rate base, and

authorized equity ratio of 10.09%, 7.90%, and 47.43% respectively. The lack of a substantial difference between

these averages suggests that authorized rates of return and equity ratios for regulated companies are not that

responsive to differences in investment choices, and are more reflective of an entire company’s rate base.

### 8

- 10

likely to have a higher debt capacity than a combustion turbine plant. Table 8

- 1 presents the capital

structure assumptions used in EPA Base Case v.5.13

.

Table 8

- 1 Capital Structure Assumptions in EPA Base Case v.5.13

Technology Utility Merchant H y brid

Combustion Turbine 55/45 40/60 51/49

Combined Cycle 55/45 55/45 55/45

Coal & Nuclear 55/45 65/35 58/43

Renewables 55/45 55/45 55/45

Retrofits 55/45 55/45 N.A.



### The risk differences across technologies are implemented by varying the capital structure. As shown in

Table 8

- 1 and discussed above, a peaking unit such as a combustion turbine is estimated to have a

capital structure of 40/60 while a base load unit such as nuclear and coal is assumed to have a capital

structure of 65/35. This is based on the expectation that less risky technologies can c arry more leverage.

As debt is less expensive than equity, this will automatically translate into a lower discount rate that is

used in deriving capital charge rate for base load technologies, and a higher discount rate that is used in

deriving capital cha rge rate for peaking technology, assuming other components of the capital charge rate

calculation remain the same.

8.2.5 Debt and Equity Shares and Technology Risk

The capitalization structure for merchant financings was estimated to be 55/45 based on empirical

analyses. This ratio is based on the assumption that the overall IPP risk was an 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 co nsidered to have “average” market risk being an

intermediate type technology. Also, in the aggregate, the five selected IPP companies 59 

### have more

combined cycle capacity in their supply mix than any other technology. Additionally, going forward, it is

expec ted 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

is appropriate to use the ROE corresponding t o a combined cycle facility.

Each generation technology was considered to have its own risk profile because base load technologies

have multiple sources of revenues, both energy and capacity, which decreases risk and facilitates

hedging relative to IPP peaking units. Nearly 75% of load is in LMP markets, and the liquidity of these

electrical energy markets creates the potential for near

- term cross commodity hedging if the plant has

significant energy sales, i.e., if the plant is non

- peaking. The potentia l for capacity revenue hedging is

more limited than for energy. Hence, greater the base load share, the lower the asset risk. Additional

differentiation among different technologies e.g. nuclear, versus coal, was not implemented because

there is a lack of publicly traded securities that provide an empirical basis for differentiating between the

risks, and hence, financing parameters for different activities.

There are two main mechanisms for reflecting the greater risk for peak load units 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 peaking units, and lower for base load units. For example, an unlevered beta and ROE

(which assumes zero leverage) could have been cal culated using the risk differentiated capital structures

and then relevered at some target 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 method was

adopted for EPA Base Case v.5.13 . Thus, even though the leverage of peaking units was lowered, the

ROE was not lowered. This raised the weighted average cost of capital and the resulting capital char ge

59 

### The merchant parameters are derived from market observations of five IPP companies

– Merchant ROE

.

### 8

- 11

rate. This effectively also raised the unlevered beta for peaking relative to combined cycle. For base load,

leverage was raised without raising ROE, effectively lowering the unlevered beta and the cost of capital.

Debt and Equity Shares

The target capi talization structure for utilities was 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 estim ated to be 55/45 based on empirical analyses and

this capitalization structure was assumed to be on average reflective of all technologies. 60



### Technology Risks

For the utility financing, EPA Base Case v.5.13 assumes that the required returns for regulated ut ilities

are independent of technology. This is a simplifying assumption, and further empirical work may be

warranted here.

Cost of Debt

The third step in calculating the discount rate was an assessment of the cost of debt. The summary of

historical assessm ent of debt rates across merchant and utility entities is summarized in Table 8

- 2

. The

utility and merchant cost of debt is assumed to be the same acro ss all technologies.

Table 8

- 2 Debt Rates for EPA Base Case v.5.13

Technology Utility Merchant H y brid

Combustion Turbine 5.72% 7.58% 6.19%

Combined Cycle 5.72% 7.58% 6.19%

Coal & Nuclear 5.72% 7.58% 6.19%

Renewables 5.72% 7.58% 6.19%

Retrofits 5.72% 7.58% N.A.



### Merchant Cost of Debt

. The cost of debt for the merchant sector was estimated to be 7.6%. It is

calculated by taking a 5

- year (2008

- 2012) weighted average of debt yields from existing company debt

with eight or more years to maturity. The weights assigned to each company debt yields were based on

that company’s market capitalization. During the most recent 5 years, none of the existing long

- term debt

exceeded twelve years to maturity, hence above a verage yields are based on debt with maturity between

eight and twelve years.

Utility Cost of Debt

The cost of debt for the utility sector was estimated to be 5.7%. It is calculated by taking a 5

- year (2008

- 2012) weighted average of debt yields from four long

- term (20 years) Bloomberg Utility Indexes with

different debt ratings. The four indices’ debt ratings ranged from BBB

- to A. The weights assigned to each

index were based on the number of regulated companies with the same debt rating. 61

60 

### In the last 3 years, the average utility debt/equity ratio was approximately 1.23, which translates to 55/45

debt/equity ratio.

61 

### In all

, 29 different regulated companies were considered when assigning weights to the Bloomberg Utility Indexes.

They are : Allete I nc., Ameren Corp., American Electric Power Co. Inc., Cleco Corp., CMS Energy Corp., Empire

District Electric Co., Great Plains Energy Inc., MGE Energy Inc. Vectren Corp., Westar Energy Inc., Wisconsin

Energy Corp., Consolidated Edison Inc., Northeast Utili ties, Southern Co., UIL Holdings Corp., Avista Corp.,

IDACORP Inc., PG&E Corp., Pinnacle West Capital Corp., and Xcel Energy Inc.

### 8

- 12

Return on Equ ity (ROE)

The final step in calculating the discount rate was the calculation of a return on equity (ROE) using a

weighted average ROE under utility financing (8.8% in nominal terms) and merchant financing (16.1% in

nominal terms) at a 75:25 utility/mercha nt ratio. These utility and merchant ROE’s are estimated

assuming a 55:45 debt/equity ratio. This resulted in a hybrid ROE of 10.6% (nominal). This ROE is kept

the same across each technology 62 

### but the risk differences across technologies are implemented th rough

the capital structure. See the discussion of capital structures in subsection 8

. 3

. 2

. 5 “Debt and Equity

Shares and Technology Risk”, and subsection 8

. 3

. 2

. 5

. 1 “Debt and Equity Shares”.

Merchant ROE

. The Independent Power Producer (IPP) after tax retur n on equity parameter was

estimated to be 16.1% (nominal). This was based on empirical analysis of stock price data of five pure

play comparable merchant generation companies, namely NRG, Dynegy, Calpine, RRI Energy, and

Mirant. 63 

### First, levered betas 64 

### (a m easure of total corporate risk, which includes business and financial

risk) for the five companies were calculated using five years (2008

- 2012) of historical stock price data.

Five years is a standard time period. Weekly returns were also used as supplemen tary data in the

analysis. Second, unlevered betas (a measure of business risk, i.e., those affected by a firm’s investment

decisions) were calculated using the estimated levered beta, the companies’ market debt/equity ratio, and

the riskiness of debt. The goal is to correctly handle business or systemic risk and financial risk. As most

comparables historically had periods of financial distress, the unlevering 65 

### approach was modified to

include the riskiness of debt, instead of purely using the Hamada equati on. 66 

### The unlevered betas were

then relevered 67 

### at the target debt/equity ratio of 55/45 to get the relevered equity betas and return on

equity. The target debt/equity ratio of 55/45 is based on average levels of debt/equity ratios across

merchant and regula ted companies over the last 3 years (2010

- 2012). 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 68 

### based on 20 year T bond rate: 3.8%

Market Risk 69 

### Premium : 1926

- 2011: 6.62%

Size 16 

### Premium: 1.14%

The risk free rate assumption of 3.8% represents a 5

- year (2008

- 2012) average of U.S. Treasury 20 year

bond rates. A common practice within the CAPM construct is to utilize the most recent U.S. Treasury 20

-

62 

### As indicated previously i n 

### Table 8

- 1 a 3% adder is applied to the cost of debt prior to adjustment for income taxes,

and to cost of equity when calculating capital charge rates for Supercritical Pulverized Coal and Integrated

Gasification Combined Cycle without Carbon Capture technologies.

63 

### Mirant and RRI Energy merged in December 2010 to form GenOn. Prior to their merger I CF analyzed these two

companies separately, while after their merger the analysis was of the merged company. Dynegy Holdings began

Chapter 11 proc eedings on November 2011. The ICF analysis of Dynegy analyzed the company data until 2011.

Parts of 2011 and 2012 data were not available for further analysis of Dynegy.

64 

### Levered beta is directly measured from the company’s stock returns with no adjustm ent made for the debt

financing undertaken by the company.

65 

### The unlevering process removes a company’s financing decision from the beta calculation. The calculation

therefore, attempts to isolate the business (operating risk) of the firm.

66 

### The Hamada equ ation is described at http://www.answers.com/topic/hamada

- equation as “A fundamental analysis

method of analyzing a firm's costs 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).

67 

### The relevering process estimates the levered beta of the firm given a target capital structure and the pu re business

risks of the firm as determined from the unlevering process.

68 

### Federal Reserve Statistical Release (H15 data), September 2012

.

69 

### Source: Stocks, Bonds, Bills, and Inflation, 2012 Yearbook Valuation Edition, Morningstar/Ibbotson’s Associates

.

### 8

- 13

y ear bond rate 70 

### which in September 2012 was 2.5%. Were EPA Base Case v.5.13 to adopt 2.5% as a

risk free rate assumption, it would lower all nominal ROEs by 1.3%. Thus, capital investment would have

a lower cost. The EPA Base Case v.5.13 assumptions deviate from that practice for several reasons:

Current rates are unsustainably low due to the latest recession, and slow pace of recovery.

Second, the EPA analysis begins in the year 2016; by that time the treasury yields are assumed to

recover from their curr ent low levels.

The EPA Base Case financial assumptions are changed infrequently, and hence, it should not use

temporary unsustainable assumptions.

Merchant and utility cost of debt, debt

- equity ratios, and historical betas are all calculated based on

the last 5 years (2008

- 2012) of historical data. The same approach to calculate the risk free rate is

used in order to remain consistent in its methodology.

The estimation of the IPP ROE described here is fairly close to what EIA has published. EIA estimates 71



### an ROE of roughly 16% by 2012.

Utility ROE

. The utility return on equity was calculated to be 8.8%. This was based on empirical analysis

of the correlation of returns on the S&P utility Index vs. the broader S&P 500 market index for the

previous five years (2008

- 2012) to determine the levered beta and then unlevering and relevering based

on a process similar to that for merchant sector. The ROE is slightly lower than what state commissions

have awarded the shareholder

- owned electric utilities recently. 72



## 8.3 Cal culation of Capital Charge Rate

8.3.1 Introduction to Capital Charge Rate Calculations

EPA Base Case v.5.13 model s a diverse set of generation and emission control technologies, each of

which requires financing. 73



### The capital charge rate is used to convert the c apital cost into a stream of levelized annual payments that

ensures capital recovery of an investment. The number of payments is equal to book life of the unit or the

years of its book life included in the planning horizon (whichever is shorter)

. Table 8

- 3 presents the capital

charge rates by technology type used in EPA Base Case 5.13. C apital charge rates are a function of

underlying discount rate, book and debt life, taxes and insurance costs, and depreciation schedule.

Table 8

- 3 U.S. Real Capital Charge Rates a 

### for EPA Base Case v.5.13

New Investment Technology Capital Capital Charge

Rate

Environmental Retrofits

- Utility Owned 12.10%

Environmental Retrofits

- Merchant Owned 16.47%

Advanced Combined Cycle 10.26%

Advanced Combustion Turbine 10.63%

Supercritical Pulverized Coal and Integrated Gasification Combined Cycle without Carbon

Capture b 

### 12.57%

70 

### An important source of statistics and common practices associated with calculating cost of capital with CAPM

model is based on the Morningstar’s 2012 issue of the Ibbotson ® 

### Cost of Capital Yearbook.

71 

### See Electricity Market Module of NEMS, EIA Annual Energ y Outlook, June 2012

.

72 

### SNL based rate case statistics for 2011 suggest nationwide average ROE rate of 10.3%.

73 

### The capital charge rates discussed here apply to new (potential) units and environmental retrofits that IPM installs.

The capital cost of existi ng and planned/committed generating units and the emission controls already on these units

are considered “sunk costs” and are not represented in the model.

### 8

- 14

Advanced Coal with Carbon Capture 9.68%

Nuclear without Production Tax Credit (PTC) 9.44%

Nuclear with Production Tax Credit (PTC) c 

### 7.97%

Biomass 9.53%

Wind, Landfill Gas, Solar and Geothermal 10.85%

Notes:

a Capital charge rates were adjusted for expected inflation and represent real rates. The expected inflation rate used to convert

future nominal to constant real dollars is 2.0%. The future i nflation rate of 2.0% is based on an assessment of implied inflation

from an analysis of yields on 10 year U.S. Treasury securities and U.S. Treasury Inflation Protected Securities (TIPS) over a

period of 5 years (2008

- 2012).

b EPA has adopted the procedure followed in EIA’s Annual Energy Outlook 2013; the capital charge rates shown for Supercritical

Pulverized Coal and Integrated Gasification Combined Cycle (IGCC) without Carbon Capture include a 3% adder t o the cost of

debt and equity

. See Levelized Cost of New Generation Resources in th e Annual Energy Outlook 2013 (p.2),

http://www.eia.gov/forecasts/aeo/er/pdf/electricity_generation.pdf

c The Energy Policy Act of 2005 (Sections 1301, 1306, and 1307) provides a production tax credit (PTC) of 18 mills/kWh for 8

years up to 6,000 MW of new nuclear capacity. The financial impact of the credit is reflected in the capital charge rate show n in

for “Nuclear with Production Tax Credit (PTC).” NEEDS v.5.13 integrates 4 ,400 MW of new nuclear capacity at V C Summer and

Vogtle nuclear power plants. Therefore, in EPA Base Case v.5.13 , only 1,600 MW of incremental new nuclear capacity will be

provided with this tax credit.



### 8.3.2 Capital Charge Rate Components

The capital charge rate is a function of the parameters that overlap in part with the discount rate such as

the level of the capital investment and recovery of capital, 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

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

Table 8

- 4 presents a summary of various assumed lives at the national level. The EPA Base Case v.5.13

assumes a book life of 15 years for retrofits. This assumption is made to account for recent trends in

financing of retrofit types of investments.

Table 8

- 4 Book Life, Debt Life and Depreciation Schedules for EPA Base Case v. 5.13

Technology Book Life

(Years) Debt Life

(Years) US MACRS Depreciation

Schedule

Combine Cycle 30 20 20

Combustion Turbine 30 15 15

Coal Steam and IGCC 40 20 20

Nuclear 40 20 15

Solar, Geothermal, Wind and Landfill Gas 20 20 5

Biomass 40 20 7

Retrofits 15 15 15

### 8

- 15

Book Life

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 74 

### typically list the period over which long

lived assets are depreciated for financial reporting purposes. The research c onducted broadly supports

the numbers outlined in the table above.

Debt Life

The debt life is assumed to be on a 20 year schedule except in the case of combustion turbine and

environmental retrofits where debt life is assumed to be on a 15 year schedule.

Depreciation Schedule

The US MACRS 75 

### depreciation schedules were obtained from IRS Publication 946 76 

### that lists the

schedules based on asset classes. The document specifies a 5 year depreciation schedule for wind

energy projects and 20 years for Electric Uti lity Steam Production plants. These exclude combustion

turbines which have a separate listing at 15 years. Nuclear Power Plants are separately listed as 15 years

as well.

Taxation and Insurance Costs

Corporate and State Income Taxes: The maximum US corpora te income tax rate 77 

### is 35%. State taxes

vary but on a national average basis, the state taxes 78 

### are 6.45%. This yields a net effective tax rate of

39.1%.

US state property taxes are approximately 0.9% based on a national average basis. This is based on

exte nsive primary and secondary research conducted by ICF using property tax rates obtained from

various state agencies.

Insurance costs are approximately 0.3%. This is based on estimates of insurance costs on a national

average basis.

8.3.3 Capital Charge Rate Calc ulation Process

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 l ifetime of the project. The sum of discounted cash flows to the equity

holders over the lifetime of the project, discounted at the cost of equity is set equal to the initial

investment. Put another way, it creates an annuity value when multiplied by the ca pital investment to

recover all capital related charges and provide an IRR equal to the required return on equity. The capital

charge rate so calculated is defined as follows:

Capital Charge Rate = EBITDA/Total Investment

74 

### SEC 10K filings of electric utilities and pure merchant 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 equipmen t; Reliant: 10

- 35

years.

75 

### 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.

76 

### IRS Publication 946, “How to Depreciate Property” , Table B

- 2, Class Lives and Recovery Periods.

77 

### Internal Revenue Service, Publication 542.

78 

### Represents weighted average state corporate marginal income tax rate.

### 8

- 16

In other words, the capital charge rate is the annuity charge that provides for the rate of return required on

invested capital, resulting from pure operations.

The discounted cash flow to the equity holders of the project is characterized in terms of the Free Cash

Flow to Equity (FCFE). F CFE is a valuation 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 valuation of assets that have finite economic lives and wh ere 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 equity 79 

### are calculated as follows:

Ca sh Flows to Equity 80 

### = EBIT (1

- tax rate)

- Interest (1

- tax rate)

+ Depreciation

- Capital Expenditures

- Working Capital Change 81



### - Principal Payments

+ New Debt Issued

79 

### An alternative definition of free cash flow to equity is as follows:

Net Income + Depreci ation

– capital expenditures

– working capital change

– Principal Payments + New Debt Issued

80 

### Property taxes and insurance are incorporated in c ash flow calculations.

81 

### NERA Economic Consulting estimates that working capital and inventory constitutes about 2% of direct capital

costs. NERA also indicates that working capital and inventories (inventories refer to the initial inventories of fuel,

consumables, and spare parts) are normally capitalized. Therefore, this item does not need to be in the capital

c harge rate. See “Independent Study to Establish Parameters of the ICAP Demand Curve for the New York

Independent System Operator”, August 27, 2010.

### 9

- 1



# 9. Coal

The next three chapters cover the representation and underlying assumptions for fuels in EPA Base Case

v. 5

. 13 . 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 h ow the coal market is represented in

EPA Base Case v.5.13 . 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 devel oped for EPA Base Case v.5.13 and the

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 dat a used to quantify the economically recoverable coal reserves, characterize their

cost, and build the 67 coal supply curves that are implemented in EPA Base Case v.5.13 . Illustrative

examples are included of the step

- by

- step approach employed in developin g the 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 considera tions that come into play in developing specific rail, barge and truck transport rates.

The last topic covered in this chapter is coal exports, imports, and non

- electric sector demand.

The assumptions for the coal supply curves and coal transportation were finalized in June 2013 , 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): TetraTech (coal transportation and team coordination),

Wood Ma ckenzie (coal supply curve development), Hellerworx (coal transportation and third party

review), and ICF (representation in IPM). The coal supply curves and transportation matrix implemented

in EPA Base Case v.5.13 are included in tables and attachments at the end of this chapter.



## 9.1 Coal Market Representation in EPA Base Case v.5.13

Coal supply, coal demand, coal quality, and the assignment of specific types of coals to individual coal

fired generating units are the four key components of the endogenous coa l market modeling framework in

EPA Base Case v.5.13 . The modeling representation attempts to realistically reflect the actual options

available to each existing coal fired power plant while aggregating data sufficiently to keep the model size

and solution time within acceptable bounds.

Each coal

- fired power plant modeled is reflected as its own coal demand region . The demand regions are

defined to reflect the coal transportation options (rail, barge, truck, conveyer belt) that are availab le to the

plant

. These demand regions are interconnected by a transportation network to at least one of the 36

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 each 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.5.13 the endogenous demand for coal is generated by coal fired power plants

interacting with a set of exogenous supply curves (see Table 9

- 24 for coal supply curve data) f or 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 the demand at a given price. The supply of and demand for each grade of

coal is linked to and affected by the supp ly of and demand for every other coal grade across supply and

demand regions. The transportation net work or matrix ( see

Excerpt from Table 9

- 23 for coal transportati on matrix data) also factors into the final determination of

delivered coal prices, given coal demand and supply. IPM derives the equilibrium coal consumption and

### 9

- 2

prices that result when the entire electric system is operating, emission, and other requirem ents are met

and total electric system costs over the modeling time horizon are minimized.

