NUREG-2242, Replacement Energy Cost Estimates for Nuclear Power Plants: 2020-2030 - Draft for Comment

NUREG-2242, Replacement Energy Cost Estimates for Nuclear Power Plants: 2020-2030 - Draft for Comment

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NUREG-2242 Replacement Energy Cost Estimates for Nuclear Power Plants:

2020-2030 Draft Report for Comment Office of Nuclear Material Safety and Safeguards

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SR-CR 10/2017

NUREG-2242 Replacement Energy Cost Estimates for Nuclear Power Plants:

2020-2030 Draft Report for Comment Manuscript Completed: October 2020 Date Published: December 2020 Prepared by:

K. Collison, ICF E. Gormsen, ICF R.F. Schofer, NRC Dr. Boddu Venkatesh, ICF Project Manager Pamela Noto, NRC Project Manager Amy Sharp, NRC Project Manager ICF Incorporated, L.L.C.

9300 Lee Highway Fairfax, VA 22031 Office of Nuclear Material Safety and Safeguards

COMMENTS ON DRAFT REPORT Any interested party may submit comments on this report for consideration by the NRC staff.

Comments may be accompanied by additional relevant information or supporting data. Please specify the report number NUREG-2242 in your comments, and send them by the end of the comment period specified in the Federal Register notice announcing the availability of this report.

Addresses: You may submit comments by any one of the following methods. Please include Docket ID NRC-2020-0266 in the subject line of your comments. Comments submitted in writing or in electronic form will be posted on the NRC website and on the Federal rulemaking website http://www.regulations.gov.

Federal Rulemaking Website: Go to http://www.regulations.gov and search for documents filed under Docket ID NRC-2020-0266.

Mail comments to: Office of Administration, Mail Stop: TWFN-7-A60M, U.S. Nuclear Regulatory Commission, Washington, DC 20555-0001, ATTN: Program Management, Announcements and Editing Staff.

For any questions about the material in this report, please contact: Pamela Noto, Project Manager, at 301-415-6795 or by e-mail at Pamela.Noto@nrc.gov.

Please be aware that any comments that you submit to the NRC will be considered a public record and entered into the Agencywide Documents Access and Management System (ADAMS). Do not provide information you would not want to be publicly available.

1 2 ABSTRACT 3 Replacement energy costs are estimated for the United States wholesale electricity market 4 regions with nuclear electricity-generating units over the 2020-2030 report period. These 5 estimates were developed to assist the U.S. Nuclear Regulatory Commission (NRC) in 6 evaluating proposed regulatory actions that (1) require safety modifications that might 7 necessitate temporary reactor outages and (2) reduce the potential for extended outages 8 resulting from a severe reactor accident. Estimates were calculated using ASEA Brown 9 Boveris (ABBs) PROMOD model and ICFs Integrated Planning Model for North America.

10 The models simulate dispatching a collection of generating units in merit order (i.e., lowest to 11 highest incremental cost of dispatch) until the regional power demand is met. Each generating 12 unit is characterized by the technology and fuel it uses to generate electricity, the units heat 13 rate, and the variable and fixed costs incurred in owning and operating the unit. To estimate the 14 replacement energy cost, the report models a Reference Case, in which all operational nuclear 15 power plants are generating, and an Alternative Case, in which a nuclear generating unit is 16 taken offline so that the next unit in merit order is dispatched to replace the lost generation. The 17 difference in market clearing prices between the two cases is the replacement energy cost.

iii

1 FOREWORD 2 This report presents updated estimates of replacement energy costs for nuclear 3 electricity-generating units in the United States. The information was developed principally for 4 the U.S. Nuclear Regulatory Commission (NRC) to use in its regulatory analyses. The NRC 5 conducts these analyses to examine the impacts of proposed regulations that require retrofitting 6 or safety modifications to nuclear power plants and to estimate the value of replacement energy 7 costs for regulatory actions that reduce the likelihood of severe reactor accidents. These 8 replacement energy cost estimates also could be used in NRC licensing actions and other 9 regulatory decisions.

10 The replacement energy cost estimates in this report were developed to update replacement 11 energy cost estimates for both short- and long-term outages provided in NUREG/CR-4012, 12 Volume 4, Replacement Energy Costs for Nuclear Electricity-Generating Units in the United 13 States: 1997-2001, published in September 1997, and NUREG/CR-6080, Replacement 14 Energy, Capacity, and Reliability Costs for Permanent Nuclear Reactor Shutdowns, published 15 in October 1993. This report provides replacement energy cost estimates between the 16 beginning of 2020 and the end of 2030. Given the length of time since these values had been 17 updated and the many market changes that have occurred in the electrical generation and 18 transmission industries due in part to deregulation, the NRC decided to develop a new approach 19 and new values.

20 The NRC contracted with ICF Incorporated, LLC, to assist in the replacement energy cost 21 analysis. The project identified key modeling parameters to be used in the replacement energy 22 cost analysis, as well as specific market areas and representative nuclear electricity-generating 23 units. Once the modeling parameters, market areas, and representative units were finalized, 24 replacement energy cost estimates were calculated to estimate the impacts of unit outages on 25 wholesale power prices in each region.

26 This analysis uses the North American Electric Reliability Corporation (NERC) areas, which 27 consist of eight regional entities used to improve the reliability of the bulk electric power system.

28 The members of the regional entities come from all segments of the electrical industry. Overlaid 29 on the NERC regional entities are regional electricity market areas, in which buyers and sellers 30 have traditionally bought and sold power and for which the transmission system operator can 31 accommodate such transactions.

32 To estimate the impact of a nuclear unit outage on the wholesale power price and, 33 consequently, the cost of replacing the lost electrical production from the unit, simulations were 34 performed to model the operation of specific power markets, with the selected first nuclear unit 35 included and then excluded from the market areas stock of operable generators.

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1 TABLE OF CONTENTS 2 ABSTRACT ................................................................................................................................. iii 3 FOREWORD ................................................................................................................................. v 4 LIST OF FIGURES ...................................................................................................................... ix 5 LIST OF TABLES ........................................................................................................................ xi 6 ABBREVIATIONS AND ACRONYMS....................................................................................... xiii 7 1 INTRODUCTION AND OBJECTIVE..................................................................................... 1-1 8 2 APPROACH ..........................................................................................................................2-1 9 2.1 Introduction ....................................................................................................................2-1 10 2.2 Modeling Methodology ................................................................................................... 2-1 11 2.3 Replacement Energy Cost Calculations ......................................................................... 2-3 12 2.4 Selection of Nuclear Generating Units to Be Taken Out of Service in Alternative 13 Cases ............................................................................................................................. 2-3 14 2.4.1 Approach to Selecting Nuclear Outage Units................................................... 2-4 15 2.4.2 Selected Units to be Taken Out of Service for Alternative Cases .................... 2-4 16 2.5 Summary of Key Input Assumptions .............................................................................. 2-5 17 2.5.1 Peak and Energy Demand Assumptions ....................................................... 2-10 18 2.5.2 Natural Gas Price Assumptions ..................................................................... 2-11 19 2.5.3 Energy and Environmental Policies ............................................................... 2-12 20 2.5.4 Recent and Firm Builds and Retirement Assumptions ................................... 2-13 21 2.5.5 New Unit Cost Assumptions2-15 22 2.6 Limitations of Model and Methodology..2-17 23 3 RESULTS..............................................................................................................................3-1 24 3.1 Market Price Impact and Replacement Energy Costs.................................................... 3-1 25 3.2 Use of Replacement Cost Estimates.............................................................................. 3-6 26 3.2.1 Example 1: Annual Energy Cost Calculation for a Region with Only One 27 Alternative Case ........................................................................... 3-7 28 3.2.2 Example 2: Annual Energy Cost Calculation for a Region with Two 29 Alternative Cases ......................................................................... 3-8 30 3.2.3 Example 3: Seasonal Energy Cost Calculation for a Region with Only 31 One Alternative Case ................................................................... 3-8 32 3.2.4 Example 4: Seasonal Energy Cost Calculation for a Region with Two 33 Alternative Cases ......................................................................... 3-9 34 4 REFERENCES ...................................................................................................................... 4-1 35 APPENDIX A OVERVIEW OF IPM ........................................................................................ A-1 36 APPENDIX B OVERVIEW OF PROMOD ............................................................................... B-1 37 APPENDIX C SELECTION OF NUCLEAR PLANTS FOR ALTERNATIVE CASES 38 (NEW ENGLAND)............................................................................................ C-1 vii

1 APPENDIX D EXISTING AND COMMITTED NUCLEAR UNITS ........................................... D-1 2 APPENDIX E DETERMINATION OF REGIONAL DEFINITIONS FOR 3 REPLACEMENT COST CALCULATIONS ..................................................... E-1 4 APPENDIX F

SUMMARY

OF ASSUMPTIONS .......................................................................F-1 5 APPENDIX G DETAILED REPLACEMENT ENERGY COSTS: 2020-2030. ......................... G-1 6 APPENDIX H STUDIES AND SOURCES OF DATA REVIEWED FOR 7 ASSUMPTIONS DEVELOPMENT .................................................................. .H-1 8

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1 LIST OF FIGURES 2 Figure 2-1 Overview of Modeling Methodology........................................................................ 2-2 3 Figure 2-2 FERC Order No. 1000 Transmission Planning Regions......................................... 2-9 4 Figure A-1 Hypothetical Chronological Hourly Load Curve and Seasonal Load Duration 5 Curve .................................................................................................................... A-8 6 Figure A-2 Stylized Depiction of a Six Segment Load Duration Curve Used in EPA 7 Platform v6 (EPA, 2019)....................................................................................... A-8 8 Figure A-3 Stylized Dispatch Order in Illustrative Load Segments.........................................A-9 9 Figure B-1: PROMOD Data Inputs and Outputs ................................................................... B-2 10 Figure C-1 ISO New England Pricing Zones .........................................................................C-2 11 Figure C-2 Simple Average Hub and Load Zone Prices, 2017 ..............................................C-3 12 Figure C-3 Day-Ahead Load-Weighted Prices........................................................................C-4 13 Figure E-1 North American Electric Interconnections ............................................................E-2 14 Figure E-2 Restructured Markets in North America ..............................................................E-3 15 Figure E-3 Western EIM .........................................................................................................E-5 16 Figure E-4 FERC Order No. 1000 Transmission Planning Regions ..................................... E-7 17 ix

1 LIST OF TABLES 2 Table 2-1 Nuclear Power Plants Selected for Analysis in Nuclear Outage Alternative 3 Cases ....................................................................................................................... 2-5 4 Table 2-2 Net Internal Peak Demand (MW) ........................................................................... 2-10 5 Table 2-3 Net Energy for Load (GWh) ................................................................................... 2-11 6 Table 2-4 Henry Hub Natural Gas Price Projections.............................................................. 2-12 7 Table 2-5 Recent and Firm Builds Assumptions for the Period 2018 through 2024 9 (MWe).....................................................................................................................2-13 10 Table 2-6 Recent and Firm Generation Retirements through 2030 (MWe) ........................... 2-14 11 Table 2-7 IPM Economic Builds through 2030 in Addition to Firm Generation Builds 12 (MW) .......................................................................................................................2-14 13 Table 2-8 IPM Economic Retirements through 2030 in Addition to Firm Generation 14 Retirements (MW) .................................................................................................. 2-15 15 Table 2-9 Performance and Unit Cost Assumptions for New Technologies .......................... 2-16 16 Table 3-1 Annual Replacement Energy Costs ......................................................................... 3-2 17 Table 3-2 Spring Season Replacement Energy Costs............................................................. 3-3 18 Table 3-3 Summer Season Replacement Energy Costs ......................................................... 3-4 19 Table 3-4 Fall Season Replacement Energy Costs ................................................................. 3-5 20 Table 3-5 Winter Season Replacement Energy Costs............................................................. 3-6 21 Table 3-6 Estimated Replacement Energy Costs for Postulated Nuclear Outages ................3-10 23 Table A-1 IPM Input .............................................................................................................. A-11 24 Table D-1 Existing and Committed Nuclear Generation Units ................................................ D-1 25 Table F-1 Peak Demand Assumptions: 2020-2030 ................................................................F-1 26 Table F-2 Energy Demand Assumptions: 2020-2030 .............................................................F-2 27 Table F-3 AEO Delivered Natural Gas Prices (2018$/MMBtu) ................................................F-3 28 Table F-4 State Renewable Portfolio Standards......................................................................F-5 29 Table F-5 State Renewable Portfolio Standard Solar Carve-Outs...........................................F-6 30 Table F-6 Regional Greenhouse Gas Initiative Cap and Trade Assumptions .......................F-7 31 Table F-7 CSAPR-Trading and Banking Rules.......................................................................F-8 32 Table F-8 Recent and Firm Builds (MW) ...............................................................................F-9 33 Table F-9 Recent and Firm Retirement Assumptions (MW) ..................................................F-11 34 Table F-10 Form EIA-860 Projected Net Generation Additions and Retirements.................F-12 35 Table F-11 IPM Economic Builds through 2030 (MW).............................................................F-13 xi

1 Table F-12 IPM Economic Retirements through 2030 (MW) .................................................F-14 2 Table F-13 IPM Projected Net Generation Additions and Retirements...................................F-16 3 Table F-14 Performance and Unit Cost Assumptions for Other New Technologies ...............F-16 4 Table G-1 ERCOT Annual and Seasonala Replacement Energy Costs ($/MWh) .................. G-1 5 Table G-2 ISO-NE Annual and Seasonal Replacement Energy Costs ($/MWh) .................... G-1 6 Table G-3 MISO Annual and Seasonal Replacement Energy Costs ($/MWh) ....................... G-2 7 Table G-4 NYISO Annual and Seasonal Replacement Energy Costs ($/MWh) ..................... G-2 8 Table G-5 PJM Annual and Seasonal Replacement Energy Costs ($/MWh) ......................... G-3 9 Table G-6 Southeast Annual and Seasonal Replacement Energy Costs ($/MWh) ................ G-3 10 Table G-7 SPP Annual and Seasonal Replacement Energy Costs ($/MWh) ......................... G-4 11 Table G-8 WECC Annual and Seasonal Replacement Energy Costs ($/MWh) ..................... G-4 12 Table H-1 Summary of Studies and Sources of Data Compared with AEO 2018 .................. H-1 xii

1 ABBREVIATIONS AND ACRONYMS 2 ABB ASEA Brown Boveri 3 ADS Anchor Data Set 4 AEO Annual Energy Outlook 5 ATB Annual Technology Baseline 6 BFB biomass-bubbling fluidized bed 7 Btu British thermal unit 8 CAGR compound annual growth rate 9 CAISO California Independent System Operator 10 CAMX WECC California/Mexico 11 CCS carbon capture and storage 12 CEC California Energy Commission 13 CPUC California Public Utility Commission 14 CRR congestion revenue right 15 CSP concentrated solar power 16 CSAPR Cross-State Air Pollution Rule 17 CO2 carbon dioxide 18 DOE U.S. Department of Energy 19 EIA U.S. Energy Information Administration 20 EIM Energy Imbalance Market 21 EPA U.S. Environmental Protection Agency 22 ERCOT Electric Reliability Council of Texas 23 ES&D Electricity Supply and Demand 24 FERC U.S. Federal Energy Regulatory Commission 25 FOM fixed operation and maintenance 26 FPL Florida Power and Light 27 FRCC Florida Reliability Coordinating Council 28 FTR financial transmission right 29 GMM Gas Market Model 30 GWh gigawatt-hour 31 HCL hydrochloric acid 32 IEPR Integrated Energy Policy Report 33 IPM Integrated Planning Model 34 IRP integrated resource plan 35 ISO independent system operator 36 ISO-NE ISO New England 37 kW kilowatt 38 kWh kilowatt-hour 39 Lb pound 40 LDC load duration curve xiii

1 LMP locational marginal prices 2 MATS Mercury and Air Toxics Standards 3 MISO Midcontinent Independent System Operator 4 MMBtu one million British thermal units 5 MRO Midwest Reliability Organization 6 MTEP MISO Transmission Expansion Plan 7 MTons million tons 8 MW megawatt 9 MWe megawatts electric 10 MWh megawatt-hour 11 NA not applicable 12 NAMGas North American market gas 13 NEMA Northeast Massachusetts/Boston 14 NEMS National Energy Modeling System 15 NERC North American Electric Reliability Corporation 16 NOx nitrous oxide 17 NPCC Northeast Power Coordinating Council 18 NRC U.S. Nuclear Regulatory Commission 19 NREL National Renewable Energy Laboratory 20 NTTG Northern Tier Transmission Group 21 NWPP Northwest Power Pool 22 NWPP-US Northwest Power Pool-United States 23 NYISO New York Independent System Operator 24 NYMEX New York Mercantile Exchange 25 O&M operation and maintenance 26 OASIS Open-Access Same-Time Information System 27 OATT open-access transmission tariff 28 PCM production cost model 29 PROMOD PROMOD model 30 PJM PJM Interconnection 31 PV photovoltaic 32 REC renewable electricity credit 33 RF ReliabilityFirst Corporation 34 RGGI Regional Greenhouse Gas Initiative 35 RMRG Rocky Mountain Reserve Group 36 RPS renewable portfolio standard 37 RTO regional transmission organization 38 SAE Statistically Adjusted End-use model 39 SCRTP South Carolina Regional Transmission Planning 40 SERC SERC Reliability Corporation 41 SERTP Southeastern Regional Transmission Planning xiv

1 SO2 sulfur dioxide 2 SPP Southwest Power Pool 3 SRSG Southwest Reserve Sharing Group 4 STP South Texas Project 5 TCC transmission congestion contract 6 TRE Texas Reliability Entity, Inc.

7 TSO transmission system operator 8 TVA Tennessee Valley Authority 9 VOM variable operation and maintenance 10 WECC Western Electricity Coordinating Council 11 Yr year 12 ZEC zero-emission credit xv

1 1 INTRODUCTION AND OBJECTIVE 2 The U.S. Nuclear Regulatory Commission (NRC) performs analyses to support a variety of 3 regulatory actions that affect nuclear power plant licensees. These include actions that reduce 4 risks or enhance the safety of nuclear power plants. Some of these regulatory actions may 5 require that a nuclear generating unit be taken out of service for a period of time to implement 6 the required change; other regulatory actions may result in reduced outages for the unit. The 7 change in energy cost represents one factor that the NRC considers when deciding to require a 8 regulatory change. This report updates previous estimates of long-term and short-term, 9 plant-specific replacement energy costs contained in NUREG/CR-6080, Replacement Energy, 10 Capacity, and Reliability Costs for Permanent Nuclear Reactor Shutdowns, (NRC, 1993) and 11 NUREG/CR-4012, Replacement Energy Costs for Nuclear Electricity-Generating Units in the 12 United States: 1997-2001, (NRC, 1997). As described below, this report modeled the 13 operation of the U.S. electricity markets over the 2020-2030 report period and calculated the 14 replacement energy costs for regions with nuclear power plants.

15 This report estimates the replacement energy costs for a range of regions, years, and 16 scenarios. It presents the inputs and generation cost outlook used as the basis for assumptions 17 for the replacement energy cost analysis 2020-2030 period. The assumptions are based on 18 information from the U.S. Energy Information Administrations (EIA) 2019 Annual Energy 19 Outlook (AEO), and many other publicly available resources from regional market operators, 20 interconnection planning collaboratives, and public utility commissions. The report provides 21 context and a more detailed understanding of the use of these assumptions, their potential 22 impact on the replacement energy cost estimates, and information on the overall approach and 23 the modeling methodology.

24 This report covers the following items:

25 26

• A discussion of the approach, including the methodology, input assumptions, the basis 27 for estimating replacement energy costs (an approach for methods capturing a range of 28 replacement cost estimates), and the differences in approach in comparison with the 29 previous NRC method (Section 2).

30 31

• A discussion of the results of the analysis and a demonstration of the use of the results 32 to calculate replacement energy costs for power plants in different regions (Section 3).

33 34

• A summary of the structure and capabilities of the two models ICFs Integrated 35 Planning Model (IPM)1 and ABBs PROMOD model (PROMOD)2 (Appendices A and 36 B).

37 38

• Additional detail on the methodology, assumptions, and results (Appendices C to G),

39 including the basis for selecting nuclear power plants to analyze for the replacement 40 energy cost calculation (Appendix C), existing and committed nuclear units 41 (Appendix D), regional definitions (Appendix E), detailed input assumptions 42 (Appendix F), detailed results (Appendix G), and summary of studies (Appendix H).

43 All cost values in this report are in nominal dollars unless otherwise specified.

1 The IPM is an ICF model used in support of ICFs public and private sector clients. IPM is a registered trademark of ICF Resources, L.L.C.

2 PROMOD is a product of ABB licensed by ICF. The version used is PROMOD IV.

1-1

1 2 APPROACH 2 2.1 Introduction 3 The NRCs regulatory analyses can examine actions that reduce risks or enhance the safety of 4 nuclear power plants and that may require that a nuclear generating unit be taken out of service 5 for some time period to implement the required change; alternatively, some regulatory actions 6 may result in reduced outages for the unit. These actions would result in changes in energy 7 generation from these units. The NRCs regulatory analyses therefore require estimates of the 8 costs of replacement energy to support cost-benefit analyses. The goal of this update is to 9 develop replacement energy costs to be used in support of regulatory analyses. This report 10 simulated the operation of the U.S. electricity markets over the 2020-2030 analysis period and 11 calculated the wholesale market prices for regions with nuclear power plants. Market clearing 12 prices represent the price at which supply equals demand for the forecasted period and the 13 specified power market.

14 In the context of this report, the term replacement energy cost refers to the difference in 15 forecasted market clearing prices between a Reference Case with the nuclear power plant 16 operating and an Alternative Case with the plant taken out of service. In this Alternative Case, 17 additional energy generation will be dispatched to replace the generation that is no longer 18 provided by the nuclear unit. This report provides projections of replacement energy costs (in 19 dollars per megawatt-hour [$/MWh]) for regions within the U.S. electricity system over the 2020 20 to 2030 analysis horizon. This report summarizes the analysis that is used to develop the 21 replacement energy cost estimates.

22 Replacement energy costs are based on the average electricity price for the duration of the 23 outage. This report provides both annual and seasonal replacement energy costs because of 24 seasonal variations in electricity prices in the electricity markets. Factors that affect seasonal 25 variations include fuel price, demand, generator unit availability, generator maintenance 26 scheduling, and renewable resource availability. For example, electricity prices are typically 27 higher during the summer due to higher demand. The appropriate replacement energy cost can 28 be applied depending on the period of the outage.

29 The remainder of this chapter describes the modeling approach, including the models applied 30 (Section 2.2); the calculations of the replacement energy costs (Section 2.3); the approach used 31 to identify the nuclear units to be taken out of service in each Alternative Case (Section 2.4);

32 and discussion of the key assumptions underlying the modeling (Section 2.5). Chapter 3 33 presents the results for the analysis.

34 2.2 Modeling Methodology 35 To determine the clearing price in the Reference and Alternative Cases, the report simulated the 36 operation of the U.S. electricity market using PROMOD, a production cost model (PCM) that 37 determines the price of electricity in each location based on the economic dispatch of 38 generation plants subject to operational constraints and limitations of the transmission system.

39 The report divided the U.S. electricity markets into eight regions as described in Section 2.5.

40 The report assessed the impact of the loss of a nuclear generating unit on energy prices in the 41 power market region in which it is located. To determine the impact, the report modeled a 42 Reference Case and up to two Alternative Cases for each region. Market clearing prices for a 43 selected number of years within the report period were modeled for each of these cases and in 44 each region.

2-1

1 Because the impact of a nuclear power plant outage within a region could varydepending on 2 the units size, its location relative to load, or significant transmission constraintsthe report 3 modeled two Alternative Cases for regions where significant variations in the impact on market 4 clearing prices as a result of these factors was expected, to estimate a range for the 5 replacement cost. Where the effect was expected to be similar, regardless of which nuclear 6 generation unit was taken out of service, only one Alternative Case was used. The nuclear 7 generation units assessed in the Alternative Cases are shown in Table 2-1 in Section 2.4.2.

8 Because PROMOD, a PCM, does not incorporate capacity expansion investment 9 decision-making capability, entry and exit (investment) decisions for future years were 10 determined exogenously using IPM. IPM is a long-term investment planning and production 11 costing model that considers fuel, emission allowance, and renewable electricity credit (REC) 12 prices. The new investments and retirements decisions from the IPM analysis were 13 incorporated into PROMOD and are reflected in the analysis to determine the replacement 14 energy costs.

15 Figure 2-1 provides a conceptual overview of the modeling methodology.

16 Figure 2-1 Overview of Modeling Methodology 17 The analysis accounted for legislation enacted in recent years in Illinois, New York, and New 18 Jersey. These states provide price support for specific nuclear generating units that were at risk 19 of being retired early due to economic factors. The report modeled the zero-emission credit 20 (ZEC) programs in IPM by explicitly requiring affected nuclear generation units to remain in the 21 market and continue to operate for the duration of the applicable ZEC program. AEO 2019 22 (DOE, 2019) uses a similar approach. In PROMOD, the report ensured that units under ZEC 23 programs dispatched fully.

