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This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Technical Report

NREL/TP-6A20- 72023

April 2019

Regional Energy Deployment System 

(ReEDS) Model Documentation: 

Version 2018

Stuart Cohen, Jon Becker, Dave Bielen, Maxwell Brown, 

Wesley Cole, Kelly Eurek, Will Frazier, Bethany Frew, 

Pieter Gagnon, Jonathan Ho, Paige Jadun, Trieu Mai, 

Matthew Mowers, Caitlin Murphy, Andrew Reimers, 

James Richards, Nicole Ryan, Evangelia Spyrou, 

Daniel Steinberg, Yinong Sun, Nina Vincent, 

and Matthew Zwerling

National Renewable Energy Laboratory
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Technical Report

NREL/TP-6A20-72023

April 2019

Regional Energy Deployment System 

(ReEDS) Model Documentation: 

Version 2018

Stuart Cohen, Jon Becker, Dave Bielen, Maxwell Brown, 

Wesley Cole, Kelly Eurek, Will Frazier, Bethany Frew, 

Pieter Gagnon, Jonathan Ho, Paige Jadun, Trieu Mai, 

Matthew Mowers, Caitlin Murphy, Andrew Reimers, 

James Richards, Nicole Ryan, Evangelia Spyrou, 

Daniel Steinberg, Yinong Sun, Nina Vincent, 

and Matthew Zwerling

National Renewable Energy Laboratory

Suggested Citation

Cohen, Stuart, Jon Becker, Dave Bielen, Maxwell Brown, Wesley Cole, Kelly Eurek, Will 

Frazier, et al. 2019. Regional Energy Deployment System (ReEDS) Model Documentation: 

Version 2018. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-72023. 

https://www.nrel.gov/docs/fy19osti/72023.pdf.
NOTICE

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, Strategic Programs. 

The views expressed herein do not necessarily represent the views of the DOE or the U.S. Government.

This report is available at no cost from the National Renewable 

Energy Laboratory (NREL) at www.nrel.gov/publications.

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov.

Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, 

NREL 46526.

NREL prints on paper that contains recycled content.
iii

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Acknowledgments

We gratefully acknowledge the many people whose efforts contributed to this report. The

ReEDS modeling and analysis team at the National Renewable Energy Laboratory (NREL) was 

active in developing and testing the ReEDS model v.2018. We also acknowledge the vast 

number of current and past NREL employees on and beyond the ReEDS team who have 

participated in data and model development, testing, and analysis. We are especially grateful to 

Walter Short who first envisioned and developed the Wind Deployment System (WinDS) and 

ReEDS models. We thank Walter Short, Ella Zhou, Owen Zinaman, Laura Vimmerstedt, Jeffrey 

Logan, Cara Marcy, Gokul Iyer, and Mike Meshek for their comments and improvements on 

successive versions of this report. Finally, we are grateful to all those who helped sponsor 

ReEDS model development and analysis, particularly supporters from the U.S. Department of 

Energy (DOE) but also others who have funded our work over the years. This report was funded 

by the DOE Office of Energy Efficiency and Renewable Energy under contract number DEAC36-08GO28308. Any errors or omissions are the sole responsibility of the authors.

ReEDS Team Members

Jon Becker Pieter Gagnon James Richards

Dave Bielen Jonathan Ho Nicole Ryan

Maxwell Brown Paige Jadun Evangelia Spyrou

Stuart Cohen Trieu Mai Daniel Steinberg

Wesley Cole Matthew Mowers Yinong Sun

Kelly Eurek Caitlin Murphy Nina Vincent

Will Frazier Andrew Reimers Matthew Zwerling

Bethany Frew
iv

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List of Abbreviations and Acronyms

AC alternating current

AEO Annual Energy Outlook

AFY acre-feet per year

ATB Annual Technology Baseline

BA balancing area

CAES compressed-air energy storage

CAIR Clean Air Interstate Rule

CC combined cycle

CCS carbon capture and sequestration

CF capacity factor

CO2 carbon dioxide

CMIP Coupled Model Intercomparison Project

CPP Clean Power Plan

CPUC California Public Utilities Commission

CSAPR Cross-State Air Pollution Rule

CSP concentrating solar power

CT combustion turbine

CV capacity value

DC direct current

DNI direct normal insolation

DOE U.S. Department of Energy

DSIRE Database of State Incentives for Renewables & Efficiency

DUPV distribution-side utility-scale photovoltaic

EAC early action credit

EGR enhanced gas recovery

EGS enhanced geothermal system

EIA U.S. Energy Information Administration

ELCC effective load carrying capability

EOR enhanced oil recovery

EPA U.S. Environmental Protection Agency

ERC emission rate credit

ERCOT Electric Reliability Council of Texas

FERC Federal Energy Regulatory Commission

GCM general circulation model

GHG greenhouse gas

GIS geographic information systems

GW gigawatt

HMI U.S. Bureau of Reclamation Hydropower Modernization Initiative

HSIP Homeland Security Infrastructure Project

HVDC high-voltage direct current

IGCC integrated gasification combined cycle

IOU investor-owned utility

IPM U.S. Environmental Protection Agency Integrated Planning Model

IPP independent power producer

ISO independent system operator
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IRS Internal Revenue Service

ITC investment tax credit

JEDI Jobs and Economic Development Impact model

km2 square kilometer

kV kilovolt

kW kilowatt

kWh kilowatt hour

LCOE levelized cost of energy

LDC load-duration curve

LOLP loss of load probability

MACRS Modified Accelerated Cost Recovery System

MATS Mercury and Air Toxic Standards

MMBtu million British thermal units

MPI materials price index

MW megawatt

MWh megawatt hour

NaS sodium-sulfur

NEB Canadian National Energy Board

NEMS National Energy Modeling System

NERC North American Electric Reliability Corporation

NG natural gas

NHAAP National Hydropower Asset Assessment Program

NLDC net load-duration curve

NOx nitrogen oxide

NPD non-powered dam

NRC Nuclear Regulatory Commission

NREL National Renewable Energy Lab

NSD new stream-reach development

NSRDB National Solar Radiation Database

O&M operation and maintenance

OGS oil-gas steam

PCM production-cost model

PRODESEN Mexican Programa de Desarrollo del Sistema Eléctrico Nacional

PSH pumped storage hydropower

PTC production tax credit

PV photovoltaic

RCP representative concentration pathway

REC renewable energy certificate

ReEDS Regional Energy Deployment System

RGGI Regional Greenhouse Gas Initiative

RLDC residual load-duration curve

RPS renewable portfolio standard

RROE rate of return on equity

RTO Regional Transmission Organization

SAM System Advisor Model

SENER Secretaría de Energía for Mexico
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SO2 sulfur dioxide

TDY typical direct normal insolation year

TEPPC Transmission Expansion Planning Policy Committee

TES thermal energy storage

TRG techno-resource group

tCO2 metric ton of carbon dioxide

TW terawatt

TWh terawatt hour

UPV utility-scale photovoltaic

VRE variable renewable energy

WACC weighted average cost of capital

WECC Western Electricity Coordination Council

WIND Wind Integration National Dataset

WinDS Wind Deployment System
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Table of Contents

1 Introduction........................................................................................................................................... 1

1.1 ReEDS History.............................................................................................................................. 1

1.2 Qualitative Model Description ...................................................................................................... 1

1.3 Linear Program Formulation Description ..................................................................................... 5

1.4 Summary of Caveats ..................................................................................................................... 7

2 Technology Descriptions..................................................................................................................... 9

2.1 Renewable Energy Resources and Technologies.......................................................................... 9

2.1.1 Land-Based Wind............................................................................................................. 9

2.1.2 Offshore Wind................................................................................................................ 13

2.1.3 Solar Photovoltaics......................................................................................................... 15

2.1.4 Concentrating Solar Power............................................................................................. 20

2.1.5 Geothermal..................................................................................................................... 24

2.1.6 Hydropower.................................................................................................................... 24

2.1.7 Biopower........................................................................................................................ 30