9.1.1 Coal Supply Regions

There are 36 coal supply regions in EPA Base Case v.5.13, 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 coal they can supply. Table 9

- 1 lists the coal supply regions included in

EPA Base Case v.5.13.

Figure 9

- 1 provides a map showing the location of both the coal supply regions listed 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 S upply 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

Gulf Lignite 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 Arkansas, North AN

West Interior Kansas KS

West Interior Missouri MO

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

Alberta Alberta, Canada AB

British Columbia British Columbia, Canada BC

Saskatchewan Saskatchewan, Canada SK

### 9

- 3 Figure 9

- 1 Map of the Coal Supply Regions in EPA Base Case v.5.13

9.1.2 Coal Demand Regions

Coal demand regions are designed to reflect coal transportation options available to power plants. Each

existing coal plant is reflected as its own individual demand region. The transportation infrastructure (i.e.,

rail, barge, or truck/conveyor belt), pr oximity to mine (i.e., mine mouth or not mine mouth), and

transportation competitiveness levels (i.e., non

- competitive, low

- cost competitive, or high

- cost

competitive) are developed specific to each coal plant (demand region).

When IPM is run, it determine s the amount and type of new generation capacity to add within each of

IPM’s 64 US model regions. These model regions reflect the administrative, operational, and

transmission geographic structure of the electricity grid. Since these new plants could be l ocated at

various locations within the region, a generic transpor t ation cost for different coal types is developed for

these new plants and the methodology for deriving that cost is described in the transpor t ation section of

this chapter. See Table 9

- 2 for the list of coal plant demand regions reflected in the transpor t ation matrix.

### 9

- 4 Table 9

- 2 Coal Demand Regions in EPA Base Case

Plant ORIS

Code Plant Name Coal Demand

Region Codes IPM Model Region for Which the Existing

Demand Region Serves as the Surrogate*

1004 Edwardsport C181

1010 Wabash River C183

10684 Argus Cogen Plant C563

1077 Sutherland C194

1554 Herbert A Wagner C227

1606 Mount Tom C232

1943 Hoot Lake C259

2682 S A Carlson C303

2943 Shelby Municipal Light Plant C339

3319 Jefferies C365

511 Trinidad C131

54407 Waupun Correctional Central Heating Plt C624

55856 Prairie State Generatng Station C637 MIS_IL

56564 John W Turk Jr Power Plant C644 SPP_WEST

56785 Virginia Tech Power Plant C651

56808 Virginia City Hybrid Energy Center C653

7 Gadsden C101

7242 Polk C495

728 Yates C151

991 Eagle Valley C176

10 Greene County C103

10003 Colorado Energy Nations Company C514

1001 Cayuga C180

10043 Logan Generating Company LP C517

10071 Portsmouth Genco LLC C518

10075 Taconite Harbor Energy Center C519

1008 R Gallagher C182

10113 John B Rich Memorial Power Station C520

1012 F B Culley C184

10143 Colver Power Project C521

10148 White Pine Electric Power C522

10151 Grant Town Power Plant C523

1024 Crawfordsville C185

1032 Logansport C186

10328 T B Simon Power Plant C528

10333 Central Power & Lime C529

10343 Foster Wheeler Mt Carmel Cogen C530

1037 Peru C187

10377 James River Genco LLC C540

10378 CPI USA NC Southport C541

10380 Elizabethtown Power LLC C542

10382 Lumberton C543

10384 Edgecombe Genco LLC C544

1040 Whitewater Valley C188

1043 Frank E Ratts C189

10464 Black River Generation C546

1047 Lansing C191

1048 Milton L Kapp C192

10495 Rumford Cogeneration C548 NENG_ME

10566 Chambers Cogeneration LP C550

10603 Ebensburg Power C552

10640 Stockton Cogen C554 WEC_CALN

10641 Cambria Cogen C555

10670 AES Deepwater C556

### 9

- 5

Plant ORIS

Code Plant Name Coal Demand

Region Codes IPM Model Region for Which the Existing

Demand Region Serves as the Surrogate*

10671 AES Shady Point LLC C557

10672 Cedar Bay Generating Company LP C558

10675 AES Thames C560 NENG_CT

10676 AES Beaver Valley Partners Beaver Valley C561

10678 AES Warrior Run Cogeneration Facility C562

1073 Prairie Creek C193

10743 Morgantown Energy Facility C564

10768 Rio Bravo Jasmin C565

10769 Rio Bravo Poso C566

10784 Colstrip Energy LP C570

108 Holcomb C113

1081 Riverside C195

1082 Walter Scott Jr Energy Center C196 MIS_MIDA

10849 Silver Bay Power C572

1091 George Neal North C197

1104 Burlington C198

1122 Ames Electric Services Power Plant C199

113 Cholla C114

1131 Streeter Station C200

1167 Muscatine Plant #1 C201

1217 Earl F Wisdom C203

1218 Fair Station C204

1241 La Cygne C206

1250 Lawrence Energy Center C207

1252 Tecumseh Energy Center C208

126 H Wilson Sundt Generating Station C115

127 Oklaunion C116 ERC_WEST

1295 Quindaro C209

130 Cross C117

1355 E W Brown C211

1356 Ghent C212

136 Seminole C118

1364 Mill Creek C216

1374 Elmer Smith C217 S_C_KY

1378 Paradise C218 S_C_TVA

1379 Shawnee C219

1381 Kenneth C Coleman C220

1382 HMP& L Station Two Henderson C221

1383 Robert A Reid C222

1384 Cooper C223

1385 Dale C224

1393 R S Nelson C225 S_D_WOTA

1552 C P Crane C226

1571 Chalk Point LLC C229

1572 Dickerson C230 PJM_SMAC

1573 Morgantown Generating Plant C231

160 Apache Station C119

1619 Brayton Point C234

165 GRDA C120

1695 B C Cobb C236

1702 Dan E Karn C237

1710 J H Campbell C238

1720 J C Weadock C239

1723 J R Whiting C240

1731 Harbor Beach C241

### 9

- 6

Plant ORIS

Code Plant Name Coal Demand

Region Codes IPM Model Region for Which the Existing

Demand Region Serves as the Surrogate*

1733 Monroe C243 MIS_LMI

1740 River Rouge C244

1743 St Clair C245

1745 Trenton Channel C246

1769 Presque Isle C247

1771 Escanaba C248

1825 J B Sims C249

1830 James De Young C250

1831 Eckert Station C251

1832 Erickson Station C252

1843 Shiras C253

1866 Wyandotte C254

1891 Syl Laskin C255

1893 Clay Boswell C256

1915 Allen S King C258

1961 Austin Northeast C260

1979 Hibbing C261

2008 Silver Lake C262

2018 Virginia C263

2022 Willmar C264

2049 Jack Watson C265

207 St Johns River Power Park C121

2076 Asbury C267

2079 Hawthorn C268

2080 Montrose C269

2094 Sibley C270

2098 Lake Road C271

2103 Labadie C272 MIS_MO

2104 Meramec C273

2107 Sioux C274

2123 Columbia C275

2132 Blue Valley C276

2144 Marshall C277

2161 James River Power Station C278

2167 New Madrid C279

2168 Thomas Hill C280

2171 Missouri City C282

2187 J E Corette Plant C283

2240 Lon Wright C284

2277 Sheldon C285

2291 North Omaha C286

2324 Reid Gardner C287 WECC_SNV

2364 Merrimack C288 NENGREST

2367 Schiller C289

2378 B L England C290 PJM_EMAC

2403 PSEG Hudson Generating Station C292

2408 PSEG Mercer Generating Station C293

2442 Four Corners C295

2451 San Juan C296

2526 AES Westover C298

2527 AES Greenidge LLC C299

2535 AES Cayuga C300 NY_Z_C&E

2549 C R Huntley Generating Station C301

2554 Dunkirk Generating Plant C302

26 E C Gaston C104

### 9

- 7

Plant ORIS

Code Plant Name Coal Demand

Region Codes IPM Model Region for Which the Existing

Demand Region Serves as the Surrogate*

2706 Asheville C304

2712 Roxboro C306

2718 G G Allen C309 S_VACA

2721 Cliffside C311

2727 Marshall C312

2790 R M Heskett C314

2817 Leland Olds C315 MAP_WAUE

2823 Milton R Young C316

2824 Stanton C317 MIS_MNWI

2828 Cardinal C318

2836 Avon Lake C322

2840 Conesville C325

2850 J M Stuart C328

2866 FirstEnergy W H Sammis C331

2876 Kyger Creek C333

2878 FirstEnergy Bay Shore C334

2914 Dover C335

2917 Hamilton C336

2935 Orrville C337

2936 Painesville C338

2952 Muskogee C340

2963 Northeastern C341

298 Limestone C122

3 Barry C100

3118 Conemaugh C345 PJM_PENE

3122 Homer City Station C346

3130 Seward C347

3136 Keystone C349

3138 New Castle Plant C350

3140 PPL Brunner Island C351 PJM_WMAC

3149 PPL Montour C352

3152 Sunbury Generation LP C353

3179 Hatfields Ferry Power Station C355

3181 FirstEnergy Mitchell Power Station C356

3287 McMeekin C360

3295 Urquhart C361

3297 Wateree C362

3298 Williams C363

3393 Allen Steam Plant C367

3396 Bull Run C368

3399 Cumberland C369

3403 Gallatin C370

3407 Kingston C373

3470 W A Parish C375

3497 Big Brown Power Company LLC C376

3775 Clinch River C378

3796 Bremo Bluff C381

3797 Chesterfield C382

3809 Yorktown C384

384 Joliet 29 C123

3845 Transalta Centralia Generation C385 WECC_PNW

3935 John E Amos C386

3943 FirstEnergy Fort Martin Power Station C390

3944 FirstEnergy Harrison Power Station C391

3948 Mitchell C395

### 9

- 8

Plant ORIS

Code Plant Name Coal Demand

Region Codes IPM Model Region for Which the Existing

Demand Region Serves as the Surrogate*

3954 Mt Storm C396

3992 Blount Street C397

4041 South Oak Creek C398

4042 Valley C399

4050 Edgewater C400

4072 Pulliam C402

4078 Weston C403

4125 Manitowoc C404

4127 Menasha C405

4140 Alma C406

4143 Genoa C407

4158 Dave Johnston C410

4162 Naughton C411

4259 Endicott Station C412

4271 John P Madgett C413

465 Arapahoe C125

469 Cherokee C126

47 Colbert C105

470 Comanche C127 WECC_CO

477 Valmont C128

492 Martin Drake C129

4941 Navajo C414

50 Widows Creek C106

50039 Kline Township Cogen Facility C580

50130 G F Weaton Power Station C581

50366 University of Notre Dame C588

50388 Phillips 66 Carbon Plant C590 WECC_SF

50397 P H Glatfelter C592

50410 Chester Operations C594

50611 WPS Westwood Generation LLC C597

50776 Panther Creek Energy Facility C599

508 Lamar Plant C130

50806 Stone Container Florence Mill C601

50835 TES Filer City Station C602

50879 Wheelabrator Frackville Energy C603

50888 Northampton Generating Company LP C604

50931 Yellowstone Energy LP C606

50951 Sunnyside Cogen Associates C607

50974 Scrubgrass Generating Company LP C609

50976 Indiantown Cogeneration LP C610

51 Dolet Hills C107

52007 Mecklenburg Power Station C611

52071 Sandow Station C612

525 Hayden C132

527 Nucla C133

54035 Roanoke Valley Energy Facililty I C614

54081 Spruance Genco LLC C615

54144 Piney Creek Project C616

54304 Birchwood Power C621

54408 UW Madison Charter Street Plant C625

54556 Corn Products Illinois C626

54634 St Nicholas Cogen Project C627

54677 CII Carbon LLC C628

54755 Roanoke Valley Energy Facility II C629

54775 University of Iowa Main Power Plant C630

### 9

- 9

Plant ORIS

Code Plant Name Coal Demand

Region Codes IPM Model Region for Which the Existing

Demand Region Serves as the Surrogate*

55076 Red Hills Generating Facility C633

55479 Wygen 1 C635

55749 Hardin Generator Project C636

56 Charles R Lowman C108

56068 Elm Road Generating Station C639 MIS_WUMS

56163 KUCC C640

56224 TS Power Plant C641 WECC_NNV

56319 Wygen 2 C642

564 Stanton Energy Center C134 FRCC

56456 Plum Point Energy Station C643 S_D_N_AR

56596 Wygen III C645 WECC_WY

56609 Dry Fork Station C646

56611 Sandy Creek Energy Station C647

56671 Longview Power LLC C649 PJM_AP

56708 CFB Power Plant C650

56786 Spiritwood Station C652 MIS_MAPP

568 Bridgeport Station C135

56848 Haverhill North Cogeneration Facility C210

57046 Archer Daniels Midland Columbus C654

59 Platte C109 SPP_NEBR

593 Edge Moor C136

594 Indian River Generating Station C137

60 Whelan Energy Center C110

6002 James H Miller Jr C415

6004 FirstEnergy Pleasants Power Station C416

6009 White Bluff C417 S_D_REST

6016 Duck Creek C418

6017 Newton C419

6018 East Bend C420

6019 W H Zimmer C421

602 Brandon Shores C138

6021 Craig C422

6030 Coal Creek C423

6031 Killen Station C424

6034 Belle River C425

6041 H L Spurlock C426

6052 Wansley C427

6055 Big Cajun 2 C428

6061 R D Morrow C429

6064 Nearman Creek C430

6065 Iatan C431 SPP_N

6068 Jeffrey Energy Center C432

6071 Trimble County C433

6073 Victor J Daniel Jr C434

6076 Colstrip C435 WECC_MT

6077 Gerald Gentleman C436

6082 AES Somerset LLC C437 NY_Z_A&B

6085 R M Schahfer C438

6089 Lewis & Clark C439

6090 Sherburne County C440

6094 FirstEnergy Bruce Mansfield C441

6095 Sooner C442

6096 Nebraska City C443

6098 Big Stone C444

6101 Wyodak C445

### 9

- 10

Plant ORIS

Code Plant Name Coal Demand

Region Codes IPM Model Region for Which the Existing

Demand Region Serves as the Surrogate*

6106 Boardman C446

6113 Gibson C447 MIS_INKY

6124 McIntosh C448

6136 Gibbons Creek C449

6137 A B Brown C450

6138 Flint Creek C451

6139 Welsh C452

6146 Martin Lake C453

6147 Monticello C454

6155 Rush Island C455

6165 Hunter C456 WECC_UT

6166 Rockport C457

6170 Pleasant Prairie C458

6177 Coronado C459

6178 Coleto Creek C460

6179 Fayette Power Project C461 ERC_REST

6180 Oak Grove C462

6181 J T Deely C463

6183 San Miguel C464

6190 Brame Energy Center C465 SPP_SE

6193 Harrington C466

6194 Tolk C467 SPP_SPS

6195 Southwest Power Station C468

6204 Laramie River Station C469

6213 Merom C470

6225 Jasper 2 C471

6248 Pawnee C473

6249 Winyah C474

6250 Mayo C475

6254 Ottumwa C476 MIS_IA

6257 Scherer C477

6264 Mountaineer C478

628 Crystal River C139

641 Crist C140

642 Scholz C141

643 Lansing Smith C142

645 Big Bend C143

6469 Antelope Valley C480

6481 Intermountain Power Project C481

663 Deerhaven Generating Station C144

6639 R D Green C482

6641 Independence C483

6648 Sandow No 4 C484

6664 Louisa C485

667 Northside Generating Station C145

6705 Warrick C486

676 C D McIntosh Jr C146

6761 Rawhide C487

6768 Sikeston Power Station C488

6772 Hugo C489

6823 D B Wilson C490

703 Bowen C147 S_SOU

7030 Twin Oaks Power One C491

708 Hammond C148

709 Harllee Branch C149

### 9

- 11

Plant ORIS

Code Plant Name Coal Demand

Region Codes IPM Model Region for Which the Existing

Demand Region Serves as the Surrogate*

7097 J K Spruce C492

7210 Cope C493

7213 Clover C494 PJM_Dom

727 Mitchell C150

733 Kraft C152

7343 George Neal South C496

7504 Neil Simpson II C497

753 Crisp Plant C153

7549 Milwaukee County C499

7737 Cogen South C501

7790 Bonanza C502

7902 Pirkey C503

8 Gorgas C102

8023 Columbia C504

8042 Belews Creek C505

8066 Jim Bridger C672

8069 Huntington C506

8102 General James M Gavin C507

8219 Ray D Nixon C508

8222 Coyote C509

8223 Springerville C510 WECC_AZ

8224 North Valmy C511

8226 Cheswick Power Plant C512

856 E D Edwards C154

861 Coffeen C155

87 Escalante C112 WECC_NM

874 Joliet 9 C158

876 Kincaid Generation LLC C159

879 Powerton C160

883 Waukegan C161

884 Will County C162 PJM_COMD

887 Joppa Steam C164

889 Baldwin Energy Complex C165

891 Havana C166

892 Hennepin Power Station C167

898 Wood River C169

963 Dallman C170

976 Marion C171

983 Clifty Creek C173

990 Harding Street C175

994 AES Petersburg C177

995 Bailly C178

997 Michigan City C179

83551 Plant Ratcliffe

- the Kemper IGCC Project C633

55360 Two Elk Generating Station C634

56664 Greene Energy Resource Recovery Project C678

70194 Genesee #3 C661

70195 Genesee C661

70243 HR Milner C662

70269 Keephills C663 CN_AB

70309 Lingan C664

70035 Belledune C658 CN_NB

70441 Poplar River C665

70449 Pt. Aconi C666 CN_NS

70450 Pt. Tupper C667

### 9

- 12

Plant ORIS

Code Plant Name Coal Demand

Region Codes IPM Model Region for Which the Existing

Demand Region Serves as the Surrogate*

70514 Shand C668

70517 Sheerness C669

70056 Boundary Dam C659 CN_SK

70562 Sundance C670

70587 Trenton NS C671

3264 W S Lee C358

3406 Johnsonville C372

3803 Chesapeake C383

C676 NY_Z_F

2480 Danskammer Generating Station C297 NY_Z_G

- I

C675 NY_Z_D

2837 FirstEnergy Eastlake C323 PJM_ATSI

C677 S_D_AMSO

10002 ACE Cogeneration Facility C513 WECC_SCE

70058 Brandon G.S. C660 CN_MB

1353 Big Sandy C210 PJM_West

*If IPM elects to build a new coal plant, that coal plant will be assigned to a particular IPM region. Therefore, the base case

modeling relies on a particular existing plant in that region

– generally one considered to be representative of average transportation

cost for plants in that region

– and uses that plant’s transportation cost as a surrogate for coal transportation cost for a projected

new coal plant.



### 9.1.3 Coal Quality Characteristics

Coal varies by heat content, SO 2 

### content

, HCl 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, subbituminous, or lignite) with their associated

heat conte nt 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 an d associated SO 2 

### ranges are shown in

Table 9

- 4 .)

.

Table 9

- 3 Coal Rank Heat Content Ranges

Coal Type Heat Content (Btu/lb) Classification

Bituminous >10,260

– 13,000 B

Subbituminous > 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

### 9

- 13

The assumptions in EPA Base Case v.5.13 on the heat, HCl, 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) 82



### .

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 compan ies, 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 repr esentative unit

configurations. Data regarding the SO 2 

### , chlorine, and ash content of the coal used was obtained along

with mercury content.

The 1998

- 2000 ICR resulted in more than 40,000 data points indicating the coal type, sulfur content,

mercury content , ash content, chlorine 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.5.13 , the ICR data points were first grouped by IPM coal

grades and IPM c oal supply regions. Using the grouped ICR data, the average heat, SO 2 

### , mercury, HCl,

and ash content were calculated for each coal grade/supply region combination. In instances where no

data were available for a particular coal grade in a specific suppl y region, the national average SO 2 

### and

mercury values for the coal grade were used as the region’s values. The coal characteristics of Canadian

coal supply regions are based on the coal characteristics of the adjacent US coal supply regions. The

resulting values are shown in Table 9

- 5

.

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) HCl Content

(lbs/MMBtu) CO 2 Content

(lbs/MMBtu)

AB SA 16.12 0.59 5.29 5.47 0.009 214.9

SB 15.60 0.94 6.06 6.94 0.013 211.0

SD 15.00 1.43 5.35 11.60 0.008 214.9

AL BB 25.50 1.09 4.18 9.76 0.012 204.7

BE 24.00 2.68 12.58 10.70 0.028 204.7

AN BG 22.00 4.23 9.36 7.83 0.079 202.8

AZ BB 21.50 1.05 5.27 7.86 0.067 207.1

BC BD 21.40 1.40 6.98 8.34 0.096 205.4

CG BB 22.74 0.90 4.09 8.42 0.021 209.6

SB 20.00 0.93 2.03 7.06 0.007 209.6

CR BB 23.36 1.05 5.27 7.86 0.067 209.6

CU BB 23.56 0.86 4.01 7.83 0.009 209.6

IL BE 23.75 2.25 6.52 6.61 0.214 203.1

BG 23.50 4.56 6.53 8.09 0.113 203.1

BH 22.00 5.58 5.43 9.06 0.103 203.1

IN BB 22.00 1.00 2.29 6.67 0.050 203.1

BE 22.70 2.31 5.21 7.97 0.036 203.1

BG 22.40 4.27 7.20 8.22 0.028 203.1

BH 22.40 6.15 7.11 8.63 0.019 203.1

KE BB 25.00 1.04 4.79 6.41 0.112 206.4

82 

### Data from the ICR can be found at h ttp://www.epa.gov/ttn/atw/combust/utiltox/mercury.html

. .