24 The report developed replacement energy costs for the 2020-2030 period; however, in the 25 interest of computational tractability, not all years were modeled explicitly. The report modeled 26 five run years in PROMOD: 2020, 2021, 2023, 2025, and 2030. Results for intermediate years 27 were linearly interpolated.

28 Summaries of the structure and capabilities of the two models IPM and ABBs PROMODare 29 provided in Appendix A and Appendix B.

2-2

1 2.3 Replacement Energy Cost Calculations 2 The report modeled the cases depending on the expected impact of the outage of different 3 nuclear generating units in the region. In the first case, the report selected the unit likely to have 4 the least impact on energy prices, referred to as the Least Critical Unit. In the second case, the 5 report selected the unit expected to have the largest impact, referred to as the Most Critical Unit.

6 The basis for selecting these units is described in Section 2.4.

7 The report calculated the replacement energy cost for a region as the change in annual average 8 energy price between the Reference Case and each Alternative Case. For regions with two 9 Alternative Cases, the report developed a replacement energy cost range with a minimum value 10 based on the impact of the Least Critical Unit and a maximum value based on the impact of the 11 Most Critical Unit. The report provided annual and seasonal replacement energy cost values for 12 each region. For each region and year, the report developed hourly energy prices and 13 calculated annual average energy prices as a simple average of hourly prices. By comparing 14 energy prices from the Reference Cases to those of the Alternative Cases, the report 15 determined the impact of the loss of nuclear generating units on electricity market prices and 16 calculated the replacement energy cost for each region.

17 To account for seasonal variations in energy prices, the report provided seasonal replacement 18 energy costs. Average seasonal prices were calculated by averaging the appropriate hourly 19 prices. The impact of a nuclear generation outage that is concentrated in a particular season 20 might be higher or even lower than the annual average, depending on the season. The impact 21 of such outages can be assessed using seasonal replacement energy costs.

22 The seasonal values were calculated as the change in average energy price between the 23 Reference Case and each Alternative Case for the months within the season. The seasons 24 were defined as:

25 26

• Winter: December (of prior year), January, February 27 28

• Spring: March, April, May 29 30

• Summer: June, July, August 31 32

• Fall: September, October, November 33 The impact of an outage of a specific nuclear generation unit on energy costs within its region 34 can be assessed by multiplying the replacement energy cost (in $/MWh) by the units loss of 35 generation (MWh). For long duration outages of several months to years, the impact can be 36 assessed using the annual replacement energy costs. For shorter duration outages of a few 37 months to a couple of seasons, the seasonal replacement energy costs can be used to derive a 38 replacement cost impact that is more reflective of the particular season in which the nuclear 39 generation unit is expected to be out of service. Section 3.2 describes how to apply the 40 replacement energy costs.

41 2.4 Selection of Nuclear Generating Units to Be Taken Out of Service in 42 Alternative Cases 43 A list of the nuclear generating units modeled is shown in Table D-1 in Appendix D. The report 44 assumes that a unit is retired based on the announced plans by their owners. The remaining 2-3

1 nuclear units were considered as candidates for being taken out of service in the Alternative 2 Cases. This section describes the approach used to select nuclear outages for the Alternative 3 Cases and shows the unit(s) selected for each region.

4 2.4.1 Approach to Selecting Nuclear Outage Units 5 The units for the Alternative Cases were selected according to the key criteria that determine 6 how the impact of unit outages on electricity market prices would vary. These criteria include 7 the location of the unit relative to congestion in the region, the size of the nuclear generating 8 unit, and proximity to load centers.

9 10

• Location relative to congestion in the region. Congestion occurs on the transmission 11 system when restrictions prevent the use of the most economic power plants to serve 12 load. When congestion occurs, less economic generation is dispatched out of economic 13 merit to serve load. This results in prices being higher in the areas limited by congestion 14 compared with areas with little or no congestion. Within a region, generators in 15 congested areas would therefore have higher replacement energy costs than would 16 those in less congested locations.

17 18

• Size of the generating unit. Older nuclear units typically are smaller than 1,000 19 megawatts electric (MWe), whereas newer units are greater than 1,000 MWe in size.3 A 20 smaller nuclear unit would require less replacement energy than would a larger unit and 21 could have a lower impact on the generation stack. The replacement cost could 22 therefore be lower for a smaller unit, all else being equal.

23 24

• Proximity to load centers. Wholesale electricity prices are usually higher in load 25 centers because they have higher demand than other locations and they are relatively 26 far from generation centers. Transmission capability limitations and transmission losses 27 in delivering power over long distances generally result in relatively higher prices in load 28 centers. It is likely that generators closer to load centers would have higher replacement 29 energy costs than would those farther away.

30 The modeling approach covered the entire contiguous U.S. electricity markets in the lower 31 48 states but focused on regions with nuclear power plants to produce the replacement cost 32 estimates. A detailed description of the selection approach along with an example is provided in 33 Appendix C. In general, the report applied expertise and judgment that leverages past and 34 ongoing power sector modeling and analysis work to assess how each unit fit the criteria and to 35 identify the appropriate unit(s) to model.

36 2.4.2 Selected Units to be Taken Out of Service for Alternative Cases 37 Table 2-1 shows the nuclear generation units assessed in the Alternative Cases. Because the 38 impact of the outage of a nuclear generating unit in the Electric Reliability Council of Texas 39 (ERCOT) is expected to be similar regardless of the unit that is out of service, only one 40 generation unit, the South Texas Project (STP) Unit 1, was selected for the Alternative Case. In 41 other regions the impact could vary significantly due to factors such as size and location of the 42 unit, therefore, two generation units were selected. For example, in New England, Millstone 43 Unit 2 was expected to have the least impact, due to its relatively smaller size. The relatively 44 larger Millstone Unit 3 represented units that would have the most impact on replacement 45 energy costs. The assessment of regional conditions suggests that the impact of the Seabrook 3

NUREG-1350, vol. 31, Information Digest 2019-2020 (NRC, 2019).

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1 Unit would be similar to that of Millstone Unit 3 (see Appendix C), therefore it was sufficient to 2 analyze Millstone Unit 2 and Millstone Unit 3 to determine the range of replacement energy 3 costs for the region.

4 Appendix D provides the complete list of existing and committed nuclear generation plants 5 modeled in the report.

6 Table 2-1 Nuclear Power Plants Selected for Analysis in Nuclear Outage Alternative 7 Cases Expected Nuclear Power Plant, Unit Capacity Primary Driver Region Impact on Name (MWe) of Impact Market Prices ISO-NE Millstone Power Station, Unit 2 868 Least Impact Unit size ISO-NE Millstone Power Station, Unit 3 1,220 Most Impact Unit size Location, Palo Verde Nuclear WECC 1,314 Most Impact proximity to load Generating Station, Unit 2 center Proximity to load WECC Columbia Generating Station 1,180 Least Impact center ERCOT South Texas Project, Unit 1 1,280 N/A N/A Nine Mile Point Nuclear NYISO 1,287 Most Impact Unit size Station, Unit 2 R E Ginna Nuclear Power NYISO Plant 582 Least Impact Unit size Wolf Creek Generating SPP 1,175 Most Impact Unit size Station, Unit 1 SPP Cooper Nuclear Station 772 Least Impact Unit size Limerick Generating Station, Unit size, PJM 1,122 Most Impact Unit 2 location Quad Cities Nuclear Power Unit size, PJM Station, Unit 1 908 Least Impact location Unit size, MISO Clinton Power Station, Unit 1 1,065 Most Impact location Prairie Island Nuclear MISO Generating Plant, Unit 2 519 Least Impact Unit size Vogtle Electric Generating Unit size, Southeast 1,152 Most Impact Plant, Unit 2 location Joseph M Farley Nuclear Unit size, Southeast Plant, Unit 1 874 Least Impact location 8 2.5 Summary of Key Input Assumptions 9 A broad range of input assumptions is required for the modeling used to support a report of this 10 kind. This includes information on the generating equipment (e.g., capacity, fixed and variable 11 operating and maintenance costs, operating constraints and regulatory limits), electric energy 12 and peak demand, fuel prices, the cost and performance characteristics of new technologies, 2-5

1 and national and state-level laws and regulations that affect operations (e.g., emissions limits, 2 and renewable portfolio standards [RPSs]4), among other inputs.

3 The report collected data and developed assumptions to represent conditions in the Reference 4 Cases and Alternative Cases, which included:

5 6

• Regional definitions for replacement cost calculations 7

8

• Peak demand and energy demand 9

10

• Natural gas prices 11 12

• Energy and environmental policies 13 14

• Recent and firm generation builds 15 16

• Recent and firm generation retirements 17 18

• New unit costs 19 All assumptions affect the results of the modeling and analysis; however, not all factors have a 20 significant impact on the replacement energy costs. To determine the most appropriate sources 21 to use to develop assumptions, the report focused on three parameters that are important for 22 the determination of replacement energy costs. The parameters included natural gas prices, 23 electricity demand, and technology cost and performance as discussed below.

24 25

• Natural gas prices. Over the past few years, natural gas has become the fuel of the 26 marginal unit of generation in most electricity markets and, thus, a major determinant of 27 electricity prices. In addition, most conventional generation plants that are currently 28 planned or under development are natural gas-fired units. In some markets, natural 29 gas-fueled plants are virtually the only non-renewable power plants currently under 30 active development. This indicates that the correlation between natural gas prices and 31 electricity prices, as well as the role of natural gas prices in setting electricity prices, is 32 likely to continue.

33 34

• Electricity demand. In addition to natural gas prices, the level of demand also affects 35 marginal energy prices. For a given hourly demand level, the market operator will 36 dispatch a subset of available generating units that will minimize the total cost of meeting 37 that load. The variable cost (fuel cost, emission allowance cost, and variable operation 38 and maintenance [VOM] cost) of operating the marginal unit (most expensive unit) sets 39 the marginal energy price in that region in that hour. As the marginal unit will change as 40 the level of demand changes, electricity demand assumptions are a critical input for 41 estimating the replacement costs of energy.

42 43

• Technology cost and performance. The cost and performance of new units are also 44 an important input for calculating the replacement energy costs. However, unlike natural 45 gas prices and electricity demand assumptions that directly impact the price setting for 4

Renewable portfolio standards are policies designed to increase the use of renewable energy sources for electricity generation. These policies require or encourage electricity suppliers to provide their customers with a stated minimum share of electricity from eligible renewable resources.

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1 energy prices as discussed above, the cost and performance of new technologies have 2 an indirect impact on the marginal energy price calculation. This is because these 3 assumptions affect the generating capacity that will be built and the generating units that 4 will retire in the future. These entry and exit decisions will change the mix of resources 5 available in a region and thus the marginal unit and its associated cost as well.

6 The assumptions were derived primarily from public sources, including:

7 8

• U.S. Department of Energy (DOE) Energy Information Administration (EIA) AEO 2019 9 Reference Case (DOE, 2019) 10 11

• North American Electric Reliability Corporation (NERC) Electricity Supply and Demand 12 (ES&D), December 2018 release (NERC, 2018) 13 14

• Environmental Protection Agency (EPA) Power Sector Modeling Platform v6 (EPA, 15 2019) 16 17

• National Renewable Energy Laboratory (NREL) Annual Technology Baseline (ATB),

18 2018 (NREL, 2018) 19 20

• EIA Form 860M (February 2019 version) (EIA, 2019) 21 22 In addition to these sources, national, interconnection-wide, inter-regional, and regional studies 23 were reviewed. Because the replacement cost report calculates costs at the regional level, 24 studies that were at least regional in scope were preferred. In addition, because replacement 25 energy costs are based on electricity prices derived from a production cost analysis, the report 26 focused on studies that included production cost or related economic assessments that would 27 have the relevant economic input parameters required to implement the replacement cost 28 simulations. Appendix H provides a summary of the studies reviewed.

29 The EIAs AEO 2019 Reference Case (DOE, 2019) was selected as the basis for development 30 of natural gas price assumptions because the AEO is national in scope and has all the data 31 elements needed for this report. Furthermore, the AEO is publicly available and readily 32 accessible; and its assumptions are used for regional, inter-regional, and national energy policy 33 studies. Also, the AEO natural gas price projections are used as the basis for gas prices in 34 several regional and inter-regional studies.

35 The NERC ES&D 2018 was selected as the source for electricity demand assumptions because 36 NERC and other operators use these assumptions in their analyses, and their regional structure 37 is consistent with the regions in PROMOD and IPM (NERC, 2018).

38 The AEO 2019 was used for capital cost assumptions for fossil and nuclear technologies, and 39 the NREL ATB 2018 was used for solar and wind technologies (DOE, 2019; NREL, 2018). Most 40 studies reviewed use the NREL ATB 2018 for new technology costs, and of the studies 41 examined, the NREL ATB was the only report, other than the AEO, that provided a full dataset 42 of new technology costs (NREL, 2018).

43 For other assumptions, EPA Platform v6 (EPA, 2019) was used. The firm builds and firm 44 retirements were based on the February 2019 version of EIA Form 860M (EIA, 2019).

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1 The remainder of this section provides a summary of the key assumptions. Additional details 2 are provided in Appendix F.

3 The report divided the U.S. electricity markets into eight regions and determined the 4 replacement energy cost for each region. The regional definitions used for the replacement cost 5 calculations were based on the U.S. Federal Energy Regulatory Commission (FERC) Order 6 No. 1000 (FERC, 2012) planning regions shown in Figure 2-2. In most areas with competitive 7 markets, the report used regional definitions that were coincident with the existing competitive 8 markets.5 For example, the New York Independent System Operator (NYISO) market was 9 considered as a single region. Therefore, the replacement energy costs determined for NYISO 10 would apply to all nuclear power plants located in that market. The exception was the California 11 Independent System Operator (CAISO) market. CAISO operates the Western Energy 12 Imbalance Market (Western EIM), a real-time energy market, which includes eight non-CAISO 13 utilities or balancing authorities, with seven entities planning to participate by 2022. The 14 Western EIM covers portions of almost all the states in the Western Interconnection. Because 15 of the scope of the Western EIM, the report considered the U.S. portion of the Western 16 Interconnection as a single region for the purposes of the calculation of replacement energy 17 cost.

18 The remaining area is the southeastern United States, which is served by vertically integrated 19 utilities in regulated markets. Although utilities serve most of their demand with generation 20 located within their service territories, there are frameworks under which utilities in regulated 21 markets can source power from locations outside their service territories in the event of 22 shortages. Further, regional transmission planning processes established under FERC Order 23 No. 1000 (FERC, 2012) include economic transmission planning studies that allow market 24 participants to request studies for the feasibility of long-term economic power transactions. The 25 three entities responsible for regional transmission planning in the southeastern U.S. under 26 FERC Order No. 1000 (FERC, 2012) are Florida Reliability Coordinating Council (FRCC), South 27 Carolina Regional Transmission Planning (SCRTP), and Southeastern Regional Transmission 28 Planning (SERTP). Because of the potential for interactions between the regions, the report 29 considered the regulated markets in the southeastern United States as a single region for the 30 purposes of the calculation of replacement energy cost.

5 The competitive markets are the seven regional transmission operator (RTO) or independent system operator (ISO) markets: CAISO, ERCOT, ISO New England, Midcontinent Independent System Operator, New York Independent System Operator, PJM Interconnection, and Southwest Power Pool.

2-8

1 2 Figure 2-2 FERC Order No. 1000 Transmission Planning Regions 3 Note: The map was annotated with black dots to show the approximate locations of existing nuclear 4 power plants. Heavy black lines have been added to distinguish the location of the eight regional 5 definitions modeled in this report.

6 Source: (FERC, 2020) 7 8 The eight regions specified for the replacement energy cost report were defined as:

9 10 1. ERCOT 11 12 2. ISO New England (ISO-NE) (ISO, 2018) 13 14 3. Midcontinent Independent System Operator (MISO) 15 16 4. NYISO 17 18 5. PJM Interconnection (PJM) 19 20 6. Southwest Power Pool (SPP) 21 22 7. Southeast, comprising FRCC, SCRTP, and SERTP 23 2-9

1 8. Western Electricity Coordinating Council (WECC), comprising CAISO, ColumbiaGrid, 2 Northern Tier Transmission Group (NTTG) and WestConnect 3 The report modeling reflected the current operation of the electricity markets. Regions in 4 PROMOD were defined consistent with the current representation in the markets. Market prices 5 from PROMOD analysis were aggregated up to the eight regions for the calculation of the 6 replacement energy costs. For example, in WECC, the report modeled utility areas in CAISO 7 as a single market administered by the system operator, with appropriate financial hurdles 8 between the region and its neighbors. Other utility areas were modeled similarly. This captured 9 the actual operation of the electricity markets. However, the replacement energy cost was 10 calculated for the entire WECC region. Prices at all nodes in WECC, including in CAISO, were 11 aggregated to determine the replacement energy cost for WECC.

12 A more detailed description of the report approach is provided in Appendix E.

13 2.5.1 Peak and Energy Demand Assumptions 14 The net internal peak demand assumptions for selected years and compound annual growth 15 rate (CAGR) are shown in Table 2-2. The net internal demand is the maximum hourly demand 16 within a given year after removing interruptible demand6. Peak demand assumptions for all 17 years is provided in Section F.1 of Appendix F.

18 Table 2-2 Net Internal Peak Demand (MW)

Year Region (Assessment Area) 2020 2025 2030 CAGR (Percent)

FRCC (FRCC) 45,608 48,290 50,534 1.03 Midwest Reliability Organization [MRO]

119,303 121,289 122,842 0.29 (MISO)

Northeast Power Coordinating Council 24,878 24,239 24,190 -0.28

[NPCC] (New England)

NPCC (New York) 31,759 31,429 31,559 -0.06 ReliabilityFirst Corporation [RF] (PJM) 144,287 147,118 151,070 0.46 SERC Reliability Corporation (SERC-42,907 44,930 47,361 0.99 East)

SERC (SERC-North) 39,935 40,477 41,121 0.29 SERC (SERC-Southeast) 45,983 47,201 46,764 0.17 SPP (SPP) 52,044 53,965 55,603 0.66 Texas Reliability Entity, Inc. [TRE]

73,706 80,677 87,666 1.75 (ERCOT)

WECC (Northwest Power Pool-United 49,075 50,767 52,343 0.65 States [NWPP]-US)

WECC (Rocky Mountain Reserve Group 12,637 13,549 14,394 1.31

[RMRG])

6 Interruptible demand is demand that the end-use customer agrees with its Load-Serving Entity via contract or agreement can be curtailed.

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1 Table 2-2 Net Internal Peak Demand (MW) (continued)

Year Region (Assessment Area) 2020 2025 2030 CAGR (Percent)

WECC (Southwest Reserve Sharing 24,298 26,650 28,788 1.71 Group [SRSG])

WECC (WECC California/Mexico 50,132 51,584 52,031 0.37

[CAMX])

2 Source: (NERC, 2018) 3 4 The net energy for load demand assumptions for selected years and CAGR between years 5 2020 and 2030 are shown in Table 2-3. Net energy for load is the projected annual electric grid 6 demand, prior to accounting for intra-regional transmission and distribution losses. Section F.1 7 of Appendix F shows the energy demand assumptions for all years of the report period.

8 Table 2-3 Net Energy for Load (GWh)

Year Region (Assessment Area) 2020 2025 2030 CAGR (Percent)

FRCC (FRCC) 236,779 245,769 253,486 0.68 MRO (MISO) 669,881 681,949 694,663 0.36 NPCC (New England) 120,395 115,594 113,400 -0.60 NPCC (New York) 155,567 153,454 153,518 -0.13 RF (PJM) 808,638 824,140 849,551 0.49 SERC (SERC-East) 214,026 221,904 233,819 0.89 SERC (SERC-North) 214,064 214,084 215,733 0.08 SERC (SERC-Southeast) 247,542 253,679 253,860 0.25 SPP (SPP) 259,341 274,090 281,854 0.84 TRE (ERCOT) 392,609 439,094 487,269 2.18 WECC (NWPP-US) 294,092 301,503 308,586 0.48 WECC (RMRG) 69,671 74,874 80,099 1.40 WECC (SRSG) 111,351 121,139 129,981 1.56 WECC (CAMX) 267,722 271,314 272,334 0.17 9 Source: (NERC, 2018) 10 11 2.5.2 Natural Gas Price Assumptions 12 Natural gas price assumptions are based on the AEO 2019 Reference Case (DOE, 2019) price 13 projections. The Henry Hub natural gas price projections for the run years are shown in Table 14 2-4. The Henry Hub natural gas price projections remain below $4/MMBtu through the report 15 period, although growing demand in domestic and export markets led to increasing prices. The 16 Henry Hub price is projected to be $3.00/MMBtu in 2021, increasing to $3.76/MMBtu in 2030.

17 EIA also provides delivered natural gas prices for the regions modeled for the AEO. The report 18 used the EIAs delivered natural gas price projections and its energy markets expertise to 2-11

1 develop price projections for the regions. Section F.2 of Appendix F shows the delivered natural 2 gas price projections for the AEO regions.

3 Table 2-4 Henry Hub Natural Gas Price Projections Year Henry Hub Natural Gas Price (2018$/MMBtu) 2020 3.08 2021 3.00 2023 3.13 2025 3.53 2030 3.76 4 Source: (DOE, 2019) 5 6 2.5.3 Energy and Environmental Policies 7 The power sector is subjected to a variety of clean energy policies that include RPSs, tax credits 8 for new solar and wind units, and ZECs for selected existing nuclear units. The report modeled 9 the RPSs and tax credits for new solar and wind units explicitly in IPM. These policies affect the 10 generating technologies chosen during the 2020-2030 period. The renewable energy credit 11 prices were an output of IPM.

12 In addition, the analysis accounted for clean energy legislation that recently passed in Illinois, 13 New York, and New Jersey, which provides price support in the form of ZECs for nuclear units 14 that are at risk of early closure because of declining profitability. The revenue a nuclear 15 generation plant receives from a ZEC program is assumed to enable the plant to continue to 16 operate for the duration of the program. The following ZEC programs are modeled:

17 18

• The New York Clean Energy Standard, established in 2016, creates ZECs that apply to 19 Fitzpatrick, Ginna, and Nine Mile Point nuclear units. The New York load-serving 20 entities are responsible for purchasing ZECs equal to their share of the statewide load, 21 providing an additional revenue source to the nuclear units holding the ZECs. The 22 program is set to cover a 12-year term.

23 24

• Illinois Future Energy Jobs Bill, passed in 2017, also creates a ZEC program covering a 25 10-year term for Clinton and Quad Cities Units 1 and 2.

26 27

• New Jersey has established a ZEC program. Salem Nuclear Generating Station Units 1 28 and 2 and Hope Creek Generating Station are eligible to receive payments during the 29 year of implementation and in the three following years and may be considered for 30 additional three-year renewal periods thereafter. Only the first three years of the 31 program are modeled in the report.

32 The analysis accounted for environmental regulations that were approved and enacted as of 33 2018. These include the Regional Greenhouse Gas Initiative (RGGI), the Cross-State Air 34 Pollution Rule (CSAPR), and the Mercury and Air Toxics Rule (MATS). Policies under 35 discussion but not enacted (e.g., Pennsylvania nuclear subsidies) were not modeled.

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1 In PROMOD, generation units bid their marginal cost. The report explicitly modeled 2 environmental compliance costs and subsidies that affect the variable costs of the generation 3 units. Emission allowance prices were modeled as adders to the variable cost of units.

4 Production tax credits for wind generators were modeled as discounts to the variable cost where 5 applicable, enabling them to bid low to negative marginal costs. Nuclear power plants covered 6 under a ZEC program were modeled in PROMOD so that these plants were connected to the 7 grid in accordance with their availability.

8 Section F.3 of Appendix F provides input assumptions on renewable policies and environmental 9 regulations.

10 2.5.4 Recent and Firm Builds and Retirement Assumptions 11 The report incorporates the generating unit inventory including the operating as well as firm 12 build units as of February 2019. The analysis modeled projections of firm generation builds and 13 retirements based on Form EIA-860 (EIA, 2019),7 generator-level specific information about 14 existing and planned units.

15 Generation addition and retirement assumptions based on Form EIA-860 data are shown in 16 Table 2-5 and Table 2-6, respectively. Table 2-5 is a summary of generation capacity that was 17 placed in service recently, or capacity that developers expect will be placed in service over the 18 next few years. For planned units not yet in service, the report identified these units as likely to 19 come online based on whether it is under construction in Form EIA-860. Projections are shown 20 through 2024, with more than half of the approximately 56 GW of capacity being in service by 21 2018. More than 90 percent were expected to be in operation by 2020. Additional detail on 22 generation capacity additions is provided in Section F.4 of Appendix F.