2.1.8 Marine Hydrokinetic Wave............................................................................................ 30

2.2 Conventional Energy Technologies............................................................................................ 30

2.3 Storage and Demand-ide Technologies....................................................................................... 33

2.3.1 Storage............................................................................................................................ 33

2.3.2 Demand Response.......................................................................................................... 36

2.3.3 Plug-In Electric Vehicles ............................................................................................... 36

2.3.4 Energy Efficiency........................................................................................................... 36

2.4 Capital Stock ............................................................................................................................... 37

2.4.1 Initial Capital Stock, Prescribed Builds and Restrictions............................................... 37

2.4.2 Retirements .................................................................................................................... 37

2.4.3 Growth Penalties............................................................................................................ 40

2.5 Regional Parameter Variations and Adjustments........................................................................ 41

3 Fuel Prices........................................................................................................................................... 44

4 Thermal Cooling Water ...................................................................................................................... 46

4.1 Cooling System Characteristics................................................................................................... 46

4.2 Water Access Constraints............................................................................................................ 51

4.3 Cooling Water Availability and Cost .......................................................................................... 51

4.4 Operational Water Constraints.................................................................................................... 55

5 Transmission ...................................................................................................................................... 56

5.1 Transmission System................................................................................................................... 56

5.2 Transmission Cost....................................................................................................................... 57

5.3 Spur Lines ................................................................................................................................... 60

5.4 International Electricity Trade..................................................................................................... 60

6 Electricity System Operation and Reliability................................................................................... 62

6.1 Electricity Load........................................................................................................................... 62

6.2 Resource Adequacy..................................................................................................................... 62

6.2.1 VRE Capacity Value ...................................................................................................... 63

6.2.2 Storage Capacity Value.................................................................................................. 65

6.2.3 Planning Reserve Margins ............................................................................................. 67

6.3 Operational Reliability ................................................................................................................ 68

6.4 Curtailment.................................................................................................................................. 70

7 Climate Impacts .................................................................................................................................. 72

7.1 Temperature Impacts on Load..................................................................................................... 72

7.2 Temperature Impacts on Power System Performance................................................................. 72

7.3 Precipitation Impacts on Cooling Water Availability ................................................................. 73
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7.4 Precipitation Impacts on Hydropower......................................................................................... 73

7.5 Climate Scenarios........................................................................................................................ 74

8 Policy Descriptions ............................................................................................................................ 75

8.1 Federal and State Emission Standards......................................................................................... 75

8.1.1 New Source Performance Standards.............................................................................. 75

8.1.2 Cross-State Air Pollution Rule....................................................................................... 75

8.1.3 Mercury and Air Toxic Standards.................................................................................. 75

8.1.4 California Carbon Cap ................................................................................................... 77

8.1.5 Regional Greenhouse Gas Initiative............................................................................... 77

8.2 Federal and State Tax Incentives................................................................................................. 78

8.2.1 Renewable Production and Investment Tax Credits ...................................................... 78

8.2.2 Carbon Oxide Sequestration Tax Credit ........................................................................ 81

8.3 State Renewable Portfolio Standards.......................................................................................... 81

8.4 Storage Mandates........................................................................................................................ 86

8.5 Other Policy Capabilities ............................................................................................................ 86

9 Capital Financing, System Cost, and Electricity Rates .................................................................. 87

9.1 Financing of Capital Stock.......................................................................................................... 87

9.2 Electric Sector Costs ................................................................................................................... 87

9.2.1 Present Value of Direct Electric Sector Cost ................................................................. 87

9.2.2 Electricity Price.............................................................................................................. 87

10 ReEDS North American Expansion .................................................................................................. 89

10.1 Fuel Price and Availability.......................................................................................................... 90

10.2 Electricity Demand...................................................................................................................... 91

10.3 Generation Fleet Representation ................................................................................................. 92

10.4 Renewable Energy Representation.............................................................................................. 94

10.4.1 Hydropower Generation Capacity.................................................................................. 94

10.4.2 Wind Resource ............................................................................................................... 94

10.4.3 Solar Resource ............................................................................................................... 94

10.4.4 Geothermal Resource ..................................................................................................... 94

10.4.5 Biomass Resource .......................................................................................................... 95

10.5 Policy and Regulation ................................................................................................................. 95

11 Model Linkages................................................................................................................................... 97

11.1 ReEDS-dGen............................................................................................................................... 97

11.2 ReEDS-PLEXOS ........................................................................................................................ 97

11.3 ReEDS-JEDI ............................................................................................................................... 98

12 References .......................................................................................................................................... 99

Appendix.................................................................................................................................................. 110

Natural Gas Supply Curves................................................................................................................ 110

Seasonal Natural Gas Price Adjustments........................................................................................... 112

Capital Cost Financial Multipliers...................................................................................................... 113

Present Value of Direct Electric Sector Cost...................................................................................... 115

Cost-of-Service Electricity Prices ...................................................................................................... 116

Marginal Electricity Prices................................................................................................................. 119

Clean Power Plan ............................................................................................................................... 121

Existing Source Performance Standard..................................................................................... 121

Clean Energy Incentive Program .............................................................................................. 124
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List of Figures

Figure 1. Map showing the ReEDS regional structure........................................................................... 2

Figure 2. Schematic of the ReEDS model structure............................................................................... 5

Figure 3. Land-based wind resource map for the contiguous United States........................................ 12

Figure 4. Estimated wind supply curves of all wind resource modeled in ReEDS (2016$) ................ 13

Figure 5. Offshore wind resource map for the contiguous United States............................................. 15

Figure 6. UPV and DUPV resource areas available for development considering land use

exclusions.................................................................................................................................... 16

Figure 7. UPV resource availability by ReEDS BA region and resource class.................................... 17

Figure 8. DUPV resource availability in each ReEDS region by resource class ................................. 18

Figure 9. Prescribed distributed PV deployment used in the ReEDS as determined by dGen............. 20

Figure 10. CSP resource availability in each ReEDS resource region by 12 resource class................ 22

Figure 11. Demonstration of ReEDS constraints and corresponding feasible space (hashed line) for

CSP with storage in Class 8 resource regions............................................................................. 23

Figure 12. Capacity factor trends versus solar multiple ....................................................................... 23

Figure 13. National capital cost supply curves for new identified hydrothermal and near-field EGS 

capacity used in the base model assumptions ............................................................................. 24

Figure 14. Modeled hydropower upgrade resource potential (DOE 2016b)........................................ 26

Figure 15. Modeled non-powered dam resource potential (DOE 2016b)............................................ 27

Figure 16. Modeled new stream-reach development resource potential (DOE 2016b) ....................... 28

Figure 17. National hydropower supply curve of capital cost versus cumulative capacity potential... 28

Figure 18. National supply curve for biomass feedstock through the 2012 solve year........................ 30

Figure 19. Modeled new PSH resource potential (DOE 2016b).......................................................... 35

Figure 20. National PSH supply curve of capital cost versus cumulative capacity potential............... 35

Figure 21. National and regional growth penalty cost curve................................................................ 40

Figure 22. Maps of regional capital cost multipliers for the various technology types........................ 43

Figure 23. Fuel price assumptions........................................................................................................ 44

Figure 24. Water availability and cost for each water type .................................................................. 54

Figure 25. Existing long-distance transmission infrastructure as represented in ReEDS .................... 56

Figure 26. Map of long-distance transmission costs............................................................................ 59

Figure 27. Map of spur-line transmission costs.................................................................................... 59

Figure 28. Net exports from Canada to the United States.................................................................... 61

Figure 29. LDC-based approach to calculating CV ............................................................................. 64

Figure 30. Map of ReEDS 134 BA regions and 18 RTO subregions................................................... 65

Figure 31. Illustration of the calculation of marginal and existing storage CV with the functional form 

curve fit for PV penetration of 7% and storage duration of four hours....................................... 66

Figure 32. Battery capacity value availability factor as a function of battery duration........................ 66