### 9

- 14 Coal

Supply

Region Coal

Grade Heat Content

(MMBtu/Ton) SO 2 Content

(lbs/MMBtu) Mercury

Content

(lbs/Tbtu) Ash Content

(lbs/MMBtu) HCl Content

(lbs/MMBtu) CO 2 Content

(lbs/MMBtu)

BD 24.80 1.44 5.97 7.45 0.087 206.4

BE 24.64 2.12 7.93 7.71 0.076 206.4

KS BG 22.00 4.84 4.09 8.47 0.133 202.8

KW BD 23.80 1.56 5.56 6.19 0.280 203.1

BG 23.80 4.46 6.90 8.01 0.097 203.1

BH 23.00 5.73 8.16 10.21 0.053 203.1

LA LE 13.80 2.49 7.32 17.15 0.014 212.6

MD BD 23.00 1.55 7.82 9.53 0.029 204.7

BE 23.20 2.78 15.62 11.70 0.072 204.7

ME LE 12.97 1.83 11.33 11.69 0.019 219.3

MO BG 22.00 4.54 5.91 9.46 0.023 202.8

MP SA 18.20 0.62 4.24 3.98 0.007 215.5

SD 17.20 1.49 4.53 10.13 0.006 215.5

MS LE 10.39 2.76 12.44 21.51 0.018 212.6

MT BB 20.90 1.05 5.27 7.86 0.067 215.5

ND LE 13.10 2.27 8.30 12.85 0.014 219.3

NS SB 19.60 0.89 4.60 14.51 0.014 209.2

SE 18.40 1.90 8.65 23.97 0.008 209.2

OH BE 24.20 3.08 18.70 7.08 0.075 204.7

BG 24.10 3.99 18.54 8.00 0.071 204.7

BH 24.20 6.43 13.93 9.13 0.058 204.7

OK BG 22.00 4.65 26.07 13.54 0.051 202.8

PC BE 24.41 2.57 17.95 9.23 0.096 204.7

BG 24.40 3.79 21.54 9.59 0.092 204.7

PW BE 26.00 2.51 8.40 5.37 0.090 204.7

BG 25.40 3.69 8.56 6.48 0.059 204.7

SK LD 13.82 1.51 7.53 11.57 0.014 219.3

LE 10.58 2.76 12.44 21.51 0.018 215.3

TN BB 26.20 1.14 3.78 10.35 0.083 206.4

BE 25.23 2.13 8.43 6.47 0.043 206.4

TX LE 13.47 3.00 14.65 25.65 0.020 212.6

LG 12.47 3.91 14.88 25.51 0.036 212.6

LH 10.68 5.67 30.23 23.95 0.011 212.6

UT BA 23.00 0.67 4.37 7.39 0.015 209.6

BE 23.90 2.34 9.20 7.41 0.095 209.6

VA BB 25.90 1.05 4.61 6.97 0.054 206.4

BD 25.20 1.44 5.67 7.97 0.028 206.4

BE 25.00 2.09 8.40 8.05 0.028 206.4

WG BB 22.00 1.13 1.82 5.58 0.005 214.3

SD 18.80 1.33 4.33 10.02 0.008 214.3

WH SA 17.60 0.58 5.61 5.47 0.010 214.3

WL SB 16.79 0.94 6.44 6.50 0.012 214.3

WN BE 25.35 2.55 10.28 7.89 0.092 204.7

BH 25.15 6.09 8.82 9.62 0.045 204.7

WS BB 24.40 1.09 5.75 9.15 0.091 206.4

BD 24.50 1.32 8.09 9.25 0.098 206.4

BE 23.83 1.94 8.80 9.89 0.102 206.4

### 9

- 15

9.1.5 Coal Grade Assignments

The grades of coal that may be used by specific 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.5.13 are shown in Table 9

- 6 . Not all of the coal grades allowed to a plant by

the coal grade assignment are necessarily available in the plant’s assigned coal demand region (due to

transportation limitations). IPM endogenously selects the coal burned by a plant by taking into account

both the constraint of the plant’s coal grade assignment and the constraint of the coals actually available

within a plant’s coal demand region.

Table 9

- 6 Example of Coal Assignments Made in EPA Base Case

Plant Name Unique ID SIP SO 2



### Limit

(lbs/MMBtu) Scrubber? Fuels Allowed

Mt Storm 3954_B_3 0.15 Yes BA,BB,BD

Mitchell 3948_B_1 1.2 Yes BA,BB,BD,BE,BG,BH

Scherer 6257_B_1 1.2 Yes BA,BB,BD,BE,BG,BH,SA,SB, SD,SE

Newton 6017_B_1 0.5 No BA,SA

Weston 4078_B_4 0.1 Yes BA,SA,SB

Sandow No 4 6648_B_4 1.2 Yes LA,LD,LE,LG,LH

Monticello 6147_B_3 1.2 Yes LA,LD,LE,LG,LH,SA,SB,SD,SE

Laramie River Station 6204_B_3 0.2 Yes LA,SA,SB

Big Cajun 2 6055_B_2B1 0.38 No SA

W A Parish 3470_B_WAP8 0.36 Yes SA,SB,SD,SE



## 9.2 Coal Supply Curves

9.2.1 Nature of Supply Curves Developed for EPA Base Case v.5.13

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

curv es for EPA Base Case v.5.13 . 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, Wood Mackenzie first mapped its internal list of coal

regions and qualities to EPA’s 36 coal supply regions (described abo ve in sections 0 ) and 1 4 coal grades

(described above in section 9.1.3 ). The combined code list is shown in Table 9

- 7 below with the IPM

supply regions appearing in the ro ws 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

- 7 ) for fo recast

years 2016, 2018, 2020, 2025, 2030, 2040, and 2050.

### 9

- 16 Table 9

- 7 Basin

- Level Groupings Used in Preparing v.5.13 Coal Supply Curves

Table 9

- 7 Basin Level Groupings Used in Preparing v.5.13 Coal Supply Curves

Bituminous Lignite Subbituminous

Coal S upply

Region Geo

Region Geo. Sub

- Region BA BB BD BE BG BH LD LE LG LH SA SB SD SE

AB Canada Alberta, Canada x x x

AK West Northwest x x

AL Appalachia Southern Appalachia x x

AN Interior West Interior x

AZ West Southwest x

BC Canada British Columbia x

CG West Rocky Mountain x x

CR West Rocky Mountain x

CU West Rocky Mountain x

IL Interior East Interior (Illinois Basin) x x x

IN Interior East Interior (Illinois Basin) x x x x

KE Appalachia Central Appalachia x x x

KS Interior West Interior x

KW Interior East Interior (Illinois Basin) x x x

LA Interior Gulf Lignite x

MD Appalachia Northern Appalachia x x

ME West Dakota Lignite x

MO Interior West Interior x

MP West Powder River Basin x x

MS Gulf Gulf Lignite Coast x

MT West Western Montana x

ND West Dakota Lignite x

NS West Southwest x x

OH Appalachia Northern Appalachia x x x

OK West West Interior x

PC Appalachia Northern Appalachia x x

PW Appalachia Northern Appalachia x x

SK Canada Saskatchewan x x

TN Appalachia Central Appalachia x x

TX Interior Gulf Lignite x x x

UT West Rocky Mountain x x

VA Appalachia Central Appalachia x x x

WG West Western Wyoming x x

WH West Powder River Basin x

WL West Powder River Basin x

WN Appalachia Northern Appalachia x x

WS Appalachia Central Appalachia x x x

### 9

- 17

9.2.2 Cost C omponents in the Supply Curves

C osts are represented as total cash costs, which is a combination of a mine’s operating cash costs plus

royalty & levies . These costs are estimated on a Free on Board (FOB) basis at the point of sale. Capital

costs (either expansionary or sustaining) are not included in the cash cost estimate . We beli eve that total

cash cost is the best metric for the supply curves as c oal prices tend to be ultimately determined by the

incremental cost of production (i.e. total cash cost).

Operating cash cost

These are the direct operating cash costs and includes, wher e appropriate, mining, coal preparation,

product transport, and overheads. No capital cost component or depreciation & amortization charge is

included. Operating cash costs co nsist of the following elements:

Mining costs

- Mining costs are the direct cos t of mining coal and associated waste material for

surface and underground operations. It includes any other mine site costs, such as ongoing

rehabilitation / reclamation, security, community development costs. It also includes the cost of

transporting ra w coal from the mining location to the raw coal stockpile at the coal preparation plant.

Coal preparation

- The cost of coal preparation includes raw coal stockpile reclaim, crushing and

screening, washing and marketable coal product stockpiling (if applicable).

Transport

- This covers all transport costs of product coal to point of sale

. Transport routes wi th

multiple modes (e.g. truck and rail) are shown as total cost per marketable ton for all stages of the

transport route

. Loading charges are included in this cost if relevant

.

Overheads

- This is any off mine site general and administration overheads tha t are essential to the

production and sale of a mine’s coal product

. Examples would be essential corporate management

or a sales and marketing charge

.

It is important to note that although the formula for calculating mine costs is consistent across region s,

some tax rates and fees vary by state and mine type. In general, there are two mine types: underground

(deep) or surface mines. Underground mining is categorized as being either a longwall (LW) or a

continuous room

- and

- pillar mine (CM). Geologic conditi ons 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 operation 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 . F urther information on operating cost methodology and assumptions can be found in Attachment

9

- 1

.

Royalties and L evies

These include, where appropriate, coal royalties, mine safety levies, health levies, industry research

levies and other production taxes.

9.2.3 Procedures Employed in Determining Mining Costs

The total cash cos ts of mines have been estimated in current year terms using public domain information

including; geological reports, reported statistics on production, labo r and input costs, and company

reports. The estimates have been validated by reference to information gained by visits to operations, and

discussions with industry participants.

Because the estimates are based only on public information and analysis, and do not represent private

knowledge of an operation’s actual costs, there may be deviations from actual costs. In instances where

confidential information is held by Wood Mackenzie, it has not been used to produce the published

estimates. Several methods are employed for cost estimation depending on the availability of information

and the diversi ty of mining operations. When possible, Wood Mackenzie analysts develop ed detailed lists

### 9

- 18

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 operating cash costs.

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. Th e 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 a nd mine and division

overhead categories into unit removal costs by equipment type. 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 information obtained from mine personnel interviews and industry contacts. Phone conversations

and conferences wi th industry professionals provide 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 M ackenzie report s 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 transp ort costs beyond

the point

- of

- sale terminal are not part of this analysis and are not reflected in the supply curves

themselves.

9.2.4 Procedure Used In Determining Mine Productivity

Projected production and stripping ratios are the key determinants of surface m ine 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 t o 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 l oss is often offset by

technology 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 balance increased stripping ratios with add ed 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

employee

- hours. Alloca ting aggregate employee hours among specific mines, production forecasts for

these mines can be converted back into mine

- specific productivity forecasts. These forecasts are then

examined on a mine

- by

- mine basis by an industry expert with region

- specific k nowledge.

A similar approach is used for underground mines. First, as background, the specific factors affecting

productivity at such mines are identified. For example, underground mines do not have stri pping ratios

. Productivity estimates for these min es 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.

### 9

- 19

9.2.5 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 cases

, 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 operators’ statements as the starting point for prod uction and reserves

forecasts. If no other material is available, interviews with company personnel will pr ovide an estimate.

Region and coal type determinations for unlisted reserves are based on public information reported for

similarly located mines

. Classifying reserves this way means considering not only a mine’s geographic

location but also its geologic conditions 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 a re assigned in much the same way.

Once a mine become s active, actual production numbers reported in corporate SEC filings and MSHA

reports are subtracted from the total reserve number to arrive at current reserve amount s

. Wood

Mackenzie consistently updat es the reserves database when announcements of new or ame nded

reserves are made public. As a final check, the Wood Mackenzie supply estimates are balanced against

the Demonstrated Reserve Base (DRB) 83 

### estimates to ensure that they do not exceed the DRB

esti mates.

9.2.6 New Mine Assumptions

New mines have been included based on information that Wood Mackenzie maintains on each supply

region. They include announced projects, coal lease applications and unassigned reserves reported by

mining companies. Where addition al reserves are known to exist, additional incremental steps have been

added and designated with the letter “N” in the “Step Name” field of the supply curves

. These incremental

steps were added based on characteristics of the specific region, typical mine size

, and cost trends. The y

do not necessarily imply a specific mine or mine type.

In the IL basin, there is a significant amount of mine projects announced and/or underway that will be

completed and available by 2016. These “on the way” mines are design ated as existing mines in the

“step name” field as they already are, or expected to be, available by the first model run year of 2016.

Wood Mackenzie has also identified technical coal reserves that may be commercial in the longer

- term,

but would most lik ely not be developed until after the completion of mine development already underway

or announced. Therefore, the new mines reflecting these additional reserves are not available until 2018.

9.2.7 Other Notable Procedures

Currency A ssumptions

For consistency with the cost basis used in EPA Base Case v.5.13, costs are converted to real 20 11 $.

Future Cost Adjustments

Changes in mine productivity are a key factor impacting the evolution of costs over time. In general, mine

productivity is expected to continue to decline

– in large part due to worsening geology and more difficult

to mine reserves Productivity has declined at

- 2.7% CAGR from 2000

- 2011 as shown in Figure 9

- 2.

83 

### Posted by the Energy Information Administration (EIA) in its Coal Production Report

.

### 9

- 20 Figure 9

- 2 Coal Mine Productivity (2000

- 2011)

Source: U.S. Department of Labor, Mine Safety and Health Administration



### Figure 9

- 3 Av erage Annual Cost Growth Assumptions by R egion

(2012

- 2050)

Figure 9

- 3 shows the compounded average annual growth rate (CAGR) of mining costs by basin over the

fore cast period. It should be noted that cost increases will ultimately be linked to market demand (as

demand grows, the faster the rate of depletion of lower cost reserves). Costs in some supply basins are

expected to increase more quickly than others due t o issues such as mining conditions, productivity,

infrastructure limitations, etc. Region

- specific information can be found in section 9.2.9

.

### 9

- 21

Supply Growth Limita tions

To the maximum extent possible, the IPM model is set up to determine the optimal volume of coal supply

which can be profitably supplied. For two of the lower cost basins (Powder River and Illinois basins),

maximum production capacities are included as constraints (production ceilings) to more accurately

reflect the upper bound of what could be produced in a given year. Those limits, represented in millions

of tons per year, are shown in Figure 9

- 4 below. These ceilings are necessary to guard against modeling

excess annual production capacity in certain basins. For instance, in the PRB, several of the “new” mines

reflect expansion min es that would not be developed until the initial mine is further depleted. In this case,

the production ceiling helps safeguard against a modeling scenario that would simultaneously produce

from both of these mines.

Figure 9

- 4 Maximum Annual Coal Production Capacity

Maximum Thermal Coal Production Capacity per Year (million tons)

2016 2018 2020 2025 2030 2040 2050

ILB 165.5 190 203.4 220.1 239.5 254.6 254.6

PRB 509 525.5 552.5 572.3 609.5 609.5 609.5



### 9.2.8 Supply Curve Development

The description below describes the development of the coal supply curves. The actual coal supply

curves can be found www.epa.gov/airmarkets/progsregs/epa

- ipm/BaseCasev513.html . For illustrative

purposes, there is also an excerpt of the coal supply curves in Table 9

- 24 of this chapt er.

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 undergr ound 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 p rocess for

developing a cumulative supply curve is illustrated in Figure 9

- 5 and Figure 9

- 6 below.

Figure 9

- 5 Illustration of Pre liminary Step in D eveloping a C umulative Coal S upply C urve

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 the 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

### 9

- 22

curve contains all mines

– new or existing as well as both underground and surface mines. The resulting

curve is a continuous supply curve but can be modified to show costs as a stepped supply curve. (Supply

curves in stepped format are used in linear programming models like IPM.) See Figure 9

- 7 for a stepped

version of the supply curve example shown in Figure 9

- 6 . 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.

Figure 9

- 6 Illustration of Final S tep in D eveloping a C umulative C oal S upply C urve

Figure 9

- 7 Example Coal Supply Curve in Stepped Format

### 9

- 23

9.2.9 EPA Base Case v.5.13 Assumptions and Outlooks for Major Supply Basins

Powder River Basin (PRB)

The PRB is somewhat unique to other US co al basins; in that producers have the ability to add significant

production volumes relatively easily and at a profit. That said, the decisions on production volumes are

largely based on the market conditions, namely the price. For instance, in a low pri ce environment

producers tend to moderate production volumes to maintain attractive prices, and choose to ramp up

production when prices are higher. The evolution of costs in the PRB will be strongly correlated to the

rate at which producers ramp up produ ction at existing mines, which as indicated will depend on market

conditions.

Wood Mackenzie anticipate s productivity at most existing PRB mining operations to decline at very

modest rates over the forecast horizon, with increasing strip ratios at least partly offset by improved usage

of labor and capital. As most PRB mines are progressing downward, the ratios of overburden to coal

(strip ratios) will increase in the future. The productivity of new mines will be quite low during the early

stages of thei r life span.

Mining at several locations is steadily proceeding production westward toward the Joint Line railroad and,

at current and forecasted levels of production, around 2019 several mines are expected to eventually

reach the line. This event will re sult in a costly movement across the railroad, requiring significant capital

investment and reduced production as the transition is made. During the move across the Joint Line

railroad, strip ratios will spike and productivity will fal l as new box cuts ar e created.

Illinois Basin (ILB)

Production costs in the Illinois basin have been steadily decreasing in recent years as new low cost mines

are opened using more efficient longwall mining techniques. Wood Mackenzie expects that average

costs will continue to decline as additional new mines are developed. However, as new low cost mines

are brought on, higher cost mines will be unable to compete. In the long

- term, the shape of the ILB

supply curve is expected to decrease in cost and increase in production capacity

.

Given its large scale growth potential , investments in rail i nfrastructure development will have to keep

pace. While Wood Mackenzie expect there to be some bottlenecks in expanding transportation in the

basin early on, they project that once utilities begin committing to taking ILB coal, railroads will make the

nec essary changes to accommodate the change. However, there is a risk that rail infrastructure in the

basin will not be able to keep up with the rate of growth in ILB which could limit the region’s otherwise

strong growth potential.

Central Appalachia (CAPP)

Geologic conditions in the CAPP region are challenging, with thin seams and few underground reserves

amenable to more efficient longwall mining techniques. Costs of production in CAPP have risen

substantially in recent years as the region has struggled w ith mining thinner seams as reserves deplete,

mining accidents have led to increased inspections, and mine permitting has become increasingly difficult

as opposition to surface mining intensifies

– with the revocation of some section 404 permits that regul ate

the discharge into US waterways. Since surface mining is the lowest cost form of production in CAPP,

reduced growth in surface mining operations is adding to increasing cost in the region

As producers cut back on production over the course of 2012 in order to manage the falling demand,

productivity suffered and production costs per ton in the region rose roughly 10%. In an effort to retain

margins, producers implemented a variety of tactics to try to keep production costs from continuing to

increas e; including, shifting more production to lower cost operations and selling lesser quality raw coal to

save on coal preparation/washing costs.

### 9

- 24

Northern Appalachia (NAPP)

M ining cost escalation in NAPP has slowed considerably recently

. Future cost for t he basin as a whole

will depend largely on the development of new reserve areas.

Northern Appalachia has an estimated 5 billion short tons (Bst) of thermal coal reserves. However, only

about 2.3 Bst is associated with currently operating mines and 90 Ms t of that with existing mines that are

idled. Many major producers within the region are within years of depleting currently assigned reserves.



## 9.3 Coal Transportation

The description below describes the transportation matrix. The actual transportation matri x can be found

www.epa.gov/airmarkets/progsregs/epa

- ipm/BaseCasev513.html . For illustrative purposes, there is also

an excerpt of the transportation matrix in Excerpt from Table 9

- 23 of this chapter.

Within the United States, steam coal for use in coal

- fired power plants is shipped via a variety of

transportation modes, including barge, con veyor belt, rail, truck, and lake/ocean vessel. A given coal

- fired

plant typically only has access to a few 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 sol iciting coal

deliveries influences transportation 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 predominately by two major rail carriers, Burlington Northern Santa Fe (BNSF)

and Union Pacific (UP). Plants in the Midwestern United States may ha ve 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 direc tly located next to mining operations (e.g., mine

- mouth plants).