23 Table 2-5 Recent and Firm Builds Assumptions for the Period 2018 through 2024 (MWe)

Technology ERCOT ISO-NE MISO NYISO PJM Southeast SPP WECC Total Combined Cycle 232 1,230 4,725 1,721 13,065 5,480 1,235 27,689 Combustion Turbine 329 629 729 124 371 155 409 1,425 4,171 Nuclear 2,200 2,200 Onshore Wind 5,166 33 2,858 158 959 3,423 2,531 15,128 Other 11 16 21 215 1 373 637 Solar Photovoltaic (PV) 1,032 7 109 10 437 2,109 15 2,554 6,273 Totals 6,770 1,915 8,421 2,034 14,832 10,159 3,848 8,118 56,097 24 Note: No new coal fired generation is projected to be built through year 2024.

25 Source: (EIA, 2019) 7 The survey Form EIA-860 (EIA, 2019) collects generator-level specific information about existing and planned generators and associated environmental equipment at electric power plants with 1 megawatt or greater of combined nameplate capacity.

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1 Table 2-6 is a summary of the assumed planned capacity retirements based on Form EIA 860 2 (EIA, 2019). The table shows generation capacity that was retired recently or that is scheduled 3 to retire by 2030. The EIA data show that about 82 percent of the approximately 96 GW 4 capacity retirements are scheduled to occur by 2025. Additional detail on generation capacity 5 retirements is provided in Section F.4 of Appendix F.

6 Table 2-6 Recent and Firm Generation Retirements through 2030 (MWe)

Technology ERCOT ISO-NE MISO NYISO PJM Southeast SPP WECC Total Coal 5,583 383 12,906 11,417 8,529 1,546 9,416 49,780 Combined 34 424 430 121 2,268 3,277 Cycle Combustion 26 25 1,798 99 777 371 237 737 4,069 Turbine Nuclear 1,205 1,928 5,181 3,260 5,361 2,240 19,174 Oil/Gas 1,692 2,098 1,394 364 3,241 9,090 17,879 Steam Other 1,010 2 183 2 242 255 169 437 2,301 Totals 9,516 2,372 22,590 3,361 19,621 9,640 5,193 24,187 96,480 7 Note: No solar photovoltaic generation is projected to be retired through year 2030.

8 Source: Form EIA-860 (EIA, 2019) 9 10 In addition to the firm generation additions and retirements, the report modeled unplanned 11 economic generation additions and retirements projected to occur over the report period. These 12 are generation capacity decisions expected to occur as a result of market conditions and are 13 based on the IPM model projections. A summary of IPMs generation capacity additions and 14 retirements are shown in Table 2-7 and Table 2-8, respectively. Additional details on IPM 15 economic builds and retirements are provided in Section F.5 of Appendix F.

16 Table 2-7 IPM Economic Builds through 2030 in Addition to Firm Generation Builds (MW)

Technology ERCOT ISO-NE MISO NYISO PJM Southeast SPP WECC Total Combined 13,242 7,974 519 6,123 12,117 11,250 51,225 Cycle Combustion 673 1,409 2,082 Turbine Onshore Wind 3,692 3,589 5,173 8,341 330 229 25,835 47,189 Other 43 4,026 1,507 3,224 2,428 680 2,985 14,893 Solar PV 14,895 5,430 2,828 36,963 15,767 4,694 21,105 101,682 Total 28,137 3,735 21,019 10,027 55,324 30,642 5,603 62,584 217,071 2-14

1 Table 2-8 IPM Economic Retirements through 2030 in Addition to Firm Generation 2 Retirements (MW)

Technology ERCOT ISO-NE MISO NYISO PJM Southeast SPP WECC Total Coal 815 534 9,505 723 12,514 22,860 1,193 48,144 Combined Cycle 1,576 1,519 110 2,975 6,180 Combustion 148 54 40 8 1,424 1,674 Turbine Nuclear 5,456 853 1,590 5,526 1,947 1,180 16,552 Oil/Gas 1,723 202 2,297 2,236 130 536 34 7,158 Other 118 547 724 74 210 1,558 949 4,180 Totals 933 4,528 15,887 5,520 16,590 30,192 2,483 7,755 83,888 3 2.5.5 New Unit Cost Assumptions 4 For its capacity expansion and retirement assessment, the staff and ICF developed 5 assumptions for new unit technologies that could potentially be placed in service during the 6 report period.

7 Selected technologies are shown in Table 2-9. Because the cost and performance 8 characteristics of new units evolve over time, Table 2-9 provides the cost assumptions for new 9 units that were used in the IPM run years shown. Additional detail on other technologies is 10 provided in Section F.6. of Appendix F.

11 Table 2-9 Performance and Unit Cost Assumptions for New Technologies Advanced Advanced Battery Solar Solar Onshore Parameter Combined Combustion Nuclear Storage Photovoltaic Thermal Wind Cycle Turbine Size (MWe) 1,100 237 2,234 30 150 100 100 First Year 2022 2021 2025 2020 2020 2022 2022 Available Lead Time 3 2 6 1 1 3 3 (Years)

Generation Economic Economic Economic Economic Generation Economic Generation Capability Dispatch Dispatch Dispatch Dispatch Profile Dispatch Profile 2021 Heat Rate 6,300 9,550 10,461 NA 0 0 0 (Btu/kWh)

Capital 768 667 5,813 1,796 939 6,675 1,460 (2018$/kW)

Fixed O&M 10.30 7.01 103.31 36.32 7.84 67.80 51.48 (2018$/kW-yr) 12 2-15

1 Table 2-9 Performance and Unit Cost Assumptions for New Technologies (continued) 2 Advanced Advanced Battery Solar Solar Onshore Parameter Combined Combustion Nuclear Storage Photovoltaic Thermal Wind Cycle Turbine Variable O&M 2.06 11.02 2.37 7.26 0.00 3.69 0.00 (2018$/MWh) 2023 Heat Rate 6,250 9,050 10,461 NA 0 0 0 (Btu/kWh)

Capital 732 625 5,651 1,673 918 6,505 1,426 (2018$/kW)

Fixed O&M 10.30 7.01 103.31 36.32 7.67 64.32 50.71 (2018$/kW-yr)

Variable O&M 2.06 11.02 2.37 7.26 0.00 3.69 0.00 (2018$/MWh) 2025 Heat Rate 6,200 8,550 10,461 NA 0 0 0 (Btu/kWh)

Capital 711 600 5,550 1,573 897 6,334 1,395 (2018$/kW)

Fixed O&M 10.30 7.01 103.31 36.32 7.50 60.85 49.94 (2018$/kW-yr)

Variable O&M 2.06 11.02 2.37 7.26 0.00 3.69 0.00 (2018$/MWh) 2030 Heat Rate 6,200 8,550 10,461 NA 0 0 0 (Btu/kWh)

Capital 658 545 5,195 1,385 844 5,909 1,329 (2018$/kW)

Fixed O&M 10.30 7.01 103.31 36.32 7.07 52.15 48.02 (2018$/kW-yr)

Variable O&M 2.06 11.02 2.37 7.26 0.00 3.69 0.00 (2018$/MWh) 3 Btu - British thermal units; kW - Kilowatt; kWh - Kilowatt-hour; kW-yr - Kilowatt-year; MW - Megawatt; 4 MWh - Megawatt-hour; NA - not applicable; O&M - operation and maintenance.

5 Source: (DOE, 2019; NREL, 2018 2-16

1 2.6 Limitations of Model and Methodology 2 Actual market outcomes will be different from the replacement energy costs calculated in this 3 report due to limitations of the model and methodology used. The PCM used for the analysis is 4 deterministic and most contingencies are predefined. It therefore might not fully capture spikes 5 in prices and volatility that could occur as a result of unexpected outages or errors in forecasts.

6 The modeling would also not account for the effect of extreme events such as the polar vortex 7 or hurricanes. The impact of future regulations and the effect of economic and other disruptions 8 due to events such as pandemics are not captured in the projections of the replacement energy 9 costs.

10 In addition, the model assumes generation units in each region are dispatched in economic 11 order subject to transmission and other constraints, and electricity market prices are based on 12 the variable cost of the marginal unit. Prices could vary in regulated markets where unit 13 commitment and dispatch decisions are conducted at the utility level, rather than at a 14 centralized utility level. Similarly, some power transactions take place under contract terms that 15 could affect commitment decisions and the order in which units are dispatched.

16 Another factor is that the replacement energy costs are based on average prices calculated for 17 an entire region. The actual market outcome within a region would depend on the location of 18 the nuclear plant that goes out of service and the location where the replacement power is 19 sourced. For example, the replacement power could be sourced from a hub that is not 20 representative of the average for the entire market or from a specific resource within the region.

21 Further, the replacement energy costs are calculated as a range using up to two nuclear 22 generation units within a region. The actual outcome could vary if a unit not included in the 23 selection goes out of service. The report also uses interpolation to determine values for 24 intermediate years (between the selected run years). Market dynamics such as fuel price 25 variations, resource builds and retirements, or demand fluctuations that occur in the 26 intermediate years could result in costs that are different from the interpolated values.

27 Finally, this report uses modeling assumptions from a multitude of sources beyond DOE 2019 28 and also differs in the modeling framework used. Therefore, the results obtained in this report 29 are expected to differ from the projections from DOE 2019.

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1 3 RESULTS 2 The report simulated the operation of the U.S. electricity markets and calculated the incremental 3 replacement energy cost for each of the regions specified in the report. This chapter discusses 4 the results and demonstrates how to use the incremental replacement energy costs to calculate 5 the replacement energy cost for specific projected generation outages.

6 3.1 Market Price Impact and Replacement Energy Costs 7 As described in Section 2.3, PROMOD runs for the Reference and Alternative Cases produce a 8 series of hourly market energy prices ($/MWh) for each run year. Replacement energy costs 9 are defined as the difference in these energy prices between the Reference and the Alternative 10 Cases. The report calculated annual and seasonal replacement energy costs for each of the 11 eight identified regions. Annual replacement energy costs are the simple average of the hourly 12 replacement energy costs.

13 Seasonal variations in factors such as gas prices, demand, and unit availabilities result in 14 corresponding seasonal variations in replacement energy costs. An outage that is concentrated 15 in a particular season might have a replacement energy cost that is lower or higher than the 16 annual value depending on the season. Seasonal replacement energy costs have been 17 provided to account for the seasonal variations. Seasonal replacement energy costs are 18 calculated as the average of the hourly replacement energy cost values in each season.

19 The impact of long duration outages (several months to years) can be assessed using the 20 annual replacement energy costs. Shorter duration outages (up to several months) can be 21 assessed using the seasonal replacement energy cost that is more reflective of the particular 22 season in which the nuclear generation unit is expected to be out of service.

23 The annual replacement energy costs for the run years are shown in Table 3-1. Detailed results 24 for the entire report period, 2020 to 2030, are shown in Appendix G. Because ERCOT has only 25 one Alternative Case, the Most Impact and Least Impact values are the same. A single 26 replacement energy cost is provided in each year in Table 3-1. In some regions some of the 27 units selected for the market price impact assessment are forecasted not to be dispatched due 28 to economic reasons or when their operating licenses expire. In NYISO, the unit assessed to 29 develop the Least Impact value is the R E Ginna Nuclear Power Plant (Ginna). The Ginna plant 30 is assumed to cease commercial operation when its current operating license expires in 2030.

31 Therefore, in 2030 only a single replacement energy cost is calculated for the region. In SPP, 32 there are no replacement energy cost values in 2023 and later because both units in the region 33 are projected to not be dispatched beginning in 2023 for economic reasons. In WECC, the 34 operating license for the unit assessed to develop the Least Impact value, Columbia Generating 35 Station, expires in January 2023. Therefore, in 2023 and beyond there is a single replacement 36 energy cost in each year.

37 Table 3-1 shows a jump in replacement energy cost in 2030 in ERCOT and ISO-NE. This is 38 because the operating licenses for Comanche Peak Nuclear Power Plant Unit 1 in ERCOT and 39 Seabrook Station in ISO-NE expire in 2030. Replacing the lost energy from these large, 40 relatively lower cost units in addition to the unit modeled offline for the replacement energy cost 41 calculation results in a higher difference in prices between the Reference Case and the 3-1

1 Alternative Case in 2030 relative to the other run years. The operating license for Nine Mile 2 Point Nuclear Station Unit 1 also expires in 2030, but the impact on the NYISO region annual 3 replacement energy cost is less pronounced because it is a relatively smaller unit 4 (approximately half the capacity of the Comanche Peak and Seabrook units).

5 Table 3-1 Annual Replacement Energy Costs Annual Replacement Energy Costs ($/MWh)a 2020 2021 2023 2025 2030 Region Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact ERCOTb 1.01 1.01 0.85 0.85 1.48 1.48 1.22 1.22 2.8 2.8 ISO-NE 2.36 1.68 3.00 2.13 2.96 2.13 3.42 2.38 6.12 4.35 MISO 0.13 0.01 0.23 0.03 0.30 0.03 0.37 0.09 0.17 0.00 NYISOc 2.04 0.92 2.14 0.98 1.73 0.72 2.19 0.80 3.77 0.00 PJM 1.02 0.08 0.67 0.09 0.74 0.19 0.79 0.16 1.16 0.17 Southeast 0.18 0.11 0.18 0.07 0.17 0.13 0.16 0.10 0.26 0.15 SPPd 0.92 0.46 0.86 0.47 0.00 0.00 0.00 0.00 0.00 0.00 WECC e 1.12 0.68 1.15 0.91 1.07 0.00 0.94 0.00 1.76 0.00 6 a Values are in nominal dollars.

7 b ERCOT has only one Alternative Case, so a single replacement energy cost is used for the region in all 8 years.

9 c The operating license for the unit assessed for Least Impact in NYISO, R E Ginna Nuclear Power Plant, 10 expires in 2030. Therefore, in 2030 this report calculates a single replacement energy cost for the 11 region.

12 d In 2023, the report assumes that the Wolf Creek Generating Station and Cooper Nuclear Station in SPP 13 will not be dispatched for economic reasons.

14 e The report assumes that beginning in 2023, the unit assessed for Least Impact, Columbia Generating 15 Station, will not be dispatched for economic reasons. Therefore, beginning in 2023 a single replacement 16 energy cost for the WECC region is modeled.

17 18 The seasonal incremental energy costs for the spring season are shown in Table 3-2. Spring 19 season replacement energy costs are based on values for the months of March, April, and May.

20 Results for all years are shown in Appendix G. The swings in seasonal replacement energy 21 costs are due to differences in the scheduled maintenance outage dates modeled in the 22 Reference Cases and the Alternative Cases. Modeling a large nuclear unit out of service in an 23 Alternative Case affects the maintenance decisions of other units in the region. Some 24 scheduled maintenance outage dates shift relative to the Reference Case. For example, a 25 power plant in one of the regions might have its scheduled maintenance outage in April in the 26 Reference Case and in May in the Alternative Case. Because maintenance outages are 27 typically scheduled in the spring and fall, the swings are pronounced in spring and fall and 28 muted in summer, when virtually no maintenance outages occur.

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1 Table 3-2 Spring Season Replacement Energy Costs Spring Season Incremental Replacement Energy Costs ($/MWh)a 2020 2021 2023 2025 2030 Region Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact ERCOTb 0.65 0.65 0.51 0.51 0.58 0.58 0.43 0.43 0.62 0.62 ISO-NE 1.78 1.12 3.35 2.38 1.46 1.27 2.25 1.58 4.41 3.19 MISO 0.00 0.03 0.33 0.10 0.27 0.09 0.32 0.04 0.04 0.00 NYISO c 1.79 0.92 2.67 1.22 0.85 0.46 1.20 0.45 1.57 0.00 PJM 0.70 0.10 0.49 0.03 0.55 0.12 0.59 0.11 0.85 0.11 Southeast 0.14 0.11 0.22 0.11 0.23 0.21 0.14 0.09 0.31 0.18 SPPd 1.09 0.52 0.96 0.47 0.00 0.00 0.00 0.00 0.00 0.00 WECCe 1.53 1.16 1.35 1.11 1.09 N/A 0.9 N/A 1.74 N/A 2 a Values are in nominal dollars.

3 b ERCOT has only one Alternative Case, so a single replacement energy cost is used for the region in all 4 years.

5 c The operating license for the unit assessed for Least Impact in NYISO, R E Ginna Nuclear Power Plant, 6 expires in 2030. Therefore, in 2030 this report calculates a single replacement energy cost for the 7 region.

8 d In 2023, the report assumes that the Wolf Creek Generating Station and Cooper Nuclear Station in SPP 9 will not be dispatched for economic reasons.

10 e The report assumes that beginning in 2023, the unit assessed for Least Impact, Columbia Generating 11 Station will not be dispatched for economic reasons. Therefore, beginning in 2023 a single replacement 12 energy cost for the WECC region is modeled.

13 14 The seasonal incremental energy costs for the summer season are shown in Table 3-3.

15 Summer season replacement energy costs are based on values for the months of June, July, 16 and August. Results for all years are shown in Appendix G. As discussed above for the spring 17 season costs, the swings in seasonal replacement energy costs are due to differences in the 18 scheduled maintenance outage dates modeled in the Reference Cases and the Alternative 19 Cases. In addition, as discussed above for the annual values, the jump in replacement energy 20 cost in 2030 in ERCOT, ISO-NE, and NYISO are partly due to the operating licenses of 21 Comanche Peak Nuclear Power Plant Unit 1 in ERCOT, Seabrook Station in ISO-NE, and Nine 22 Mile Point Nuclear Station Unit 1 in NYISO expiring.

3-3

1 Table 3-3 Summer Season Replacement Energy Costs Summer Season Replacement Energy Costs ($/MWh)a 2020 2021 2023 2025 2030 Region Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact ERCOTb 2.07 2.07 1.62 1.62 3.03 3.03 2.91 2.91 8.32 8.32 ISO-NE 2.86 1.97 3.19 2.23 5.75 4.10 6.20 4.22 11.73 8.19 MISO 0.36 0.07 0.30 0.01 0.34 0.00 0.44 0.02 0.07 0.01 NYISOc 2.38 1.18 2.05 0.99 2.15 0.95 3.35 1.15 6.23 0.00 PJM 1.18 0.10 0.69 0.14 0.87 0.22 1.02 0.29 1.65 0.20 Southeast 0.10 0.07 0.15 0.06 0.15 0.08 0.23 0.18 0.33 0.18 SPP d 0.91 0.55 1.27 0.73 0.00 0.00 0.00 0.00 0.00 0.00 WECC e 0.96 0.56 1.19 0.63 1.27 0.00 1.19 0.00 2.01 0.00 2 a Values are in nominal dollars.

3 b ERCOT has only one Alternative Case, so a single replacement energy cost is used for the region in all 4 years.

5 c The operating license for the unit assessed for Least Impact in NYISO, R E Ginna Nuclear Power Plant, 6 expires in 2030. Therefore, in 2030 this report calculates a single replacement energy cost for the 7 region.

8 d In 2023, the report assumes that the Wolf Creek Generating Station and Cooper Nuclear Station in SPP 9 will not be dispatched for economic reasons.

10 e The report assumes that beginning in 2023, the unit assessed for Least Impact, Columbia Generating 11 Station will not be dispatched for economic reasons. Therefore, beginning in 2023 a single replacement 12 energy cost for the WECC region is modeled.

13 14 The seasonal incremental energy costs for the fall season are shown in Table 3-4. Fall season 15 replacement energy costs are based on values for the months of September, October, and 16 November. Results for all years are shown in Appendix G. As discussed above for the spring 17 season costs, the swings in seasonal replacement energy costs are due to differences in the 18 scheduled maintenance outage dates modeled in the Reference Cases and the Alternative 19 Cases.

3-4

1 Table 3-4 Fall Season Replacement Energy Costs Fall Season Replacement Energy Costs ($/MWh) a 2020 2021 2023 2025 2030 Region Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact ERCOTb 0.89 0.89 0.74 0.74 1.68 1.68 1.03 1.03 1.76 1.76 ISO-NE 1.97 1.44 2.51 1.83 1.40 1.00 1.90 1.49 4.63 3.40 MISO 0.05 0.00 0.15 0.00 0.43 0.16 0.64 0.23 0.47 0.14 NYISO c 1.42 0.70 1.77 0.91 1.31 0.50 1.26 0.65 2.82 0.00 PJM 0.80 0.14 0.60 0.13 0.69 0.19 0.68 0.17 0.99 0.14 Southeast 0.17 0.14 0.16 0.07 0.15 0.16 0.13 0.05 0.22 0.15 SPPd 0.99 0.51 0.70 0.38 0.00 0.00 0.00 0.00 0.00 0.00 WECC e 0.88 0.41 0.97 0.65 0.99 0.00 0.87 0.00 1.96 0.00 2 a Values are in nominal dollars.

3 b ERCOT has only one Alternative Case, so a single replacement energy cost is used for the region in all 4 years.

5 c The operating license for the unit assessed for Least Impact in NYISO, R E Ginna Nuclear Power Plant, 6 expires in 2030. Therefore, in 2030 this report calculates a single replacement energy cost for the 7 region.

8 d In 2023, the report assumes that the Wolf Creek Generating Station and Cooper Nuclear Station in SPP 9 will not be dispatched for economic reasons.

10 e The report assumes that beginning in 2023, the unit assessed for Least Impact, Columbia Generating 11 Station will not be dispatched for economic reasons. Therefore, beginning in 2023 a single replacement 12 energy cost for the WECC region is modeled.

13 14 The seasonal incremental energy costs for the winter season are shown in Table 3-5. Winter 15 season replacement energy costs are based on values for the months of December in the 16 previous year, and January and February in the prevailing year. Results for all years are shown 17 in Appendix G. As discussed above for the spring season costs, the swings in seasonal 18 replacement energy costs are due to differences in the scheduled maintenance outage dates 19 modeled in the Reference Cases and the Alternative Cases.

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1 Table 3-5 Winter Season Replacement Energy Costs Winter Season Incremental Replacement Energy Costs ($/MWh)a 2020 2021 2023 2025 2030 Region Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact ERCOTb 0.41 0.41 0.47 0.47 0.57 0.57 0.54 0.54 0.44 0.44 ISO-NE 2.91 2.25 3.00 2.25 3.38 2.24 3.22 2.17 3.55 2.52 MISO 0.10 0.00 0.18 0.00 0.17 0.00 0.01 0.01 0.14 0.18 NYISOc 2.76 0.99 2.17 0.81 2.72 0.98 2.92 0.96 4.34 0.00 PJM 1.28 0.01 1.10 0.03 0.87 0.17 0.86 0.11 1.16 0.22 Southeast 0.29 0.11 0.20 0.10 0.16 0.07 0.11 0.07 0.19 0.10 SPP d 0.73 0.29 0.56 0.27 0.00 0.00 0.00 0.00 0.00 0.00 WECC e 1.33 0.67 1.16 1.22 0.88 0.00 0.78 0.00 1.29 0.00 2 a Values are in nominal dollars.

3 b ERCOT has only one Alternative Case, so a single replacement energy cost is used for the region in all 4 years.

5 c The operating license for the unit assessed for Least Impact in NYISO, R E Ginna Nuclear Power Plant, 6 expires in 2030. Therefore, in 2030 this report calculates a single replacement energy cost for the 7 region.

8 d In 2023, the report assumes that the Wolf Creek Generating Station and Cooper Nuclear Station in SPP 9 will not be dispatched for economic reasons.

10 e The report assumes that beginning in 2023, the unit assessed for Least Impact, Columbia Generating 11 Station, will not be dispatched for economic reasons. Therefore, beginning in 2023 a single replacement 12 energy cost for the WECC region is modeled.

13 14 3.2 Use of Replacement Cost Estimates 15 Wholesale power prices are higher during the on-peak hours (daytime hours) of the day than 16 the off-peak hours (nighttime hours) because average hourly loads are lower during the off-peak 17 hours1.967593e-4 days <br />0.00472 hours <br />2.810847e-5 weeks <br />6.4685e-6 months <br /> and on weekends and holidays. Because generators are dispatched in merit order (from 18 low-cost to high-cost alternatives), the lower the average load the lower the incremental cost of 19 dispatched power. Typically, power is more expensive during the summer cooling season and 20 the winter heating season compared with the cost of power during the spring and fall 21 (i.e., shoulder periods).

22 Given that it is not possible to predict when an outage might occur or how long it might last, the 23 average annual 24-hour prices projected for each of the postulated nuclear unit outages are a 24 good representation of what the price of replacement power would be for each hour of the 25 postulated outage. The increase in the wholesale power price would affect any market 26 participant that had to purchase power in the spot market during the postulated outage periods.

27 However, most power transactions take place under the terms of a contract rather than in the 28 spot market, and the contract prices are not typically tied (or indexed to) the spot market price.