Figure 33. Map of ReEDS 134 BA regions and 18 NERC reliability subregions................................ 67

Figure 34. Planning reserve margin by NERC region.......................................................................... 68

Figure 35. Regions defined in the ReEDS-Canada model ................................................................... 90

Figure 36. Map of EIA Census Division boundaries extended to Canadian provinces........................ 91

Figure A-1. The nine census divisions defined by EIA (EIA 2014) .................................................. 110

Figure A-2. β values for the nine census divisions............................................................................. 111

Figure A-3. Natural gas futures prices from the New York Mercantile Exchange for July 10, 2014 112

Figure A-4. Natural gas futures prices from Figure A-3 separated by season.................................... 113
x

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List of Tables

Table 1. Definition of ReEDS Time-Slice .................................................................................................... 3

Table 2. Characteristics of Land-Based Wind TRGs.................................................................................. 11

Table 3. Characteristics of offshore wind TRGs......................................................................................... 14

Table 4. Characteristics of UPV and DUPV Resource Classes.................................................................. 16

Table 5. Characteristics of CSP Technology Storage Options ................................................................... 20

Table 6. Resource Classes for CSP Plants Using a Solar Multiple of 2.4 .................................................. 21

Table 7. Multipliers Applied to Full-Load Heat Rates to Approximate Heat Rates for Part-Load

Operation................................................................................................................................ 32

Table 8. Emissions Rate by Generator Type in Pounds per MMBtu.......................................................... 33

Table 9. Lifetimes of Renewable Energy Generators and Batteries........................................................... 38

Table 10. Most Common Estimated Lifetimes of Conventional Energy Generators (ABB 2018)............. 38

Table 11. Amount of Nuclear Power Plant Capacity (in GW) in Each Bin................................................ 39

Table 12. Nuclear Power Plant Lifetime for Each Scenario by Bin (years) ............................................... 39

Table 13. Minimum Base Values for Assessing Excessive Growth........................................................... 41

Table 14. Allowed Power and Cooling Technology Combinations in ReEDS .......................................... 47

Table 15. Capital Cost Multipliers for Power-Cooling Technology Combinations ................................... 48

Table 16. Operations and Maintenance Cost Multipliers for Power-Cooling Technology Combinations. 49

Table 17. Heat Rate Multipliers for Power-Cooling Technology Combinations ....................................... 49

Table 18. Water Withdrawal Rates for Power-Cooling Technology Combinations (gal/MWh)................ 50

Table 19. Water Consumption Rates for Power-Cooling Technology Combinations (gal/MWh)............. 50

Table 20. Summary of Operating Reserve Requirements........................................................................... 69

Table 21. Flexibility Parameters of the ReEDS Generation Technologies................................................. 70

Table 22. Cost of Regulation Reserves....................................................................................................... 70

Table 23. Actual and Modeled Schedule of Wind and Solar Tax Credits from the Consolidated 

Appropriations Act, 2016....................................................................................................... 80

Table 24. Default State Tax Incentives....................................................................................................... 81

Table 25. Effective State RPS Requirements in ReEDS............................................................................. 84

Table 26. Pending Cross-Border Transmission Lines as of January 2016 (DOE 2016a)........................... 93

Table 27. International REC Trading Rules Implemented in ReEDS ........................................................ 96
1

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1 Introduction

The Regional Energy Deployment System (ReEDS) model is a capacity expansion and dispatch 

model for the contiguous U.S. electric power sector that relies on system-wide least cost 

optimization to estimate the type and location of future generation and transmission capacity.

1

1.1 ReEDS History

The ReEDS model heritage traces back to National Renewable Energy Laboratory’s (NREL’s)

seminal electric sector capacity expansion model, called the Wind Deployment System (WinDS)

model. The WinDS model was developed in 2003 to examine long-term market penetration of 

wind in the electric power sector (Short et al. 2003). From 2003 to 2008, WinDS was used in a 

variety of wind-related analyses, including the production of hydrogen from wind power, the 

impacts of state-level policies on wind deployment, the role plug-in hybrid electric vehicles in 

wind markets, the impacts of high wind penetration on U.S wind manufacturing, the potential 

for offshore wind, the benefits of storage to wind power, and the feasibility of producing 20% 

of U.S. electricity from wind power by 2030 (DOE 2008). In 2006, a variation of WinDS was 

developed to analyze concentrating solar power (CSP) potential and its response to state and 

federal incentives. In 2009, WinDS was recast as ReEDS—a generalized tool for examining 

the long-term deployment interactions of multiple technologies in the power sector (Short et 

al. 2009). 

Since 2009, ReEDS has been the primary analytical tool in several studies, including the 

Hydropower Vision (DOE 2016b), Wind Vision (DOE 2015), SunShot Vision (DOE 2012), and 

Renewable Electricity Futures (NREL 2012). NREL currently uses ReEDS to publish an annual 

Standard Scenarios report, which provides a U.S. electric sector outlook under a wide range of 

possible futures (Sullivan et al. 2015b; Cole, Mai, et al. 2016; Cole et al. 2017). ReEDS has also 

been used to examine impacts of a range of existing and proposed energy policies (Lantz et al. 

2014; Mai et al. 2015, 2015; Gagnon et al. 2017). Other recent studies have used ReEDS to 

examine the role of natural gas, high renewable scenarios, and other important issues for the U.S. 

electricity sector (Mignone et al. 2012; Logan et al. 2013; Clemmer et al. 2013; Mai et al. 2014; 

Sullivan et al. 2015a; Cole et al. 2015; Cole, Lewis, et al. 2016; Richards and Cole 2017; Cole 

et al. 2018). The ReEDS website2 includes an up-to-date list of publications that use ReEDS.

1.2 Qualitative Model Description

To represent the competition between the many electricity generation, storage, and transmission 

options, ReEDS identifies the least-cost mix of technologies that meet regional electric power 

demand requirements, based on grid reliability (reserve) requirements, technology resource 

constraints, and policy constraints. This cost minimization routine is performed sequentially for 

two-year periods beginning in 2010. Simulations are typically concluded in the 2050 solve year, 

but a new input data extension framework now allows ReEDS to run as far as a 2100 solve year.

3

 1 “Regional Energy Deployment System Model,” NREL, https://www.nrel.gov/analysis/reeds/ 2 “Regional Energy Deployment System Model Publications,” NREL, 

https://www.nrel.gov/analysis/reeds/publications.html 3 By default, all input data are assumed to remain constant after the final available data year except for electricity 

load. Load growth is extended by extrapolating linearly from the last 10 years of available load data. The data 

extension framework allows a developer to modify the extension technique to any functional form desired for a 

given input parameter.
2

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Some of the major outputs of ReEDS include the amount and location of generator capacity, 

annual generation from each technology, storage capacity expansion, transmission capacity 

expansion, total electric sector costs, electricity price, fuel demand and prices, and carbon 

dioxide (CO2) emissions.

ReEDS can simulate the contiguous United States, Canada, and Mexico, though the model is 

typically used to study the contiguous United States. In Section 10, we discuss the model and 

data requirements for continental simulation. Within the United States, ReEDS serves load and 

constructs generating capacity in 134 model balancing areas (BAs), shown in Figure 1. The 

model BAs are not designed to represent or align perfectly with real balancing authority areas; 

they are county aggregates intended to represent model nodes where electricity supply and 

demand is balanced. The model’s aggregated transmission network connects those BAs and is 

composed of roughly 300 representative lines across the three asynchronous interconnections: 

the Western Interconnection, the Eastern Interconnection, and Electric Reliability Council of 

Texas (ERCOT). The BAs also respect state boundaries, allowing the model to represent 

individual state regulations and incentives. The BAs are further subdivided into 356 resource 

regions that describe wind and CSP resource supply and quantity to have more spatial granularity 

for these resources. Resource regions also consist of one or more counties. All other renewable 

resources are represented at the BA level of spatial resolution. Additional geographical layers 

used for defining model characteristics include 3 electricity interconnects, 18 model regional 

transmission operators designed after existing regional transmission operators, 19 North 

American Electric Reliability Corporation (NERC) reliability subregions, 9 census divisions as 

defined by the U.S. Census Bureau, and 48 states.