In recent years, transportation rates for most modes of coal transportation 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 belo w reflect expected long

- run equilibrium

transportation rates as of March 2012, when the coal transportation rate assumptions for EPA Base Case

v.5.13 were finalized. The forecasted changes in transportation rates during the 2016

- 2050 forecast

period reflec t expected changes in long

- term equilibrium transportation rate levels, including the long

- term

market dynamics that will drive these pricing levels.

All rates are represented in 2011 real dollars.

9.3.1 Coal Transportation Matrix Overview

Description

In previo us versions of EPA Base Case using IPM, the coal transportation matrix connected coal supply

regions with coal demand regions that represented the aggregated coal demand from several coal

- fired

generating plants. In EPA Base Case v.5.13, the demand side o f the coal transportation matrix has been

expanded, so that each of the approximately 560 U.S. and Canadian coal

- fired generating plants included

in EPA Base Case v.5.13 is individually represented in the coal transportation matrix. This allows the

coal t ransportation routings, coal transportation distances, and coal transportation rates associated with

each individual coal

- fired generating plant to be estimated more accurately in EPA Base Case v. 5.13.

### 9

- 25

The coal transportation matrix shows the total coal transportation rate which is expected to be required to

transport coal from selected coal supply regions to each individual coal

- fired generating plant.

The coal supply regions associated with each coal

- fired generating plant in EPA Base Case v.5.13 are

largely unchanged from previous versions of IPM. The coal supply regions associated with each coal

fired generating plant are the coal supply regions which were supplying each plant as of late 2011, have

supplied each plant in previous years, or are consi dered economically and operationally feasible sources

of additional coal supply during the forecast period in EPA Base Case v. 5.13 (2016

- 2050.) A more

detailed discussion of the coal supply regions can be found in previous sections.

Methodology

Each coal supply region and coal

- fired generating plant is conn ected via a transportation link, which can

include multiple transportation modes. For each transportation link, co st estimates, in terms of $/ton , were

calculated u tilizing 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

- 8

.

Figure 9

- 8 Calculation of Multi

- Mode Transportation Costs (Example)

9.3.2 Calculation of Coal Transportation Distances

Definition of applicable supply/demand regions

Coal

- fired generating plants are linked to coal supply regions based on historical coal deliveries, as well

as based on the potential for new coal supplies to serve each coal

- fired generating plant going forward. A

generating plant will almost always have transportation links w ith more than one supply region, depending

on the various coal types that can be physically delivered and burned at that particular plant. On

average, each coal

- fired generating plant represented in IPM is linked with about nine coal supply

regions. Some plants may have more than the average number of transportation links and some may

have fewer, depending on the location of each plant, the transportation modes available to deliver coal to

each plant, the boiler design and emissions control technologies associated with each plant, and other

factors that affect the types of coal that can be burned at each plant.

For “mine

- mouth” plants (plants for which the current coal supply is delivered from a single nearby mine,

generally by conv eyor belt or using truck transportation) that are 200 MW or larger, Hellerworx and

Tetratech have estimated the cost of constructing facilities that would allow rail delivery of alternative coal

supplies, and the transportation rates associated with the de livery of alternative coal supplies. This

includes the construction of rail spurs (between one and nine miles in length depending on the proximity

of each plant to existing railroad lines) to connect each plant with existing railroad lines.

Transportat ion Links for Existing Coal

- Fired Plants

Transportation routings from particular coal supply regions to particular coal

- fired generating plants were

developed based on third

- party software 84 

### and other industry knowledge available to Hellerworx and

Tetratech. Origins for each coal supply region were based on significant mines or other significant

84 

### Rail routing and mileage calculations utilize ALK Technologies PC*Miler software.

### 9

- 26

delivery points within the supply region, and the destination points were plant

- specific for each coal

- fired

generating plant represented in IPM. 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

Transportation links for new coal

- fired plants that were under construction as of March 2012 were

developed using the same methodology as for existing plants, and these committed new plants were

included in IPM as of their expected date of commercial operation.

Coal tran sportation costs for new coal

- fired plants not yet under construction (i.e., coal transportation

costs for new coal plants modeled by IPM) were estimated by selecting an existing coal plant within each

IPM Region whose coal supply alternatives, and coal tr ansportation costs, were considered

representative of the coal supply alternatives and coal transportation costs that would likely be faced by

new coal plants within that same IPM Region. This methodology helps ensure that coal transportation

costs for n ew coal plants are properly integrated with and assessed fairly vis

- à

- vis existing coal

- fired

assets with in the IPM model ing structure

.

9.3.3 Overview of Rail Rates

Competition within the railroad industry is limite d. Two major railroads in the W estern U.S. (B NS F and

U P ) and two major railroads in the E astern U.S. (CSX and N S ) currently originate most of the U.S. coal

traffic that moves by rail.

In recent years, railroads have increased coal transportation rates in real terms wherever they have the

opportunity. H owever, 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,

as of March 2012, the differential between rates at cap tive plants and rates at competitively

- served plants

was relatively narrow. T he current relatively small differentials between captive and competitive rates are

ex pected 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, r ail rates are classified as being either captive or competitive (see Table

9

- 8 ) , 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

- 8 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).

### 9

- 27

Rail Rates

As previously discussed, rail rates are subdivided into three competitive categories: captive, high

- cost

competitive, and low

- cost competitive. Moves are further subdivided based on the distance that the coal

supply must move over rail lines: <200 miles, 2 00

- 299 miles, 300

- 399 miles, 400

- 649 miles, and 650+

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 assum ptions 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 railroads, and projected productivity improve ments. 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 2011 real dollars.

Rates A pplicable to Eastern M o ves

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 railro ad (i.e., they are captive plants). The practical effect of

this is that CSX and NS do not compete aggressively at the limited number of plants that have access to

both major railroads, and the rates for high

- cost competitive plants tend to be similar to the rates for

captive plants. Table 9

- 9 presents the 2012 eastern rail rates.

Table 9

- 9 Assumed Eastern Rail Rates for 2012

(20 11 mills/ton

- mile)

Mileage Block Captive High

- Cost Competitive Low

- Cost Competitive

< 200 85 85 72

200

- 299 71 71 60

300

- 399 69 69 59

400

- 649 61 61 52

650+ 43 43 37



### Rates A pplicable to Midwestern M oves

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 Midwestern U.S. also includes a higher proportion of barge

- serv ed and truck

served plants than is the case in the Eastern or Western U.S. Table 9

- 10 depicts 2012 rail rates in the

Midwest.

Table 9

- 10 Assumed Midwestern Rail Rates for 2012

(20 11 mills/ton

- mile)

Mileage Block Captive High

- Cost Competitive Low

- Cost Competitive

< 200 85 85 72

200

- 299 67 67 57

300

- 399 49 49 42

400

- 649 46 46 39

650+ 43 43 37



### Rates A pplicable to Western M oves

Rail moves within the Western U.S. are handled predominately by BNSF and UP. Due to industry

concerns about potential future regulation of carbon dioxide ( CO 2 

### ) emissions and other factors, it now

### 9

- 28

appears very unlikely that the CP will construct a third rail line into the PRB, so this analysis assumes the

PRB will continue to be served only by BNSF and UP. 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 , 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. In the west, there are enough

plants that have access to both BNSF and UP or a neutral carrier that the western railroads are

concerned of losing coal volume to the competing railroad, and do offer more of a rate discount to plants

that can access both railroads (e.g., high

- cost competitive).

Non

- PRB Coal Moves

The assumed non

- PRB western rail rates for 2012 are shown in Table 9

- 11

.

Table 9

- 11 Assumed Non

- PRB Western Rail Rates for 2012

(20 11 mills/ton

- mile)

Mileage Block Captive High

- Cost Competitive Low

- Cost Competitive

< 300 53 45 45

300+ 28 25 25



### The assumed PRB western rail rates for 2012 are available in Table 9

- 12

.

PRB Moves Confined to BNSF/UP Rail Lines

Table 9

- 12 Assumed PRB Western Rail Rates for 2012

(20 11 mills/ton

- mile)

Mileage Block Captive High

- Cost Competitive Low

- Cost Competitive

< 300 32 27 27

300+ 26 23 23



### PRB Moves Transferring to Eastern R ailroads

For PRB coal moving west

- to

- east, the coal transportation matrix assumes that the applicable low

- cost

competitive assumption is applied to the BNSF/UP portion of the rail mileage, and an assumption of either

$2.20 per ton or 41 mills per ton

- mile (whicheve r 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 R ates

Truck rates include loadin g 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

- 13 are expected

long

- term equilibrium levels reflective of current rates as of March 2012, and expected changes in labor

costs, fuel prices, and steel prices.

Table 9

- 13 Assumed Truck Rates for 2012

(20 11 Real Dollars)

Market Loading Cost ($/ton) Transport (mills/ton

- mile)

All Markets 1.00 120

### 9

- 29

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 that 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

- 14 are expected long

term equilibrium levels reflective of current rates as of March 2012, and expected changes in labor costs,

fuel prices, and steel prices.

Table 9

- 14 Assumed Barge Rates for 2012

(20 11 Real Dollars)

Type of Barge Movement Loading Cost

($/ton) Transport

(mills/ton

- mile)

Upper Mississippi River, and Downstream on the Ohio River System 2.70 9.7

Upstream on the Ohio River System 2.45 11.5

Lower Mississippi River 2.70 6.9

Notes:

1. The Upper Mississippi River is the portion of the Mississippi River north of St. Louis.

2. The Ohio River System includes the Ohio, Big Sandy, Kanawha, Allegheny, and Monongahela Rivers.

3. The Lower Mississippi River is the portion of the Mississippi River south of St. Louis.



### Rates for transportation of coal by lake vessel on the Great Lakes were forecast on a plant

- specific basis,

taking into account the lake vessel distances applicable to each movement, the expected backhaul

economics applicable to each movement (if any), and the expected changes in labor costs and fuel and

steel prices over the long

- term.

9.3.6 Transportation Rates for Imported Coal

Transportation rates for import ed coal reflect expectations regarding the long

- term equilibrium level for

ocean vessel ra tes, taking into account expected long

- run equilibrium levels for fuel and steel prices, and

expected continued strong demand for shipment of dry bulk commodities (es pecially coal and iron ore)

from China and other Asian nations.

In EPA Base Case v.5.13, 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 that railroads are

unlikely to quote rail rates that will allow imported coal to be cost

- competitive at rail

- served plants.

Moreover, import rates are highe r 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 plan t),

whereas most of the New England plants can take imported coal directly by vessel. The assumed costs

are summarized in

Excerpt from Table 9

- 23

.

### 9

- 30

9.3.7 Other Transportati on 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 be tween different transportation modes (e.g., rail

- to

- barge or

truck

- to

- barge)

– see Table 9

- 15

.

Table 9

- 15 Assumed Other Transportation Rates for 2012

(20 11 Real Dollars)

Type of Transportation Rate ($/ton)

Rail

- to

- Barge Transfer 1.50

Rail

- to

- Vessel Transfer 2.00

Truck

- to

- Barge Transfer 2.00

Rail Switching Charge for Short line 2.00

Conveyor 1.00



### 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 3 3 % of the rail industry’s operating costs in 20 10 , and fuel ac counted for an additional 18%. The

remaining 4 9 % of the rail industry’s costs relate primarily to locomotive and railcar ownership and

maintenan ce, and track construction and maintenance.

The RCAF 85 

### Unadjusted for P roductivity (RCAF

- U), which tracks operating expenses for the rail industry,

increased at an annualized rate of 3.3 %/year between the second quarter of 2008 and the fourth quarter

of 2011, see Table 9

- 9

, more than double the increase of 1.5% /year in general inflation (GDP

- IPD) over

the s ame period. This is largely the result of unusually steep increases in labor costs, which reflected the

effect of new labor agreements negotiated prior to the economic downturn that occurred in late 2008 and

2009. Hellerworx expects that going forward, th e rail industry’s labor costs will increase at a more

moderate rate (assumed to be 1% more than overall inflation), which is more in line with longer

- term

historical increases in these costs.

According to data from the AAR, the net change in the rail ind ustry’s fuel costs between 2Q2008 and

4Q2011 was a nominal decline of about 9% (or an annualized decline of about 2.6% per year. Over the

same time period, equipment and other costs for the rail industry increased by an average of about 2.0%

per year, onl y slightly faster than overall inflation of 1.5% per year.

85 

### 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

- 31 Figure 9

- 9 Rail Cost Indices Performance

(2Q2008

- 4Q2011)

The other major transportation modes used to ship coal (barge and truck) have cost drivers broadly

similar to those for rail transportation (labor costs, f uel 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 d ifferent tr ansportation modes. Estimates as shown in Figure 9

- 10 show that, at 20 12

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

- 32 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) 86 

### forecast o f long

- term

equilibrium prices for diesel fuel used in the transportation sector (see Table 9

- 16 ) shows expected prices

ranging from about $3.83 /gallon in 2012 to ab out $4.58/gallon in 2035 (2011 real dollars ) . This represents

an annual real increase in diesel fuel prices of about 0.8%/year during 2012

- 2035. The coal

transportation rate forecast for EPA Base Case v.5.13 assume s that this average rate of increase in

d iesel fuel prices will apply over EPA’s entire forecast period (2016

- 2050).

Table 9

- 16 EIA AEO Diesel Fuel Forecast, 2012

- 2030

(2011 Real Dollars)

Year Rate ($/gallon)

2012 3.83

2015 3.84

2020 4.06

2025 4.27

2030 4.48

2035 4.58

Annualized % Change, 2025

- 2035 0.8%

Source: EIA

86 

### As noted at the beginning of this section, the coal transportation rate assumptions for EPA Base Case v5.13 were

finalized in March 2012 . At that time, the Annual Energy Outlook 20 12 forec ast was the latest available.



### 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Rail Barge Truck

Fuel Other

### 9

- 33

Iron Ore Prices

ABARE S ’s 87 

### forecast of iron ore prices as depicted in Table 9

- 17 shows an expect ation that iron ore

prices will decline by about 22 % in real terms for their 5

- year forecast period (20 12

- 201 7 ) as a whole.

Table 9

- 17 ABARES Forecast of Iron Ore Prices

2011 US$/metric tonne

ABARE Forecast of Average Contract Price for Australian Iron Ore Exports, 2012 137

ABARE Forecast for 2013 129

ABARE Forecast for 2014 125

ABARE Forecast for 2015 121

ABARE Forecast for 2016 115

ABARE Forecast for 2017 107

Total Percent Change (2012

- 2017)

- 22%

Source: ABARES, Resources and Energy Quarterly, March 2012.



### Labor C osts

As noted earlier, l abor costs for the rail industry are expected to increase approximately 1% faster than

overall inflation, on average over the forecast period. Due to the fact that competition is stronger in the

barge and trucking industries than in the rai l industry, labor costs in the barge and truck industries are

e xpected to increase at approximately the same rate as overall inflation, on average over the forecast

period.

Productivity G ains

The most recent data published by AAR (covering 200 6

- 20 10 ) shows that rail industry productivity

increased at an annualized ra te of approximately 0.8% 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, t he potential for significant productivity gains in the tru cking industry is relatively

limited since truck load sizes, operating speeds, and truck driver hours are all regulated by law. Although

increased lock outages and the associated congestion on the inland waterway system as the river

infrastructure ages may reduce the rate of future productivity gains in the barge industry, limited

productivity gains are expected to occur, and these productivity gains are expected to be largely passed

through to shippers since the barge industry is highly competitive.

Lon g

- Term Escalation of Coal Transportation Rates

Based on the foregoing discussion, rail rates are expected to escalate at an average rate of 0.5% per

year in real terms during 2013

- 2050. Over the same period, barge and lake vessel rates are expected to

decline at an average rate of 0.2% per year, which reflects some pass

- through of productivity gains in

thos e highly competitive industries. Truck rates are expected to escalate at an average rate of 0.4%/year

during 2013

- 2050, rates for conveyor transport ation and transloading services are expected to be flat in

real terms, on average over the forecast period.

The basis for these forecasts is summarized in

Table 9

- 18

.

87 

### ABARE S (the Australian Bureau of Agricultural and Resource Economics and Sciences ) is a branch of the

Australian government that forecasts prices and trade volumes for a wide variety of commodities that Australia

exports. Au stralia is a major exporter of iron ore, accounting for about 41 % of total worldwide iron ore exports in

20 11 . See www.daff.gov.au/abares

.

### 9

- 34 Table 9

- 18 Summary of Expected Escalation for Coal Transportation Rates, 2013

- 2050

9.3.10 Other Considerations

Estimated Construction Costs for Railcar Unloaders and Rail Spurs at Mine

- Mouth Plants

In order to allow mine

- mouth generating plants (i.e., coal

- fired generating plants which take all of their

current coal supply from a single nearby mine) to access additional types of coal, the costs of

constructing facilities that would allow rail delivery of coal wa s estimated for almost all 88 

### of the mine

mouth generating plants with total capacity of 200 MW or more

.

The facilities needed for rail delivery of coal to generating plants of this relatively large size were assumed

to be: a) a rotary dump railcar unloader capable of handling unit train coal shipments, which is estimated

to cost about $25 million installed (in 2011$). b) at least three miles of loop track, which would allow for

one trainload of coal to be unloaded, and a second trainload of coal to simulta neously be parked on the

plant site preparatory to unloading, and c) at least one mile of additional rail spur track to connect the

trackage on the plant site with the nearest railroad main line. Since construction costs for rail trackage

capable of handl ing coal trains is estimated at about $3 million per mile (in 2011$), the minimum

investment required to construct the facilities needed for rail delivery of coal was estimated at $37 million.

In some cases, the length of the rail spur required to reach t he nearest main line (which was estimated on

a plant

- specific basis) is considerably longer than one mile. In cases where a rail spur longer than one

mile was required to reach the main line, the cost of the additional trackage was estimated using the

sam e construction cost of $3 million per mile (2011$) referenced earlier.

88 

### The costs of rail coal delivery were not estimated for mine

- mouth plant s located in the Powder River Basin or

Illinois Basin coal fields, since the coal reserves in these coal fields are among the largest, and among the cheapest

to mine, anywhere in the United States.

### 9

- 35

The total cost of the facilities required for rail delivery of coal was converted to an annualized basis based

on each plant’s historical average coal burn from 2007

- 2011, and a capit al recovery factor of 11.29%.

The cost of transporting additional types of coal to each mine

- mouth generating plant was then calculated

using the same methodology described earlier in this section, and added to the annualized cost for the

rail delivery fac ilities, to arrive at an estimated “all

- in” cost for delivering additional types of coal to the

mine

- mouth plants.



## 9.4 Coal Exports, Imports, and Non

- Electric Sectors Demand

The coal supply curves used in EPA Base Case v.5.13 represent the total steam coal s upply in the United

States. While the U.S. power sector is the largest cons umer of native coal

– roughly 93 % of mined U.S.

coal in 2 012 was used in electricity generation

– non

- electricity demand must also be taken into

consideration in IPM modeling in ord er 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

electricity generation, and these changes in the coal supply must also be detailed in the modeling of the

coal supply available to coal power plants. The projections for imports, exports, non

- electric sector coal

demand , and coal to liquids demand are based on EIA’s AEO 2013

.

In EPA Base Case v.5.13, coal exports, coal

- serving residenti al, commercial and industrial demand, and

coal to liquids demand are designed to correspond as closely as possible to the projections in AEO 2013

both in terms of the coal supply regions and coal grades that meet this demand. The projections used

exclude e xports to Canada, as the Canadian market is modeled endogenously within IPM. First, the

subset of coal supply regions and coal grades in EPA Base Case v.5.13 are identified that are contained

in or overlap geographically with those in EIA Coal Market Modul e (CMM) supply regions and coal grades

that are projected as serving exports and non

- electric sector demand in AEO 2013. Next, coal for exports

and non

- electricity demand are constrained by CMM supply region and coal grade to meet the levels

projected in A EO 2013. These levels are shown in Table 9

- 19

.

Table 9

- 19 Coal Exports

Name 2016 2018 2020 2025 2030 2040

- 2050

Alaska/Washington

- Subbituminous Low Sulfur 1.37 1.44 1.52 1.71 2.04 2.84

Central Appalachia

- Bituminous Medium Sulfur 9.33 9.08 8.78 7.58 7.73 6.33

East Interior

- Bituminous High Sulfur 16.54 18.23 20.10 25.65 32.74 45.51

Northern Appalachia

- Bituminous High Sulfur 4.18 4.15 4.07 3.58 3.65 2.98

Northern Appalachia

- Bituminous Medium Sulfur 0.44 0.32 0.23 0.10 0.10 0.10

Rocky Mountain

- Bituminous Low Sulfur 3.21 3.54 3.90 3.92 4.73 4.45

Western Montana

- Subbituminous Low Sulfur 8.22 9.07 4.85 12.83 16.49 27.28

Wyoming Southern PRB

- Subbituminous Low Sulfur 0.42 0.31 6.21 0.10 0.10 0.10



### Table 9

- 20 and Table 9

- 21 . (Since the AEO 2013 time horizon extends to 2040 and EPA Base Case

v.5.13 to 2050, the AEO projected levels for 2040 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.5 .13 than in AEO 2013, the specific

regions and coal grades that serve export and non

- electric sector demand are not pre

- specified but

modeled.