29 It is difficult to determine what fraction of any hours power transactions for delivery at a given 30 price hub might be affected by the postulated nuclear unit outages. Therefore, no attempt was 3-6

1 made to calculate any other costs that could result from the impact on the outage beyond the 2 direct cost to purchase replacement power.

3 This section discusses the use of the replacement energy cost estimates to assess the impact 4 of the outage of a nuclear generation unit on energy prices in a region. The report 5 demonstrates the calculation using four examples to illustrate variations in approach depending 6 on the number of Alternative Cases developed for the region and the duration of the outage.

7 The report uses the following illustrations:

8 9

• Example 1: Annual energy cost calculation for a region with only one Alternative Case 10 11

• Example 2: Annual energy cost calculation for a region with two Alternative Cases 12 13

• Example 3: Seasonal energy cost calculation for a region with only one Alternative Case 14 15

• Example 4: Seasonal energy cost calculation for a region with two Alternative Cases 16 3.2.1 Example 1: Annual Energy Cost Calculation for a Region with Only One 17 Alternative Case 18 The ERCOT region has only one Alternative Case. This illustrates the annual replacement 19 energy cost calculation for ERCOT using STP Unit 1 in 2020.

20 21 Capacity of STP Unit 1= 1,280 MW (from Table D-1) 22 23 Representative capacity factor of STP Unit 1 = 90 percent8 24 25 Estimated annual power production from STP Unit 1 in 2020 26 27 = 1,280 MW x 0.9 x 8,784 hours0.00907 days <br />0.218 hours <br />0.0013 weeks <br />2.98312e-4 months <br /> per year9 28 29 = 10,119,168 MWh 30 31 Annual replacement energy cost in 2020 for the ERCOT region = $1.01/MWh (from 32 Table 3-1) 33 34 Annual replacement energy cost for STP Unit 1 in 2020 35 36 = 10,119,168 MWh x $1.01/MWh 37 38 = $10,192,435 8

Historical capacity factors for individual nuclear power plant units are available in NUREG-1350 (NRC, 2019a). The 10-year capacity factor for South Texas Project Unit 1 ranges from 78 percent to 101 percent over the 11 year period of 2008 to 2018 with an average capacity factor of 90 percent.

https://www.nrc.gov/reading-rm/doc-collections/datasets/ (NRC, 2019b).

9 The number of hours in year 2020 is 8,784 because 2020 is a leap year. For a standard year, the number of hours in a year is 8,760.

3-7

1 3.2.2 Example 2: Annual Energy Cost Calculation for a Region with Two Alternative 2 Cases 3 Two Alternative Cases were modeled for the WECC region, which produce a low and high 4 estimate of replacement energy costs. When determining the replacement energy cost for a 5 specific facility the user would need to determine where the value for that facility is likely to fall 6 given factors such as location, size, and proximity to load centers. The report illustrates the 7 annual replacement energy cost calculation for WECC using Diablo Canyon Unit 1 in 2020.

8 Diablo Canyon Unit 1 has a capacity of 1,122 MW. This is lower than the capacities of 9 Columbia Generating Station (1,180 MW), used to develop the Least Impact values, and Palo 10 Verde Unit 2 (1,314 MW), used to develop the Most Impact values. In terms of location on the 11 grid and the impact of factors such as transmission congestion on market prices, Diablo Canyon 12 is likely to be more similar to Palo Verde. The impact of the proximity to load centers will likely 13 be similar to the other two. The report will therefore assume that the replacement energy cost 14 will be the average of the Most Impact and Least Impact values.

15 The calculation of the energy cost is shown below:

16 17 Capacity of Diablo Canyon Unit 1= 1,122 MW (from Table D-1) 18 19 Representative capacity factor of Diablo Canyon Unit 1 = 91 percent 20 21 Estimated annual power production from Diablo Canyon Unit 1 in 2020 22 23 = 1,122 MW x 0.91 x 8,784 hours0.00907 days <br />0.218 hours <br />0.0013 weeks <br />2.98312e-4 months <br /> per year 24 25 = 8,968,640 MWh 26 27 Annual replacement energy cost in 2020, calculated from WECC Most Impact and Least 28 Impact values 29 30 = $(1.12 + 0.68)/2/MWh 31 32 = $0.90/MWh (from Table 3-1) 33 34 Annual replacement energy cost for Diablo Canyon Unit 1 in 2020 35 36 = 8,968,640 MWh x $0.90/MWh 37 38 = $8,071,776 39 3.2.3 Example 3: Seasonal Energy Cost Calculation for a Region with Only One 40 Alternative Case 41 The ERCOT region has only one Alternative Case. The report illustrates the seasonal 42 replacement energy cost calculation for ERCOT using STP Unit 1 in summer 2020.

43 44 Capacity of STP Unit 1 = 1,280 MW (from Table D-1) 45 46 Representative capacity factor of STP Unit 1 during the three summer months = 100 percent 3-8

1 Estimated power production from STP Unit 1 during the three 2020 summer months 2

3 = 1,280 MW x 1 x 2,208 hours0.00241 days <br />0.0578 hours <br />3.439153e-4 weeks <br />7.9144e-5 months <br /> per summer season 4

5 = 2,826,240 MWh 6

7 Incremental replacement energy cost in summer 2020 = $2.07/MWh (from Table 3-3) 8 9 Replacement energy cost for STP Unit 1 in summer 2020 10 11 = 2,667,264 MWh x $2.07/MWh 12 13 = $5,850,317 14 3.2.4 Example 4: Seasonal Energy Cost Calculation for a Region with Two Alternative 15 Cases 16 Two Alternative Cases were modeled for the WECC region, which produce a low and high 17 estimate of replacement energy costs. When determining the replacement energy cost for a 18 specific facility the user would need to determine where the value for that facility is likely to fall 19 given factors such as location, size, and proximity to load centers. The report illustrates the 20 annual replacement energy cost calculation for WECC using Diablo Canyon Unit 1 in summer 21 2020.

22 23 As discussed in Section 3.2.2, the report assumed the replacement energy cost will be the 24 average of the Most Impact and Least Impact values for the WECC region. The calculation of 25 the energy cost is shown below:

26 27 Capacity of Diablo Canyon Unit 1= 1,122 MW (from Table D-1) 28 29 Representative capacity factor of Diablo Canyon Unit 1 during the three summer months =

30 100 percent 31 32 Estimated power production from Diablo Canyon Unit 1 in summer 2020 33 34 = 1,122 MW x 1 x 2,208 hours0.00241 days <br />0.0578 hours <br />3.439153e-4 weeks <br />7.9144e-5 months <br /> per summer season 35 36 = 2,477,376 MWh 37 38 Incremental replacement energy cost in summer 2020 39 40 = $(0.96 + 0.56)/2/MWh 41 42 = $0.76/MWh (from Table 3-3) 43 44 Replacement energy cost for Diablo Canyon Unit 1 in summer 2020 45 46 = 2,477,376 MWh x $0.76/MWh 47 48 = $1,882,806 3-9

1 Table 3-6 shows the range of prices for replacement power in 2020 for a postulated outage 2 lasting a day and a year for each of the units modeled in this report using a 100 percent 3 capacity factor.

4 Table 3-6 Estimated Replacement Energy Costs for Postulated Nuclear Outages 2020 2020 Annual 2020 Annual Unit 2020 Unit Incremental Incremental Market Incremental Nuclear Unit Size Output Replacement Outage Cost per Region Outage Cost (MWe) (MWh)a,b Energy Cost Day

($ millions)

($/MWh) ($ thousands)

South Texas Project, Unit 1 1,280 11,243,520 ERCOT $1.01 $31.027 $11.356 Millstone Power Station, Unit 2 868 7,624,512 ISO-NE $1.68 $34.998 $12.809 Millstone Power Station, Unit 3 1,220 10,716,480 ISO-NE $2.36 $69.101 $25.291 Prairie Island Nuclear Generating Plant, Unit 2 519 4,558,896 MISO $0.01 $0.125 $0.046 Clinton Power Station, Unit 1 1,065 9,354,960 MISO $0.13 $3.323 $1.216 R E Ginna Nuclear Power Plant 582 5,112,288 NYISO $0.92 $12.851 $4.703 Nine Mile Point Nuclear Station, Unit 2 1,287 11,305,008 NYISO $2.04 $63.012 $23.062 Quad Cities Nuclear Power Station, Unit 1 908 7,975,872 PJM $0.08 $1.743 $0.638 Limerick Generating Station, Unit 2 1,122 9,855,648 PJM $1.02 $27.467 $10.053 Joseph M Farley Nuclear Plant, Unit 1 874 7,677,216 Southeast $0.11 $2.307 $0.844 Vogtle Electric Generating Plant, Unit 2 1,152 10,119,168 Southeast $0.18 $4.977 $1.821 Cooper Nuclear Station 772 6,781,248 SPP $0.46 $8.523 $3.119 Wolf Creek Generating Station, Unit 1 1,175 10,321,200 SPP $0.92 $25.944 $9.496 Columbia Generating Station 1,180 10,365,120 WECC $0.68 $19.258 $7.048 Palo Verde Nuclear Generating Station, Unit 2 1,314 11,542,176 WECC $1.12 $35.320 $12.927 5 a Unit output = unit size x number of hours per year = unit size by 8,784 (accounts for leap year with 6 366 days). For a standard year, the number of hours is 8,760.

7 b The calculations in this table assumes a 100 percent capacity factor for this example.

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1 4 REFERENCES 2 NRC Guidance 3 U.S. Nuclear Regulatory Commission, Replacement Energy, Capacity, and Reliability Costs for 4 Permanent Nuclear Reactor Shutdowns, NUREG/CR-6080, October 1993, ADAMS Accession 5 No. ML20076F500. (NRC, 1993).

6 U.S. Nuclear Regulatory Commission, Replacement Energy Costs for Nuclear Electricity-7 Generating Units in the United States: 1997-2001, NUREG/CR-4012, Volume 4, 8 September 1997, ADAMS Accession No. ML20073J435. (NRC, 1997).

9 U.S. Nuclear Regulatory Commission, 2019-2020 Information Digest, NUREG-1350, Volume 10 31, August 2019, ADAMS Accession No. ML19242D326. (NRC, 2019a).

11 U.S. Nuclear Regulatory Commission, NRC Datasets, September 2019. Available at:

12 https://www.nrc.gov/reading-rm/doc-collections/datasets/. (NRC, 2019b).

13 Other Guidance 14 California Independent System Operator (CAISO), Western Energy Imbalance Market, 2020.

15 Available at: https://www.westerneim.com/Pages/About/default.aspx. (CAISO, 2020).

16 Florida Power and Light (FPL), Ten Year Power Plant Site Plan 2019-2028, April 2019. (FPL, 17 2019).

18 ISO New England, 2017 Annual Markets Report, May 2018. Available at: https://www.iso-19 ne.com/static-assets/documents/2018/05/2017-annual-markets-report.pdf. (ISO, 2018).

20 National Renewable Energy Laboratory (NREL), Annual Technology Baseline (ATB), 2018, 21 2018. (NREL, 2018).

22 North American Electric Reliability Corporation (NERC), Electricity Supply and Demand 23 (ES&D), December 2018. (NERC, 2018).

24 Southeastern Regional Transmission Planning (SERTP), 2018 Economic Planning Studies, 25 November 2018. (SERTP, 2018).

26 U.S. Department of Energy (DOE) Energy Information Administration (EIA) Annual Energy 27 Outlook (AEO) 2019 Reference Case. (DOE, 2019).

28 U.S. Energy Information Administration, Form 860M, February 2019. (EIA, 2019).

29 U.S. Environmental Protection Agency (EPA), Power Sector Modeling Platform v6, 2019.

30 (EPA, 2019).

31 U.S. Federal Energy Regulatory Commission, Order No. 888 Promoting Wholesale 32 Competition Through Open Access Non-discriminatory Transmission Services by Public 33 Utilities; Recovery of Stranded Costs by Public Utilities and Transmitting Utilities, 2006.

34 (FERC, 2006).

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1 U.S. Federal Energy Regulatory Commission, Order No. 890 Preventing Undue Discrimination 2 and Preference in Transmission Service, 2007. (FERC, 2007).

3 U.S. Federal Energy Regulatory Commission, Order No. 1000 Transmission Planning and Cost 4 Allocation by Transmission Owning and Operating Utilities, 2012. (FERC, 2012).

4-2

1 APPENDIX A 2 OVERVIEW OF IPM 3 This appendix provides an overview of IPM, the software that is used to project economic 4 generation capacity additions or retirements over the report period.

5 The report used IPM to support analysis of the electric power sector. The EPA, in addition to 6 state air regulatory agencies, utilities, and public and private sector entities, has used IPM 7 extensively for various air regulatory analyses, market studies, strategy planning, and economic 8 impact assessments.

9 IPM is a well-established model of the electric power sector designed to help government and 10 industry analyze a wide range of issues related to this sector. The model represents economic 11 activities in key components of energy marketsfuel markets, emissions markets, and 12 electricity markets. Because the model captures the linkages in electricity markets, it is well 13 suited for developing integrated analyses of the impacts of alternative regulatory policies on the 14 power sector. In the past, applications of IPM have included capacity planning, environmental 15 policy analysis and compliance planning, wholesale price forecasting, and power plant asset 16 valuation.

17 A.1 Purpose and Capabilities 18 IPM is a dynamic linear programming model that generates optimal decisions under the 19 assumption of perfect foresight. It determines the least-cost method of meeting energy and 20 peak demand requirements over a specified period. In its solution, the model considers several 21 key operating or regulatory constraints that are placed on the power, emissions, and fuel 22 markets. The constraints include but are not limited to emissions limits, transmission 23 capabilities, renewable generation requirements, and fuel market constraints. The model is 24 designed to accommodate complex treatment of emissions regulations involving trading, 25 banking, and special provisions affecting emissions allowances, as well as traditional 26 command-and-control emissions policies.

27 IPM represents power markets through model regions that are geographical entities with distinct 28 operational characteristics. The model regions are largely consistent with the North American 29 Electric Reliability Corporation (NERC) assessment regions, and with the organizational 30 structures of the Regional Transmission Organizations (RTOs) and the Independent System 31 Operators (ISOs) that handle dispatch on most of the U.S. bulk power grid. IPM calculates the 32 least-cost arrangement of electricity supply (capacity and generation) within each model region 33 to meet assumed future load (electricity demand) while constrained by a transmission network 34 of bulk transfer limitations on inter-regional power flows. All utility-owned existing electric 35 generating units, including renewable resources, as well as independent power producers and 36 cogeneration facilities selling electricity to the grid, are modeled.

37 IPM provides a detailed representation of new and existing resource options. These include 38 fossil, nuclear, renewable, and non-conventional options. Fossil options include coal steam, 39 oil/gas steam, combined cycles, and gas-fired simple cycle combustion turbines. Renewable 40 options include wind, landfill gas, geothermal, solar thermal, solar PV, and biomass.

41 Non-conventional options include fuel cell, pump storage, and battery storage.

A-1

1 IPM can incorporate a detailed representation of fuel markets and can endogenously forecast 2 fuel prices for coal, natural gas, and biomass by balancing fuel demand and supply for electric 3 generation. The model also includes detailed fuel quality parameters to estimate emissions 4 from electric generation.

5 IPM provides estimates of air emissions changes, regional wholesale energy and capacity 6 prices, incremental electric power system costs, changes in fuel use, and capacity and dispatch 7 projections.

8 A.2 Applications 9 IPMs structure, formulation, and setup make it adaptable and flexible. The necessary level of 10 data, modeling capabilities exercised, and computational requirements can be tailored to the 11 strategies and policy options being analyzed. This adaptability has made IPM suitable for a 12 variety of applications. These include:

13 Air Regulatory Assessment: Because IPM contains extensive air regulatory modeling features, 14 state and federal air regulatory agencies have used the model extensively in support of air 15 regulatory assessment.

16 Integrated Resource Planning: IPM can be used to perform least-cost planning studies that 17 simultaneously optimize demand-side options (load management and efficiency), renewable 18 options, and traditional supply-side options.

19 Options Assessment: IPM allows industry and regulatory planners to screen alternative 20 resource options and option combinations based on their relative costs and contributions to 21 meeting customer demands.

22 Cost and Price Estimation: IPM produces estimates of energy prices, capacity prices, fuel 23 prices, and allowance prices. Industry and regulatory agencies have used these cost reports for 24 due diligence, planning, litigation, and economic impact assessment.

25 A.3 Model Structure and Formulation 26 IPM employs a linear programming structure that is particularly well suited for analysis of the 27 electric sector to help decision makers plan system capacity and model the dispatch of 28 electricity from individual units or plants. The model consists of three key structural 29 components:

30 31

• A linear objective function 32 33

• A series of decision variables 34 35

• A set of linear constraints 36 The sections below describe the objective function, key decision variables, and key constraints 37 included in IPM.

A-2

1 A.3.1 Objective Function 2 IPMs objective function is to minimize the total, discounted net present value of the costs of 3 meeting demand, power operation constraints, and environmental regulations over the entire 4 planning horizon. The objective function represents the summation of all the costs incurred by 5 the electricity sector on a net present value basis. These costs, which the linear programming 6 formulation attempts to minimize, include the cost of new plant and pollution control 7 construction, fixed and variable operating and maintenance costs, and fuel costs. Many of 8 these cost components are captured in the objective function by multiplying the decision 9 variables by a cost coefficient. Cost escalation factors are used in the objective function to 10 reflect changes in cost over time. The applicable discount rates are applied to derive the net 11 present value for the entire planning horizon from the costs obtained for all years in the planning 12 horizon.

13 A.3.2 Decision Variables 14 Decision variables represent the values for which the IPM model is solving, given the 15 cost-minimizing objective function and the set of electricity system constraints. The model 16 determines values for these decision variables that represent the optimal least-cost solution for 17 meeting the assumed constraints. Key decision variables represented in IPM are described in 18 detail below.

19 Generation Dispatch Decision Variables: IPM includes decision variables that represent the 20 generation from each model power plant.10 For each model plant, a separate generation 21 decision variable is defined for each possible combination of fuel, season, model run year, and 22 segment of the seasonal load duration curve (LDC) applicable to the model plant. In the 23 objective function, each plants generation decision variable is multiplied by the relevant heat 24 rate and fuel price (differentiated by the appropriate step of the fuel supply curve) to obtain a 25 fuel cost. It is also multiplied by the applicable VOM cost rate to obtain the VOM cost for the 26 plant.

27 Capacity Decision Variables: IPM includes decision variables that represent the capacity of 28 each existing model plant and capacity additions associated with potential (new) units in each 29 model run year. In the objective function, the decision variables that represent existing capacity 30 and capacity additions are multiplied by the relevant fixed operation and maintenance (FOM) 31 cost rates to obtain the total FOM cost for a plant. The capacity addition decision variables are 32 also multiplied by the investment cost and capital charge rates to obtain the capital cost 33 associated with the capacity addition.

34 Transmission Decision Variables: IPM includes decision variables that represent the electricity 35 transmission along each transmission link between model regions in each run year. In the 36 objective function, these variables are multiplied by variable transmission cost rates to obtain 37 the total cost of transmission across each link.

38 Emission Allowance Decision Variables: For emissions policies where allowance trading 39 applies, IPM includes decision variables that represent the total number of emission allowances 40 for a given model run year that are bought and sold in that or subsequent run years. In the 41 objective function, these year-differentiated allowance decision variables are multiplied by the 10 Model plants are aggregate representations of real-life electric generating units. They are used by IPM to model the electric power sector.

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1 market price for allowances prevailing in each run year. This formulation allows IPM to capture 2 the inter-temporal trading and banking of allowances.

3 Fuel Decision Variables: For each type of fuel and each model run year, IPM defines decision 4 variables that represent the quantity of fuel delivered from each fuel supply region to model 5 plants in each demand region.

6 A.3.3 Constraints 7 Model constraints are implemented in IPM to accurately reflect the characteristics of and the 8 conditions faced by the electric sector. Among the key constraints included are:

9 Reserve Margin Constraints: Regional reserve margin constraints capture system reliability 10 requirements by defining a minimum margin of reserve capacity (in megawatts) per year beyond 11 the total capacity needed to meet future peak demand that must remain in service to that region.

12 These reserve capacity constraints are derived from reserve margin targets that are assumed 13 for each region based on information from NERC, RTOs, or ISOs. If existing plus planned 14 capacity is not sufficient to satisfy the annual regional reserve margin requirement, the model 15 will build the required level of new capacity.

16 Demand Constraints: The model categorizes regional annual electricity demand into seasonal 17 load curves that are used to form winter (December 1-February 28), winter shoulder 18 (March 1-April 30, October 1-November 30), and summer (May 1-September 30) LDC11. The 19 seasonal load segments, when taken together, represent all the hourly electricity load levels that 20 must be satisfied in a region in the particular season for a particular model run year. As such, 21 the LDC defines the minimum amount of generation required to meet the regions electrical 22 demand during the specific season. These requirements are incorporated in the models 23 demand constraints.

24 Capacity Factor Constraints: These constraints specify how much electricity each plant can 25 generate (a maximum generation level), given its capacity and seasonal availability.

26 Turn Down Constraints: The model uses these constraints to take into account the cycling 27 capabilities of the units, i.e., whether or not they can be shut down at night or on weekends, or 28 whether they must operate at all times, or at least at some minimum capacity level. These 29 constraints ensure that the model reflects the distinct operating characteristics of peaking, 30 cycling, and base load units.

31 Emissions Constraints: IPM can endogenously consider an array of emissions constraints for 32 sulfur dioxide (SO2), nitrous oxide (NOx), hydrochloric acid (HCL), mercury, and carbon dioxide 33 (CO2). Emissions constraints can be implemented on a plant-by-plant, regional, or system-wide 34 basis. The constraints can be defined in terms of a total tonnage cap (e.g., tons of SO2) or a 35 maximum emission rate (e.g., lb/MMBtu of NOx). The scope, timing, and definition of the 36 emissions constraints depend on the required analysis.

11 The seasonal definitions of the IPM and PROMOD models are different. While IPM models three seasons, PROMOD is modeled at an hourly level and Section 2.3 and Table 3-5 summarize the hourly results from PROMOD run into four seasons.

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1 Transmission Constraints: IPM can simultaneously model any number of regions linked by 2 transmission lines. The constraints define either a maximum capacity on each link, or a 3 maximum level of transmission on two or more links (i.e., joint limits) to different regions.

4 Fuel Supply Constraints: These constraints define the types of fuel that each model plant is 5 eligible to use and the supply regions that are eligible to provide fuel to each specific model 6 plant. A separate constraint is defined for each model plant.

7 A.4 Key Methodological Features of IPM 8 IPM is a flexible modeling tool for obtaining short- and long-term projections of production 9 activity in the electric generation sector. The projections obtained using IPM are not statements 10 of what will happen, but they are estimates of what might happen given the assumptions and 11 methodologies used. This section provides an overview of the essential methodological and 12 structural features of IPM.

13 A.4.1 Model Plants 14 Model plants are a central structural component that IPM uses in three ways: (1) to represent 15 aggregations of existing generating units, (2) to represent retrofit and retirement options that are 16 available to existing units, and (3) to represent potential (new) units that the model can build.

17 Existing Units: Theoretically, there is no predefined limit on the number of units that can be 18 included in IPM. However, to keep model size and solution time within acceptable limits, IPM 19 utilizes model plants to represent aggregations of actual individual generating units. The 20 aggregation algorithm groups units with similar characteristics for representation by model 21 plants with a combined capacity and weighted-average characteristics that are representative of 22 all the units comprising the model plant. Model plants are defined to maximize the accuracy of 23 the models cost and emissions estimates by capturing variations in key features of those units 24 that are critical to the analysis.

25 Retrofit and Retirement Options: IPM also utilizes model plants to represent the retrofit and 26 retirement options that are available to existing units. Existing model plants are provided with a 27 wide range of options for retrofitting with emissions control equipment as well as with an option 28 to retire.

29 The options available to each model plant are predefined at the models setup. The retrofit and 30 retirement options are themselves represented in IPM by model plants, which, if actuated in a 31 model run, take on all or a portion of the capacity initially assigned to a model plant, which 32 represents existing generating units.12 In setting up IPM, parent-child-grandchild relationships 33 are predefined between each existing model plant (parent) and the specific retrofit and 34 retirement model plants (children and grandchildren) that may replace the parent model plant 35 during a model run. The child and grandchild model plants are inactive in IPM unless the model 36 finds it economical to engage one of the options provided, e.g., retrofit with particular emissions 37 controls or retire.

12 IPM has a linear programming structure whose decision variables can assume any value within the specified bounds subject to the constraints. Therefore, IPM can generate solutions where model plants retrofit or retire a portion of the model plants capacity. IPMs standard model plant outputs explicitly present these partial investment decisions.

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1 Theoretically, there are no limits on the number of child, grandchild, and even great-grandchild 2 model plants (i.e., retrofit and retirement options) that can be associated with each existing 3 model plant. However, model size and computational considerations dictate that the number of 4 successive retrofits be limited.