4

Figure 1. Map showing the ReEDS regional structure

ReEDS includes 3 interconnections, 134 model BAs, and 356 wind and CSP resource regions.

 4 These additional geographical layers defined in ReEDS do not necessarily align perfectly with the actual regions, 

except for state boundaries, which are accurately represented.
3

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Table 1. Definition of ReEDS Time-Slice

Time-Slice Hours/Year Season Time of Day Period

H1 736 Summer Overnight 10 p.m. to 6 a.m.

H2 644 Summer Morning 6 a.m. to 1 p.m.

H3 328 Summer Afternoon 1 p.m. to 5 p.m.

H4 460 Summer Evening 5 p.m. to 10 p.m.

H5 488 Fall Overnight 10 p.m. to 6 a.m.

H6 427 Fall Morning 6 a.m. to 1 p.m.

H7 244 Fall Afternoon 1 p.m. to 5 p.m.

H8 305 Fall Evening 5 p.m. to 10 p.m.

H9 960 Winter Overnight 10 p.m. to 6 a.m.

H10 840 Winter Morning 6 a.m. to 1 p.m.

H11 480 Winter Afternoon 1 p.m. to 5 p.m.

H12 600 Winter Evening 5 p.m. to 10 p.m.

H13 736 Spring Overnight 10 p.m. to 6 a.m.

H14 644 Spring Morning 6 a.m. to 1 p.m.

H15 368 Spring Afternoon 1 p.m. to 5 p.m.

H16 460 Spring Evening 5 p.m. to 10 p.m.

H17 40 Summer Peak 40 highest demand hours of 

H3

ReEDS serves load and maintains operational reliability using a reduced-order dispatch in 17 timeslices within each model year, as defined in Table 1. Each of the four seasons is modeled with a 

representative day of four chronological time-slices: overnight, morning, afternoon, and evening.5

The 17th time-slice is a summer “superpeak” representing the top 40 hours of summer load. 

Though this schedule does allow the model to capture seasonal and diurnal variations in demand,

wind, and solar profiles, it is insufficient to address some of the shorter time-scale challenges 

associated with unit commitment and economic dispatch, especially under scenarios with high 

penetration of variable generation (e.g., wind and solar). To more accurately represent how 

renewable grid integration might affect investment and dispatch decisions, the ReEDS model 

includes parameters designed to address intra-time-slice variability and uncertainty of wind, solar,

and other variable renewable resources. These parameters, including capacity value for system 

adequacy, forecast error reserve requirements, and curtailment estimates, are each discussed in

detail in Section 6.

The major conventional thermal generating technologies represented in ReEDS include simple 

and combined cycle natural gas, several varieties of coal, oil/gas steam, and nuclear. In addition 

to representing these technologies, ReEDS characterizes many renewable technologies, including 

geothermal, hydropower, biopower, wind, and solar. Electricity storage technologies include

 5 The modeled load for each time-slice is an average of all hours represented by that time-slice. Therefore, the 

representative days are non-chronological across seasons. 
4

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pumped storage hydropower (PSH), compressed-air energy storage (CAES), batteries, and CSP 

with thermal storage.

With a system-wide central-planner perspective, ReEDS is not designed to evaluate distributed 

generation adoption decisions. For this reason, ReEDS analysis is supported by the dGen model

(Sigrin et al. 2016)—a market-penetration model well-suited to producing scenarios of market 

uptake of distributed rooftop photovoltaics (PV). ReEDS uses dGen6 adoption scenarios to 

exogenously specify the amount and location of new rooftop PV.

7

ReEDS is structured as a sequence of individual, but interacting8 optimization problems, each 

representing a two-year period from 2010 to a maximum end year of 2100. Each ReEDS 

scenario launches with an infrastructure base representing installed generation and transmission 

capacity as of December 31, 2010. New infrastructure that came online from 2011 through the 

present is prescribed into the ReEDS system in the proper model year, and recently 

decommissioned units are removed in the same way. Similarly, generators with scheduled online 

dates are included as prescribed builds in future years, and scheduled retirements are set to be 

removed from the fleet as appropriate. Additionally, ReEDS inputs include an equipment 

lifetime for each technology that is used to retire capacity as it ages. In certain types of scenarios, 

some existing stock can be underutilized due to, for example, high fuel prices or emissions 

standards. ReEDS facilitates “economic” retirements of underutilized coal capacity if usage (i.e.,

capacity factor) falls below a certain threshold. Economic coal retirement in ReEDS is applied 

starting in 2022 with an increasingly stringent threshold of underutilization through 2040.9 The 

model does not include capacity factor-based or economic retirements of any other capacity 

type.10

ReEDS tracks emissions of CO2, sulfur dioxide (SO2), nitrogen oxides (NOx), and mercury from 

both generators and fossil-fueled storage technologies such as CAES. Limits on these emissions 

can be placed at the national, regional, or state level. Applying a carbon tax instead of a cap is 

another available policy option; the tax level and trajectory can be defined exogenously.

Annual electric loads and fuel price supply curves are exogenously specified to define the system 

boundaries for each period of the optimization. The data source for most load and fuel inputs is 

the most recent Annual Energy Outlook (AEO) from the U.S. Energy Information 

Administration (EIA).

11 Coal and uranium fuels are assumed to be price-inelastic; ReEDS can 

 6 For documentation of the dGen model, see Sigrin et al. (2016). 7 ReEDS and dGen can interact dynamically, though that capability is not used by default. See Cole, Lewis, et al. 

(2016) for more information. 8 Because ReEDS is a sequential model, it is path-dependent, such that solutions in a given solve period inform 

the starting point of the next optimization year. 9 The minimum capacity factor threshold is 6% in 2022, increases linearly to 50% by 2040, and remains 

flat thereafter. Because ReEDS does not include endogenous economic retirements, this construct is an effort to 

estimate realistic coal retirement behavior that occurs for a range of reasons including but not exclusive to low 

capacity factors.

10 A technology “agnostic” method would be preferred but has not been developed. Nevertheless, we implement this 

technique for coal because historical and recent market developments have shown coal capacity to be particularly 

sensitive to reduced capacity factors (Van Atten 2017; Larsen et al. 2018; Jell and Bowman 2018) 11 Any inputs to ReEDS v.2018 from AEO are sourced from the 2018 edition of the AEO (EIA 2018) or its 

assumptions (EIA 2017) unless otherwise specified.
5

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demand as much of those fuels as it likes at the AEO-specified price. However, natural gas prices 

are defined by regional supply curves so that the prices respond to changes in electric sector 

demand for gas (Section 3).

1.3 Linear Program Formulation Description

This section qualitatively describes the basic linear program formulation of ReEDS. The model 

is recursive-dynamic in that it solves a linear program for each of the 21 two-year periods as it 

moves successively from 2010 to a maximum of 2100. 

Figure 2. Schematic of the ReEDS model structure

The objective function in the ReEDS linear program is a minimization of both capital and 

operating costs for the U.S. electric sector, including:

• The net present value of the cost of adding new generation, storage, and 

transmission capacity

• The present value of operating expenses over the evaluation period12 (e.g., expenditures 

for fuel and operation and maintenance [O&M]) for all installed capacity

• The cost of several categories of ancillary services and storage

• The cost or incentive applied by any policies that directly charge or credit generation 

or capacity

 12 The current default is 20 years.
6

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• Penalties for rapid capacity growth as a proxy for manufacturing, supply chain, and 

siting/permitting limitations

By minimizing these costs and meeting the system constraints (discussed below), the linear 

program determines the types of new capacity to construct in each region during each period to 

minimize systemwide cost. Simultaneously, the linear program determines how generation and 

storage capacity should be dispatched to provide the necessary energy and operating reserves in 

each of the 17 time-slices. The capacity factor for each dispatchable technology therefore is an 

output of the model and not an input assumption.