Table 9

- 20 Residential, Commercial, and Industrial Demand

Name 2016 2018 2020 2025 2030 2040

- 2050

Alaska/Washington

- Subbituminous Low Sulfur 0.59 0.59 0.59 0.59 0.59 0.60

Central Appalachia

- Bituminous Low Sulfur 4.02 4.03 4.05 4.08 4.08 4.28

Central Appalachia

- Bituminous Medium Sulfur 11.68 11.68 11.75 11.82 11.83 12.41

### 9

- 36 Name 2016 2018 2020 2025 2030 2040

- 2050

East Interior

- Bituminous High Sulfur 7.04 7.00 7.00 6.97 6.89 7.04

East Interior

- Bituminous Medium Sulfur 0.82 0.83 0.83 0.83 0.83 0.85

Northern Appalachia

- Bituminous High Sulfur 1.63 1.62 1.62 1.62 1.61 1.66

Northern Appalachia

- Bituminous Medium Sulfur 3.04 3.05 3.06 3.08 3.08 3.24

Rocky Mountain

- Bituminous Low Sulfur 4.05 4.06 4.08 4.10 4.10 4.36

Southern Appalachia

- Bituminous Low Sulfur 0.17 0.17 0.17 0.17 0.18 0.19

Southern Appalachia

- Bituminous Medium Sulfur 1.13 1.13 1.14 1.15 1.16 1.24

Wyoming Southern PRB

- Subbituminous Low Sulfur 2.58 2.56 2.56 2.55 2.52 2.58

Dakota Lignite

- Lignite Medium Sulfur 6.37 6.34 6.34 6.31 6.25 6.38

Wyoming Northern PRB

- Subbituminous Low Sulfur 5.04 5.04 5.06 5.09 5.09 5.31

West Interior

- Bituminous High Sulfur 0.67 0.67 0.68 0.69 0.69 0.74

Arizona/New Mexico

- Bituminous Low Sulfur 0.46 0.47 0.47 0.47 0.47 0.50

Arizona/New Mexico

- Subbituminous Medium Sulfur 0.11 0.11 0.11 0.11 0.12 0.12

Western Wyoming

- Subbituminous Low Sulfur 1.03 1.03 1.04 1.04 1.05 1.12

Western Wyoming

- Subbituminous Medium Sulfur 1.12 1.13 1.13 1.14 1.15 1.24

Gulf Lignite

- Lignite High Sulfur 0.84 0.85 0.85 0.87 0.87 0.93



### Table 9

- 21 Coal to Liquids Demand

Name 2016 2018 2020 2025 2030 2040

- 2050

Rocky Mountain

- Bituminous Low Sulfur 0 0 0 5.61 3.36 4.02

Wyoming Southern PRB

- Subbituminous Low Sulfur 0 0 0 0.00 0.00 8.94

Wyoming Northern PRB

- Subbituminous Low Sulfur 0 0 0 0.42 5.49 0.00

Western Montana

- Subbituminous Low Sulfur 0 0 0 0.00 0.00 1.36



### Imported coal is only available to 39 coal facilities which are eligible to receive imported coal. These

facilities which may receive imported coal, along with the cost of transporting this coal to the demand

regions, are in Excerpt from

Excerpt from Table 9

- 23

. The total US imports of steam coal are limit ed to AEO 2013 projections as

shown in Table 9

- 22

.

Table 9

- 22 Coal Import Limits

2016 2018 2020 2025 2030 2040

- 2050

Annual Coal Imports Cap (Million Short Tons) 1.50 0 0 3.60 3.78 34.28

### 9

- 37



## Attachment 9

- 1 Mining Cost Estimation Methodology a nd Assumptions

Labor Costs

Productivity and labor cost rates are utili z ed to estimate the total labor cost associated with the mining

operation. This excludes labor involved in any coal processing / preparation plant.

Labor productivity is used to calculate mine labor and salaries by applying an average cost per employee

ho ur to the labor productivity figure reported by MSHA or estimated based on comparable mines.

Labor costs rates 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.

Surfa ce Mining

The prime (raw coal) strip ratio and overburden volume is estimated on a year by year basis. Estimates

are entered of the amount of overburden 89 

### moved each year, split by method to allow for different unit

mining costs. The unit rate cost for eac h method excludes any drill and blast costs, and labor costs, as

these are accounted for separately. Drill and blast costs are estimated as an average cost per volume of

prime overburden. If applicable, dragline re

- handle is estimated separately and a su mmation gives the

total overburden moved.

The different overburden removal methods are:

Dragline

- the estimated volume of prime overburden moved

Dragline re

- handle

- the estimated volume of any re

- handled overburden

Truck and shovel

- including excavato rs.

Other

- examples would be dozer push, front end loader, or cast blasting. If overburden is moved by

cast blasting the unit rate is taken to be zero as the cost is already included in the drill and blast

estimate.

Surface mining costs also include the c ost of coal mining estimated on a raw ton basis

.

Underground Mining

Raw coal production is split by type into either continuous miner or longwall. Cost estimates can be input

either on a unit rate or a fixed dollar amount, as the cost structure of undergro und mining generally has a

large fixed component from year to year. Costs are divided into:

Longwall

Continuous miner

Underground services

Underground services costs cover categories such as ventilation, conveyor transport, gas drainage,

secondary roof sup port etc.

89 

### Overburden refers to the surface soil and rock that must be removed to uncover the coal.

### 9

- 38

Mine Site Other

This covers any mine site costs that are outside the direct production process. Examples are ongoing

rehabilitation/reclamation, security, community development costs

.

Raw Haul

Costs for transporting raw coal from the mining loca tion to the raw coal stockpile at the coal preparation

plant or rail load out. A distance and a unit rate allows for an increasing cost over time if required

.

Excerpt from Table 9

- 23 Coal Transportation Matrix in EPA Base Case v.5 .1 3

This is a small excerpt of the data in Table 9

- 23. The complete data set in spreadsheet format can be

downloaded via the link found at www.epa.gov/airmarkets/progsregs/epa

- ipm/BaseCasev513.html

Link

# Plant Name ORIS

Plant

Code Coal

Supply

Region

Code Coal Supply Region

Description Total Cost

(2012 Rate in

201 1$/Ton) Escalation/Year

(2013

- 2025) Escalation/Year

(2026

- 2050)

1 Aurora Energy LLC Chena 79 AK Alaska $3.52 1.0050 1.0050

2 Eielson AFB Central Heat &

Power Plant 50392 AK Alaska $4.32 1.0050 1.0050

3 Healy 6288 AK Alaska $1.00 1.0000 1.0000

4 Barry 3 CG Colorado, Green River $44.85 1.0039 1.0039

5 Barry 3 CR Colorado, Raton $42.85 1.0039 1.0039

6 Barry 3 CU Colorado, Uinta $48.85 1.0040 1.0040

7 Barry 3 IL Illinois $20.50 1.0031 1.0031

8 Barry 3 IN Indiana $24.00 1.0034 1.0034

9 Barry 3 KE Kentucky East $26.04 1.0031 1.0031

10 Barry 3 KW Kentucky West $19.78 1.0031 1.0031

11 Barry 3 PW Pennsylvania, West $25.77 1.0028 1.0028

12 Barry 3 WH Wyoming, Powder River Basin

(8800) $43.13 1.0039 1.0039

13 Barry 3 WL Wyoming, Powder River Basin

(8400) $42.90 1.0039 1.0039

14 Barry 3 WN West Virginia, North $23.04 1.0028 1.0028

15 Barry 3 WS West Virginia, South $27.45 1.0031 1.0031

16 Barry 3 I1 Imports

- 1 (Colombia) $14.75 0.9995 0.9995

17 Charles R Lowman 56 CG Colorado, Green River $45.25 1.0039 1.0039

18 Charles R Lowman 56 CR Colorado, Raton $43.25 1.0039 1.0039

19 Charles R Lowman 56 CU Colorado, Uinta $49.25 1.0040 1.0040

20 Charles R Lowman 56 IL Illinois $20.90 1.0031 1.0031



### Table 9

- 24 Coal Supply Curve s in EPA Base Case v.5 .1 3

This is a small excerpt of the data and graphs in Table 9

- 24 . The complete data set in spreadsheet

format can be downloaded via the link found at www.epa.gov/airmarkets/pr ogsregs/epa

ipm/BaseCasev513.html

.

Year Coal Supply

Region Coal

Grade Step

Name Heat Content

(MMBtu/short ton) Cost of Production

(2011$/short ton) Coal Production (Million

short tons per annum) End 2015 Coal Reserves

(Million short tons)

2016 AL BB E1 25.5 47.51 0.09 0.19

2016 AL BB E2 25.5 75.16 0.06 0.30

2016 AL BB E3 25.5 81.84 1.18 8.37

2016 AL BB E4 25.5 88.23 0.14 1.39

2016 AL BB E5 25.5 96.45 0.47 4.51

2016 AL BB E6 25.5 101.89 0.07 0.69

2016 AL BB E7 25.5 103.68 0.10 0.94

2016 AL BB E8 25.5 110.04 0.08 0.75

2016 AL BB N1 25.5 115.74 0.12 500.00

2016 AL BE E1 24 35.96 0.21 0.36

### 9

- 39 Year Coal Supply

Region Coal

Grade Step

Name Heat Content

(MMBtu/short ton) Cost of Production

(2011$/short ton) Coal Production (Million

short tons per annum) End 2015 Coal Reserves

(Million short tons)

2016 AL BE E2 24 47.51 0.30 0.37

2016 AL BE E3 24 52.89 3.41 13.66

2016 AL BE E4 24 71.05 0.38 1.87

2016 AL BE E5 24 90.23 2.20 18.68

2016 AL BE E6 24 102.49 2.64 25.32

2016 AL BE E7 24 104.83 0.30 2.80

2016 AL BE E8 24 137.98 0.09 0.90

2016 AL BE N1 24 108.27 0.28 500.00

### 10

- 1



# 10. Natural Gas

This chapter describes how natural gas supply, demand, and costing are modeled in EPA Base Case

v.5.13 . Section 0 indicates that natural gas supply dynamics are directly (i.e., endogenously) modeled in

the base case. Section 10.2 gives an overview of the new natural gas module. Sections 10.3 and 10.4

describe the very detailed pr ocess

- engineering model and data sources used to characteri ze North

American conventional and unconventional 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 production and the assumptions (in the form of cost indices) used to depict

expected changes in costs over the 201 6

- 2050 modeling time horizon

.

Section 10.5 describes how liquefied natural gas (LNG) imports are represented in the natural gas

module. The section covers the assumptions regarding liquefaction facilitie s, LNG supply, regasification

capacity, and related costs. Section 10.6 turns to demand

- side issues, in particular, how non

- power

sector residentia l, commercial, and industrial consumer demand is represented. This section also

describes the use of the gas demand sub

- module to model LNG exports. 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/withdrawal 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 exploration and development of

hydrocarbon resource s. The chapter concludes in Section 10.10 with a discussion of key gas market

parameters in the natural gas report of EPA Base Case v.5.13

.



## 10.1 Overview of IPM’s Natural Gas Module

I n EPA Base Case v.5.13 natural gas supply, demand, transportation, storage, and related costs are

modeled directly in IPM through the incorporation of a natural gas module. N atural gas supply curve s are

generated endogenously for each region , and the balance between the natural gas supply and demand is

solved in all regi ons simultaneously. Figure 10

- 1 and Figure 10

- 2 illustrate the integration of the natural

gas module in IPM

. The integration allows direct interaction between the electric and the gas modules

and captures the overall gas supply and demand dynamic

.

To a certain extent, the design and assum ptions 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,

simplifications 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) segments. The gas module also employs a s imilar run year concept as

in IPM where, in order to manage model size, individual calendar year s over the entire modeling period

are mapped to a lesser number of run years. In the current version, bo th modules use the same run year

mapping

.

### 10

- 2 Figure 10

- 1 Modeling and Data Structure in EPA Base Case v.5.13

Figure 10

- 2 Natural Gas Module in EPA Base Case v.5.13

### 10

- 3



## 10.2 Key Components of the New IPM Natural Gas Module

The gas module is a full supply/demand equilibrium model of the North American ga s market. Most of the

structure and data for the gas module are derived from ICF ’s Gas Market Model (GMM ). It consists of

11 8 supply/demand/storage nodes

, 1 5 LNG regasification (import) fac ility locations , and 3 LNG export

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 11 8 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 Resource s are modeled by a set of base year resource cost curves, which represent

und iscovered resource availability or recoverable resource as a function of exploration & development

(E&D) cost for 81 supply region s. “Resource Appreciation” 90 

### 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 eco nomically recover the resources. The construction of the resource cost

curve s 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 provides a list of the

supply regions and a mapping of the regions to the modeling nodes.

Natural Gas production from the 81 supply regions is calculated from the resource cost cu rves 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 supply 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 specified as inputs. The model has the capabili ty to expand regasification capacity.

However, due to a current excess of LNG regasification capacity and robust natural gas supply in the

U.S. and Canada combined with a relatively low electricity demand growth assumption in the EPA Base

Case v.5.13 , the regasification expansion feature is currently turned off. If future economic growth

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 IPM through node

- level interruptible and firm demand curve s 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

consumption is calculated within the IPM electricity dispatch mo dule. Figure 10

- 5 shows the geographic

locations of the demand region s.

LNG export modeling

The gas module does not curr ently have a specific sub

- module for LNG exports. The modeling of

LNG export is currently performed within the gas demand sub

- module using a set of fixed or

inelastic firm demand curves. T he EPA Base Case v.5.13 includes two LNG export term inals in

the U.S. Gulf Coast and one LNG export terminal in Western Canada. The LNG export modeling

is discussed in more detail in Section 10.6 bel ow.

90 

### Resource appreciation represents growth in ultimate resource estimates attributed to success in extracting

resource from known plays such as natural gas from shale , coal seams, offshore deepwater, and gas hydrates that

ar e not included in the resource base estimates.

### 10

- 4 Figure 10

- 3 Gas Transmission Network Map

Table 10

- 1 List of Nodes

Node Name Supply Demand Transit,

Import/

Export Underground

Storage Peakshaving Storage

(existing and potential)

1 New England X X

2 Everett TRANS X

3 Quebec X X X

4 New York City X X

5 Niagara X X X X

6 Southwest PA 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 East KY/TN X X X X

19 MD/DC/Northern VA X X

20 Wisconsin X X X

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

27 Great Plains X

28 Kansas X X X X

29 East Colorado X X X X

### 10

- 5

Node Name Supply Demand Transit,

Import/

Export Underground

Storage Peakshaving Storage

(existing and potential)

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

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 X

74 Caroline X X X X

75 Empress X

76 Saskatchewan X X X X

77 Manitoba X X X

78 Dawn X X X X

79 Philadelphia X X

80 West Virginia X X X X

81 Eastern Canada Demand X X

### 10

- 6

Node Name Supply Demand Transit,

Import/

Export Underground

Storage Peakshaving Storage

(existing and potential)

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

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

112 Southeast Missouri X X X X

113 Uinta/Piceance X X X X

114 South MS/AL X X X X

115 West KY/TN X X X

116 Kosciusko MS X

117 Northeast PA X X X

118 Leidy X X X

### 10

- 7 Figure 10

- 4 Gas Supply Region s Map

Table 10

- 2 List of Gas Supply Region s

Supply Region Number Node Number Region Name

1 5 Niagara

2 6 Southwest PA

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 Eastern KY/TN

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

### 10

- 8 Supply Region Number Node Number Region Name

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

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

- 9 Supply Region Number Node Number Region Name

78 115 Western KY/TN

79 3 Eastern Canada Onshore

80 117 NE PA/SC NY

81 118 Leidy



### Figure 10

- 5 Gas Demand Region s Map

Natural gas pipeline network is modeled by 380 transmission links or segments (excluding pipeline

connections with LNG import 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 interstate 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. 91



### 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 Pipeline s

Link Pipeline

1

- 4 Iroquois Pipeline Co

1

- 104 Tennessee Gas Pipeline Co

1

- 104 Algonquin Gas Trans Co

3

- 104 Iroquois Pipeline Co

5

- 6 Tennessee Gas Pipeline Co

5

- 104 Tennessee Gas Pipeline Co

5

- 117 Tennessee Gas Pipeline Co

6

- 5 National Fuel Gas Supply Co

6

- 11 Dominion Trans (CNG)

6

- 11 Columbia Gas Trans Corp

6

- 19 Dominion Trans (CNG)

6

- 79 Texas Eastern Trans Corp

91 

### In this context “load factor” refers to the percentage of the pipeline capacity that is utilized at a given time.

### 10

- 10 Link Pipeline

6

- 80 Dominion Trans (CNG)

6

- 80 Columbia Gas Trans Corp

6

- 118 Dominion Trans (CNG)

6

- 118 Tennessee Gas Pipeline Co

8

- 18 Southern Natural Gas Co

8

- 95 Transcontinental Gas Pipeline Co

8

- 96 Southern Natural Gas Co

9

- 8 Southern Natural Gas Co

10

- 96 Florida Gas Trans Co

11

- 6 Texas Eastern Trans Corp

11

- 6 Tennessee Gas Pipeline Co

11

- 80 Columbia Gas Trans Corp

12

- 11 Columbia Gas Trans Corp

12

- 17 ANR Pipeline Co

12

- 17 Panhandle Eastern Pipeline Co

12

- 98 ANR Pipeline Co

13

- 11 Dominion Trans (CNG)

13

- 11 Texas Eastern Trans Corp

13

- 14 Panhandle Eastern Pipeline Co

14

- 12 Panhandle Eastern Pipeline Co

14

- 12 ANR Pipeline Co

14

- 13 Texas Eastern Trans Corp

14

- 98 Trunkline Gas Co

15

- 14 Panhandle Eastern Pipeline Co

15

- 16 Nat Gas Pipeline Co of America

16

- 20 ANR Pipeline Co

16

- 98 ANR Pipeline Co

17

- 78 Great Lakes Gas Trans Ltd

17

- 98 Panhandle Eastern Pipeline Co

17

- 99 Michcon

18

- 8 East Tennessee Nat Gas Co

18

- 11 Texas Eastern Trans Corp

18

- 11 Tennessee Gas Pipeline Co

18

- 13 Columbia Gas Trans Corp

18

- 80 Columbia Gas Trans Corp

18

- 80 Tennessee Gas Pipeline Co

18

- 92 East Tennessee Nat Gas Co

19

- 79 Transcontinental Gas Pipeline Co

19

- 92 Columbia Gas Trans Corp

19

- 93 Dominion Trans (CNG)

21

- 15 Panhandle Eastern Pipeline Co

23

- 20 ANR Pipeline Co

23

- 22 Great Lakes Gas Trans Ltd

23

- 99 Great Lakes Gas Trans Ltd

23

- 106 Great Lakes Gas Trans Ltd

24

- 16 Nat Gas Pipeline Co of America

25

- 23 Great Lakes Gas Trans Ltd

26

- 24 Nat Gas Pipeline Co of America

27

- 24 Williston Basin Pipeline Co

### 10

- 11 Link Pipeline

27

- 41 Williston Basin Pipeline Co

28

- 15 Panhandle Eastern Pipeline Co

28

- 16 ANR Pipeline Co

28

- 21 Panhandle Eastern Pipeline Co

28

- 26 Nat Gas Pipeline Co of America

28

- 29 Colorado Interstate Gas

28

- 68 Colorado Interstate Gas

28

- 108 Nat Gas Pipeline Co of America

28

- 109 Southern Star Central (Williams)

30

- 31 Colorado Interstate Gas

30

- 48 Northwest Pipeline Corp

30

- 113 Northwest Pipeline Corp

31

- 28 Southern Star Central (Williams)

31

- 29 Colorado Interstate Gas

32

- 33 El Paso Nat Gas Co

32

- 33 Transwestern Pipeline Co

32

- 113 Northwest Pipeline Corp

33

- 63 El Paso Nat Gas Co

33

- 68 Transwestern Pipeline Co

33

- 97 El Paso Nat Gas Co

33

- 101 El Paso Nat Gas Co

33

- 101 Transwestern Pipeline Co

34

- 27 Williston Basin Pipeline Co

34

- 31 Wyoming Interstate Co

36

- 37 Socal Gas

36

- 103 Socal Gas

37

- 38 Pacific Gas & Electric

40

- 41 Northwest Energy

41

- 83 Williston Basin Pipeline Co

43

- 73 Terasen (BC Gas)