5 Potential (New) Units: IPM also uses model plants to represent new generation capacity that 6 may be built during a model run. All the model plants representing new capacity are predefined 7 at setup, differentiated by type of technology, regional location, and years available. When it is 8 economically advantageous to do so (or otherwise required by reserve margin constraints to 9 maintain electric reliability), IPM builds one or more of these predefined model plants by 10 raising its generation capacity from zero during a model run. In determining whether it is 11 economically advantageous to build new plants, IPM takes into account cost differentials 12 between technologies, expected technology cost improvements, and regional variations in 13 capital costs that are expected to occur over time.

14 A.4.2 Model Run Years 15 Another important structural feature of IPM is the use of model run years to represent the full 16 planning horizon being modeled. Although IPM can represent an individual year in an analysis 17 time horizon, mapping each year in the planning horizon into a representative model run year 18 enables IPM to perform multiple year analyses while keeping the model size manageable. IPM 19 takes into account the costs in all years in the planning horizon but reports results only for 20 model run years.

21 Often models like IPM include a final model run year that is not included in the analysis of 22 results. This technique reduces the likelihood that modeling results in the last represented year 23 will be skewed due to the modeling artifact of having to specify an end point in the planning 24 horizon, whereas economic decision-making will continue to take information into account from 25 years beyond the models time horizon. This should be considered when assessing model 26 projections from the last output year.

27 A.4.3 Cost Accounting 28 As noted earlier in this appendix, IPM is a dynamic linear programming model that solves for the 29 least-cost investment and electricity dispatch strategy for meeting electricity demand subject to 30 resource availability and other operating and environmental constraints. The cost components 31 that IPM considers in deriving an optimal solution include the costs of investing in new capacity 32 options, the cost of installing and operating pollution control technology, fuel costs, and the 33 operation and maintenance costs associated with unit operations. Several cost accounting 34 assumptions are built into IPMs objective function that ensures a technically sound and 35 unbiased treatment of the cost of all investment options offered in the model. These features 36 include:

37 38

• All costs (in real dollars) in IPMs single multi-year objective function are discounted to a 39 base year. Because the model solves for all run years simultaneously, discounting to a 40 common base year ensures that IPM properly captures complex inter-temporal cost 41 relationships.

42 43

• Capital costs in IPMs objective function are represented as the net present value of 44 levelized stream of annual capital outlays, not as a one-time total investment cost. The 45 payment period used in calculating the levelized annual outlays never extends beyond A-6

1 the models planning horizon. It is either the book life of the investment or the years 2 remaining in the planning horizon, whichever is shorter. This approach avoids 3 presenting artificially lower capital costs for investment decisions taken closer to the 4 models time horizon boundary simply because some of that cost would typically be 5 serviced in years beyond the models view. This treatment of capital costs ensures both 6 realism and consistency in accounting for the full cost of each of the investment options 7 in the model.

8 9

• The cost components informing IPMs objective function represent the composite cost 10 over all years in the planning horizon rather than just the cost in the individual model run 11 years. This permits the model to capture more accurately the escalation of the cost 12 components over time.

13 A.4.4 Modeling Wholesale Electricity Markets 14 IPM is designed to simulate electricity production activity in a manner that would minimize 15 production costs, as is the intended outcome in wholesale electricity markets. For this purpose, 16 the model captures transmission costs and losses between IPM model regions, but it is not 17 designed to capture retail distribution costs. However, the model implicitly includes distribution 18 losses because net energy for load,13 rather than delivered sales, is used to represent electricity 19 demand in the model. Additionally, the production costs calculated by IPM are the wholesale 20 production costs. In reporting costs, the model does not include embedded costs, such as 21 carrying charges of existing units, which may ultimately be part of the retail cost incurred by 22 end-use consumers.

23 A.4.5 Load Duration Curves 24 IPM uses LDCs to provide realism to the dispatching of electric generating units. Unlike a 25 chronological electric load curve, which is an hourly record of electricity demand, the LDCs are 26 created by rearranging the hourly chronological electric load data from the highest to lowest 27 (MW) value. To aggregate such load detail into a format enabling this scale of power sector 28 modeling, EPA applications of IPM use a 24-step piecewise linear representation of the LDC.

29 IPM can include any number of user-defined seasons. A season can be a single month or 30 several months. Use of seasonal LDCs rather than annual LDCs allows IPM to capture 31 seasonal differences in the level and patterns of customer demand for electricity. For example, 32 in most regions air conditioner cycling only impacts customer demand patterns during the 33 summer season. The use of seasonal LDCs also allows IPM to capture seasonal variations in 34 the generation resources available to respond to the customer demand depicted in an LDC. For 35 example, power exchanges between utility systems may be seasonal in nature. Some air 36 regulations affecting power plants are also seasonal in nature. This can impact the type of 37 generating resources that are dispatched during a particular season. Further, because of 38 maintenance scheduling for individual generating units, the capacity and utilization for these 39 supply resources also vary between seasons.

13 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 delivered to others through interchange. It includes distribution losses.

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1 Within IPM, LDCs are represented by a discrete number of load segments, or generation 2 blocks, as illustrated in Figure A-1.

3 4 Figure A-1 Hypothetical Chronological Hourly Load Curve and Seasonal Load Duration 5 Curve 6 Hourly load curves from FERC Form 714 and ISO/RTOs are used to derive future seasonal LDCs for 7 each IPM run year in each IPM region. The results of this process are individualized seasonal LDCs that 8 capture the unique hourly electricity demand profile of each region. The LDCs change over time to reflect 9 projected changes in load factors because of future variations in electricity consumption patterns.

10 11 The EPA Platform v6 (EPA, 2019) uses 24 load segments in its seasonal LDCs. Figure A-2 12 illustrates and the following text describes the 24-segment LDCs used in EPA Platform v6 (EPA, 13 2019). Length of time and system demand are the two parameters, which define each segment 14 of the LDC. The load segment represents the amount of time (along the x-axis) and the 15 capacity that the electric dispatch mix must be producing (represented along the y-axis) to meet 16 system load. In EPA Platform v6 (EPA, 2019), the hours in the LDC are initially clustered into 17 six segments. Segment 1 incorporates 1 percent of all hours in the season with the highest 18 load. Segments 2 to 6 have 4 percent, 10 percent, 30 percent, 30 percent, and 25 percent of 19 the hours, respectively, with progressively lower levels of demand. Each of these segments is 20 further separated into four time-of-day categories to result in a possible maximum of 24 load 21 segments. This approach better accounts for the impact of solar generation during periods of 22 high demand. The four time-of-day categories are 8 PM-6 AM, 6 AM-9 AM, 9 AM-5 PM and 23 5 PM-8 PM. Plants are dispatched to meet this load based on economic considerations and 24 operating constraints. The most cost-effective plants are assigned to meet load in all 25 24 segments of the LDC.

26 27 Figure A-2 Stylized Depiction of a Six Segment Load Duration Curve Used in EPA 28 Platform v6 (EPA, 2019) 29 Each segment is further divided into four time-of-day categories resulting in 24-segment LDCs.

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1 A.4.6 Dispatch Modeling 2 In IPM, the dispatching of electricity is based on the variable cost of generation. In the absence 3 of any operating constraints, units with the lowest variable cost generate first. The marginal 4 generating unit, i.e., the power plant that generates the last unit of electricity, sets the energy 5 price. Physical operating constraints also influence the dispatch order. For example, IPM uses 6 turn down constraints to prevent base load units from cycling, i.e., switching on and off. Turn 7 down constraints often override the dispatch order that would result based purely on the 8 variable cost of generation. Variable costs in combination with turn down constraints enable 9 IPM to dispatch generation resources in a technically realistic fashion.

10 Figure A-3, below, depicts a stylized dispatch order based on the variable cost of generation of 11 resource options. In this figure, two hypothetical load segments are subdivided according to the 12 type of generation resource that responds to the load requirements represented in that 13 segment. Notice that the generation resources with the lowest operating cost (i.e., hydro and 14 nuclear) respond first to the demand represented in the LDC and are accordingly at the bottom 15 of the dispatch stack. They are dispatched for the maximum possible number of hours 16 represented in the LDC because of their low operating costs.

17 Generation resources with the highest operating cost (e.g., peaking turbines) are at the top of 18 the dispatch stack, because they are dispatched last and for the minimum possible number of 19 hours2.199074e-4 days <br />0.00528 hours <br />3.141534e-5 weeks <br />7.2295e-6 months <br />. In the load segment with non-dispatchable generating capacity such as solar, the 20 conventional power plants are dispatched to the residual load level where residual load is 21 defined as the difference between the total load and the load met by non-dispatchable 22 resources.

23 24 Figure A-3 Stylized Dispatch Order in Illustrative Load Segments 25 Note: This figure does not include all the plant types that are modeled in EPA Platform v6 (EPA, 2019).

26 Intermittent renewable technologies, such as wind and solar, are considered non-dispatchable and are 27 assigned a specific hourly generation profile.

28 A-9

1 A.4.7 Fuel Modeling 2 Another key methodological feature of IPM is its capability to model the full range of fuels used 3 for electric power generation. The cost, supply, and (if applicable) quality of each fuel included 4 in the model are defined during model setup. Fuel price and supply are represented in EPA 5 Platform v6 (EPA, 2019) in one of two alternative ways: (1) through a set of supply curves (coal, 6 natural gas, and biomass) or (2) through an exogenous price stream (fuel oil and nuclear fuel).

7 With the first approach, the model endogenously determines the price for that fuel by balancing 8 the supply and demand. IPM uses fuel quality information (e.g., the sulfur, chlorine or mercury 9 content of different types of coal from different supply regions) to determine the emissions 10 resulting from combustion of that fuel.

11 A.4.8 Transmission Modeling 12 IPM includes a detailed representation of existing transmission capabilities between model 13 regions. The maximum transmission capabilities between regions are specified in IPMs 14 transmission constraints. Due to uncertainty surrounding the building of new transmission lines 15 in the United States, IPMs capability to model the building of new transmission lines is not 16 exercised. However, that capacity of the model is described in case it is applied in future 17 analyses. Additions to transmission lines are represented by decision variables defined for 18 each eligible link and model run year. In IPMs objective function, the decision variables 19 representing transmission additions are multiplied by new transmission line investment cost and 20 capital charge rates to obtain the capital cost associated with the transmission addition.

21 A.4.9 Perfect Competition and Perfect Foresight 22 Two key methodological features of IPM are its assumptions of perfect competition and perfect 23 foresight. The former means that IPM models production activity in wholesale electric markets 24 on the premise that these markets operate within a market structure of perfect competition. The 25 model does not explicitly capture any market imperfections such as market power, transaction 26 costs, informational asymmetry, or uncertainty. However, if desired, appropriately designed 27 sensitivity analyses or redefined model parameters can be used to gauge the impact of market 28 imperfections on the wholesale electric markets.

29 IPMs assumption of perfect foresight implies that agents know precisely the nature and timing 30 of conditions in future years that affect the ultimate costs of decisions along the way. For 31 example, under IPM there is complete foreknowledge of future electricity demand, fuel supplies, 32 and other variables (including regulatory requirements) that are subject to uncertainty and 33 limited foresight. Modelers frequently assume perfect foresight to establish a decision-making 34 framework that can estimate cost-minimizing courses of action given the best-guess 35 expectations of these future variables that can be constructed at the time the projections are 36 made.

37 A.4.10 Scenario Analysis and Regulatory Modeling 38 One of the most notable features of IPM is its detailed and flexible modeling features enabling 39 for scenario analysis involving different outlooks of key drivers of the power sector and 40 environmental regulations. Treatment of environmental regulations is endogenous in IPM. That 41 is, by providing a comprehensive representation of compliance options, IPM enables 42 environmental decisions to be made within the model based on least-cost considerations, rather 43 than exogenously imposing environmental choices on model results. For example, unlike other A-10

1 models that enter allowance prices as an exogenous input during model setup, IPM obtains 2 allowance prices as an output of the endogenous optimization process of finding the least-cost 3 compliance options in response to air regulations. In linear programming terminology, the 4 shadow prices of the respective emissions constraints are the standard output produced in 5 solving a linear programming problem.

6 A.5 Model Inputs and Outputs 7 A.5.1 Data Parameters for Model Inputs 8 IPM requires input parameters that characterize the U.S. electricity system, economic outlook, 9 fuel supply, and air regulatory framework.

10 Table A-1 lists the key input parameters required by IPM.

11 Table A-1 IPM Input Category Key Input Parameters Existing Plant Capacities Generating Heat Rates Resources Fuels Used Availability Fixed and Variable O&M Costs Minimum Generation Requirements (Turn Down Constraint)

Output Profile for Non-Dispatchable Resources Emissions Limits or Emission Rates for NOx, SO2, HCl, CO2, and Mercury Existing Pollution Control Equipment and Retrofit Options New Cost and Operating Characteristics Generating Resource Limits and Generation Profiles Resources Other System Regional Specification Requirements Inter-regional Transmission Capabilities Reserve Margin Requirements for Reliability System Specific Generation Requirements Electricity Electricity Demand Demand Peak Load Load Curves Financial Capital Charge Rates Outlook Fuel Supply Fuel Supply Curves for Coal, Gas, and Biomass Fuel Price Fuel Quality Transportation Costs for Coal, Natural Gas, and Biomass Regulatory Air Regulations for NOx, SO2, HCl, CO2, and Mercury Outlook Other Air Regulations Nuclear Unit Zero-Emission Credit Programs Non-air Regulations (Affecting Electric Generating Unit Operations)

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1 A.5.2 Model Outputs 2 IPM can produce a variety of output reports. These range from extremely detailed reports, 3 which describe the results for each model plant and run year, to summary reports, which 4 present results for regional and national aggregates. Individual topic areas can be included or 5 excluded at the users discretion. Standard IPM reports cover the following topics:

6 7

• Generation and capacity mix 8

9

• Capacity additions and retirements 10 11

• Capacity and energy prices 12 13

• Power production costs 14 15

• Fuel consumption 16 17

• Fuel supply and demand 18 19

• Fuel prices for coal, natural gas, and biomass 20 21

• Emissions (NOx, SO2, HCl, CO2, and mercury) 22 23

• Emission allowance prices 24 25 A.6 Appendix A References 26 U.S. Environmental Protection Agency (EPA), Power Sector Modeling Platform v6, 2019.

27 (EPA, 2019).

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1 APPENDIX B 2 OVERVIEW OF PROMOD 3 This appendix provides an overview of PROMOD, the software that was used to simulate 4 electricity market operations and derive electricity price projections for replacement energy cost 5 calculations.14 6 For over 40 years, energy firms have been using PROMOD for a variety of applications that 7 include locational marginal price (LMP) forecasting, financial transmission right (FTR) valuation, 8 environmental analysis, asset valuations (generation and transmission), transmission 9 congestion analysis, and purchased power agreement evaluations.

10 PROMOD provides valuable information on the dynamics of the marketplace by determining the 11 effects of transmission congestion, fuel costs, generator availability, bidding behavior, and load 12 growth on market prices. PROMOD performs a daily or weekly commitment and hourly or 13 sub-hourly dispatch, recognizing both generation and transmission impacts at the nodal and 14 zonal level.

15 PROMOD forecasts hourly and sub-hourly energy prices, unit generation, revenues and fuel 16 consumption, external market transactions, transmission flows, and congestion and loss prices.

17 PROMOD is built on robust data structures. This includes the ability to enter time-based data 18 changes at the hourly and sub-hourly granular level and detailed generator data inputs. In 19 addition to unit capacity changes, users can enter data describing future changes to generator 20 data.

21 B.1 Price Forecasting 22 PROMOD performs a security-constrained unit commitment and economic dispatch that is 23 co-optimized with operating reserve requirements, similar to how transmission/independent 24 system operators (TSOs/ISOs) set schedules and determine prices, to provide forecasts of 25 LMPs. LMP may be reported for selected nodes, user-defined hubs, or load-weighted or 26 generator-weighted hubs; this may be further broken down into a reference price, a congestion 27 price (showing individual flowgate contributions to congestion), and a marginal loss price.

28 B.2 Transmission and Congestion Valuation 29 PROMOD performs valuation of transmission, congestion and associated financial 30 instrumentssuch as FTRs, congestion revenue rights (CRRs), and transmission congestion 31 contracts (TCCs)by providing all market participants and energy companies with the powerful 32 tools needed to quantify market prices, identify binding constraints, and evaluate economic 33 impacts of the specific constraints that have strategic significance to specific portfolios and 34 business needs.

35 B.3 Renewable Energy Valuation 36 PROMOD simulates the effects of intermittent energy schedules from wind, solar, and other 37 renewable projects on transmission congestion, and forecasts the amount of energy that may 14 The source of information for this appendix is ABB, unless otherwise stated.

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1 be curtailed considering the opportunity costs from production tax and renewable energy 2 credits. This information enables the user to evaluate renewable projects and their impacts on 3 the wider generation and transmission system.

4 B.4 Economic Transmission Analysis 5 PROMOD provides market participants and energy companies with the ability to evaluate the 6 economic benefit, changes in transmission congestion, and impact to generation assets 7 associated with transmission expansion and outage scheduling. By simulating the energy 8 market in detail, users can see the LMP and its components, transmission flows, and the 9 behavior of the generating units.

10 B.5 Zonal Power Market Analysis 11 PROMOD simulates, on an hourly and sub-hourly basis, the applicable region under a variety of 12 conditions. This information is then used to quantify the operating risks associated with each 13 facility and develop a detailed forecast of zonal market clearing prices and system operation 14 under these conditions. PROMOD is also used to perform long-term, transportation-based 15 simulations of regions with robust hourly unit commitment and sub-hourly dispatch decisions, 16 using the capacity expansion determined by ABBs Reference Case, Capacity Expansion or 17 Market Power solutions. Figure B-1 summarizes PROMOD inputs and outputs.

18 19 Figure B-1: PROMOD Data Inputs and Outputs 20 Source: PJM 21 22 B.6 Appendix B References 23 ABB, ABB Ability' PROMOD-Generation and transmission modeling system with nodal and 24 zonal price forecasting, 2018. Brochure available for download at: https://www.hitachiabb-25 powergrids.com/offering/product-and-system/enterprise/energy-portfolio-management/market-26 analysis/promod.

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1 PJM Interconnection LLC, PJM PROMOD Overview, 2017. Available at:

2 https://www.pjm.com/-/media/committees-groups/subcommittees/cs/20170811/20170811-item-3 02-pjm-promod-overview.ashx.

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1 APPENDIX C 2 SELECTION OF NUCLEAR PLANTS FOR ALTERNATIVE CASES (NEW 3 ENGLAND) 4 This appendix illustrates the use of the criteria to determine the Alternative Case(s) for the 5 calculation of replacement energy costs for ISO-NE (ISO, 2018).

6 The nuclear power plants currently in operation in the ISO-NE (ISO, 2018) market are:

7 8

• Millstone Power Station, Unit 2 and Unit 3 9

• Seabrook Station 10 The Pilgrim Nuclear Power Station was shut down permanently on May 31, 2019, so it is not 11 considered in the determination of replacement energy costs. The report considers only 12 Millstone Power Station and Seabrook Station.

13 Regarding location relative to congestion in the region, there is no significant difference between 14 Millstone and Seabrook. Historically, ISO-NE had congestion that created relatively high 15 electricity prices in locations such as southwest Connecticut and the Greater Boston area. Over 16 the past few years, however, transmission providers in the region have implemented large 17 transmission projects that have significantly reduced congestion and led to relatively flat prices 18 in the market, with variations due to losses. To illustrate, Figure C-1 shows pricing zones in 19 ISO-NE. In addition to providing prices for specific locations or nodes on the system, ISO-NE 20 provides prices for the eight load zones. The zonal prices are aggregations of the nodal prices 21 and are calculated as load-weighted-average prices of all the nodes within a load zone. In 22 addition, ISO-NE provides prices for a central Hub. The Hub is a collection of internal nodes 23 intended to represent an uncongested price for electric energy, facilitate energy trading, and 24 enhance transparency and liquidity in the marketplace.15 The Hub price is calculated as a 25 simple average of price at 32 nodes in central New England where little congestion is evident.

15 ISO New England, 2017 Annual Markets Report, May 17, 2018, page 48, available at: https://www.iso-ne.com/staticassets/documents/2018/05/2017-annual-markets-report.pdf (ISO, 2018).

C-1

1 2 Figure C-1 ISO New England Pricing Zones 3 Source: ISO New England (ISO, 2018) 4 5 Figure C-2 shows the simple average zonal and hub prices in 2017. The differences between 6 zonal prices and between zonal and hub prices were relatively small. ISO-NEs 2017 Annual 7 Markets Report (ISO, 2018) states that the Maine load zone had the lowest average prices in 8 the region in 2017. Maines prices averaged $0.86 per MWh and $2.55 per MWh lower than the 9 Hubs prices for the day-ahead and real-time markets, respectively.16 The report also adds that 10 NEMA [Northeast Massachusetts/Boston] had the highest average prices in both the 11 day-ahead and real-time markets. NEMA average prices were slightly higher than the Hubs 12 prices, by $0.10 per MWh and $0.83 per MWh, respectively.17 16 Ibid, page 52.

17 Ibid.

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1 2 Figure C-2 Simple Average Hub and Load Zone Prices, 2017 3 Source: (ISO, 2018) 4 5 Figure C-3 shows load-weighted monthly average prices for the ISO-NE (ISO, 2018) over the 6 5-year span from 2013 to 2017. As indicated in the ISO-NE (ISO, 2018) report, load-weighted 7 energy prices by load zone from 2013 to 2017 indicate a pattern that varies considerably by 8 year and month, but typically not by load zone.18 Extreme prices occurred in periods such as 9 the months of January and February in 2013 to 2015 due to high natural gas prices.

10 The foregoing shows that there will be relatively small difference in the impact of an outage of a 11 unit at Millstone or Seabrook based on congestion at the units location.

18 Ibid, page 53.

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1 2 Figure C-3 Day-Ahead Load-Weighted Prices 3 Source: (ISO, 2018) 4 5 There is some variation in the sizes of the units. Millstone Power Station Unit 2 and Unit 3 are 6 868 MW and 1,220 MW, respectively. Seabrook Station is 1,251 MW. Because there isnt a 7 significant difference in unit capacities between Millstone Unit 3 and Seabrook Station, the 8 report expects that there will be a relatively small difference in the impact of an outage at either 9 unit. The replacement energy cost impact of an outage at Millstone Unit 2 might be different 10 from an outage at either of the other two units.

11 Regarding proximity to load centers both power plants are relatively close to the ISO-NEs major 12 load centers, which are in southern New England. In New England, power generally flows from 13 generation centers in the north and east to load centers in the south and west. Because 14 Millstone is more embedded in the south and west of the market it will be downstream of flows, 15 relative to Seabrook. Therefore, to the extent proximity to load centers is a factor, the impact 16 will be stronger for Millstone than Seabrook; however, the effect is likely to be relatively small.

17 The report therefore expects that there will be relatively small difference in the impact of an 18 outage of a unit at Millstone or Seabrook based on the units proximity to load centers.

19 To summarize, considering the criteria for selection of nuclear power plants to analyze to 20 determine replacement energy cost for ISO-NE:

21 22

• Location relative to congestion in the region: Relatively small difference in impact on 23 market if either Millstone Unit or the Seabrook Unit is selected. The outage of any unit 24 will be sufficient to determine replacement energy costs for the region.

25 26

• Size of power plant: Relatively small difference in impact on the market if either Millstone 27 Unit 3 or Seabrook Station is selected, but the impact of Millstone Unit 2 might be C-4

1 different, and likely lower. Millstone Unit 2 should be selected for least impact and either 2 Millstone Unit 3 or Seabrook Station should be selected for most impact.

3 4

• Proximity to load centers: Relatively small difference in pricing based on the units 5 proximity to load centers if either Millstone Unit or Seabrook Unit is selected. The 6 outage of any unit will be sufficient to determine replacement energy costs for the region.

7 C.1 Appendix C References 8 ISO New England, 2017 Annual Markets Report, May 2018. Available at: https://www.iso-9 ne.com/static-assets/documents/2018/05/2017-annual-markets-report.pdf. (ISO, 2018).

C-5

1 APPENDIX D 2 EXISTING AND COMMITTED NUCLEAR UNITS 3 Table D-1 is a list of existing and committed nuclear generation units modeled in service in the 4 replacement energy cost report. The Planned Retirement Year is the year the unit is expected 5 to be taken out of service based on the owners announced plan or the units operating license 6 expiration date. IPM simulates electricity production activity in a manner that would minimize 7 production costs, by dispatching generation in the market in economic merit order, subject to 8 transmission and other system limitations, to determine prices. For modeling purposes, the IPM 9 Economic Dispatch Curtailment column identifies when a unit is not to be economically 10 dispatched for the purposes of calculating replacement energy costs.