The constraints that govern how ReEDS builds and operates capacity fall into several main 

categories, including:

• Load constraints: Sufficient power must be generated within or imported by the 

transmission system to meet the projected load in each of the 134 BAs in each of the 17 

time-slices. The annual demand and the time-slice-specific electricity demand in future 

years are based on projections for each NERC subregion. Within each NERC subregion, 

the load distribution between BAs and the load shape in each BA is retained for all years.

• Planning reserve constraints: There must be sufficient firm-generating capacity 

available in each region to meet the forecasted peak demand plus an additional reserve 

(safety) margin (NERC 2010). Dispatchable technologies contribute their full capacity 

toward planning reserves. For variable renewable energy (VRE) technologies, ReEDS 

uses a load-duration curve (LDC) approximation to estimate the effective load-carrying 

capacity of both existing capacity and potential capacity additions to determine their 

contribution to meeting the reserve margin. Firm capacity can also be contracted from 

one region to another if transmission capacity is available.13

• Operating reserve constraints: These constraints ensure enough capacity is available 

to meet unexpected changes in generation and load in each reserve-sharing group 

(see Section 6.3) and time-slice. ReEDS accounts for the following operating reserve 

requirements: regulation reserves, spinning reserves, and flexibility reserves.

• Generator operating constraints: Technology-specific constraints bound the minimum 

and maximum power production and capacity commitment based on physical limitations 

and assumed average outage rates. 

• Transmission constraints: Power transfers among regions are constrained by the 

nominal carrying capacity of transmission lines that connect the regions and governed

by a linearized direct current (DC) power flow representation. Firm power contracts for 

planning reserves are also subject to transmission limits. A detailed description of the 

transmission constraints can be found in Section 5. 

• Resource constraints: Many renewable technologies, including wind, solar, geothermal, 

biopower, and hydropower, are spatially heterogeneous and constrained by quantity 

available at each location. Several of the technologies include cost- and resource-quality 

considerations in resource supply curves to account for depletion, transmission, and 

 13 Hydropower’s contribution to planning reserves depends on its categorization as dispatchable or non-dispatchable, 

which is discussed in Section 2.1.6.
7

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competition effects. The resource assessments that seed the supply curves come from 

various sources and are discussed in Section 2, where characteristics of each technology

are also provided. 

• Emissions constraints: ReEDS can limit or cap the emissions from fossil-fueled 

generators for SO2, NOx, mercury, and CO2. The emission limit and the emission per

megawatt-hour by fuel and plant type are inputs to the model. In carbon-constrained 

scenarios, CO2 can be either capped or taxed, and either a cap or tax can be finely 

adjusted to match proposed legislation. Alternatively, emissions intensities can also be 

limited to certain bounds in ReEDS.

• Renewable portfolio standards or clean electricity standards: ReEDS allows users 

to input renewable portfolio standards (RPSs) or clean electricity standards constraints 

at the national and state levels. All renewable generation is considered eligible under a

national RPS requirement. The renewable generation sources include hydropower, wind, 

CSP, geothermal, PV, and biopower (including the biomass fraction of co-firing plants). 

The eligibility of technologies for state RPSs depends on the state’s specific requirements

and thus varies by state. RPS targets over time are based on an externally defined profile. 

Penalties for noncompliance can be imposed for each megawatt-hour shortfall occurring 

in the country or a given state. In the same way, a clean energy standard constraint can be 

implemented to include non-renewable clean energy resources, such as nuclear, fossil 

fuels with carbon capture and sequestration (CCS), or natural gas.

1.4 Summary of Caveats

Though ReEDS represents many aspects of the U.S. electricity system, it necessitates

simplifications, as all models do. We offer a list of some important limitations and caveats that 

result from these simplifications.

• System-wide optimization: ReEDS takes a system-wide, least-cost perspective that does 

not necessarily reflect the perspectives of individual decision makers, including specific 

investors, regional market participants, or corporate or individual consumer choice; nor 

does it model contractual obligations or noneconomic decisions. In addition, like other 

optimization models, ReEDS finds the absolute (deterministic) least-cost solution that 

does not fully reflect real distributions or uncertainties in the parameters; however, the 

heterogeneity resulting from the high spatial resolution of ReEDS mitigates this effect

to some degree.

• Resolution: Though ReEDS has high spatial, temporal, and process resolution for 

models of its class and scope, it cannot generally represent individual units and 

transmission lines, and it does not have the temporal resolution to characterize detailed 

operating behaviors, such as ramp rates and minimum plant runtime. It also does not 

represent all intra-annual time scales, such as weekly or monthly trends, as ReEDS intraannual time slices are designed to characterize a typical day in each season. 

• Foresight and behavior: The model is not inter-temporally optimized, and except for 

limited foresight of future natural gas prices and CO2 prices (if applicable), model 

decision-making does not account for anticipated changes to markets and policies. 
8

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For example, ReEDS does not endogenously model banking and borrowing of credits for 

carbon, renewable, or clean energy policy between solve periods. 

• Project pipeline: The model incorporates data of planned or under-construction projects, 

but these data likely do not include all projects in progress.

• Manufacturing, supply chain, and siting: The model does not explicitly simulate 

manufacturing, supply chain, or siting and permitting processes. Potential bottlenecks or 

delays in project development stages for new generation or transmission are not fully 

reflected in the results. All technologies are assumed to be available at their defined 

capital cost in any quantity up to their technical resource potential. Penalties for rapid 

growth are applied in ReEDS; however, these do not fully consider all potential 

manufacturing or deployment limits. Dates associated with cost inputs in the model 

reflect project costs for the commercial operation date but not necessarily when 

equipment is ordered.

• Financing: Though the model can use annually varying financing parameters to capture 

near-term market conditions and technology-specific financing to account for differences 

in typical investment strategies across technologies, ReEDS cannot fully represent 

differences in project financing terms across markets or ownership types and thus does 

not allow multiple financing options for a given technology.

• Technology learning: Future technology improvements are considered exogenously and 

thus are not a function of deployment in each scenario.

• Power sector: ReEDS models the only power sector within its defined regional scope 

(contiguous United States or United States with Canada and/or Mexico), and it does not 

represent the broader U.S. or global energy economy. For example, competing uses of 

resources (e.g., natural gas) across sectors are not dynamically represented in ReEDS, 

and end-use electricity demand is exogenously input into ReEDS.

Notwithstanding these limitations—many of which exist in other similar tools—the modeling 

approach considers complex interactions among numerous policies and technologies while 

ensuring electric system reliability requirements are maintained within the resolution and scope 

of the model. In doing so, ReEDS can comprehensively estimate the system cost and value of 

a wide range of technology options given a set of assumptions, and we can use the model to 

generate self-consistent future deployment portfolios.
9

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2 Technology Descriptions

This section describes the electricity generating technologies included in ReEDS. Cost and 

performance assumptions for these technologies are not included in this report but are taken

directly from the 2018 Annual Technology Baseline (ATB) (NREL 2018a).

14

2.1 Renewable Energy Resources and Technologies

Because renewable energy technologies are a primary focus area of the ReEDS model, they are 

characterized in great detail. Their characterization encompasses resource assessments,15

projected technology improvements, grid interconnection costs, and operational implications of 

integration. Renewable energy technologies modeled include land-based and offshore wind 

power, solar PV (both distributed and utility), CSP with and without thermal storage,

16

hydrothermal geothermal, near-field enhanced geothermal systems (EGS), deep EGS, run-of-theriver and traditional hydropower (including upgrades and non-powered dams), dedicated 

biomass, co-fired biomass, land-fill gas, and marine hydrokinetic wave technologies. The input 

assumptions, data sources, and treatments of these technologies are discussed in the following 

sections. Transmission considerations for renewable energy technologies are discussed in 

Section 5.3.

17

2.1.1 Land-Based Wind

Wind technologies are modeled using representative turbine technologies by region depending 

on wind resource quality. Details of the wind resource data and technology representation can be 

found in Appendix H of the Wind Vision study (DOE 2015). In the current version of ReEDS, 

we have relied on the same data sources and approach; however, we extend the wind resource 

data to lower-quality wind sites.