44

- 45 Northwest Pipeline Corp

45

- 46 Northwest Pipeline Corp

46

- 48 Northwest Pipeline Corp

48

- 47 Northwest Pipeline Corp

51

- 66 Texas Eastern Trans Corp

54

- 8 Transcontinental Gas Pipeline Co

54

- 8 Southern Natural Gas Co

55

- 114 Transcontinental Gas Pipeline Co

56

- 18 Tennessee Gas Pipeline Co

56

- 54 Transcontinental Gas Pipeline Co

56

- 54 Southern Natural Gas Co

56

- 58 Gulf South (Koch)

56

- 114 Gulf South (Koch)

57

- 58 Tennessee Gas Pipeline Co

57

- 58 Southern Natural Gas Co

57

- 58 Texas Eastern Trans Corp

58

- 56 Transcontinental Gas Pipeline Co

58

- 56 Southern Natural Gas Co

58

- 56 Tennessee Gas Pipeline Co

### 10

- 12 Link Pipeline

58

- 60 Transcontinental Gas Pipeline Co

58

- 60 Southern Natural Gas Co

58

- 60 Texas Eastern Trans Corp

58

- 60 Tennessee Gas Pipeline Co

58

- 60 Florida Gas Trans Co

58

- 114 Florida Gas Trans Co

58

- 114 Gulf South (Koch)

58

- 116 Texas Eastern Trans Corp

59

- 57 Tennessee Gas Pipeline Co

60

- 61 Trunkline Gas Co

60

- 61 Gulf South (Koch)

60

- 61 ANR Pipeline Co

60

- 61 Tennessee Gas Pipeline Co

60

- 65 Nat Gas Pipeline Co of America

61

- 18 Tennessee Gas Pipeline Co

61

- 56 Southern Natural Gas Co

61

- 115 ANR Pipeline Co

61

- 115 Trunkline Gas Co

61

- 116 Texas Eastern Trans Corp

62

- 60 Tennessee Gas Pipeline Co

62

- 60 ANR Pipeline Co

62

- 60 Trunkline Gas Co

62

- 60 Transcontinental Gas Pipeline Co

62

- 60 Texas Eastern Trans Corp

63

- 53 El Paso Nat Gas Co

63

- 64 Epgt Texas Pipeline (Valero)

63

- 64 Txu Lonestar Gas Pipeline

63

- 65 Oasis

63

- 66 Epgt Texas Pipeline (Valero)

63

- 68 Epgt Texas Pipeline (Valero)

63

- 68 Nat Gas Pipeline Co of America

63

- 97 El Paso Nat Gas Co

64

- 65 Txu Lonestar Gas Pipeline

64

- 108 Nat Gas Pipeline Co of America

65

- 60 Trunkline Gas Co

65

- 60 Transcontinental Gas Pipeline Co

65

- 60 Texas Eastern Trans Corp

65

- 61 Tennessee Gas Pipeline Co

65

- 107 Nat Gas Pipeline Co of America

66

- 51 Tennessee Gas Pipeline Co

66

- 65 Epgt Texas Pipeline (Valero)

66

- 65 Texas Eastern Trans Corp

66

- 65 Tennessee Gas Pipeline Co

66

- 65 Nat Gas Pipeline Co of America

66

- 65 Transcontinental Gas Pipeline Co

67

- 65 Nat Gas Pipeline Co of America

67

- 66 Transcontinental Gas Pipeline Co

68

- 28 Nat Gas Pipeline Co of America

68

- 108 Nat Gas Pipeline Co of America

### 10

- 13 Link Pipeline

77

- 25 Great Lakes Gas Trans Ltd

78

- 106 Union Gas

79

- 105 Texas Eastern Trans Corp

79

- 105 Transcontinental Gas Pipeline Co

80

- 11 Dominion Trans (CNG)

80

- 19 Columbia Gas Trans Corp

80

- 92 Columbia Gas Trans Corp

83

- 31 Colorado Interstate Gas

92

- 18 Dominion Trans (CNG)

92

- 93 Columbia Gas Trans Corp

94

- 19 Transcontinental Gas Pipeline Co

94

- 92 Transcontinental Gas Pipeline Co

94

- 93 Transcontinental Gas Pipeline Co

95

- 94 Transcontinental Gas Pipeline Co

97

- 102 El Paso Nat Gas Co

98

- 99 ANR Pipeline Co

99

- 17 Great Lakes Gas Trans Ltd

101

- 35 El Paso Nat Gas Co

101

- 36 Socal Gas

101

- 37 Pacific Gas & Electric

101

- 102 El Paso Nat Gas Co

102

- 36 Socal Gas

104

- 1 Iroquois Pipeline Co

104

- 4 Tennessee Gas Pipeline Co

104

- 79 Columbia Gas Trans Corp

105

- 4 Transcontinental Gas Pipeline Co

105

- 4 Texas Eastern Trans Corp

105

- 104 Algonquin Gas Trans Co

106

- 5 Tennessee Gas Pipeline Co

107

- 15 Nat Gas Pipeline Co of America

107

- 61 Gulf South (Koch)

107

- 61 Centerpoint Energy (Reliant)

107

- 64 Txu Lonestar Gas Pipeline

107

- 111 Texas Eastern Trans Corp

108

- 28 ANR Pipeline Co

108

- 107 Nat Gas Pipeline Co of America

108

- 109 Nat Gas Pipeline Co of America

108

- 110 Centerpoint Energy (Reliant)

109

- 21 Southern Star Central (Williams)

110

- 107 Nat Gas Pipeline Co of America

110

- 109 Centerpoint Energy (Reliant)

110

- 111 Centerpoint Energy (Reliant)

111

- 112 Texas Eastern Trans Corp

111

- 115 Centerpoint Energy (Reliant)

112

- 15 Nat Gas Pipeline Co of America

113

- 30 Wyoming Interstate Co

114

- 54 Transcontinental Gas Pipeline Co

114

- 96 Florida Gas Trans Co

115

- 14 Trunkline Gas Co

### 10

- 14 Link Pipeline

115

- 14 ANR Pipeline Co

116

- 18 Texas Eastern Trans Corp

117

- 5 Dominion Trans (CNG)

117

- 104 Dominion Trans (CNG)

117

- 105 Transcontinental Gas Pipeline Co

117

- 118 Transcontinental Gas Pipeline Co

117

- 118 Dominion Trans (CNG)

117

- 118 Tennessee Gas Pipeline Co

117

- 118 National Fuel Gas Supply Co

118

- 5 National Fuel Gas Supply Co



### Natural gas storage is modeled by 190 underground and LNG peak shaving 92 

### storage facilities that are

link ed to individual nodes. The underground storage is grouped into three categories based on storage

“Days Service” 93 

### : (1) 20

- day for high deliverability 94 

### storage such as salt caverns, (2) 80

- day for

depleted 95 

### and aquifer 96 

### reservoirs, and (3) over 80 days mai nly for depleted reservoirs. The level of gas

storage withdrawals and injections are calculated within the supply and demand balance algorithm based

on working gas 97 

### levels, gas prices, and extraction/injection rates and costs. Starting year of 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 price s are market clearing prices derived from the supply and demand balance at each of

the model’s nodes for each segment of IPM’s electricity sector’s seasonal load duration curve (LDC). On

the supply

- side, prices are determined by production and storage price curves that reflect prices as a

function of produ ction 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 curve s 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

- 2040 Input Assumptions

The time horizon of the EPA’s Base Case v.5.13 extends through 2050 . Projections through the year

2040 in EPA’s Base Case v.5.13 are based on a detailed bottom

- up development of natural gas

assumptions from available data sou rces. Beyond 20 4 0, where detailed data are not readil y available,

various technically plausible simplifying assumptions were made. For example, natural gas demand

growth from 20 4 0 to 2050 for the non

- power sectors (i.e. residential, commercial, and industrial) is

92 

### LNG peak shaving facilities supplement deliveries of natural gas during times of peak perio ds. 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.

93 

### “Days Service” refers to the number of days required to completely withdraw the maximum working gas inventory

associated with an underground storage facility.

94 

### High deliverability storage is depleted reservoir storage facility or Salt Cavern storage whose design allows a

relatively quick turno ver of the working gas capacity.

95 

### 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.

96 

### 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.

97 

### The term “working gas” refers to natural gas that has been injected into an und erground 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 adequa te pressure and

deliverability rates throughout the withdrawal season.

### 10

- 15

assumed to be the same as the level of growt h from 2020 to 20 4 0. Resource growth assumptions (for

resource appreciation) that were applied for pre

- 20 4 0 are extended beyond 20 4 0. Post

- 20 4 0 price

projections for crude oil and natural gas liquid s 98 

### (NGL s ) are assumed to be flat at 20 4 0 price levels. The

pre

- 20 4 0 price projections were adapted from AEO 20 13

.

Figure 10

- 6 Natural Gas Storage Facility Node Map



## 10.3 Resource Characterization 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.5.13 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 constraints

affecting E&D activities.

The HSM was designed for the simulation, forecasting and analysis of natural gas, crude oil and natural

gas liquid s supply and cost trends in the United States and Canada . The HSM includes a highly detailed

description of both the undiscovered and discovered resources in the U

. S

. and Canada. The resource

base is descr ibed on a field

- 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 re presentation of potential gas resources and the technologies

98 

### Those hydrocarbons in natural gas that are separated from the gas as liquids in gas processing or cycling plants.

Generally such liquids consist of ethane, propane , butane, and heavie r hydrocarbons.

### 10

- 16

with which those resources can be proven 99 

### and produced. The degree and timing by which resources

are proven and produced are determined in the model through discounted cashflow analyses of

alter native 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 undevelope d resource accounting and detailed well, reserve

addition, decline rate, and financial results. These results are utilized to provide estimates of base year

economically recoverable natural gas resource s and remaining reserves as a function of E&D cost for the

81 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 oil and

natural gas liquid s, na tural gas reserves to production ratio, and natural gas requirement for lease and

plant use. 100



### 10.3.1 Resource and Reserves 101 

### Assessment

Data sources : The HSM uses the U.S. Geological Survey (USGS), Minerals Management Se rvice

(MMS), and Canadian Gas Potential Committee (CGPC) play

- level 102 

### resource assessments as the

starting point for the new field/new pool 103 

### assessments. Beyond the resource assessment data, ICF has

access to numerous databases that were used f or the HSM model development and other analysis.

Completion

- level 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 production data for

Outer Continental Shelf (OCS) areas and state production reports.

In the area of unconventional gas 104 

### , ICF has worked for many years with the Gas Research Institute

(GRI)/Gas Technology Institute (GTI) to dev elop 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

99 

### The term “proven” refers to the estimat ion 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 exi sting economic and operating conditions. Among the factors considered are drilling results, prod uction, and

historical trends. Proven reserves are the most certain portion of the resource base.

100 

### 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.

101 

### When referring to natural gas a distinction is m ade between “resources” and “reserves.” “Resources” are

concentrations of natural gas that are or may become of potential economic interest. “Reserves” are that p a rt of the

natural gas resource that has been fully evaluated and determined to be commercial ly viable to produce.

102 

### 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 ty pe

.

103 

### A “pool” is a subsurface accumulation of oil and other hydrocarbons . Pools are not necessarily big caverns . T hey

can be small oil

- filled pores. A “field” is an accumulation of hydrocarbons in the subsurface of sufficient size to be of

economic int erest. A field can consist of one or more pools.

104 

### 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 sedimenta ry 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.

### 1 0

- 17

database was used to help develop the HSM’s “cells”, which represent resources in a specific geograph ic

area, characterizing the unconventional resource in each basin, historical unconventional reserves

estimates and typical decline curves. 105 

### ICF has recently revised the unconventional gas resource

assessments based on new gas industry information on the geology, well production characteristics, and

cost s. The new assessments include major shale units such as the Fort Worth Barnett Shale, the

Marcellus Shale, the Haynessv ille 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. 106



### In Canada , gas compositi on data are obtained from provincial agencies. These data were used to

develop dry gas 107 

### production/reserves by region and processing costs in the HSM and to characterize

ethane rejection 108 

### 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 during 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 provincial 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 gener al categories:

new fields/new pools, field appreciation, and unconventional gas. The methodology for resource

characterization and economic evaluation differs for each.

Conventional resource

– new fields/new pools : The modeling of conventional resource is based on a

modified “Arps Roberts” equation 109 

### 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 its 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 equati on 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 rates 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 exploration activity in these a reas. 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.

105 

### 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. Exponenti al, harmonic, and hyperbolic equations are typically used to

represent the decline curve.

106 

### Raw gas production refers to the volumes of natural gas extracted from underground sources, whereas ne t gas

production refers to the volume of purified, marketable natural gas leaving the natural gas processing plant.

107 

### Natural gas is a combustible mixture of hydrocarbon gases. Although consisting primarily of methane, the

composition of natural gas can va ry 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 ca lled 'wet'.

108 

### 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 g as 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 r ejection by region can play a role in determining the production

level and cost of natural gas by region.

109 

### “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.

### 10

- 18

Conventional resource

– field appreciation : The model maintains inventories of potential resources that

can be proved from already discovered fields. These inventories are referre d 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 o f new fields that are added to the discovered fields’ data set during the model

simulation.

Unconventional resource : Originally, the assessments of the unconventional resources were based on

t he Enhanced Recovery Module (or ERM) within the HSM . The ERM c overs 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 corre spond 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 inter val 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 affec ting 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.

As described earlier, ICF ha s recently revised the unconventional gas resource assessments based on

new gas industry information on the geology, well production characteristics, and cost s. The new

assessment method is a “bottom

- up” approach that first generates estimates of unrisked and risked gas

in

- place (GIP) from maps of depth, thickness, organic content, and thermal maturity. Then ICF uses a

reservoir simulator to estimate well recoveries and production profiles. Unrisked GIP is the amo unt of

original gas

- in

- place determined to be present based upon geological factors without risk reductions.

Risked GIP includes a factor to reduce the total gas volume on the basis of proximity to existing

production and geologic factors such as net thick ness (e.g., remote areas, thinner areas, and areas of

high thermal maturity have higher risk). ICF calibrates well recoveries with specific geological settings to

actual well recoveries by using a rigorous method of analysis of historical well data.

10.3.2 Fronti er 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 ar e

subject to similar resource assessment and economic evaluation procedures as applied to other regions.

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 ha ve been put forward for b ringing this

Alaska North Slope and Mackenzie Delta gas to market

.

In developing the gas resource assumptions for EPA Base Case v.5.13 , two gas pipeline projects were

identified for bringing the two frontier gas supply resource s to the markets in the U.S. and Canada

. However, due to uncertainties in the economics and the timing of these pipeline projects, they are not

included in the EPA Base Case v.5.13.

10.3.3 Use of the HSM resource and reserves data in EPA Base Case using IPM v.5.13 Natural

Gas Module

The base year for the integrated gas

- electricity module in EPA Base Case using IPM v.5.13 is 201 6

. Having a base year in the future has implications 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 201 5 to provide

beginning of year ( B OY) 201 6 reserves and resources as the starting point for the integrated run from

201 6 onwa rd. This in turn requires the reserves and resources data to be provided for the BOY 2015

. Since the data from the HSM are as of B OY 20 11

, adjustments have to be made to account for reserves

### 10

- 19

development, production, and also resource appreciation between 2011 and 2014. In the EPA Base

Case using IPM v.5.13, these adjustments are made based on a four

- year production and reserves

development forecast using the GMM and a set of resource appreciation growth assumptions. The

resource growth assumptions are d iscussed in “Undiscovered Resource Appreciation ” section below.

Table 10

- 4 provides a snapshot of the starting natural gas resource and reserve assumptions for the EPA

Base Case v.5.13 . In this table, undiscovered resources represent the economic volume of dry gas that

could be discovered and developed with current technology through exploration and development at a

specified maximum wellhead gas price. Since the IPM natural gas module differentiates conventional gas

from u nconventional gas, these are shown separately in Table 10

- 4

. The conventional gas is

subcategorized into non

- associated gas from gas fields and associ ated gas 110 

### from oil fields. The

unconventional gas is subdivided into coalbed methane (CBM) , shale gas, and tight gas. In Table 10

- 4

, the shale gas resource availability in the Northeast region is constrained by as assumption of limited

access in accordance with current permitting procedures mostly affecting the Marcellus play . The full

resource is about 925 Tcf.

The reserves are rema ining dry gas volumes to be produced from existing developed fields. For EPA

Base Case v.5.13 the maximum wellhead price for the resource cost curves is capped at $1 6 /MMBtu (in

real 20 11 dollars). The ultimate potential undiscovered resources available a re actually higher than those

presented in Table 10

- 4 but it would cost more than $1 6 /MMBtu to recover them. (It is important to note

that this price is for wet 111 

### gas at the w ellhead in the production nodes . The dry gas price at the receiving

nodes can be higher than $1 6 /MMBtu which depends on the share of dry gas, lease and plant use, gas

processing cost, production O&M cost, and pipeline transportati on costs.) The approach used in the HSM

to derive these costs is described more fully in section 10.4 below.

Table 10

- 4 U.S. and Canada Natural Gas Resource s and Reserves

Region Beginning of Year 2015

Undiscovered Dry Gas Resource (Tcf) Dry Gas Reserves (Tcf)

Lower 48 Onshore Non Associated 2 

### ,049 325

Conventional (includes tight) 566 101

Northeast 49 9

Gulf Coast 144 18

Midcontinent 48 16

Southwest 19 13

Rocky Mountain 2 

### 88 46

West Coast 18 0

Shale Gas 1,408 2 

### 12

Northeast 647 79

Gulf Coast 492 89

Midcontinent 151 2 

### 2

Southwest 67 15

Rocky Mountain 50 8

West Coast 0

-

Coalbed Methane 75 11

Northeast 10 1

Gulf Coast 4 1

110 

### A ssociated 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

.

111 

### A mixture of hydrocarbon compounds and small quantities of various non

- hydrocarbons 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, buta ne, and pentane. Typical non

- hydrocarbon

gases that may be present in reservoir natural gas are water vapor, carbon dioxide, hydrogen sulfide, nitrogen and

trace amounts of helium.

### 10

- 20 Region Beginning of Year 2015

Undiscovered Dry Gas Resource (Tcf) Dry Gas Reserves (Tcf)

Midcontinent 10 1

Southwest

- -

Rocky Mountain 50 9

West Coast 1

-

Lower 48 Offshore Non Associated 85 6

Gulf of Mexico 85 6

Pacific

- 0

Atlantic

- -

Associated

- Dissolved Gas 116 13

Alaska 51 10

Total U.S. 2 

### ,300 355

Canada Non Associated 858 59

Conventional and Tight 104 30

Shale Gas 723 2 

### 4

Coalbed Methane 31 5

Canada Associated

- Dissolved Gas 4 3

Total Canada 862 62

Total U.S and Canada 3,162 416



### Figure 10

- 7 presents dry gas resource cost curve s for the B OY 201 5 initializing gas assumptions for EPA

Base Case v.5.13 . The resource cost curves show the undiscovered recoverable dry gas resources at

different price levels. The curves do not include dry gas reserves. Separate reso urce cost curves are

shown for conventional, shale, coalbed methane (CBM), and tight gas. The recoverable resources

shown at maximum wellhead prices in these graphs are those tabulated in Table 10

- 4 under

“Undiscovered Dry Gas Resource ” 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 reserves

category. Figure 10

- 8 diagrams the exploration & development and production processes and the

associated costs required to bring undiscovered resource into reserves and production.

### 10

- 21 Figure 10

- 7 Resource Cost Curve s at the Beginning of Year 201 5

10.3.4 Undiscovered Resource Appreciation

Undiscovered resource appreciation is additional resource s from hydrocarbon plays that were not

included in the resource base estimates. It differs from field appreciation or reserves appreciation

catego ry discussed above which comes from already discovered fields. Natural gas from shales, coal

seams, offshore deepwater, and gas hydrates may not be included in the resource base assessments

due to lack of knowledge and technology to economically recover t he resource. As new technology

becomes available, these untapped 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 year for

conventional gas and 0.75% per year for unconventional gas. The B OY 201 5 undiscovered recoverable

gas resources in Table 10

- 4 and Figure 10

- 7 include resource appreciation between 2011 and 2014

.

### 10

- 22 Figure 10

- 8 Exploration & Development and Production Processes and Costs to Bring

Undiscovered Resource into Reserves and Production



## 10.4 Exploration, Development, and Production Cost s and Constraint s

10.4.1 Exploration and Development Cost

Exploration and development (E&D) cost or resource cost is the expenditure for activ ities 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 d rivers 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 a nd

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 nee d 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. Generally, 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 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 level of E&D investments i n 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 impr ove productions and lower the costs. This

is reflected in higher cost reduction factors for the onshore regions.