11 Table D-1 Existing and Committed Nuclear Generation Units IPM Economic Planned Unit State Capacity On Line Dispatch Model Plant Name Retirement ID Name (MW) Year Curtailment Region Year Yeara Arkansas 1 Arkansas 833 1974 2034 2023 MISO Nuclear One Arkansas 2 Arkansas 985 1980 2039 2039 MISO Nuclear One Beaver Valley 1 Pennsylvania 907 1976 2021 2021 PJM Power Stationb Beaver Valley 2 Pennsylvania 901 1987 2022 2022 PJM Power Stationb Braidwood 1 Illinois 1,183 1988 2047 2047 PJM Station Braidwood 2 Illinois 1,154 1988 2048 2048 PJM Station Browns Ferry 1 Alabama 1,266 1974 2034 2034 Southeast Nuclear Plant Browns Ferry 2 Alabama 1,268 1975 2034 2034 Southeast Nuclear Plant Browns Ferry 3 Alabama 1,270 1977 2037 2037 Southeast Nuclear Plant Brunswick Steam Electric 1 North Carolina 938 1977 2037 2037 Southeast Plant Brunswick Steam Electric 2 North Carolina 932 1975 2035 2035 Southeast Plant Byron Station 1 Illinois 1,164 1985 2045 2045 PJM Byron Station 2 Illinois 1,136 1987 2047 2047 PJM Callaway Plant 1 Missouri 1,190 1984 2045 2023 MISO 12 D-1

1 Table D-1 Existing and Committed Nuclear Generation Units (continued)

IPM Economic Planned Unit State Capacity On Line Dispatch Model Plant Name Retirement ID Name (MW) Year Curtailment Region Year Yeara Calvert Cliffs Nuclear Power 1 Maryland 866 1975 2035 2035 PJM Plant Calvert Cliffs Nuclear Power 2 Maryland 842 1977 2037 2037 PJM Plant Catawba South 1 1,160 1985 2044 2044 Southeast Nuclear Station Carolina Catawba South 2 1,150 1986 2044 2044 Southeast Nuclear Station Carolina Clinton Power 1 Illinois 1,065 1987 2027 2027 MISO Station Columbia Generating 2 Washington 1,180 1984 2044 2023 WECC Station Comanche Peak Nuclear 1 Texas 1,205 1990 2030 2030 ERCOT Power Plant Comanche Peak Nuclear 2 Texas 1,195 1993 2033 2033 ERCOT Power Plant Cooper 1 Nebraska 772 1974 2034 2023 SPP Nuclear Station Davis Besse Nuclear Power 1 Ohio 894 1977 2037 2037 PJM Station Diablo Canyon Nuclear Power 1 California 1,122 1985 2025 2025 WECC Plant Diablo Canyon Nuclear Power 2 California 1,118 1986 2026 2026 WECC Plant Donald C Cook 1 Michigan 1,009 1975 2035 2035 PJM Nuclear Plant Donald C Cook 2 Michigan 1,060 1978 2038 2038 PJM Nuclear Plant Dresden Nuclear Power 2 Illinois 902 1970 2030 2030 PJM Station Dresden Nuclear Power 3 Illinois 895 1971 2031 2031 PJM Station Duane Arnold 1 Iowa 601 1975 2020 2020 MISO Energy Center D-2

1 Table D-1 Existing and Committed Nuclear Generation Units (continued)

IPM Economic Planned Unit State Capacity On Line Dispatch Model Plant Name Retirement ID Name (MW) Year Curtailment Region Year Yeara Edwin I Hatch 1 Georgia 876 1975 2035 2023 Southeast Nuclear Plant Edwin I Hatch 2 Georgia 883 1979 2038 2023 Southeast Nuclear Plant Enrico Fermi 2 Michigan 1,141 1988 2045 2023 MISO Nuclear Plant Grand Gulf 1 Mississippi 1,401 1985 2045 2023 MISO Nuclear Station H B Robinson South Steam Electric 2 741 1971 2031 2023 Southeast Carolina Plant Hope Creek Generating 1 New Jersey 1,190 1986 2046 2023 PJM Station Indian Point Nuclear 2 New York 1,012 1973 2020 2020 NYISO Generating Indian Point Nuclear 3 New York 1,039 1976 2021 2021 NYISO Generating James A Fitzpatrick 1 New York 853 1976 2035 2030 NYISO Nuclear Power Plant Joseph M Farley Nuclear 1 Alabama 874 1977 2037 2037 Southeast Plant Joseph M Farley Nuclear 2 Alabama 883 1981 2041 2041 Southeast Plant LaSalle County 1 Illinois 1,135 1984 2042 2042 PJM Station LaSalle County 2 Illinois 1,134 1984 2044 2044 PJM Station Limerick Generating 1 Pennsylvania 1,120 1986 2045 2045 PJM Station Limerick Generating 2 Pennsylvania 1,122 1990 2049 2049 PJM Station McGuire 1 North Carolina 1,158 1981 2041 2041 Southeast Nuclear Station McGuire 2 North Carolina 1,158 1984 2043 2043 Southeast Nuclear Station D-3

1 Table D-1 Existing and Committed Nuclear Generation Units (continued)

IPM Economic Planned Unit State Capacity On Line Dispatch Model Plant Name Retirement ID Name (MW) Year Curtailment Region Year Yeara Millstone 2 Connecticut 868 1975 2036 2036 ISO-NE Power Station Millstone 3 Connecticut 1,220 1986 2046 2046 ISO-NE Power Station Monticello Nuclear 1 Minnesota 617 1971 2031 2023 MISO Generating Plant Nine Mile Point 1 New York 626 1969 2030 2030 NYISO Nuclear Station Nine Mile Point 2 New York 1,287 1987 2047 2047 NYISO Nuclear Station North Anna 1 Virginia 948 1978 2038 2038 PJM Power Station North Anna 2 Virginia 944 1980 2041 2041 PJM Power Station Oconee South 1 847 1973 2033 2033 Southeast Nuclear Station Carolina Oconee South 2 848 1974 2034 2034 Southeast Nuclear Station Carolina Oconee South 3 859 1974 2035 2035 Southeast Nuclear Station Carolina Palisades 1 Michigan 784 1972 2022 2022 MISO Nuclear Plant Palo Verde Nuclear 1 Arizona 1,311 1986 2045 2045 WECC Generating Station Palo Verde Nuclear 2 Arizona 1,314 1986 2046 2046 WECC Generating Station Palo Verde Nuclear 3 Arizona 1,312 1988 2048 2048 WECC Generating Station Peach Bottom Atomic Power 2 Pennsylvania 1,245 1974 2034 2034 PJM Station Peach Bottom Atomic Power 3 Pennsylvania 1,248 1974 2035 2035 PJM Station Perry Nuclear 1 Ohio 1,240 1987 2026 2026 PJM Power Plant 2

D-4

1 Table D-1 Existing and Committed Nuclear Generation Units (continued)

IPM Economic Planned Unit State Capacity On Line Dispatch Model Plant Name Retirement ID Name (MW) Year Curtailment Region Year Yeara Point Beach 1 Wisconsin 598.1 1970 2031 2031 MISO Nuclear Plant Point Beach 2 Wisconsin 598 1972 2033 2033 MISO Nuclear Plant Prairie Island Nuclear 1 Minnesota 521 1974 2034 2034 MISO Generating Plant Prairie Island Nuclear 2 Minnesota 519 1974 2035 2035 MISO Generating Plant Quad Cities Nuclear Power 1 Illinois 908 1972 2033 2033 PJM Station Quad Cities Nuclear Power 2 Illinois 911 1972 2033 2033 PJM Station R E Ginna Nuclear Power 1 New York 582 1970 2030 2030 NYISO Plant River Bend 1 Louisiana 968 1986 2026 2023 MISO Station Salem Nuclear Generating 1 New Jersey 1,170 1977 2037 2037 PJM Station Salem Nuclear Generating 2 New Jersey 1,158 1981 2040 2040 PJM Station Seabrook New 1 1,251 1990 2030 2030 ISO-NE Station Hampshire Sequoyah 1 Tennessee 1,152 1981 2041 2041 Southeast Nuclear Plant Sequoyah 2 Tennessee 1,126 1982 2042 2042 Southeast Nuclear Plant Shearon Harris Nuclear Power 1 North Carolina 932 1987 2047 2030 Southeast Plant South Texas 1 Texas 1,280 1988 2048 2048 ERCOT Project South Texas 2 Texas 1,280 1989 2049 2049 ERCOT Project St Lucie Plant 1 Florida 981 1976 2036 2036 Southeast St Lucie Plant 2 Florida 987 1983 2043 2043 Southeast 2

D-5

1 Table D-1 Existing and Committed Nuclear Generation Units (continued)

IPM Economic Planned Unit State Capacity On Line Dispatch Model Plant Name Retirement ID Name (MW) Year Curtailment Region Year Yeara Surry Power 1 Virginia 838 1972 2032 2032 PJM Station Surry Power 2 Virginia 838 1973 2033 2033 PJM Station Susquehanna Steam Electric 1 Pennsylvania 1,247 1983 2043 2043 PJM Station Susquehanna Steam Electric 2 Pennsylvania 1,247 1985 2044 2044 PJM Station Turkey Point Nuclear 3 Florida 802 1972 2033 2033 Southeast Generating Turkey Point Nuclear 4 Florida 802 1973 2033 2033 Southeast Generating Virgil C South Summer 1 971 1984 2043 2023 Southeast Carolina Nuclear Station Vogtle Electric Generating 1 Georgia 1,150 1987 2047 2047 Southeast Plant Vogtle Electric Generating 2 Georgia 1,152 1989 2049 2049 Southeast Plant Vogtle Electric Generating 3 Georgia 1,100 2022 2062 NA Southeast Plant Vogtle Electric Generating 4 Georgia 1,100 2023 2063 NA Southeast Plant Waterford Steam Electric 3 Louisiana 1,165 1985 2025 2023 MISO Station Watts Bar 1 Tennessee 1,123 1996 2036 2023 Southeast Nuclear Plant Watts Bar 2 Tennessee 1,122 2016 2056 NA Southeast Nuclear Plant Wolf Creek Generating 1 Kansas 1,175 1985 2045 2023 SPP Station 2 a The IPM Economic Dispatch Curtailment Year is a calculated value and represents when a unit is 3 projected not to be economically dispatched for the purposes of calculating replacement energy costs 4 based on modeling performed in this report.

5 b Beaver Valley Units 1&2 are assumed to be curtailed in the model beginning after 2021 and 2022 based 6 on a March 2018 announcement by the owner that was rescinded in March 2020.

D-6

1 D.1 Appendix D References 2 U.S. Environmental Protection Agency (EPA), Power Sector Modeling Platform v6, 2019.

3 (EPA, 2019).

4 U.S. Nuclear Regulatory Commission, Commercial Nuclear Power Reactors - Operating 5 Reactors, 2019. Available at: https://www.nrc.gov/reading-rm/doc-collections/datasets/

D-7

1 APPENDIX E 2 DETERMINATION OF REGIONAL DEFINITIONS FOR REPLACEMENT 3 COST CALCULATIONS 4 As shown in Figure E-1, the U.S. electricity system covering the lower 48 states and the District 5 of Columbia is divided into three major interconnections:19 6

7

• The Eastern Interconnection covers the area east of the Rocky Mountains.

8 9

• The Western Interconnection covers the Rocky Mountains and areas to the west.

10 11

• The ERCOT covers most, but not all, of Texas.

12 The interconnections operate largely independently from each other except for a few direct 13 current connections that allow for limited transfers of power between them. Utilities within each 14 are interconnected and synchronized. Because of the electrical connections or network, a 15 problem in one part of an interconnection can propagate to other parts of the interconnection if 16 the appropriate safeguards are not in place. The network also allows generation to be sited in 17 one part of the interconnection and serve load in other parts. The interconnections are also 18 subdivided into electricity markets, with generators in a market generally serving load in that 19 market, although imports and exports of power are allowed.20 19 The Eastern Interconnection includes parts of Canada, and the Western Interconnection includes parts of Canada and Mexico.

20 U.S. electricity markets have wholesale and retail components. Wholesale electricity involves the sale of electricity between generators, utilities, and load-serving entities. Retail electricity involves the sale to consumers. The NRC is focused on wholesale electricity impacts; therefore, this report focuses on wholesale electricity markets.

E-1

1 2 Figure E-1 North American Electric Interconnections 3 Source: (NERC, 2018). The Canadian province of Quebec is a separate interconnection, which is not 4 shown on the map.

5 6 Some U.S. wholesale electricity markets are regulated, whereas others are restructured 7 competitive markets. In regulated electricity markets, vertically integrated utilities own 8 generation, transmission, and distributions systems and are responsible for serving consumers 9 in the market. In restructured markets the generation, transmission, and distribution functions 10 are unbundled, generation is competitive, and operation of the transmission system is 11 transferred to an independent, not-for-profit market operator. As shown in Figure E-2, the 12 seven restructured, competitive markets in the United States are:

13 14

• CAISO 15 16

• ERCOT 17 18

• ISO-NE 19 20

• Midcontinent ISO (MISO) 21 22

• New York ISO (NYISO) 23 24

• PJM 25 26

• SPP E-2

1 2 Figure E-2 Restructured Markets in North America 3 Source: (IRC, 2020) Available at http://isorto.org.

4 5 The competitive markets are characterized by a relatively high level of price transparency. The 6 market operators publish electricity prices for hundreds of locations as frequently as every 7 5 minutes, and these are aggregated to determine hourly prices. The markets also have liquid 8 trading hubs that are used for transactions by market participants. In general, market operators 9 dispatch generation in the market in economic merit order, subject to transmission and other 10 system limitations, to determine prices. The marginal unit sets prices as determined by the 11 economic merit order of the supply offers (the generation stack). If a nuclear power plant that is 12 part of the generation stack is taken out of service, the operator would redispatch generation 13 and rebuild the stack. Prices would change if a generator with a different cost profile becomes 14 the new marginal unit. Therefore, the loss of a nuclear power generating unit could affect prices 15 in the market, and units of different sizes could affect prices differently. For example, the larger 16 a nuclear plant, the more likely it is to affect power prices and the larger the impact is likely to 17 be. Further, if a nuclear power plant goes out of service in a competitive market, replacement 18 power can be purchased from the spot market. Purchases could also be based on futures or 19 structures, such as long-term contracts built on underlying market prices.

20 Consistent with the foregoing, in areas with competitive markets the report uses regional 21 definitions that are coincident with the existing competitive markets. For example, NYISO was E-3

1 considered as a single region for the purposes of the report. Therefore, the report determines 2 replacement energy costs for NYISO that apply to all nuclear power plants located in that 3 market.

4 The exception is CAISO. CAISO operates the Western EIM, which currently includes eight 5 non-CAISO utilities or balancing authorities, with seven entities planning to participate by 2022.

6 As shown in Figure E-3, the EIM covers portions of almost all the states in the Western 7 Interconnection. The EIM is a real-time energy market. In other competitive markets, 8 participants commit to sell or purchase power usually a day ahead of the time when the power 9 would be used.

10 This is referred to as the day-ahead market. Shortly before the actual time for the power to be 11 consumed, the operator makes adjustments, if necessary, to balance fluctuations (imbalances) 12 in demand and supply caused by unexpected events, such as load forecast errors, generation 13 outages, or transmission line limitations. These adjustments are made in the real-time market.

14 Because the EIM is only a real-time market, participants do not make prior commitments for 15 sales or purchases, such as the commitments in a day-ahead market. Rather, participants buy 16 and sell power close to the time electricity is consumed. The EIM gives system operators 17 real-time visibility across neighboring grids, and it helps balance supply and demand at 18 relatively lower cost (CAISO, 2020).21 19 Two nuclear power plants in the Western Interconnection are included in the reportColumbia 20 Generating Station in Washington State and Palo Verde Nuclear Generating Station in Arizona.

21 Diablo Canyon Nuclear Power Plant units in California are scheduled to retire by 2026 and 22 therefore were not assessed explicitly for replacement energy costs. Because of the scope of 23 the Western EIM, it is likely that the outage of any of the nuclear power plants would affect 24 prices in several parts of the Western Interconnection. Therefore, Western Interconnection is 25 treated as a single region for the purposes of calculating replacement energy cost.

21 See www.westerneim.com for additional information.

E-4

1 2 Figure E-3 Western EIM 3 Source: (CAISO, 2020) 4 The remaining area is the southeastern United States, which is served by vertically integrated 5 utilities in regulated markets. Although utilities serve most of their demand with generation 6 located within their service territories, some own or contract for generation capacity outside of 7 their service territories and reserve transmission capacity to transport the power to serve their 8 customers (FPL, 2019; GPC, 2019).22 9 In addition, under FERC Order No. 888 (FERC, 2006), public utilities are required to provide 10 open-access transmission service on a comparable basis to the transmission service they 11 provide themselves. Each public utility is required to file an open-access non-discriminatory 22 For example, Florida Power and Light (FPL) owns generation capacity in central Georgia, and Gulf Power owns generation capacity in central Georgia and Mississippi. See FPL Ten Year Power Plant Site Plan, 2019-2028, April 2019 (http://newhampshiretransmission.com/company/pdf/10-year-site-plan.pdf) (FPL, 2019) and Gulf Power Ten Year Site Plan, 2019-2028, April 1, 2019 (http://www.psc.state.fl.us/Files/PDF/Utilities/Electricgas/TenYearSitePlans/2019/Gulf%20Power.pdf).

(GPC, 2019).

E-5

1 transmission tariff that contains minimum terms and conditions of non-discriminatory service.

2 Further, Order No. 889 established rules governing Open-Access Same-Time Information 3 System (OASIS), an information sharing system that is used to provide or request transmission 4 services. Each utility has an OASIS site, which among other things, provides information about 5 available transmission capability and a process for requesting transmission service on a 6 non-discriminatory basis.23 Therefore, there are frameworks under which utilities in regulated 7 markets can source power from locations outside their service territories in the event of 8 shortages. The outage of a nuclear power plant in one utilities service territory could therefore 9 impact power flows and electricity prices in neighboring service territories.

10 Under FERC Order No. 1000 (FERC, 2012), public utility transmission providers are required to 11 participate in a regional transmission planning process that satisfies the transmission planning 12 principles of Order No. 890 (FERC, 2007) and produces a regional transmission plan.24 13 Figure E-4 shows the transmission planning regions formed in compliance with Order No. 1000 14 (FERC, 2012). Three entities are responsible for regional transmission planning in the 15 southeastern U.S.

16 17

• FRCC 18 19

• SCRTP 20 21

• SERTP 22 The regional transmission planning processes include economic transmission planning studies 23 that allow market participants to request studies for the feasibility of long-term economic power 24 transactions. For example, in 2018 SERTP evaluated economic planning studies for the 25 transfer of 1,000 MW of power between Santee Cooper and neighboring transmission systems 26 (Duke Energy Carolinas, Duke Energy Progress, and Southern Balancing Authority Area).25 27 Because of the potential for interactions between the regions, the report considers the regulated 28 markets in the southeastern United States as a single region for the purposes of the calculation 29 of replacement energy cost.

23 For additional information, see FERC, 2006.

24 For additional information on Order No. 1000 see FERC, 2012. FERC Order No. 890 (FERC, 2007) was designed to: (1) strengthen the pro forma open-access transmission tariff (OATT), to ensure that it achieves its original purpose of remedying undue discrimination; (2) provide greater specificity to reduce opportunities for undue discrimination and facilitate the Commission's enforcement; and (3) increase transparency in the rules applicable to planning and use of the transmission system.

25 SERTP 2018 Economic Planning Studies, November 29, 2018, available at http://www.southeasternrtp.com/docs/general/2018/2018-SERTP-Economic-Study-Results-FINAL.pdf.

(SERTP, 2018).

E-6

1 2 Figure E-4 FERC Order No. 1000 Transmission Planning Regions 3 Source: (FERC, 2012). The map was annotated with black dots to show the approximate locations of 4 existing nuclear power plants. Heavy black lines have been added to distinguish the location of the eight 5 regional definitions modeled in this report.

6 7 The following eight regional definitions are used for the replacement cost analysis:

8 9 1. ERCOT 10 11 2. ISO-NE 12 13 3. MISO 14 15 4. NYISO 16 17 5. PJM 18 19 6. SPP 20 21 7. Southeast, comprising FRCC, SCRTP, and SERTP 22 23 8. WECC, comprising CAISO, ColumbiaGrid, NTTG and WestConnect E-7

1 E.1 Appendix E References 2 California Independent System Operator (CAISO), Western Energy Imbalance Market, 2020.

3 Available at: https://www.westerneim.com/Pages/About/default.aspx. (CAISO, 2020).

4 Florida Power and Light (FPL), Ten Year Power Plant Site Plan 2019-2028, April 2019.

5 (FPL, 2019). Available at http://newhampshiretransmission.com/company/pdf/10-year-site-6 plan.pdf.

7 Gulf Power Company (GPC), Ten Year Site Plan 2019-2028, April 2019. (GPC, 2019).

8 Available at 9 http://www.psc.state.fl.us/Files/PDF/Utilities/Electricgas/TenYearSitePlans/2019/Gulf%20Power.

10 pdf.

11 ISO/RTO Council, 2020. (IRC, 2020). Available at: https://isorto.org/.

12 North American Electric Reliability Corporation (NERC), Electricity Supply and Demand 13 (ES&D), December 2018. (NERC, 2018).

14 Southeastern Regional Transmission Planning (SERTP), 2018 Economic Planning Studies, 15 November 2018. (SERTP, 2018).

16 U.S. Federal Energy Regulatory Commission, Order No. 888 Promoting Wholesale 17 Competition Through Open Access Non-discriminatory Transmission Services by Public 18 Utilities; Recovery of Stranded Costs by Public Utilities and Transmitting Utilities, 2006.

19 (FERC, 2006).

20 U.S. Federal Energy Regulatory Commission, Order No. 890 Preventing Undue Discrimination 21 and Preference in Transmission Service, 2007. (FERC, 2007).

22 U.S. Federal Energy Regulatory Commission, Order No. 1000 Transmission Planning and Cost 23 Allocation by Transmission Owning and Operating Utilities, 2012. (FERC, 2012).

E-8

1 APPENDIX F 2

SUMMARY

OF ASSUMPTIONS 3 This appendix provides additional details on the assumptions used for the replacement 4 energy cost report. It includes:

5 6

• Peak and energy demand assumptions 7

8

• Delivered natural gas prices 9

10

• Environmental assumptions, including state RPS requirements, RGGI assumptions, 11 CSAPR rules 12 13

• Recent and firm generation builds and retirements 14 15

• IPM economic builds and retirements 16 F.1 Peak and Energy Demand Assumptions 17 The report developed electricity peak and energy assumptions for the 2020 to 2030 report period 18 from NERC Electricity Supply & Demand (ES&D) information (Table F-1). Net internal demand 19 (peak demand) is the maximum hourly demand within a given year after removing interruptible 20 demand. Net energy for load is the projected annual electric grid demand, prior to accounting for 21 intra-regional transmission and distribution losses (Table F-2).