Wind turbine models can be classified into three different International Electrotechnical 

Commission turbine ratings (I–III), which are designed for a range of annual average wind 

speeds.18 The Class I turbines have smaller rotors relative to the size of the generator, or a higher 

specific power (watts per meter squared, or W/m2), and they are therefore rated to withstand 

higher winds. In the lowest-speed wind resources, Class III turbines are primarily used to gain 

the highest capacity factor possible in lower wind speeds. Interpolating cost and performance 

 14 The Annual Technology Baseline cost projections include the impacts of the PV import tariffs. 15 All renewable resource assessments are independent and mutually exclusive of each other due to their unique 

nature and to allow ReEDS to dynamically evaluate cost-optimal capacity expansion without any upstream ranking 

of which technologies would be preferred at a given site. This implementation ignores any possible land-use 

conflicts between multiple technologies at the same site, but spatial aggregation and resource heterogeneity is 

expected to alleviate this limitation.

16 CSP refers to solar thermal power and not concentrating PV. 17 Where given in the sections below, renewable energy resource potential values refer to the resource potential 

represented in ReEDS and not the total technical resource potential. The renewable potential capacity modeled in 

ReEDS includes exclusions in the pre-processing steps for the model, such as site exclusions, assumed transmission 

access limits, or a narrower set of technologies considered. Lopez et al. (2012) present renewable technical potential 

for the United States.

18 International Electrotechnical Commission Class I turbines are used with an annual average wind speeds of 10 

meters/second (m/s) and higher; Class III are used with an annual average wind speed of 7.5 m/s and lower. A blend 

of Class II and Class III turbines is used at annual average wind speeds of 7.5–8.5 m/s; while a blend of Class II and 

Class I turbines is used at annual average wind speeds of 8.5–10 m/s. 
10

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across the three International Electrotechnical Commission classes allows monotonic functions 

of cost and performance by annual average wind speed. Cost and performance assumptions for 

land-based wind plants are based on expected cost and performance for a turbine (representative 

or interpolated) appropriate for the average annual wind speed at the site. 

The resource assessment for land-based wind starts with a resource map of hourly wind speeds 

for the United States and offshore areas (for offshore wind, see Section 2.1.2). Land area is 

filtered to exclude a standard set of areas considered unlikely to be developed for environmental

or technical reasons: federal and state protected areas (e.g., parks, wilderness areas, and wildlife 

sanctuaries), areas covered by water, urban areas, wetlands, airports, and rough terrain. Areas 

classified as non-ridge-crest forest, non-ridge-crest U.S. Forest Service and U.S. Department of 

Defense lands, and state forests (where available in geographic information systems [GIS]) are 

50% excluded. The remaining resource totals more than 11,632 gigawatts (GW).

Individual wind sites are grouped into ten resource classes (a.k.a., techno-resource groups or 

TRGs) for ReEDS, based on estimated levelized cost of energy for present-day technology

(Table 2).

19 The modeling and assessment of individual wind sites was facilitated using NREL’s 

Renewable Energy Potential (reV) tool. The meteorological data for onshore wind are from the 

Wind Integration National Dataset (WIND) Toolkit, using long-term average data for the 

resource assessment. Each class includes representative costs (capital and O&M) and expected 

output (capacity factor) along with cost and performance improvements over time. Figure 3

shows the land-based wind resource data modeled in ReEDS for all 10 TRGs, where the highestquality wind resources belong to TRG 1 and the lowest to TRG 10. Each TRG is then further 

differentiated by a supply curve for grid interconnection costs in each region. See Section 5.3 for 

a discussion of interconnection supply curves for accessing the wind resource. Figure 4 shows 

estimated 2014 supply curves (levelized cost of energy versus resource) of all wind resources 

modeled in ReEDS, including land-based and offshore wind.

Distinct wind production profiles are also modeled for each TRG and wind resource region. 

In addition, to inform the statistical parameterizations for capacity value and curtailment, we 

use hourly production data and correlations between regions and TRGs. A 2012 weather year is 

used for the generation of the hourly onshore wind profile data.

 19 The wind resource is not evenly binned into the 10 TRGs. The best resource sites (TRGs 1–4) are grouped into 

smaller bins to improve resolution for sites that are likely to be most competitive.
11

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Table 2. Characteristics of Land-Based Wind TRGs 

TechnoResource 

Group (TRG)

Wind Speed 

Range (m/s)

Weighted 

Average 

Wind Speed 

(m/s)

Weighted 

Average Net 

CF (%)

Potential Wind 

Plant Capacity 

(GW)

TRG1 8.2–13.5 8.7 47.4 100

TRG2 8.0–10.9 8.4 46.2 200

TRG3 7.7–11.1 8.2 45.0 400

TRG4 7.5–13.1 7.9 43.5 800

TRG5 6.9–11.1 7.5 40.7 1,600

TRG6 6.1–9.4 6.9 36.4 1,600

TRG7 5.4–8.3 6.2 30.8 1,600

TRG8 4.7–6.9 5.5 24.6 1,600

TRG9 4.0–6.0 4.8 18.3 1,600

TRG10 1.0–5.3 4.0 11.1 2,132

More information can be found in the NREL Annual Technology Baseline (NREL 2018b).
12

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Figure 3. Land-based wind resource map for the contiguous United States
13

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Figure 4. Estimated wind supply curves of all wind resource modeled in ReEDS (2016$)

2.1.2 Offshore Wind

There is substantial diversity in offshore wind generators, in distance from shore, water depth, 

and resource quality. ReEDS subdivides offshore wind potential into fifteen resource classes: 

five for fixed-bottom turbine designs and ten for floating designs. Fixed bottom offshore wind 

development is limited to resources < 60 meters [m] in depth using either current-technology 

monopile foundations (0–30 m); or jacket (truss-style) foundations (30–60 m). Offshore wind 

using a floating anchorage could be developed for greater depths and are assumed the only 

feasible technology for development for resource deeper than 60 m. Within each category, the 

classes are distinguished by resource quality, and then supply curves differentiate resource by 

cost of accessing transmission in a similar fashion as land-based wind.

Eligible offshore area for wind development includes open water within the U.S.-exclusive 

economic zone having a water depth less than 1,000 m, including the Great Lakes. As with landbased resource, offshore zones are filtered to remove areas considered unsuitable for 

development, including national marine sanctuaries, marine protected areas, wildlife refuges, 

shipping and towing lanes, offshore platforms, and ocean pipelines. The offshore technology 

selection is made using the Offshore Wind Cost Model, which selects the most economically 

feasible technology for developing a wind resource (Beiter and Stehly 2016). More than 2,000 

GW of technical offshore wind potential remain after applying the exclusions.

Current-day cost data are derived from the published data of the global offshore wind industry as 

well as estimates from recent development activity on the Atlantic Coast of the United States

(Tegen et al. 2012; Wiser and Bolinger 2017; NREL 2018b). These data are coupled with 

engineering assessments and distance-based cost functions (specific to the offshore export cable 

and incremental construction cost associated with moving farther from shore) to determine 

expected site-specific costs for technology across a broad range of water depths and distances 

from shore (Beiter et al. 2017).
14

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Other aspects of our model representations for offshore wind follow the same methods as those 

for land-based wind (see Appendix H of the Wind Vision study [DOE 2015]). Figure 5 shows 

the offshore wind resource potential modeled in ReEDS using the 15 TRGs. The first five TRGs 

represent fixed-bottom offshore wind resources with TRG 1 representing the lowest-cost 

resource based on levelized cost of energy (LCOE). TRGs 6–15 represent floating offshore wind 

resources with TRG 6 being the lowest cost based on LCOE. The three lowest cost TRGs, for 

both fixed-bottom and floating-bottom turbines, have smaller bin sizes to capture the highest 

quality resources with greater resolution.