### 10

- 23 Figure 10

- 9 E&D and Production Technology Improvement Factor

Figure 10

- 10 shows E&D cost needed to discover and develop 2.5%, 5%, and 7.5% of the remaining

undiscovered resource in BOY 2015 by natural gas supply region.

### 10

- 24

Figure 10

- 10 Incremental E&D Cost ( B OY 201 5 ) by Percentage of Dry Gas Resource Found

### 10

- 25

10.4.2 Resource Discovery and Drilling Constraint s

As mentioned above the simulation in HSM also provides 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 tim e dependent. The resource discovery constraint is the same for all regions

and is assumed to be 6 % of the remaining undiscovered resource (column 4 in Table 10

- 5 ). The drilling

requirement constraint (column 5 in Table 10

- 5 ) varies from 2,500 feet for every billion cubic feet of

incremental resourc e discovered ( feet/Bcf ) for offshore U.S. and between 3,000 feet/Bcf to 10,000

feet/Bcf for onshore regions and offshore Canada.

Table 10

- 5 Exploration and Development Assumptions for EPA Base Case v.5.13

Region Fraction of

Hydrocarbons

that are

Natual 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

(Fraction) (Fraction) (Fraction) (Ft/Bcf) (Fraction)

(5) Niagara 0.02 0.12 0.06 10,000 0.05

(6) Leidy 0.01 0.02 0.06 4,556 0.03

(11) East Ohio 0.10 0.01 0.06 9,400 0.01

(14) Indiana 0.00 0.99 0.06 10,000 0.02

(15) South Illinois 0.00 0.96 0.06 10,000 0.30

(16) North Illinois 0.00 1.00 0.06 10,000 0.30

(18) Tennessee/Kentucky 0.11 0.02 0.06 10,000 0.04

(21) Northern Missouri 0.11 0.00 0.06 10,000 0.04

(28) Kansas 0.12 0.25 0.06 7,454 0.04

(29) East Colorado 0.11 0.03 0.06 9,349 0.05

(30) Opal 0.08 0.29 0.06 4,862 0.05

(32) San Juan Basin 0.11 0.04 0.06 6,323 0.13

(34) North Wyoming 0.11 0.00 0.06 3,688 0.05

(36) SOCAL Area 0.08 0.56 0.06 9,320 0.13

(37) Enhanced Oil Recovery Region 0.04 0.74 0.06 10,000 0.13

(38) PGE Area 0.08 0.61 0.06 9,376 0.13

(41) Montana/North Dakota 0.05 0.64 0.06 10,000 0.13

(45) Pacific Northwest 0.14 0.00 0.06 10,000 0.02

(49) Eastern Canada Offshore 0.03 0.00 0.06 10,000 0.06

(54) North Alabama 0.07 0.04 0.06 6,099 0.03

(55) Alabama Offshore 0.01 0.84 0.06 2,500 0.03

(57) East Louisiana Shelf 0.04 0.74 0.06 2,500 0.04

(58) Eastern Louisiana Hub 0.13 0.24 0.06 6,884 0.04

(59) Viosca Knoll/Desoto/Miss Canyon 0.07 0.56 0.06 2,500 0.04

(60) Henry Hub 0.13 0.25 0.06 6,927 0.04

(61) North Louisiana Hub 0.11 0.01 0.06 9,823 0.04

(62) Central and West Louisiana Shelf 0.04 0.74 0.06 2,500 0.04

(63) Southwest Texas 0.17 0.36 0.06 7,925 0.05

(64) Dallas/Ft Worth 0.06 0.05 0.06 4,510 0.05

(65) E. TX (Katy) 0.14 0.42 0.06 8,819 0.05

(66) S. TX 0.12 0.24 0.06 7,596 0.05

(67) Offshore Texas 0.09 0.31 0.06 2,500 0.05

(68) NW TX 0.22 0.08 0.06 7,584 0.05

(69) Garden Banks 0.07 0.49 0.06 2,500 0.04

(70) Green Canyon 0.07 0.53 0.06 2,500 0.04

(71) Eastern Gulf 0.04 0.71 0.06 2,500 0.04

### 10

- 26 Region Fraction of

Hydrocarbons

that are

Natual 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

(Fraction) (Fraction) (Fraction) (Ft/Bcf) (Fraction)

(72) North British Columbia 0.01 0.00 0.06 9,948 0.08

(74) Caroline 0.04 0.04 0.06 9,752 0.10

(76) Saskatchewan 0.01 0.54 0.06 10,000 0.07

(80) West Virginia 0.06 0.00 0.06 3,539 0.05

(83) Wind River Basin 0.11 0.01 0.06 7,013 0.05

(86) MacKenzie Delta 0.00 1.00 0.06 10,000 0.08

(87) South Alaska 0.05 0.59 0.06 10,000 0.08

(89) North Alaska 0.04 0.62 0.06 10,000 0.99

(90) Arctic 0.00 1.00 0.06 10,000 0.08

(92) Southwest VA 0.00 0.00 0.06 5,787 0.02

(96) North Florida 0.01 0.94 0.06 9,937 0.21

(98) Southwest Michigan 0.08 0.09 0.06 10,000 0.04

(99) Northern Michigan 0.05 0.21 0.06 7,946 0.04

(107) Carthage 0.07 0.02 0.06 3,228 0.05

(108) Southwest Oklahoma 0.15 0.06 0.06 6,905 0.04

(109) Northeast Oklahoma 0.16 0.03 0.06 9,089 0.04

(110) Southeastern Oklahoma 0.16 0.02 0.06 4,445 0.04

(111) Northern Arkansas 0.00 0.05 0.06 4,437 0.04

(113) Uinta/Piceance 0.10 0.13 0.06 7,715 0.05

(114) South MS/AL 0.06 0.16 0.06 7,012 0.03

(115) West KY/TN 0.11 0.06 0.06 10,000 0.04

(116) Kosciusko MS 0.11 0.00 0.06 10,000 0.04

(117) Northeast PA 0.01 0.01 0.06 3,394 0.04

(118) Leidy 0.01 0.01 0.06 3,993 0.04



### 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 capacity co nstraint shows the number of drilling rigs initially available

in the BOY 201 5 . The initial rig counts are 4,050 rigs for onshore, 1 2 5 rigs for offshore shelf, and 1 2 5 rigs

for offshore deepwater and the numbers can change over time controlled by rig reti rement and rig growth

constraint s. The drilling rig retirement constraint is the 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 re tirement

and growth are assumed to be the same for all drilling category and the constraint s 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 drilling 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 a nd offshore (which in this case

includes both shelf and deep shelf).

### 10

- 27 Figure 10

- 11 Drilling Rig Speed Constraint

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 ran ges 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 0.0 6 (or 17 years of R/P). Coalbed methane gas has

the lowest P/R ratio with average of 0.0 4 (or 25 years of R/P).

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 201 5 is estimated to be $0.5 4 /MMBtu (in real

20 11 dollars) and is assumed to be the same for all supply regions. The production O&M cost is

expected to decline ov er time due to improvements in production technology. In the model the same

technology improvement factor shown in Figure 10

- 9 is applied to the produ ction 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. Table

10

- 5 shows the shares of NGL (column 2) and crude oil (column 3) by supply region. W et gas production

from the wellhead is processed in gas processing plants to produce pipeline quality dry gas. Node level

gas processing cost for IPM natural gas module is obtained from the GMM . The processing cost varies

from $0.07/MMBtu (of wet gas in real 20 11 dollars) to $0. 61 /MMBtu with average of $0.2 3 /MMBtu.

10.4.5 Le ase and Plant Gas Use

The term “lease and plant gas” 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. The data for leas e 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 ). Lease and plant for North Alaska is set to 0.99 to represent the portion of gas

production that is re

- injected back into the Slope’s oil reservoirs

.

### 10

- 28



## 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 database of existing and potential liquefaction and regasification

facilities and worldwide LNG import/export activities, the model uses a simulation procedure to create the

BOY 201 5 North American 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 regasi fication facility are set by available firm contracts and swing supplies. Three point LNG

supply curve s 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 Cru de (RACC) price. 112 

### 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 Asian and European

LNG consumers.

The individual LNG supply curves from the GMM LNG model are aggregated to create total North

American LNG supply curve s 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 nat ural gas module. Two LNG supply curves, one for winter and one for summer, are

specified for each year starting from 201 5 until 2054 to capture growth as well as seasonal variation of

the LNG supplies. Figure 10

- 12 shows the North American LNG supply cur ves for the winters and

summers of 201 5 and 2050.

112 

### 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 cost s.

### 10

- 29 Figure 10

- 12 North American LNG Supply Curves

10.5.2 Regasification Facilities

For the EPA Base Case, 15 North American LNG regasification facilities are considered in the IPM

natural gas module. Table 10

- 6 lists the 1 5 facilities, the destination nodes where the LNG are delivered,

and the BOY 201 5 capacity for each of the regasification facility. Figure 10

- 13 provides a map of these

facilities. Existing Penuelas LNG facilities in Puerto Rico are not included because they are not part of

the natural gas network in the I PM gas module. In EPA Base Case v.5.13 . 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 Beginning of Year 2015

Regasification Capacity

(Bcf/day)

1 Cove Point (7) Cove Point TRANS 1.50

2 Elba Island (9) Elba Is TRANS 2.40

3 Everett (2) Everett TRANS 0.70

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

### 10

- 30

No LNG Regasification Facility Node Location Beginning of Year 2015

Regasification Capacity

(Bcf/day)

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 (81) Eastern Canada Demand 1.00

12 Sabine Pass (60) Henry Hub 2.60

13 Gulf LNG Energy LLC (114) South MS/AL 1.00

14 Northeast Gateway (1) New England 0.80

15 Manzanillo (51) Reynosa Imp/Exp 0.75



### Figure 10

- 13 North American LNG Regasification Facilities Map

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 specified for each facility and year where the minimum constraint is used to

force the model to add regasification capacity and the maximum constraint is the upper bound for the

capacity expansion.

The decision of whether to expand regasification capacity is controlled by the two constraints and by a

levelized capital cost for regasification capacity expansion. The BOY 201 5 levelized capital cost for

capacity expansion (in real 20 11 dollars per MMBt u 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

### 10

- 31

constraint s for the capacity expansion can be used to turn on or off the regas ification 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 t he 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 pos sible options/projects

(including regasification capacity expansions) 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,

regasification capacity e xpansion 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.5.13 . EPA scenario results show v ery low total LNG

utilizations throughout the projection period because of robust natural gas supply in the U.S. and Canada

combined with a relatively low electricity demand growth assumption . The results 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 curves 113 

### . 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 i ndustrial 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 econometric models representing the non

- power sector demand. Since the

GMM econometric mode ls are functions 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 module demand curves. Second, projections are made using the GMM econometric models

and assembled into monthly gas demand curve s by sector and demand node. Third, using a second

model, seasonal and load segment specific demand curves are derived from the monthly gas demand

curves. The s ections below describe each of these steps in further detail.

10.6.1 Step 1: Developing Sector Specific Econometric Models of Non

- Power Sector Demand

Residential/Commercial Sector

The GMM econometric models of residential and commercial demand are ba sed on regression analysis

of historical data for 4 1 regions and are adjusted to reflect conservation, efficiency, and technology

changes over time. The 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 Administra tion (DOE/EIA) data from January 1984 through

December 2002 for the U.S. and monthly Statistics Canada data from January 1988 through December

2000 for Canada.

The GMM econometric models showed node

- level residential and commer cial 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.

113 

### “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

- 32

U.S. Industrial Sector

The GMM econometric model of U.S. in dustrial 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 Pr imary 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 ov er time for most industries is controlled by industrial production indices.

Energy intensity 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 c ategory 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 space heating. Like the forecasts for process heat, the GMM

econometric modeling showed “other end uses” for each industrial sector t o be a function of growth in

output, the energy intensity trend, and the price elasticity.

In addition to these demand models, a separate regression model was use to characterize the chemicals

sector’s demand for natural gas as a feedstock for ammonia, met hanol, and non

- refinery hydrogen.

Growth in the chemicals industry 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 i ncrease, 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 sec tor 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 used in the

residential/commercial sectors. Sub

- sectors of Canadian industri al 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

- 33

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 curve s. To do this the functions are first populated with

the macroeconomic assumptions that are consistent with those used in EPA Base Case v.5.13. Then, a

range of natural gas prices are fed into the regression functions. At each gas price the regression

functions report out projected monthly 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 segment s in the load duration curve s 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 alloc ated to the firm gas demand curves, and the remaining

90% of the industrial demand is allocated to the interruptible gas demand curve s.

IPM, the power sector model, has to take into account natural gas demand faced by ele ctric generating

units that dispatch in different segments of the load duration curves, since demand for natural gas and its

resulting price may be very different for units dispatch ing in the peak load segment than it is for units

dispatch ing in the 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.5.13 , th ere 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 applicable

season and prev ailing load conditions to produce the final non

- electric sector gas demand curves that are

used in IPM.

In EPA Base Case v.5.13 , each of the summer and winter periods uses 6 load segments for pre

- 2030

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 pr e

- 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 same definitions of “Low Shoulder” and “Base” load segments are applied to both pre

2030 and po st

- 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

.

Table 10

- 7 Summer and Winter Load Segments in EPA Base Case v.5.13

Pre 2030 Post 2030

1 Needle Peak

1 Peak

2 Near Peak

3 High Shoulder

2 High Shoulder

4 Middle 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.

### 10

- 34

The non

- electric sector demand curve s (firm and interruptible) are generated based on GMM regressions

described above with macroeconomic assumptions consistent with those of EPA Base Case v.5.13 . 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 Figure 10

- 15

. Figure 10

- 14 is very inelastic; only a sma ll 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 dema nd 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.5.13 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 assumed here

.

Figure 10

- 14 Examples of Firm Demand Curve s by Electric Load Segment

Figure 10

- 15 Examples of Interruptible Demand Cu rve s by Electric Load Segment

### 10

- 35

10.6.4 The Use of Firm Gas Demand to Represent LNG Exports

As described earlier, t he gas module does not currently have a specific sub

- module for LNG exports. In

t he EPA Base Case v.5.13 , the LNG exports are treated as firm demand in the form of fixed or inelastic

firm demand curves . Three additional demand nodes are added to represent the three LNG export

terminals, two in the U.S. Gulf Coast and one in Western Canada

. The U.S. Gulf Coast LNG nodes are

linked to nodes (60) Henry Hub and (65) E. TX (Katy) and the Western Canada LNG export node is linked

to node (72) North British Columbia

. The assumptions for LNG exports from the U.S. Gulf Coast , starting

from 2016, are adapted from AEO 2013. The assumptions for LNG exports from Western Canada

, starting from 2017, are derived from GMM LNG Model. Figure 10

- 16 shows LNG exports projection from

the U.S. and Canada.

Figure 10

- 16 LNG Export Assumptions in EPA Base Case v.5.13

.



## 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 380 114 

### gas pipeline corridors (including bi

directional links) between the 118 nodes ( Figure 10

- 3 ). Each corridor is chara cterized by maximum

capacity and a “value of service” (discount curve) relationship that determines the market value of

capacity as a function of load factor. 115 

### 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

- 17 shows the flow and capacity of five

pipeline corridors in New England in 2020. Gas flows into New England along three pipeline corridors

114 

### Excluding LNG import Terminal nodes and t heir pipeline connections.

115 

### See footnote 88 above for a definition of “load factor.”

### 10

- 36

(indicated in Figure 10

- 17 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

- 17 ). New England also receives gas via the

Everett LNG terminal (indicated in Figure 10

- 17 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 Engl and (indicated in Figure

10

- 17 by the arrow that points away from the region).

Figure 10

- 17 New Engla nd Pipeline Corridors in 2020

10.7.2 Pipeline Transportation Cost s

In the IPM natural gas module, the natural gas moves 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 116 

### (i.e., the market value of capacity) for each period in the forecast for each pipeline

link. The 4

- point pipeline discount curves in the IPM natural gas module are simplified forms of the more

robust continuous discount curves from the GMM pipeline module. The GMM pipeline discount curve s

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

pipeline 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

- 18 depicts the BOY 201 5

discount curve for the pipeline corridor connecting nodes (61) North Louisiana Hub and (18)

Tennessee/Kentucky. Cost growth factors shown in

116 

### 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

- 37

Figure 10

- 19 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.

Figure 10

- 18 Example Pipeline Discount Curve

Figure 10

- 19 Pipeline Cost Growth Factor

10.7.3 Pipeline Capacity Expansion Logic

Initial pipeline capacity, derived from GMM , includes existing capacities and planned capacities that are

expected to be operational from the beginning of 201 5 . The IPM natural gas module has the capability to

endogenously expand the pipeline capacity. The decision of whether to expand pipeline capacity is

controlled by two constraints, which stipulate minimum and maximum capacity additions and by the

levelized capital cost of expa nding pipeline capacity in the specific corridor and year. The minimum

capacity addition constraint forces the model to add capacity in a specified corridor and year. The

### 10

- 38

maximum capacity constraint is the upper bound on capacity addit ions in a specified corridor and year.

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 117 

### as their maximum

capacity additio n requirement. Where this occurs, the pipeline expansion is only controlled by the

pipeline capital cost.

The model is allowed to add capacity to a pipeline corridor within the minimum and maximum capacity

addition constraint s if the cost of the 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 pos sible options/projects including capacity additions for

pipeline 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 wa y, pipeline corridor expansion

projects will compete with each other and even with other projects such as LNG regasification capacity

expansions, storage expansions, etc.

For EPA Base Case v.5.13 , pipeline corridors connecting North Alaska (node 89) and Ma ckenzie Delta

(node 86) to North British Columbia (node 72) have the minimum and maximum capacity addition

constraints. Due to uncertainties of these pipeline projects as discussed in Section 3, the North Alaska

and Mackenzie Delta pipeline project s are n ot made available throughout the projection. Both capacity

addition constraints for North Alaska and Mackenzie delta pipeline corridors are set to zero.

Expansions in other pipeline corridors are not restricted. The model is allowed to build capacity to any

pipeline corridors at any time as long as it contributes to minimization of the objective function.

The BOY 2015 levelized pipeline capital cost (in real 20 11 dollars per MMBtu/Day of pipeline capacity

addition) is specified for each of the 380 pipeline links. The cost growth factors shown in

Figure 10

- 19 are applied to derive the cost increase over time. The average levelized capital cos t for

pipeline capacity expansion for 201 5 is $1 65 per MMBtu/Day.



## 10.8 Gas Storage

T he IPM natural gas module has 11 8 underground storage facilities that are linked to 51 nodes. The

underground storage is grouped into t hree categories based on storage “Days Service.” 118



### “20

- Day” high deliverability storage

– 37 storage facilities

“80

- Day” depleted/aquifer reservoirs

– 41 storage facilities

“Over 80 Days” depleted/aquifer reservoirs

– 40 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

.

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 1 1 8 such X’s which correspond to the 1 1 8 underground storage facilities noted

117 

### In the model this is achieved by assigning a large number, e.g., 100 Bcfd

, for every year where there is no

constraint on maximum capacity

.

118 

### See footnote 90 above for a definition of “Days Service.”

### 10

- 39

in the previous paragraph. These 1 1 8 X’s appear in 51 rows, which represent the linked nodes noted in

the previous paragraph. The identities of these nodes are found in column 1 (“Node”). Simila rly, 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

Node Underground Storage Facility LNG Peakshaving Facility

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) Southwest PA 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) East KY/TN 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 X

(31) Cheyenne 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 X

(41) Montana/North Dakota X X X

(45) Pacific Northwest X X X

(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

### 10

- 40 Node Underground Storage Facility LNG Peakshaving Facility

20

- Day 80

- Day Over 80 Days Existing Potential

(61) North Louisiana Hub X 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

(114) South MS/AL X X X

(115) West KY/TN X X X

(117) Northeast PA X X X

(118) Leidy X X



### 10.8.1 Storage Capacity and Injection/Withdrawal Constraint s

The expected working gas capacity as of B OY 201 5 by location 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 B OY 201 5 GMM storage input assumption in the IPM gas module, Table

10

- 9 shows the total working gas capacity and the aver age daily injection and withdrawal rates as

percentage of working gas capacity for the four types of storage. Note that these are aggregated values

### 10

- 41

(i.e., totals and averages); the actual GMM B OY 201 5 inputs to the IPM gas module vary by location and

stor age type.

Table 10

- 9 Storage Capacity and Injection/Withdrawal Rates ( B OY 201 5 )

Working Gas

Capacity (Bcf) Average Daily Injection Rate

(Percent of WG Capacity) Average Daily Withdrawal

Rate (Percent of WG Capacity)

Underground Storage

20 Day 622 6.3% 9.6%

80 Day 3,522 1.4% 2.3%

Over 80 Days 1,235 0.6% 1.0%

Total 5,379

LNG Peakshaving Storage 84 0.4% 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 BOY 201 5 variable cost or commodity 119 

### charge for underground storage facilities is assumed

to be 1. 6 cents/MMBtu (in real 2011 dollars) an d is the same for all underground storage nodes and

types. The variable cost for LNG peakshavi ng facility is much higher at 37.4 cents/MMBtu as it includes

variable costs for gas liquefaction (in gas injection cycl e ) and LNG regasification (in gas withdrawal cycle ).