22 23 Table F-1 Peak Demand Assumptions: 2020-2030 Net Internal Demand (MW)

Region FRCC MRO NPCC NPCC RF SERC SERC SERC SPP TRE WECC WECC WECC WECC Assessment New New SERC- SERC- SERC- NWPP-FRCC MISO PJM SPP ERCOT RMRG SRSG CAMX Area England York E N SE US 2020 45,608 119,303 24,878 31,759 144,287 42,907 39,935 45,983 52,044 73,706 49,075 12,637 24,298 50,132 2021 46,170 119,646 24,511 31,581 144,672 43,257 39,982 46,158 52,410 75,422 49,495 12,806 24,668 50,275 2022 46,653 120,003 24,396 31,469 145,166 43,598 40,092 46,406 53,194 76,854 49,682 12,952 25,222 50,550 2023 47,144 120,424 24,317 31,414 145,885 44,100 40,296 46,662 53,485 78,258 50,141 13,202 25,712 50,201 2024 47,753 120,788 24,264 31,406 146,459 44,490 40,354 46,936 53,694 79,500 50,456 13,369 26,158 51,447 2025 48,290 121,289 24,239 31,429 147,118 44,930 40,477 47,201 53,965 80,677 50,767 13,549 26,650 51,584 2026 48,897 121,629 24,249 31,473 147,862 45,432 40,748 47,876 54,238 82,006 51,046 13,695 27,021 51,380 2027 49,508 122,227 24,288 31,533 148,706 45,928 40,820 46,976 54,528 83,338 51,409 13,844 27,473 51,471 2028 49,508 122,126 24,326 31,599 149,688 46,435 40,881 46,607 54,873 84,677 51,672 14,024 27,828 51,645 2029 50,018 122,483 24,258 31,579 150,377 46,896 41,001 46,686 55,237 86,158 52,007 14,208 28,304 51,838 2030 50,534 122,842 24,190 31,559 151,070 47,361 41,121 46,764 55,603 87,666 52,343 14,394 28,788 52,031 24 Source: (NERC, 2018)

F-1

1 Table F-2 Energy Demand Assumptions: 2020-2030 Net Energy for Load (GWh)

Region FRCC MRO NPCC NPCC RF SERC SERC SERC SPP TRE WECC WECC WECC WECC Assessment New New SERC- SERC- SERC- NWPP-FRCC MISO PJM SPP ERCOT RMRG SRSG CAMX Area England York E N SE US 2020 236,779 669,881 120,395 155,567 808,638 214,026 214,064 247,542 259,341 392,609 294,092 69,671 111,351 267,722 2021 238,483 672,266 118,949 154,567 808,882 215,557 213,647 248,432 265,942 401,983 295,659 70,869 113,463 268,124 2022 240,380 675,220 117,870 153,898 812,908 216,856 213,691 249,788 267,318 412,593 297,547 71,392 116,076 269,637 2023 241,710 679,319 117,039 153,593 816,817 218,138 213,861 251,006 271,312 422,216 298,914 72,987 117,962 270,617 2024 244,035 680,250 116,249 153,476 822,364 220,369 214,277 252,444 272,734 431,139 300,409 73,974 119,851 270,940 2025 245,769 681,949 115,594 153,454 824,140 221,904 214,084 253,679 274,090 439,094 301,503 74,874 121,139 271,314 2026 247,849 684,148 115,196 153,504 828,788 224,309 214,223 256,182 275,174 448,093 302,145 75,761 122,817 271,302 2027 250,053 687,133 114,981 153,691 833,712 226,671 214,622 253,400 276,116 457,273 303,565 76,793 124,326 271,324 2028 250,053 689,634 114,766 153,926 841,206 229,719 215,398 252,584 277,200 466,667 305,631 77,896 126,021 271,405 2029 251,764 692,144 114,081 153,722 845,368 231,760 215,565 253,221 279,518 476,856 307,105 78,990 127,986 271,869 2030 253,486 694,663 113,400 153,518 849,551 233,819 215,733 253,860 281,854 487,269 308,586 80,099 129,981 272,334 2 Source: (NERC, 2018) 3 4 F.2 Delivered Natural Gas Price Assumptions 5 The EIA AEO (DOE, 2019) provides delivered natural gas prices by state within the U.S. electricity 6 market. The report used the delivered natural gas prices shown in Table F-3 as the basis for the 7 natural gas price projections for the modeling and replacement cost calculations.

F-2

1 2 Table F-3 AEO Delivered Natural Gas Prices (2018$/MMBtu)

MN, WV, CT, IA, MD, IN, TX, MA, NY, ND DC, MT, CO, IL, KY, AL, LA, AZ, OR, Year Season ME, PA, OH SD, DE, GA FL WY, UT, CA MI, TN MS OK, NM WA NH, NJ NE, VA, ID NV WI AR RI, VT MO, NC, KS SC 2020 Winter 5.02 3.47 2.98 3.44 3.59 3.75 3.41 4.14 3.46 3.36 2.92 2.95 3.36 3.72 3.38 3.52 2020 Summer 3.79 3.42 3.09 3.51 3.44 3.41 3.65 3.90 3.37 3.16 3.22 2.90 3.49 3.75 3.27 3.81 2020 Spring/Fall 3.71 3.31 2.89 3.25 3.29 3.59 3.40 3.97 3.34 3.13 2.90 4.95 3.49 3.57 3.19 3.44 2021 Winter 4.82 3.42 2.98 3.39 3.50 3.77 3.35 4.02 3.34 3.30 2.91 2.92 3.31 3.58 3.32 3.49 2021 Summer 3.67 3.34 3.09 3.51 3.38 3.37 3.54 3.83 3.27 3.09 3.17 2.83 3.44 3.73 3.23 3.82 2021 Spring/Fall 3.64 3.22 2.87 3.19 3.18 3.50 3.30 3.83 3.26 3.02 2.86 5.04 3.38 3.47 3.19 3.40 2022 Winter 4.88 3.49 3.09 3.46 3.59 3.85 3.40 4.03 3.38 3.34 2.98 2.95 3.36 3.60 3.36 3.54 2022 Summer 3.77 3.41 3.21 3.61 3.49 3.42 3.58 3.86 3.34 3.13 3.25 2.89 3.49 3.85 3.29 3.93 2022 Spring/Fall 3.61 3.25 2.96 3.27 3.26 3.53 3.32 3.84 3.30 3.05 2.91 5.13 3.43 3.54 3.26 3.44 2023 Winter 5.03 3.62 3.30 3.59 3.76 3.94 3.48 4.15 3.53 3.46 3.09 2.98 3.48 3.76 3.47 3.67 2023 Summer 3.92 3.61 3.52 3.85 3.76 3.62 3.82 4.08 3.57 3.34 3.49 3.09 3.73 4.10 3.51 4.12 2023 Spring/Fall 3.69 3.38 3.19 3.39 3.45 3.66 3.46 3.97 3.44 3.21 3.08 5.32 3.58 3.71 3.42 3.58 2024 Winter 5.28 3.77 3.54 3.80 3.97 4.12 3.67 4.35 3.71 3.64 3.29 3.25 3.69 3.99 3.70 3.74 2024 Summer 4.19 3.89 3.92 4.20 4.12 3.96 4.19 4.46 3.92 3.69 3.83 3.49 4.08 4.45 3.86 4.28 2024 Spring/Fall 3.76 3.53 3.40 3.58 3.62 3.81 3.60 4.14 3.60 3.37 3.22 5.52 3.77 3.86 3.59 3.49 2025 Winter 5.40 3.91 3.77 3.98 4.16 4.31 3.81 4.53 3.91 3.82 3.46 3.38 3.87 4.22 3.84 3.93 2025 Summer 4.35 4.11 4.20 4.45 4.39 4.20 4.49 4.75 4.18 3.97 4.11 3.75 4.37 4.75 4.15 4.64 2025 Spring/Fall 3.86 3.67 3.61 3.79 3.83 4.02 3.82 4.35 3.81 3.59 3.45 5.69 3.97 4.07 3.80 3.74 2026 Winter 5.44 3.96 3.86 4.05 4.26 4.39 3.89 4.62 3.99 3.92 3.56 3.53 3.99 4.36 3.99 4.05 2026 Summer 4.37 4.13 4.27 4.52 4.49 4.25 4.53 4.81 4.23 4.04 4.19 3.84 4.48 4.89 4.26 4.76 2026 Spring/Fall 3.82 3.71 3.72 3.88 3.89 4.07 3.88 4.44 3.91 3.69 3.55 6.11 4.11 4.18 3.92 3.79 3

F-3

1 Table F-3 AEO Delivered Natural Gas Prices (2018$/MMBtu) (continued)

MN, WV, CT, IA, MD, IN, TX, MA, NY, ND DC, MT, CO, IL, KY, AL, LA, AZ, OR, Year Season ME, PA, OH SD, DE, GA FL WY, UT, CA MI, TN MS OK, NM WA NH, NJ NE, VA, ID NV WI AR RI, VT MO, NC, KS SC 2027 Winter 5.44 3.97 3.91 4.08 4.31 4.41 3.92 4.66 4.00 3.96 3.61 3.53 4.03 4.41 4.02 3.97 2027 Summer 4.35 4.11 4.30 4.56 4.52 4.24 4.53 4.82 4.26 4.06 4.22 3.85 4.51 4.88 4.31 4.69 2027 Spring/Fall 3.81 3.71 3.75 3.91 3.88 4.09 3.91 4.47 3.93 3.71 3.59 5.72 4.13 4.21 3.81 3.79 2028 Winter 5.43 3.98 3.94 4.11 4.35 4.44 3.95 4.68 4.03 3.99 3.65 3.54 4.06 4.47 4.10 3.98 2028 Summer 4.42 4.20 4.42 4.67 4.63 4.33 4.65 4.96 4.41 4.20 4.35 3.91 4.63 5.03 4.44 4.81 2028 Spring/Fall 3.81 3.75 3.81 3.96 3.94 4.14 3.96 4.52 4.00 3.76 3.66 5.77 4.17 4.29 3.81 3.86 2029 Winter 5.49 3.93 3.96 4.12 4.37 4.30 3.95 4.71 4.05 4.02 3.66 3.42 4.03 4.49 4.03 3.97 2029 Summer 4.38 4.16 4.42 4.67 4.63 4.32 4.65 4.96 4.41 4.22 4.35 3.88 4.60 5.00 4.39 4.76 2029 Spring/Fall 3.76 3.76 3.83 3.97 3.94 4.13 3.96 4.54 4.00 3.80 3.67 5.55 4.17 4.30 3.90 3.69 2030 Winter 5.51 3.93 3.98 4.12 4.39 4.31 3.95 4.72 4.03 4.03 3.68 3.43 3.99 4.51 3.99 3.99 2030 Summer 4.45 4.23 4.50 4.73 4.70 4.39 4.67 5.03 4.50 4.28 4.42 3.91 4.65 5.04 4.45 4.74 2030 Spring/Fall 3.74 3.68 3.82 3.95 3.93 4.05 3.94 4.53 3.99 3.79 3.68 5.53 4.17 4.31 3.86 3.60 2 Note: winter-Dec/Jan/Feb/Mar, summer-June/July/Aug/Sep, and spring/fall-Apr/May/Oct/Nov.

3 Source: (DOE, 2019).

F-4

1 F.3 Environmental Assumptions 2 Renewable portfolio standards (RPS) are policies designed to increase generation of electricity 3 from renewable resources. These policies require or encourage electricity producers within a 4 given jurisdiction to supply a certain minimum share of their electricity from designated 5 renewable resources. Generally, these resources include wind, solar, geothermal, biomass, 6 and some types of hydroelectricity, but may include other resources such as landfill gas, 7 municipal solid waste, and tidal energy. Twenty-nine States and the District of Columbia in the 8 U.S. have enforceable RPS or other mandated renewable capacity policies. Table F-4 shows 9 the RPS percentages applied to modeled electricity sale projections by state for each year 10 between 2020 and 2030. The RPS in Hawaii is not modeled and is outside the scope of this 11 report.

12 Table F-4 State Renewable Portfolio Standards Renewable Portfolio Standards (Percent)

State 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Arizona 5.69 6.25 6.82 7.39 7.96 8.53 8.53 8.53 8.53 8.53 8.53 California 33.00 35.75 38.50 41.25 44.00 46.67 49.33 52.00 54.67 57.33 60.00 Colorado 21.25 21.25 21.25 21.25 21.25 21.25 21.25 21.25 21.25 21.25 21.25 Connecticut 25.00 26.50 28.00 30.00 32.00 34.00 36.00 38.00 40.00 42.00 44.00 District of 20.00 20.00 20.00 20.00 23.00 26.00 29.00 32.00 35.00 38.00 42.00 Columbia Delaware 14.46 15.18 15.90 16.62 17.35 18.07 18.07 18.07 18.07 18.07 18.07 Iowa 0.64 0.64 0.63 0.63 0.62 0.62 0.62 0.61 0.61 0.60 0.60 Illinois 8.95 9.79 10.63 11.47 12.31 13.15 13.99 13.99 13.99 13.99 13.99 Massachusetts 20.50 21.50 22.50 23.50 24.50 25.50 26.50 27.50 28.50 29.50 30.50 Maryland 28.00 30.50 31.85 34.65 37.45 40.00 42.50 45.50 47.50 49.50 50.00 Maine 40.00 40.00 40.00 40.00 40.00 40.00 40.00 40.00 40.00 40.00 40.00 Michigan 12.50 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 Minnesota 25.66 25.66 25.66 25.66 25.66 28.43 28.43 28.43 28.43 28.43 28.43 Missouri 7.09 10.63 10.63 10.63 10.63 10.63 10.63 10.63 10.63 10.63 10.63 Montana 10.39 10.39 10.39 10.39 10.39 10.39 10.39 10.39 10.39 10.39 10.39 North Carolina 5.56 6.95 6.95 6.95 6.95 6.95 6.95 6.95 6.95 6.95 6.95 New Hampshire 19.10 19.80 20.50 21.20 22.10 23.00 23.00 23.00 23.00 23.00 23.00 New Jersey 23.43 28.60 32.10 35.60 38.90 42.30 45.00 47.85 50.24 52.57 54.71 New Mexico 15.84 19.87 23.91 27.94 31.98 36.02 37.82 39.62 41.42 43.22 45.02 Nevada 17.35 17.35 17.35 17.35 17.35 21.90 27.38 32.85 38.33 43.81 43.81 New York 24.10 25.31 27.10 28.89 30.69 32.48 34.27 36.06 37.85 39.64 41.44 Ohio 5.79 6.68 7.57 8.46 9.35 10.24 11.13 11.13 11.13 11.13 11.13 Oregon 14.08 14.08 14.08 14.08 14.08 21.05 21.23 21.42 21.60 21.78 27.59 Pennsylvania 7.50 8.00 8.00 8.00 8.00 8.00 8.00 8.00 8.00 8.00 8.00 Rhode Island 16.00 17.50 19.00 20.50 22.00 23.50 25.00 26.50 28.00 29.50 31.00 Texas 4.26 4.22 4.18 4.15 4.11 4.07 4.03 3.99 3.96 3.92 3.88 Vermont 61.80 62.40 63.00 67.60 68.20 68.80 73.40 74.00 74.60 79.20 79.80 Washington 11.80 11.80 11.80 11.80 11.80 11.80 11.80 11.80 11.80 11.80 11.80 Wisconsin 9.65 9.65 9.65 9.65 9.65 9.65 9.65 9.65 9.65 9.65 9.65 13 Source: (EPA, 2019; DSIRE, 2020)

F-5

1 States often drive renewable energy projects to a particular technology by providing carve-out 2 provisions that mandate that a certain percentage of electricity generated come from a 3 particular technology. A solar carve-out requires a specific share of electricity generation is met 4 by solar photovoltaics. Table F-5 shows the RPS solar carve-out percentages applied to 5 modeled electricity sale projections for each year between 2020 and 2030 for applicable states.

6 Table F-5 State Renewable Portfolio Standard Solar Carve-Outs RPS Solar Carve-Outs (Percent)

State 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 District of 1.58 1.85 2.18 2.50 2.60 2.85 3.15 3.45 3.75 4.10 4.50 Columbia Delaware 1.63 1.81 1.99 2.17 2.35 2.53 2.53 2.53 2.53 2.53 2.53 Illinois 0.96 1.05 1.14 1.23 1.32 1.41 1.50 1.50 1.50 1.50 1.50 Massachusetts 0.16 0.17 0.18 0.18 0.19 0.20 0.21 0.22 0.22 0.23 0.24 Maryland 6.00 6.75 7.25 8.75 10.25 11.50 12.50 13.50 14.50 14.50 14.50 Minnesota 1.19 1.19 1.19 1.19 1.19 1.19 1.19 1.19 1.19 1.19 1.19 Missouri 0.14 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 North Carolina 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 New Hampshire 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 New Jersey 4.90 5.10 5.10 5.10 4.90 4.80 4.50 4.35 3.74 3.07 2.21 New Mexico 3.17 3.17 3.17 3.17 3.17 3.17 3.17 3.17 3.17 3.17 3.17 Nevada 1.04 1.04 1.04 1.04 1.04 1.31 1.31 1.31 1.31 1.31 1.31 Ohio 0.23 0.27 0.30 0.34 0.37 0.41 0.45 0.45 0.45 0.45 0.45 Pennsylvania 0.44 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 7 Source: (EPA, 2019; DSIRE, 2020) 8 9 The report implemented applicable environmental regulations in the Reference Case that were 10 approved and enacted as of 2018.

11 The Regional Greenhouse Gas Initiative (RGGI) was the first mandatory cap-and-trade program 12 in the United States to limit carbon dioxide from the power sector. The states currently 13 participating are Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, 14 New York, Rhode Island, and Vermont. New Jersey will rejoin in 2020. The RGGI requires 15 fossil fuel power plants with capacity greater than 25 MW to obtain an allowance for each ton of 16 carbon dioxide emitted annually. Power plants within the region may comply by purchasing 17 allowances from quarterly auctions, other generators within the region, or offset projects.

18 Table F-6 shows the RGGI assumptions that are modeled in the report.

F-6

1 Table F-6 Regional Greenhouse Gas Initiative Cap and Trade Assumptions Item RGGI Coveragea All fossil units > 25 MW Timing Annual Size of Initial Bank (MTons) 2021: 49,442 2021: 75,148 2022: 72,873 2023: 70,598 2024: 68,323 2025: 66,048 Total Allowances (MTons) 2026: 63,773 2027: 61,498 2028: 59,223 2029: 56,948 2030-2054: 54,673 2 a RGGI states are Connecticut, Delaware, Maine, New Hampshire, New York, Vermont, Rhode Island, 3 Massachusetts, and Maryland.

4 The Cross-State Air Pollution Rule (CSAPR) is a U.S. Environmental Protection Agency (EPA) 5 regulation that addresses air pollution from upwind states that crosses state lines and affects air 6 quality in downwind states. The rule regulates sulfur dioxide and oxide of nitrogen power plant 7 emissions, which contribute to smog and soot pollution in downwind states. Table F-7 shows 8 the CSAPR assumptions that are modeled in IPM.

F-7

1 Table F-7 CSAPR-Trading and Banking Rules CSAPR CSAPR Update Update CSAPR-SO2 - CSAPR-SO2 - CSAPR - Rule - Ozone Rule Item Region 1 Region 2 Annual NOx Season NOx - Ozone Region 1 Season NOx -

Region 2 All fossil units > All fossil units > All fossil units > All fossil units > All fossil units >

Coverage 25 MWa 25 MWb 25 MWc 25 MWd 25 MWe Ozone Season Ozone Season Timing Annual Annual Annual (May- (May-September) September)

The bank Size of The bank The bank The bank The cap in 2021 starting in Initial starting in 2021 starting in 2021 starting in 2021 includes 21% of 2021 is Bank is assumed to be is assumed to be is assumed to be banking assumed to be (MTons) zero zero zero zero Total 2021: 411.9106 2021-2054: 2021-2054: 2021-2054: 2021-2054:

Allowances 2022-2054:

1372.631 597.579 1069.256 24.041 (MTons) 313.24 2 Notes:

3 a Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Missouri, New Jersey, New York, North Carolina, 4 Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, Wisconsin 5 b Alabama, Georgia, Kansas, Minnesota, Nebraska, South Carolina 6 c Alabama, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Maryland, Michigan, Minnesota, Missouri, 7 Nebraska, New Jersey, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, 8 Virginia, West Virginia, Wisconsin 9 d Alabama, Arkansas, Iowa, Illinois, Indiana, Kansas, Kentucky, Louisiana, Maryland, Michigan, Missouri, 10 Mississippi, New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Tennessee, Texas, Virginia, 11 Wisconsin, West Virginia 12 e Georgia 13 Source: (EPA, 2019) 14 15 The Mercury and Air Toxics Standards (MATS) for Power Plants Rule is an EPA regulation to 16 reduce emissions of heavy metals, including mercury, arsenic, chromium, and nickel; and acid 17 gases, including hydrochloric acid and hydrofluoric acid. The rule applies to electric generating 18 units larger than 25 MW that burn coal or oil to generate electricity for sale and distribution 19 through the national electric grid to the public. This report incorporates the impact of this rule.

20 F.4 Recent and Firm Builds and Retirements Assumptions 21 The IPM and PROMOD modeling for NRCs replacement energy cost report requires current 22 projections of firm generation builds and retirements. These were based on the EIA Form 860 23 (EIA, 2019), generator-level specific information about existing and planned units. The recent 24 and firm builds include those units that have been recently installed or are currently under 25 construction. Generation capacity addition and retirement assumptions are shown in Table F-8 26 and Table F-9, respectively. Table F-10 shows the net change in capacity due to expected 27 builds and retirements.

F-8

1 Table F-8 Recent and Firm Builds (MW)

Technology 2018 2019 2020 2021 2022 2023 2024 Total ERCOT Combined Cycle 232 232 Combustion 226 103 329 Turbine Onshore Wind 1,971 2,553 643 5,167 Other 2 10 12 Solar PV 442 590 1,032 ISO-NE Combined Cycle 745 485 1,230 Combustion 90 539 629 Turbine Onshore Wind 33 33 Other 8 8 16 Solar PV 7 7 MISO Combined Cycle 644 1,235 1,700 1,146 4,725 Combustion 262 250 218 730 Turbine Onshore Wind 1,595 1,192 70 2,857 Solar PV 109 109 NYISO Combined Cycle 705 1,016 1,721 Combustion 123 2 125 Turbine Onshore Wind 158 158 Other 2 19 21 Solar PV 10 10 PJM Combined Cycle 8,850 1,783 1,373 1,060 13,066 Combustion 337 33 370 Turbine Onshore Wind 415 543 958 Solar PV 170 169 98 437 Southeast Combined Cycle 3,170 2,310 5,480 Combustion 25 130 155 Turbine Nuclear 1,100 1,100 2,200 Other 14 189 12 215 Solar PV 919 1,190 2,109 2

F-9

1 Table F-8 Recent and Firm Builds (MW) (continued)

Technology 2018 2019 2020 2021 2022 2023 2024 Total SPP Combustion 409 409 Turbine Onshore Wind 1,297 1,730 396 3,423 Other 1 1 Solar PV 15 15 WECC Combined Cycle 29 1,206 1,235 Combustion 547 524 274 80 1,425 Turbine Onshore Wind 917 393 480 240 500 2,530 Other 306 44 23 373 Solar PV 1,266 1,288 2,554 2 Source: EIA Form 860 (EIA, 2019).

F-10

1 Table F-9 Recent and Firm Retirement Assumptions (MW)

Technology 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Total ERCOT Coal 4,273 470 840 5,583 Combustion 22 4 26 Turbine Oil/Gas 862 420 410 1,692 Steam Nuclear 1,205 1,205 Other 92 518 23 302 75 1,010 ISO-NE Coal 383 383 Combined 34 34 Cycle Combustion 6 19 25 Turbine Nuclear 677 1,251 1,928 Other 1 1 2 MISO Coal 2,667 671 313 245 1,556 1,302 2,688 154 1,632 1,678 12,906 Combined 360 44 20 424 Cycle Combustion 336 54 195 4 122 153 52 535 59 288 1,798 Turbine Nuclear 601 784 1,165 968 1,065 598 5,181 Oil/Gas 1,505 337 239 18 2,098 Steam Other 64 36 42 20 17 1 3 183 NYISO Combustion 88 2 2 4 3 99 Turbine Nuclear 1,012 1,039 1,208 3,260 Other 2 2 PJM Coal 3,166 3,655 1,948 850 1,288 510 11,417 Combustion 115 386 233 30 13 777 Turbine Nuclear 608 803 1,808 1,240 902 5,361 Oil/Gas 1,001 393 1,394 Steam 2

F-11

1 Table F-9 Recent and Firm Retirement Assumptions (MW) (continued)

Other 129 29 65 1 5 1 4 8 242 Southeast Coal 2,791 1,821 593 1,356 870 582 516 8,529 Combined 121 121 Cycle Combustion 196 29 135 12 371 Turbine Oil/Gas 278 11 75 364 Steam Other 119 128 8 255 SPP Coal 436 650 460 1,546 Combustion 2 82 71 82 237 Turbine Oil/Gas 1,159 239 243 78 107 593 93 183 190 112 244 3,241 Steam Other 109 60 169 WECC Coal 2,250 670 585 1,039 1,955 2,560 357 9,416 Combined 624 535 703 179 227 2,268 Cycle Combustion 165 86 142 165 6 172 737 Turbine Nuclear 1,122 1,118 2,240 Oil/Gas 1,914 1,933 2,208 1,629 241 268 113 102 352 330 9,090 Steam Other 168 139 125 3 1 1 437 2 Source: (EIA, 2019) 3 Based on the Form EIA-860 data contained in Table F-8 and Table F-9, the net generation additions and 4 retirements for years 2018 through 2030 are summarized in Table F-10.

5 Table F-10 Form EIA-860 Projected Net Generation Additions and Retirements Megawatts by Year Total Net Additions Parameter (Retirements) 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 (MW)

Annual Net Additions 2,904 3,212 (3,158) (6,505) (3,629) (1,711) (7,989) (5,694) (3,372) (5,199) (530) (3,388) (5,324) (40,383)

(Retirements) 6 F-12

1 F.5 IPM Economic Builds and Retirement Details 2 Projections of IPM economic builds and retirements are detailed in Table F-11 and Table F-12, 3 respectively.