Table 3. Characteristics of Offshore Wind TRGs

TRG

Wind 

Speed 

Range 

(m/s)

Weighted 

Average 

Wind 

Speed 

(m/s)

Weighted 

Water 

Depth (m)

Weighted 

Distance 

Site to 

Cable 

Landfall 

(km)

Weighted 

Average 

Net CF 

(%)

Potential 

Wind Plant 

Capacity 

(GW)

FixedBottom

TRG 1 8.5–9.0 8.6 13 6 45 12

TRG 2 8.0–8.5 8.4 16 9 43 25

TRG 3 8.0–8.5 8.3 19 15 42 50

TRG 4 8.0–8.5 8.3 26 36 41 320

TRG 5 7.5–8.0 7.9 36 72 37 320

Floating

TRG 6 9.5–10 9.7 130 24 51 12

TRG 7 9.5–10 9.7 145 40 50 25

TRG 8 9.5–10 9.5 139 50 49 50

TRG 9 9.0–9.5 9.4 136 70 48 100

TRG 10 9.0–9.5 9.1 140 94 45 200

TRG 11 8.5–9.0 8.7 323 118 42 200

TRG 12 8.0–8.5 8.1 404 123 37 200

TRG 13 7.5–8.0 7.8 474 138 35 200

TRG 14 7.0–7.5 7.4 615 130 33 200

TRG 15 7.5–8.0 7.5 797 199 31 143

More information can be found in the NREL Annual Technology Baseline (NREL 2018b).
15

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Figure 5. Offshore wind resource map for the contiguous United States

2.1.3 Solar Photovoltaics

ReEDS differentiates between three solar photovoltaic technologies: large-scale utility PV 

(UPV), distribution-side utility-scale PV (DUPV), and rooftop PV. Investments in UPV and 

DUPV are evaluated directly in ReEDS, while rooftop PV deployment and performance are 

exogenously input into ReEDS from the dGen model.

UPV in ReEDS represents utility-scale single-axis-tracking PV systems with a representative size

of 100 megawatts (MW) and an array density of 39 MW per square kilometer (km2) with an 

inverter loading ratio of 1.3. Resource potential is assumed to be located on large parcels outside 

urban boundaries, excluding federally protected lands, inventoried “roadless” areas, U.S. Bureau of 

Land Management areas of critical environmental concern, and areas with slope greater than 5%. 

Each eligible UPV site is characterized by a raw hourly (8,760) irradiance profile that is 

representative of the solar resource within a 10 km2 contiguous area. Each of these UPV sites are 

compiled into supply curves for each of the 134 ReEDS BA regions with 9 PV resource classes, 

which are further differentiated by cost to connect to the transmission network (process described 

in Section 5.3). The nine resource classes reflect different resource qualities based on the annual 

average direct normal irradiance (DNI) reported by the latest National Solar Radiation Database 

(NSRDB), assuming a tilt angle equal to the latitude (Table 2). The UPV supply curves input into 

ReEDS include 117 terawatts (TW) of potential, which is shown by resource class in Figure 6.
16

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Table 4. Characteristics of UPV and DUPV Resource Classes

Class Irradiance

(kWh/m2/day)

Potential UPV 

Capacity (GW)

Weighted 

Average

UPV Capacity 

Factor (%)

Potential DUPV 

Capacity (GW)

Weighted 

Average

DUPV Capacity 

Factor (%)

1 3.0–3.5 167 12.2 14 12.6

2 3.5–4.0 16,870 16.8 184 16.7

3 4.0–4.5 30,238 18.6 434 17.9

4 4.5–5.0 37,438 19.6 511 19.1

5 5.0–5.5 20,372 21.8 222 21.1

6 5.5–6.0 11,868 23.9 116 23.8

7 6.0–6.5 332 24.4 4 24.8

8 6.5–7.0 0 n/a 0 n/a

9 7.0–7.5 0 n/a 0 n/a

Figure 6. UPV and DUPV resource areas available for development considering land 

use exclusions
17

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Figure 7. UPV resource availability by ReEDS BA region and resource class

DUPV in ReEDS assumes utility-scale single-axis-tracking systems with a representative size of 

1 MW and an array density of 39 MW/km2 with an inverter loading ratio of 1.3. These systems 

are located within or are directly connected to distribution networks in urban areas. Resource 

potential estimates for DUPV are made by defining the eligible area as open space within urban 

areas and near existing distribution substations. Parking lots, roads, and urbanized areas are 

excluded by identifying areas with imperviousness greater than or equal to 1% (Fry et al. 2011). 

Additional exclusions for landmarks, parks, and water further exclude areas deemed unlikely for 

development. Each DUPV site within the resulting eligible rural land areas is characterized by a 

raw hourly (8,760) irradiance profile that is representative of the solar resource within a 10 km2

contiguous area. This process returns an urban DUPV potential of nearly 1.48 TW across the 

contiguous United States (see Figure 7), with resource potential generally correlated with region 

size and population, and with higher-quality potential in the Southwest. The two order-ofmagnitude difference in resource capacity relative to UPV is a result of the significantly smaller 

land area with access to the distribution networks. (Figure 6) Similar to UPV, these values are 

input into ReEDS as supply curves by BA region and the nine PV resource classes from Table 2.
18

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Figure 8. DUPV resource availability in each ReEDS region by resource class

DUPV systems have lower infrastructure requirements than large-scale rural UPV systems; we 

assume they connect to existing nearby distribution substations at about 13 kilovolts (kV), 

whereas the representative UPV system connects to a high-voltage bus at 230 kV and may 

require a spur line several miles long to reach that connection point. The cost of the spur line is 

handled separately in the accessibility supply curve (Section 5.3), but the additional transformers 

and power electronics associated with the larger systems and higher-voltage interconnections add 

cost and losses to the UPV systems. On the other hand, the larger UPV systems benefit from 

economies of scale. On balance, we assume a per-kW capital cost penalty of 8.7%20 and 3.5%

higher delivered energy (i.e., reduced losses) for DUPV relative to UPV.

 20 Represents the average of union and non-union total system cost without transmission between a 10MW (proxy 

for DUPV) and 100MW (UPV) system (Fu et al. 2015).
19

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Performance characteristics for UPV and DUPV were developed using NREL’s Renewable 

Energy Potential (reV) Tool, using multiyear hourly weather files from the National Solar 

Radiation Database21 at 4-km by 4-km parcels throughout the contiguous United States from 

1998 to 2016. No changes or improvements in capacity factor over time are assumed for PV

technologies. For each ReEDS BA region, resource quality classifications were made by 

averaging across the 1998–2016 period for all available parcels. Hourly generation profiles were 

taken from 2012. The generation profiles from all the regions in a BA for each resource class 

were averaged to provide ReEDS with average capacity factors by time-slice and resource class.

To mitigate excessive wheeling of distributed PV generation, ReEDS assumes all power 

generated by both DUPV and rooftop PV systems is permitted to be exported to neighboring 

BAs only when total generation in the source region exceeds the load for a given time-slice. 

UPV-generated electricity, in contrast, can be exported in all time-slices and regions.

Degradation of the efficiency of solar PV capacity over time is also modeled at 0.5%/year

(Jordan and Kurtz 2013). This degradation is modeled by reducing the capacity of PV that 

generates energy by 0.5%/year.

Rooftop PV includes commercial, industrial, and residential systems. These systems are assumed 

to have an inverter loading ratio of 1.1. dGen, a consumer adoption model for the contiguous

U.S. rooftop PV market, is used to develop future scenarios for rooftop PV capacity, including 

the capacity deployed by BA and the pre-curtailment energy production by that capacity (Sigrin 

et al. 2016). The default dGen trajectories used in this version of ReEDS are based on the 

residential and commercial PV cost projections as described in the 2018 ATB (NREL 2018a). 

There are 14 unique rooftop PV trajectories available in ReEDS (see Figure 8) that are from the 

2018 Standard Scenarios report (W. J. Cole et al. 2018). These trajectories were created by 

running a ReEDS scenario and feeding the electricity price outputs from ReEDS back into dGen. 