The variable cost is assumed to be the same for all LNG peakshaving nodes. A storage cost growth

factor shown in Figure 10

- 20 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.

Figure 10

- 20 Storage Cost Growth Factor

119 

### Storage c ommodity (variable ) c harge is g enerally 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

- 42

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 stipulate 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 constrai nt 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

- 21 shows projected maximum storage expansion constraints for the “80

- day” category storage

facility in supply area Katy, Texas.

Figure 10

- 21 Example Maximum Storage Capacity Expansion

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 optimal solution, i.e., minimizes the overall costs to the power

se ctor, 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 constra int s and selects the combination of options/projects that

provide the minimum objective function value. In this way, storage capacity expansion projects will

compete with each other and even with other projects such as LNG regasificatio n capacity expansions,

pipeline expansions, etc.

The BOY 201 5 levelized storage capital cost (in real 20 11 dollars per MMBtu of storage capacity addition)

is specified for each of the 190 storage facilities. Tab le 10

- 10 lists the average BOY 2015 levelized

storage capital cost for the four types of storage facility. Amongst the underground storage faciliti es the

higher capital costs represent more storage cycles 120 

### 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 about 20 percent and 4 0

120 

### One storage cycle is the theoretical time required to com pletely 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

- 43

percent, respectively, higher than that o f 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

- 20 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.

Tab le 10

- 10 Base Year 201 5 Average Levelized Storage Capital Cost

Storage Type Average Levelized

Storage Capital Cost

(2011 $/MMBtu)

Underground Storage

20

- Day 1.19

80

- Day 0.99

Over 80 Days 0.83

LNG Peakshaving Storage 5.34



## 10.9 Fuel Price s

10.9.1 Crude Oil and Natural Gas Liquid s 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 the IPM natural gas module are shown in Figure 10

- 22

. These price projections were adapted from AEO 20 13 . No attempt was made to project prices beyond

20 4 0 ot her than to assume that prices remain at their 20 4 0 levels. The projected prices shown in Figure

10

- 22 are expressed in units of 20 11 $ per MMBtu. Using a crude oil Btu content of 5.8 MMBtu/Bbl, the

projected crude oil prices in Figure 10

- 22 can be translated into the more familiar units of dollars per

barrel (Bbl), in which case, prices in this figure are equivalent to $ 93 /Bbl in 201 5 , $1 02 /Bbl in 2020,

$1 26 /Bbl in 2020

, and constant at $1 55 /Bbl from 20 4 0 (in real 20 11 dollars) onward.

Figure 10

- 22 Crude Oil and NGL Price s

### 10

- 44

10.9.2 Natural Gas Prices

Node

- level natural gas prices are output s 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 price s are what are called “shadow prices” or “dual variable values”

associated with the node mass balance constraints at the optimal LP so lution.



## 10.10 Outputs and Glossary of Terms

10.10.1 Output s from the IPM Natural Gas Module

The EPA Base Case v.5.13 reports natural gas consumption (in TBtu), Henry Hub and delivered natural

gas prices (in $/MMBtu). It also reports 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 b y census division (in $/MMBtu), and inter

- regional pipeline flows and LNG imports (in Bcf).

10.10.2 Glossary of Terms Used in this Section

For ease of reference Table 10

- 11 assembles in one table terms that have been defined in footnotes

throughout this chapter.

Table 10

- 11 Glossary of Natural Gas Terms Used in Documentation

Term Defini tion

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.

### 10

- 45 Term Defini tion

Basis In natural gas discussions “basis” refers to differences in the pric e 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 differ ence in natural gas prices between any two nodes at

the same instance in time.

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 g as 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

co nsisting 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 liquid s (NGL), which collectively refers to ethane,

propan e, 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 dema nd “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 deliverabilit y 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 c ooling 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 for m 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 so urces.

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 s (NGL) Those hydrocarbons in natural gas that are s eparated from the gas as liquids

in gas processing or cycling plants. Generally such liquids consist of ethane,

propane , butane, and heavier hydrocarbons.

### 10

- 46 Term Defini tion

Play A “play” refers to a set of known or postulated natural gas (or oil)

accumulations sharing simi lar 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 th e 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

conside red 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

di scussing 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 n atural 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

- to

- production (R/P) ratio Res erves

- 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 “resource s” 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

co mmercially 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 g as 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

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.

T he 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

- 47 Term Defini tion

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.

Undergrou nd 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 vari ous

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 nonhy drocarbon 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 stora ge 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.

### 11

- 1



# 11. Other Fuels and Fuel Emission Factor Assumptions

Besides coal (chapter 9 ) and natural gas (chapter 10 ), EPA Base Case v.5.13 also includes assumptions

for residual fuel oi l, distillate 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 the previous chapter, there is an endoge nous resource costing model for natural gas

built into EPA Base Case v.5.13. Coal is represented via an elaborate set of supply curves and a detailed

representation of the associated coal transport network. Together they are designed to capture the

intrica cies of the resource base and market for these fuels which accounted for about 68% of U.S. electric

generation in 2012. As with coal, the price and quantity of biomass combusted is determined by balancing

supply and demand using a set of geographically dif ferentiated supply curves. 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 pri ce

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.5.13.



## 11.1 Fuel Oil

Two petroleum derived fuels are included in EPA Base Case v.5.13. 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. 5.13 are from AEO 201 3 and are shown in Table 11

- 1

. They are regionally

differentiated according to the NEMS (National Energy Modeling System) regions used in AEO 201 3 and

are mapped to their corresponding IPM regions for use in EPA Base Case v. 5.13

.

Table 11

- 1 Fuel Oil Prices by NEMS Region in EPA Base Case v. 5.13

Residual Fuel Oil Prices (2011$/MMBtu)

NEMS Region 2016 2018 2020 2025 2030 2040

- 2050

ERCT 21.94 26.06 30.09 41.37 53.54 82.89

FRCC 16.93 21.06 25.09 36.37 48.54 77.88

MROE 83.45 87.58 91.61 102.89 115.05 144.40

MROW 20.32 24.45 28.48 39.76 51.92 81.27

NEWE 11.00 11.65 12.27 14.04 24.33 23.74

NYCW 12.29 12.94 13.56 15.33 17.50 22.14

NYLI 12.29 12.94 13.56 15.33 17.50 22.14

NYUP 12.29 12.94 13.56 15.33 17.50 22.14

RFCE 15.05 16.02 16.63 19.13 21.30 25.94

RFCM 92.33 96.45 100.48 111.76 123.93 153.28

RFCW 84.13 88.26 92.29 103.57 115.73 145.08

SRDA 20.92 25.05 29.08 40.36 52.52 81.87

SRGW 83.45 87.58 91.61 102.89 115.05 144.40

SRSE 16.09 20.22 24.25 35.53 47.69 77.04

SRCE 82.14 86.26 90.29 101.57 113.74 143.09

SRVC 16.09 16.74 17.36 19.13 31.30 60.65

SPNO 20.32 24.45 28.48 39.76 51.92 81.27

SPSO 21.94 26.06 30.09 41.37 53.54 82.89

AZNM 21.94 26.06 30.09 41.37 53.54 82.89

CAMX 10.31 14.44 18.47 29.75 41.91 71.26

NWPP 24.78 25.46 26.07 28.17 30.15 34.93

RMPA 83.91 88.03 92.06 103.34 115.51 144.86

### 11

- 2 Distillate Fuel Oil Prices (2011$/MMBtu)

NEMS Region 2016 2018 2020 2025 2030 2040

- 2050

ERCT 21.40 22.17 22.96 25.13 27.30 32.56

FRCC 21.56 22.35 23.13 25.29 27.46 32.72

MROE 20.44 21.23 22.01 24.18 26.43 31.68

MROW 20.09 20.90 21.69 23.85 26.04 31.30

NEWE 21.17 21.96 22.75 24.91 27.08 32.34

NYCW 20.36 21.15 21.93 24.09 26.26 31.52

NYLI 20.36 21.15 21.93 24.09 26.26 31.52

NYUP 20.36 21.15 21.93 24.09 26.26 31.52

RFCE 20.74 21.53 22.33 24.48 26.68 31.94

RFCM 20.44 21.23 22.01 24.18 26.43 31.68

RFCW 20.67 21.46 22.25 24.41 26.63 31.89

SRDA 21.40 22.17 22.96 25.13 27.30 32.56

SRGW 20.40 21.19 21.97 24.14 26.38 31.63

SRSE 21.32 22.08 22.86 25.03 27.19 32.46

SRCE 20.92 21.70 22.48 24.64 26.83 32.09

SRVC 21.56 22.35 23.13 25.29 27.46 32.72

SPNO 20.07 20.86 21.64 23.80 25.98 31.25

SPSO 21.31 22.08 22.87 25.04 27.21 32.47

AZNM 22.08 23.01 23.73 25.88 28.13 33.29

CAMX 21.76 22.75 23.47 25.62 27.87 33.02

NWPP 21.68 22.61 23.41 25.55 27.81 32.97

RMPA 22.08 23.01 23.73 25.88 28.13 33.29



## 11.2 Biomass

Biomass is offered as a fuel for existing dedicated biomass power plants and potential (new) biomass

direct fired boilers. 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. (Se e section 5.3 for a discussion of the representation of biomass co

firing in EPA Base Case v.5.13.)

EPA Base Case v.5.13 uses biomass supply curves based on those in AEO 2013. These NEMS

- coal

demand region level supply curves are translated into state

- le vel supply curves for use in EPA Base Case

v.5.13 using proportions developed from agricultural statistic district (ASD) level intermediate AEO 2011

biomass supply curves. Plants demand biomass from the supply curve corresponding to the state in

which they are located. No inter

- state 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 model

run year. Each supply curve contains 74 price steps for each run year. The supply component of the

curve represents the aggregate supply in each state of four types of biomass fuels: urban wood waste

and mill residue, public forestry residue, private forestry residue and agricultural residue 121



### . The price

compo nent of the curve includes transportation costs, which AEO 122 

### assumed to be $12/ dry ton for all

four biomass types in all states. The supply curves represent the state

- specific delivered biomass fuel

cost at the plant gate. IPM adds a storage cost of $20/dry ton to each step of the agricultural residue

supply curve s to reflect the limited agricultural growing season 123



### .

121 

### The AEO 2013 biomass supply is described in the NEMS Renewable Fuels Module documentation,

http://www.eia.gov/forecasts/aeo/assumptions/pdf/renewable.pdf

122 

### http://www.eia.gov/forecasts/aeo/nems/documentation/renewable/pdf/m069(2013).pdf , p. 83.

123 

### http://www.extension.iastate.edu/agdm/crops/pdf/a1

- 22.pdf

, http://www.rand.org/content/dam/rand/pubs/technical_reports/2011/RAND_TR876.pdf

### 11

- 3 Excerpt from Table 11

- 2 Biomass Supply Curve s in EPA Base Case v. 5.13

This is a small excerpt of the data in Excerpt from Table 11

- 2 . The complete data set in spreadsheet

format can be downloaded via the link found at www.epa.gov/airmarket s/progsregs/epa

ipm/BaseCasev513.html

Year Biomass Supply Region Step Name Cost of Production

(2011$/MMBtu) Biomass Production

(TBtu/Year)



### 2016 AL BM01 0 0

2016 AL BM02 1.42 0.87

2016 AL BM03 1.74 0

2016 AL BM04 2.06 25.12

2016 AL BM05 2.38 0

2016 AL BM06 2.71 29

2016 AL BM07 3.03 0

2016 AL BM08 3.35 2.44

2016 AL BM09 3.67 0

2016 AL BM10 3.79 0

2016 AL BM11 4.00 2.78

2016 AL BM12 4.13 0.23

2016 AL BM13 4.32 0

2016 AL BM14 4.48 2.59

2016 AL BM15 4.64 3.83

2016 AL BM16 4.82 0.89

2016 AL BM17 4.96 0

2016 AL BM18 5.16 0.28

2016 AL BM19 5.28 9.94

2016 AL BM20 5.51 0.24

2016 AL BM21 5.61 0

2016 AL BM22 5.85 0.53

2016 AL BM23 5.93 18.47

2016 AL BM24 6.19 0.13

2016 AL BM25 6.25 0.74

2016 AL BM26 6.54 0

2016 AL BM27 6.57 0.74

2016 AL BM28 6.88 0

2016 AL BM29 6.89 0.74

2016 AL BM30 7.22 0.74

2016 AL BM31 7.22 0.14

2016 AL BM32 7.54 0.74

2016 AL BM33 7.57 0.07

2016 AL BM34 7.86 0.74

2016 AL BM35 7.91 0.10

2016 AL BM36 8.18 0.74

2016 AL BM37 8.25 0

2016 AL BM38 8.51 0.74

2016 AL BM39 8.60 0

2016 AL BM40 8.83 0.74

2016 AL BM41 8.94 0

2016 AL BM42 9.15 0.74

2016 AL BM43 9.28 0.28

2016 AL BM44 9.47 0.74

2016 AL BM45 9.62 0

2016 AL BM46 9.79 0.74

2016 AL BM47 9.97 0

2016 AL BM48 10.12 0.74

### 11

- 4

Year Biomass Supply Region Step Name Cost of Production

(2011$/MMBtu) Biomass Production

(TBtu/Year)



### 2016 AL BM49 10.31 0

2016 AL BM50 10.44 0.74

2016 AL BM51 10.65 0

2016 AL BM52 10.76 0.74

2016 AL BM53 11.00 0

2016 AL BM54 11.08 0.74

2016 AL BM55 11.34 0

2016 AL BM56 11.41 0.74

2016 AL BM57 11.68 0.13

2016 AL BM58 11.73 0.74

2016 AL BM59 12.03 0

2016 AL BM60 12.05 0.74

2016 AL BM61 12.37 0

2016 AL BM62 12.37 0.74

2016 AL BM63 12.69 0.74

2016 AL BM64 13.02 0.74

2016 AL BM65 13.34 0.74

2016 AL BM66 13.66 0.74

2016 AL BM67 13.98 0.74

2016 AL BM68 14.31 0.74

2016 AL BM69 14.63 0.74

2016 AL BM70 14.95 0.74

2016 AL BM71 15.27 0.74

2016 AL BM72 15.59 0.74

2016 AL BM73 15.92 0.74

2016 AL BM74 16.24 0.74

The supply curves in Excerpt from Table 11

- 2 represent the biomass available to both the electric and

non

- electric sectors. In any given region at any point in time the power sector demand from IPM has to be

combined with the non

- electr ic sector demand for biomass to obtain the price faced by the power sector.

The non

- electric sector demand distribution is by census division based on AEO 2013

. Table 11

- 3 shows

the non

- electric sector demand by run year and census divisions.

Table 11

- 3 Non

- Electric Biomass Demand by Census Division in EPA Base Case v.5.13

Non

- Electric Biomass Demand (TBtu)

Census Division 2016 2018 2020 2025 2030 2040

- 2050

1 CT, MA, ME, NH, RI, and VT

-- -- -- 1.87 0.80 0.51

2 NJ, NY, and PA

-- 0.0004 1.70 1.76 1.62 1.15

3 IL, IN, MI, OH, and WI 2.16 2.16 1.16 0.26 2.06 9.01

4 IA, KS, MN, MO, ND, NE, and SD 5.74 5.74 3.07 0.70 0.22 0.14

5 DE, FL, GA, MD, NC, SC, VA, and WV 6.80 6.80 5.13 7.69 5.38 3.98

6 AL, KY, MS, and TN

-- -- -- -- 2.92 1.71

7 AR, LA, OK, and TX 1.08 1.08 0.58 0.13 2.33 1.34

8 AZ, CO, ID, MT, NM, NV, UT, and WY 1.10 1.10 0.59 0.13 0.89 0.53

9 CA, OR, and WA 2.70 2.70 7.35 7.02 3.35 1.19



### Once the non

- electricity demand for biomass is factored in, biomass prices in EPA Base Case v.5.13 are

derived endogenously based on the aggregate power sector demand for biomass in each state. The

results are unique market

- clearing prices for each state. All plants using biomass from that state face the

same market

- clearing price.

### 11

- 5



## 11.3 Nuclear Fuel

The AEO 2013 price assumption for nuclear fuel is used as the nuclear fuel price assumption for 2016

- 2050 in EPA Base Case v.5.13. The 2016, 2018, 2020, 2025, 2030 and 2040 prices are 0.89, 0.90, 0.90,

0.96, 1.01 and 1.06 2011 $/MMBtu, respectively.



## 11.4 Waste Fuels

Among the “modeled fuels” shown for existing generating units in the NEEDS v.5.13 (the database which

serves as the source of data on existing units for EPA Base Case v.5.13), are a number of waste fuels,

including w aste c oal , petroleum c oke , f ossil w aste , n on

- f ossil w aste , t ires , and m unicipal solid w aste

(MSW)

. Table 11

- 4 describes these fuels, shows the extent of their representation in NEEDS , and then

indicates the assumptions adopted in EPA Base Case v. 5.13 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 Ca se

v. 5.13 . Potential new generating units that the model “builds” are not given the option to burn these 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 a re included under coal.

Table 11

- 4 Waste Fuels in NEEDS v. 5.13 and EPA Base Case v. 5.13

NEEDS

Description Supply and Cost

Modeled

Fuel Number

of Units Capacity

(MW) Modeled

By Assumed

Price

Waste

Coal 27 2,432 “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 fluidized

- bed combustors. For some uses,

waste coal may be pa rtially 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.gov/tools/glossary/index. cfm?id=W Supply

Curve

Based on

AEO

2013 AEO

2013

Petroleum

Coke 22 3,170 A residual product, high in carbon content and low in

hydrogen, from the cracking process used in crude oil refining Price

Point $83.3/Ton

Fossil

Waste 60 412 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 143 1,600 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

bi omass. Price

Point 0

Tires 2 46 Discarded vehicle tires. Price

Point 0

Municipal

Solid

Waste 179 2,279 “Residential solid waste and some nonhazardous commercial,

institutional, and industrial wastes.”

http://www.eia.doe.gov/glossary/index.cfm Price

Point 0



## 11.5 Fuel Emission Factors

Table 11

- 5 brings together all the fuel emission factor assumptions as implemented in EPA Base Case

v.5.13. For sulfur dioxide, chlorine, 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

### 11

- 6

detailed treatment of the topic. Nitrogen oxides ( NO

x 

### ) are not included in Table 11

- 5 because NO

x 

### emissions are a factor of the combustion process, and are not primarily fuel based.

Table 11

- 5 Fuel Emission Factor Assumptions in EPA Base Case v. 5.13

Fuel Type Carbon Dioxide

(lbs/MMBtu) Sulfur Dioxide

(lbs/MMBtu) a 

### Mercury

(lbs/TBtu) a 

### HCl

(lbs/MMBtu) a



### Coal

Bituminous 202.8

- 209.6 0.67

- 6.43 1.82

- 26.07 0.005

- 0.280

Subbituminous 209.2

- 215.8 0.58

- 1.90 2.03

- 8.65 0.006

- 0.014

Lignite 212.6

- 219.3 1.46

- 5.67 7.51

- 30.23 0.011

- 0.036

Natural Gas 117.1 0 0.00014 0

Fuel Oil

Distillate 161.4 0

- 2.65 0.48 0

Residual 173.9 1.04 0.48 0

Biomass

-- b 

### 0.08 0.57 0

Waste Fuels

Waste Coal 204.7 7.14 63.9 0.0921

Petroleum Coke 225.1 7.27 2.66 c 

### 0.0213

Fossil Waste 321.1 0.08 0 0

Non

- Fossil Waste 0 0 0 0

Tires 189.5 1.65 3.58 0

Municipal Solid Waste 91.9 0.35 71.85 0

Notes:

a Also see Table 5

- 9

b CO 2 emissions from biomass are not currently included in EPA Base Case v.5.13 . CO 2 emission factors are not currently

available for the four aggregate biomass fuels used in the biomass supply representation in EPA Base Case v. 5.13. EPA is

currently developin g methods to estimate the amount of CO 2 emitted on

- site during biomass co

- firing at coal fired power plants.

c A previous computational error in the mercury emission factor for petroleum coke as presented in Table 6

- 3 of the EPA report

titled Control of M ercury Emissions from Coal

- fired Electric Utility Boilers: Interim Report Including Errata, 3

- 21

- 02 was corrected

(from 23.18 lbs/TBtu to 2.66 lb/TBtu) based on re

- examination of the 1999 ICR data for petroleum coke and implementation of a

procedure for fl agging and excluding outlier values above the 95 percentile value.