4 Table F-11 IPM Economic Builds through 2030 (MW)

Technology 2021 2023 2025 2030 Total ERCOT Combined Cycle 302 4,455 8,486 13,242 Solar PV 5,475 5,822 3,598 14,895 ISO-NE Onshore Wind 2,141 1,551 3,692 Other 43 43 MISO Combined Cycle 3,331 1,510 3,133 7,974 Onshore Wind 2,336 55 462 736 3,589 Other 1,914 2,089 23 4,026 Solar PV 794 2,209 54 2,372 5,430 NYISO Combined Cycle 519 519 Onshore Wind 3,680 1,493 5,173 Other 1,479 28 1,507 Solar PV 24 2,804 2,828 PJM Combined Cycle 1,157 2,471 2,495 6,123 Combustion Turbine 397 77 198 673 Onshore Wind 5,533 2,314 494 8,341 Other 1,091 733 1,400 3,224 Solar PV 5,278 7,110 10,980 13,596 36,963 Southeast Combined Cycle 5,976 3,085 3,056 12,117 Onshore Wind 318 12 330 Other 1,826 312 289 2,428 F-13

1 Table F-11 IPM Economic Builds through 2030 (MW) (continued)

Technology 2021 2023 2025 2030 Total Solar PV 1,861 2,378 96 11,432 15,767 SPP Onshore Wind 229 229 Other 467 214 680 Solar PV 4,694 4,694 WECC Combined Cycle 4,859 2,712 3,679 11,250 Combustion Turbine 1,393 17 1,409 Onshore Wind 6,277 4,518 263 14,777 25,835 Other 2,320 257 103 304 2,985 Solar PV 5,424 888 7,276 7,518 21,105 2 Table F-12 IPM Economic Retirements through 2030 (MW)

Technology 2023 2025 2030 Total ERCOT Coal 815 815 Other 118 118 ISO-NE Coal 534 534 Combined Cycle 1,576 1,576 Combustion Turbine 148 148 Oil/Gas 1,723 1,723 Other 547 547 MISO Coal 9,436 35 34 9,505 Nuclear 5,456 5,456 Oil/Gas 202 202 Other 724 724 NYISO Coal 686 37 723 3

F-14

1 Table F-12 IPM Economic Retirements through 2030 (MW) (continued)

Technology 2023 2025 2030 Total Combined Cycle 1,519 1,519 Combustion Turbine 54 54 Nuclear 853 853 Oil/Gas 1,728 569 2,297 Other 74 74 PJM Coal 9,643 2,600 271 12,514 Combustion Turbine 40 40 Nuclear 1,590 1,590 Oil/Gas 2,236 2,236 Other 210 210 Southeast Coal 20,528 570 1,761 22,860 Combined Cycle 110 110 Combustion Turbine 8 8 Nuclear 4,594 932 5,526 Oil/Gas 130 130 Other 1,558 1,558 SPP Nuclear 1,947 1,947 Oil/Gas 536 536 WECC Coal 1,193 1,193 Combined Cycle 2,975 2,975 Combustion Turbine 1,424 1,424 Nuclear 1,180 1,180 Oil/Gas 34 34 Other 836 113 949 2 Based on the IPM projections contained in Table F-11 and Table F-12, the net generation 3 additions and retirements for years up to 2030 are summarized in Table F-13.

F-15

1 Table F-13 IPM Projected Net Generation Additions and Retirements Megawatts by Year Total Parameter 2021 2023 2025 2030 (MW)

Additions 47,034 45,868 35,566 88,604 217,071 Retirements 0 (75,864) (3,811) (4,212) (83,888)

Net 47,034 (29,996) 31,755 84,392 133,183 2

3 F.6 Performance and Unit Cost Assumptions for Other Electric Generation 4 Technologies 5 For its capacity expansion and retirement assessment, the report developed assumptions for 6 new unit technologies that could potentially be placed in service during the report period.

7 Table F-14 provides details on other electric generation technologies assessed for the modeling 8 and analysis. The first year a technology is available is based on the year the analysis was 9 performed (2019) and the lead time for the technology. The year specific capital cost and heat 10 rate estimates were obtained from EIA.

11 Table F-14 Performance and Unit Cost Assumptions for Other New Technologies Ultrasupercritical Ultrasupercritical Biomass-Bubbling Parameter Landfill Gas Coal with 30% CCS Coal with 90% CCS Fluidized Bed (BFB)

Size (MW) 650 650 50 50 First Year 2023 2023 2023 2022 Available Lead Time 4 4 4 3 (Years) 2023 Heat Rate 9,574 10,852 13,500 18,000 (Btu/kWh)

Capital 4,853 5,367 3,660 8,417 (2018$/kW)

Fixed O&M 72.12 83.75 114.39 425.38 (2018$/kW-yr)

Variable O&M 7.31 9.89 5.70 9.47 (2018$/MWh) 2025 Heat Rate 9,221 9,257 13,500 18,000 (Btu/kWh)

Capital 4,773 5,278 3,604 8,311 (2018$/kW)

Fixed O&M 72.12 83.75 114.39 425.38 (2018$/kW-yr)

Variable O&M 7.31 9.89 5.70 9.47 (2018$/MWh) 2030 Heat Rate 9,221 9,257 13,500 18,000 (Btu/kWh) 12 F-16

1 Table F-14 Performance and Unit Cost Assumptions for Other New Technologies 2 (continued)

Fixed O&M 72.12 83.75 114.39 425.38 (2018$/kW-yr)

Variable O&M 7.31 9.89 5.70 9.47 (2018$/MWh) 3 Btu - British thermal units; CCS - carbon capture and storage; kW - Kilowatt; kWh - Kilowatt-hour; kW-yr 4 - Kilowatt-year; MW - Megawatts; MWh - Megawatt-hour; O&M - operation and maintenance 5 Source: DOE 2019. (DOE, 2019)

F-17

1 F.7 Appendix F References 2 Database of State Incentives for Renewables & Efficiency (DSIRE), North Carolina State 3 University: NC Clean Energy Technology Center, 2020. Available at https://www.dsireusa.org/.

4 (DSIRE, 2020) 5 North American Electric Reliability Corporation (NERC), Electricity Supply and Demand 6 (ES&D), December 2018. (NERC, 2018).

7 U.S. Department of Energy (DOE) Energy Information Administration (EIA) Annual Energy 8 Outlook (AEO) 2019 Reference Case. (DOE, 2019).

9 U.S. Energy Information Administration, Form 860M, February 2019. (EIA, 2019).

10 U.S. Environmental Protection Agency (EPA), Power Sector Modeling Platform v6, 2019.

11 (EPA, 2019).

F-18

1 APPENDIX G 2 DETAILED REPLACEMENT ENERGY COSTS:

3 2020-2030 4 Table G-1 ERCOT Annual and Seasonala Replacement Energy Costs ($/MWh)

Year Annual Winter Spring Summer Fall 2020 1.01 0.41 0.65 2.07 0.89 2021 0.85 0.47 0.51 1.62 0.74 2022 1.17 0.54 0.55 2.33 1.21 2023 1.48 0.57 0.58 3.03 1.68 2024 1.35 0.59 0.52 2.97 1.36 2025 1.22 0.54 0.43 2.91 1.03 2026 1.54 0.49 0.47 3.99 1.18 2027 1.85 0.48 0.5 5.07 1.33 2028 2.17 0.47 0.54 6.15 1.47 2029 2.49 0.45 0.58 7.23 1.62 2030 2.8 0.44 0.62 8.32 1.76 5 a Winter: Dec/Jan/Feb/, spring: Mar/April/May, summer: June/July/Aug, and fall: Sep/Oct/Nov.

6 7 Table G-2 ISO-NE Annual and Seasonal Replacement Energy Costs ($/MWh)

Annual Winter Spring Summer Fall Year Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact 2020 2.36 1.68 2.91 2.25 1.78 1.12 2.86 1.97 1.97 1.44 2021 3.00 2.13 3.00 2.25 3.35 2.38 3.19 2.23 2.51 1.83 2022 2.98 2.13 3.26 2.21 2.40 1.82 4.47 3.17 1.95 1.42 2023 2.96 2.13 3.38 2.24 1.46 1.27 5.75 4.10 1.40 1.00 2024 3.19 2.26 3.19 2.15 1.86 1.42 5.98 4.16 1.65 1.25 2025 3.42 2.38 3.22 2.17 2.25 1.58 6.20 4.22 1.90 1.49 2026 3.96 2.77 3.27 2.23 2.68 1.90 7.30 5.02 2.45 1.87 2027 4.50 3.17 3.34 2.30 3.11 2.22 8.41 5.81 2.99 2.26 2028 5.04 3.56 3.41 2.37 3.55 2.54 9.52 6.60 3.54 2.64 2029 5.58 3.95 3.48 2.45 3.98 2.86 10.62 7.40 4.09 3.02 2030 6.12 4.35 3.55 2.52 4.41 3.19 11.73 8.19 4.63 3.40 G-1

1 Table G-3 MISO Annual and Seasonal Replacement Energy Costs ($/MWh)

Annual Winter Spring Summer Fall Year Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact 2020 0.13 0.01 0.10 0.00 0.00 0.03 0.36 0.07 0.05 0.00 2021 0.23 0.03 0.18 0.00 0.33 0.10 0.30 0.01 0.15 0.00 2022 0.26 0.03 0.17 0.00 0.30 0.10 0.32 0.00 0.29 0.08 2023 0.30 0.03 0.17 0.00 0.27 0.09 0.34 0.00 0.43 0.16 2024 0.33 0.06 0.05 0.00 0.29 0.06 0.39 0.00 0.54 0.19 2025 0.37 0.09 0.01 0.01 0.32 0.04 0.44 0.02 0.64 0.23 2026 0.23 0.07 0.00 0.11 0.33 0.00 0.13 0.02 0.46 0.21 2027 0.21 0.05 0.03 0.13 0.26 0.00 0.12 0.02 0.46 0.19 2028 0.20 0.04 0.07 0.15 0.19 0.00 0.10 0.02 0.47 0.18 2029 0.19 0.02 0.10 0.16 0.11 0.00 0.09 0.01 0.47 0.16 2030 0.17 0.00 0.14 0.18 0.04 0.00 0.07 0.01 0.47 0.14 2 Table G-4 NYISO Annual and Seasonal Replacement Energy Costs ($/MWh)

Annual Winter Spring Summer Fall Year Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact 2020 2.04 0.92 2.76 0.99 1.79 0.92 2.38 1.18 1.42 0.70 2021 2.14 0.98 2.17 0.81 2.67 1.22 2.05 0.99 1.77 0.91 2022 1.93 0.85 2.43 0.91 1.76 0.84 2.10 0.97 1.54 0.70 2023 1.73 0.72 2.72 0.98 0.85 0.46 2.15 0.95 1.31 0.50 2024 1.96 0.76 2.74 0.96 1.02 0.45 2.75 1.05 1.29 0.57 2025 2.19 0.80 2.92 0.96 1.20 0.45 3.35 1.15 1.26 0.65 2026 2.51 0.93 3.18 1.07 1.27 0.51 3.93 1.37 1.57 0.72 2027 2.83 1.05 3.47 1.22 1.35 0.58 4.51 1.60 1.88 0.79 2028 3.14 1.18 3.76 1.36 1.42 0.65 5.08 1.82 2.20 0.86 2029 3.46 1.30 4.05 1.50 1.50 0.72 5.66 2.05 2.51 0.92 203026 3.77 N/A 4.34 N/A 1.57 N/A 6.23 N/A 2.82 N/A 26 The current operating license for the R E Ginna Nuclear Power Plant expires in 2030.

G-2

1 Table G-5 PJM Annual and Seasonal Replacement Energy Costs ($/MWh)

Annual Winter Spring Summer Fall Year Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact 2020 1.02 0.08 1.28 0.01 0.70 0.10 1.18 0.10 0.80 0.14 2021 0.67 0.09 1.10 0.03 0.49 0.03 0.69 0.14 0.60 0.13 2022 0.70 0.14 0.89 0.08 0.52 0.07 0.78 0.18 0.64 0.16 2023 0.74 0.19 0.87 0.17 0.55 0.12 0.87 0.22 0.69 0.19 2024 0.77 0.17 0.85 0.20 0.57 0.12 0.95 0.25 0.69 0.18 2025 0.79 0.16 0.86 0.11 0.59 0.11 1.02 0.29 0.68 0.17 2026 0.87 0.16 0.93 0.07 0.64 0.11 1.15 0.27 0.74 0.17 2027 0.94 0.16 0.99 0.11 0.69 0.11 1.27 0.25 0.81 0.16 2028 1.01 0.16 1.05 0.15 0.74 0.11 1.40 0.23 0.87 0.15 2029 1.09 0.17 1.10 0.18 0.80 0.11 1.52 0.21 0.93 0.15 2030 1.16 0.17 1.16 0.22 0.85 0.11 1.65 0.20 0.99 0.14 2 Table G-6 Southeast Annual and Seasonal Replacement Energy Costs ($/MWh)

Annual Winter Spring Summer Fall Year Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact 2020 0.18 0.11 0.29 0.11 0.14 0.11 0.10 0.07 0.17 0.14 2021 0.18 0.07 0.20 0.10 0.22 0.11 0.15 0.06 0.16 0.07 2022 0.17 0.10 0.16 0.06 0.23 0.16 0.15 0.07 0.16 0.11 2023 0.17 0.13 0.16 0.07 0.23 0.21 0.15 0.08 0.15 0.16 2024 0.16 0.12 0.13 0.07 0.19 0.15 0.19 0.13 0.14 0.10 2025 0.16 0.10 0.11 0.07 0.14 0.09 0.23 0.18 0.13 0.05 2026 0.18 0.11 0.15 0.09 0.18 0.11 0.25 0.18 0.15 0.07 2027 0.20 0.12 0.16 0.09 0.21 0.13 0.27 0.18 0.17 0.09 2028 0.22 0.13 0.17 0.09 0.24 0.14 0.29 0.18 0.19 0.11 2029 0.24 0.14 0.18 0.09 0.27 0.16 0.31 0.18 0.20 0.13 2030 0.26 0.15 0.19 0.10 0.31 0.18 0.33 0.18 0.22 0.15 G-3

1 Table G-7 SPP Annual and Seasonal Replacement Energy Costs ($/MWh)

Annual Winter Spring Summer Fall Year Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact 2020 0.92 0.46 0.73 0.29 1.09 0.52 0.91 0.55 0.99 0.51 2021 0.86 0.47 0.56 0.27 0.96 0.47 1.27 0.73 0.70 0.38 2022 0.90 0.49 0.53 0.31 0.91 0.48 1.34 0.77 0.76 0.39 2023 a 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2024 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2025 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2026 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2027 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2028 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2029 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2030 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2 a The IPM model forecasts that the Wolf Creek Generating Station and the Cooper Nuclear Station in 3 SPP would not be economically dispatched beginning in 2023.

4 Table G-8 WECC Annual and Seasonal Replacement Energy Costs ($/MWh)

Annual Winter Spring Summer Fall Year Most Least Most Least Most Least Most Least Most Least Impact Impact Impact Impact Impact Impact Impact Impact Impact Impact 2020 1.12 0.68 1.33 0.67 1.53 1.16 0.96 0.56 0.88 0.41 2021 1.15 0.91 1.16 1.22 1.35 1.11 1.19 0.63 0.97 0.65 2022 1.11 1.13 0.96 1.20 1.22 1.20 1.23 0.96 0.98 0.83 2023 a 1.07 0.00 0.88 0.00 1.09 0.00 1.27 0.00 0.99 0.00 2024 1.01 0.00 0.86 0.00 0.99 0.00 1.23 0.00 0.93 0.00 2025 0.94 0.00 0.78 0.00 0.90 0.00 1.19 0.00 0.87 0.00 2026 1.11 0.00 0.88 0.00 1.07 0.00 1.35 0.00 1.09 0.00 2027 1.27 0.00 0.98 0.00 1.23 0.00 1.52 0.00 1.30 0.00 2028 1.44 0.00 1.09 0.00 1.40 0.00 1.68 0.00 1.52 0.00 2029 1.60 0.00 1.19 0.00 1.57 0.00 1.85 0.00 1.74 0.00 2030 1.76 0.00 1.29 0.00 1.74 0.00 2.01 0.00 1.96 0.00 5 a The IPM model forecasts that the Columbia Generating Station in WECC would not be economically 6 dispatched beginning in 2023.

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1 APPENDIX H 2 STUDIES AND SOURCES OF DATA REVIEWED FOR ASSUMPTIONS 3 DEVELOPMENT 4 Table H-1 Summary of Studies and Sources of Data Compared with AEO 2018 Modeling Natural Gas Electricity New Build Study/Report Vintage Scope Legend Method Prices Demand Costs AEO 2018 2018 National Long-term AEO 2018 AEO 2018 AEO 2018 AEO Capacity National Energy (NEMS (NEMS 2018 Expansion Modeling Endogenous) Endogenous) and System (NEMS Production Endogenous)

Cost Modeling EPA Platform v6 2018 National Long-term IPM and ICFs AEO 2018 for AEO 2018 for EPA November 2018 Capacity Gas Market Energy, and fossil and Reference Case Expansion Model (GMM) NERC ES&D nuclear, and and and AEO 2018 NREL ATB for Production for Peak Load renewables Cost Modeling MISO 2018 Regional Production NYMEX, Wood MISO Internal NREL ATB for MISO Transmission Cost Mackenzie No (Module E 50/50 solar and wind Expansion Plan Carbon, and EIA load forecast (MTEP) 2018 forecasts growth rate)

PJM Market 2018 Regional Production Combination of January 2018 - AEO Efficiency Cost NYMEX forward PJM Load 2018 Analysis 2018 prices and a Forecast Report fundamental forecasting model ERCOT 2018 2018 Texas - EIA 2018 AEO ERCOT - ERCOT Regional Interconnect High Oil and 2018Regional Transmission Gas Resource Transmission Plan and Technology Plan Case NYISO 2018 Regional Production AEO as the 2018 Gold Book - NYISO Congestion Cost starting point Forecast Assessment and Resource Integration Studies 2017 and 2018 5

H-1

1 Table H-1 Summary of Studies and Sources of Data Compared with AEO 2018 2 (continued)

Modeling Natural Gas Electricity Study/Report Vintage Scope New Build Costs Legend Method Prices Demand California 2018 Regional - WECC burner CECs 2017 Natural Gas Units: North Public Utility tip price IEPR E3s 2014 review of American Commission estimate using capital costs for Market (CPUC)- California WECC, Capital Cost Gas Energy Review of Generation (NAMGas)

Commission's Technologies.

(CECs) 2017 Integrated Renewable RESOLVE Energy Policy Resources:

Report (IEPR) Developed by Black &

Demand Veatch for the Forecast CPUCs RPS Calculator v.6.3.For the 2019-2020 IRP CPUC plans to use NREL ATB WECC Anchor - Western Production California - - NAMGas Dataset Interconnect Cost Energy Commission's NAMGas-trade Model projections WestConnect 2019 Western Production WECC 2028 WECC - NAMGas Regional Interconnect Cost Anchor Data Set 2028Anchor Transmission (ADS) PCM Data Set Planning Version 1.0 (ADS) PCM 2018-2019 (2028 ADS PCM Version 1.0 V1.0) (2028 ADS PCM V1.0)

NREL 2018 2018 National Long-term AEO 2018 - Fossil and Nuclear- AEO 2018 ATB Capacity AEO 2018; Wind -

Expansion Forecasting Wind and Energy Costs and Production Cost Drivers: The Cost Views of the World's Modeling Leading Experts-Wiser et al. (2016);

Enabling the SMART Wind Power Plant of the Future Through Science-Based Innovation (Technical Report) - Dykes et al.

(2017); Solar photovoltaic (PV) and CSP - Internal NREL analysis; Hydro -

DOE 2016, Geothermal - EIA NEMS 3

H-2

1 Table H-1 Summary of Studies and Sources of Data Compared with AEO 2018 2 (continued)

New Modeling Natural Gas Study/Report Vintage Scope Electricity Demand Build Legend Method Prices Costs Southwest Power 2018 Regional Production Cost ABB Projection - - SPP Pool 2019 ITP Benchmarking FPLs 2017 Ten- 2017 Utility Production Cost Forward + FPLs econometric - FPL Year Site Plan: Key Proprietary model with projections Forecasts and PIRA Energy for the national and Resource Plan Group Florida economies Projection obtained from IHS Global Insight Tennessee Valley 2019 Regional Long-term - TVAs Statistically - TVA Authority (TVA) 2019 Capacity Adjusted End-use Integrated Resource Expansion and model (SAE)

Plan (IRP) Production Cost Modeling 3 CSP-concentrated solar power; NYMEX - New York Mercantile Exchange H-3

1 H.1 Appendix H References 2 Electric Reliability Council of Texas, 2018 Regional Transmission Plan," Version 1.0, December 3 2018.

4 Energy and Environmental Economics, Inc., RESOLVE Model Documentation: Inputs &

5 Assumptions, September 2017.

6 Florida Power & Light Company, Florida Power & Light Companys 2017 Ten Year Power Plant 7 Site Plan, April 2017. Available at 8 http://frcc.com/Planning/Shared%20Documents/Load%20and%20Resource%20Plans/FRCC%2 9 0Presentations%20and%20Utility%2010-10 Year%20Site%20Plans/2017%20TYSPs/2017%20TYSP%20-%20FPL.pdf.

11 Midcontinent Independent System Operator, 2018 MISO Transmission Expansion Planning 12 Report," December 2018.

13 National Renewable Energy Laboratory (NREL), NREL 2018 Annual Technology Baseline, 14 July 2018.

15 New York Independent System Operator, 2017 Congestion Assessment and Resource 16 Integration Studies Report, April 2018.

17 Nikki Roberts, Southwest Power Pool, SPP 2019 ITP Benchmarking Presentation, July 2018.

18 Available at 19 https://www.spp.org/Documents/58240/2019%20ITP%20Benchmarking_Final_20180703.pdf.

20 PJM Interconnection LLC, 2018 Market Efficiency Process Scope and Input Assumptions, 2018.

21 Available at: https://www.pjm.com/-/media/committees-22 groups/committees/teac/20181011/20181011-2018-market-efficiency-analysis-23 assumptions.ashx.

24 Tennessee Valley Authority, 2019 Integrated Resource Plan Volume I - Final Resource Plan.

25 June 2019. Available at https://tva-azr-eastus-cdn-ep-tvawcm-prd.azureedge.net/cdn-26 tvawcma/docs/default-source/default-document-library/site-content/environment/environmental-27 stewardship/irp/2019-documents/tva-2019-integrated-resource-plan-volume-i-final-resource-28 plan.pdf?sfvrsn=44251e0a_4.

29 U.S. Energy Information Administration (EIA), Annual Energy Outlook 2019, January 2019.

30 Available at https://www.eia.gov/outlooks/archive/aeo19/.

31 U.S. Environmental Protection Agency, (EPA), Documentation for EPAs Power Sector 32 Modeling Platform v6 Using the Integrated Planning Model, November 2018.

33 Western Electricity Coordinating Council, WECC Anchor Data Set Data Development and 34 Validation Manual Version 1.0, July 2018. Available at 35 https://www.wecc.org/Reliability/ADS%20DDVM%20V1.0.docx.

36 WESTCONNECT Regional Planning, WESTCONNECT Regional Transmission Planning 2018-37 19 Planning Cycle Model Development Report, January 2019. Available at 38 https://doc.westconnect.com/Documents.aspx?NID=18282&dl=1.

H-4

NUREG-2242 Draft Replacement Energy Cost Estimates for Nuclear Power Plants: 2020-2030 - December 2020 Draft Report for Comment K. Collison and E. Gormsen, ICF Technical R.F. Schofer, NRC Dr. Boddu Venkatesh, ICF Project Manager Pamela Noto and Amy Sharp, NRC Project Managers Division of Rulemaking, Environmental, and Financial Services Office of Nuclear Material Safety and Safeguards U.S. Nuclear Regulatory Commission Washington D.C. 20555-0001 Same as above P. Noto, A. Sharp, F. Schofer Replacement energy costs are estimated for the United States wholesale electricity market regions with nuclear electricity-generating units over the 2020-2030 report period. These estimates were developed to assist the U.S. Nuclear Regulatory Commission (NRC) in evaluating proposed regulatory actions that (1) require safety modifications that might necessitate temporary reactor outages and (2) reduce the potential for extended outages resulting from a severe reactor accident. Estimates were calculated using ASEA Brown Boveris (ABBs) PROMOD model and ICFs Integrated Planning Model for North America.

The models simulate dispatching a collection of generating units in merit order (i.e., lowest to highest incremental cost of dispatch) until the regional power demand is met. Each generating unit is characterized by the technology and fuel it uses to generate electricity, the units heat rate, and the variable and fixed costs incurred in owning and operating the unit. To estimate the replacement energy cost, the report models a Reference Case, in which all operational nuclear power plants are generating, and an Alternative Case, in which a nuclear generating unit is taken offline so that the next unit in merit order is dispatched to replace the lost generation. The difference in market clearing prices between the two cases is the replacement energy cost.

Economic consequences Electric utility economics Onsite property Regulatory analyses Replacement energy costs Replacement power Energy projections

Federal Recycling Program

NUREG-2242 Replacement Energy Cost Estimates for Nuclear Power Plants: 2020-2030 December 2020 Draft