The trajectories incorporate existing net metering policy as of spring 2018, and they include the 

investment tax credit (ITC) as discussed in Section 8.2.

 21 https://nsrdb.nrel.gov/
20

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Figure 9. Prescribed distributed PV deployment used in the ReEDS as determined by dGen

For ReEDS simulations beyond 2050, dGen projections are extended to 2100 assuming no 

changes to input conditions after 2050.

The dashed line shows the Mid-case scenario, while each individual line represents an individual 

dGen scenario.

2.1.4 Concentrating Solar Power

Concentrating solar power (CSP) technology options in ReEDS encompass a subset of possible 

thermal system configurations, with and without thermal storage, as shown in Table 3. The two 

system types access the same resource potential, which is divided into 12 resource classes based 

on DNI (Table 4). The CSP resource and technical potential is based on the latest version of 

NSRDB. Details of the CSP resource data and technology representation can be found in 

Appendix B of Murphy et al. (C. Murphy et al. 2019). By default, recirculating and dry cooling 

systems are allowed for future CSP plants getting built in ReEDS.

Table 5. Characteristics of CSP Technology Storage Options

System Configuration22 Dispatchability Capacity Value Curtailments

No Storage solar multiple: 1.4 insolation-dependent variable/

statistical

variable/

statistical

With Storage 6–12 storage hours, 

solar multiple: 1.6–2.5

dispatchable firm none

 22 The solar multiple is defined as the ratio of the design solar field aperture area to the aperture area required to 

produce the power cycle design thermal input (and power output) under reference environmental conditions. 

0

20

40

60

80

100

120

140

160

180

DPV Capacity (GWAC)

Sensitivity Range
21

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The CSP resource classes are defined by power density of DNI, developable land area having 

been filtered based on land cover type, slope, and protected status. CSP resource in each resource 

region is therefore represented as a supply curve of MW of solar collector potential, assuming a 

power density of 14.9 MW/km2

. Performance for each CSP resource class was developed using 

2012 hourly resource data (Sengupta et al. 2018) from representative sites of each resource 

region. The 2012 weather files are processed through the CSP modules of the System Advisor 

Model (SAM) to develop performance characteristics for each CSP resource class and 

representative CSP system considered in ReEDS. As with wind and PV technologies, CSP 

resources are further distinguished by grid accessibility in each resource region (see Section 5.3).

Table 6. Resource Classes for CSP Plants Using a Solar Multiple of 2.4

Resources are then scaled in ReEDS by the ratio of the model-determined solar multiple and 2.4.

Resource 

Class

DNI

(kWh/m2/day)

Weighted Average 

Field CF for SM=1 

system (%)a

Available Resource 

(GW)

Class1 5–5.25 17 2,641

Class2 5.25–5.5 18 1,925

Class3 5.5–5.75 19 1,495

Class4 5.75–6 20 1,725

Class5 6–6.25 21 1,850

Class6 6.25–6.5 22 1,282

Class7 6.5–6.75 23 1,252

Class8 6.75–7 0.244 1,098

Class9 7–7.25 0.255 1,381

Class10 7.25–7.5 0.266 1,251

Class11 7.5–7.75 0.276 677

Class12 >7.75 0.285 114

a The field capacity factors shown here are from SAM (version 2017.09.05, SDK version 181) 

simulation assuming a solar multiple (SM)=1 system. This field capacity factor is meant to 

represent the upper limit of corresponding turbine capacity factor for all systems with SM>1.

The representative CSP system without storage used to define system performance in ReEDS is a 

100 MW trough system with a solar multiple of 1.4. As CSP systems without storage are nondispatchable, output capacity factors are defined directly from SAM results. The average annual 

capacity factors for the solar fields of these systems range from 20% (Class 1 resource) to 29% 

(Class 12 resource).
22

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 10. CSP resource availability in each ReEDS resource region by 12 resource class

The representative system for any new CSP with thermal energy storage (TES) is a tower-based 

configuration with a molten salt heat transfer fluid and a thermal storage tank between the 

heliostat array and the steam turbine.23 CSP with storage is optimally built within ReEDS by 

considering three major design components (field, turbine, storage) within six constraints as 

shown in Figure 10. These constraints ensure the systems can be assumed to be fully 

dispatchable within the energy limitations imposed by the following:

• For a given turbine, the storage tank must hold enough energy for 6–12 hours of rated 

power output (horizontal lines in Figure 10).24

• The maximum allowed solar multiple is 2.5 for tower systems (vertical green line). 

• Upper and lower bounds on annual system capacity factor create lower and alternate 

upper limits on solar multiple (vertical orange and blue lines); ReEDS assumes the array 

must be sized to produce an average annual capacity factor between 40% and 65%. 

• A minimum storage-to-solar-multiple ratio ensures curtailments are not excessive; in 

other words, the storage tank size must keep pace with increasing solar multiples to avoid 

excessive curtailments (diagonal line—derived from the data in Figure 11).

The data in Figure 11 show the turbine capacity factor for a range of CSP system configurations

(field, turbine, and storage) as simulated by SAM. The capacity factor erodes for high solar 

multiple systems due to curtailments arising from insufficient storage. This relationship between 

storage and solar multiple is used to define the minimum storage to solar multiple ratio constraint 

in Figure 10 (diagonal line).

 23 Historical and announced trough-based systems are characterized with technology-appropriate characteristics. 24 This limit on storage duration is in not a technical limit, and shorter duration storage has been considered for CSP.
23

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ReEDS considers CSP-with-storage system configurations within the resulting feasible space 

(hashed lines in Figure 10). Higher solar multiples result in higher annual average capacity 

factors, and more storage allows the systems to be more flexible, although both options increase 

capital costs per kilowatt of installed turbine capacity.

Figure 11. Demonstration of ReEDS constraints and corresponding feasible space (hashed line) 

for CSP with storage in Class 8 resource regions

Figure 12. Capacity factor trends versus solar multiple
24

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

For CSP with storage, plant turbine capacity factors by time-slice are an output of the model, not 

an input, as ReEDS can dispatch collected CSP energy independent of irradiation. Instead, the 

profile of power input from the collectors (solar field) of the CSP plants are model inputs, based 

on SAM simulations from the 2012 weather files.

2.1.5 Geothermal

Geothermal resource has several subcategories in ReEDS. Hydrothermal resource represents 

potential sites that involve tapping existing hot water. The hydrothermal potential included in the 

base supply curve consists of only identified sites, with a separate supply curve for undiscovered 

hydrothermal resource. EGS sites are geothermal resources that have sufficient temperature but 

lack the natural permeability, in-situ fluids, or both to be hydrothermal systems. Near-field EGS 

is a subset of EGS that implies proximity to existing or known hydrothermal sites.

The default geothermal resource assumptions allow for new construction at identified 

hydrothermal sites and near-hydrothermal-field enhanced geothermal systems (near-field EGS). 

Though undiscovered hydrothermal and additional deep EGS resources are not allowed under 

default assumptions due to uncertain feasibility, these geothermal resource categories can be 

included as additional resource in ReEDS. The identified hydrothermal and near-field EGS 

supply curves are based on the analysis described in (Augustine 2011) and are shown in Figure 

12. The hydrothermal and near-field EGS resource potential is derived from the U.S. Geological 

Survey’s 2008 Geothermal Resource Assessment (Williams, Reed, and Mariner 2008), while the 

deep EGS resource potential is based on an update of the EGS potential from the Massachusetts 

Institute of Technology (Tester et al. 2006). As with other technologies, geothermal cost and 

performance projections are from the ATB (NREL 2018a).

Figure 13. National capital cost supply curves for new identified hydrothermal and near-field EGS 

capacity used in the base model assumptions

2.1.6 Hydropower

The existing hydropower fleet representation is informed by historical performance data. From 

the nominal hydropower capacity in each BA, seasonal capacity adjustments are used for 

Western Electricity Coordinating Council (WECC) regions based on data from the Transmission 

Expansion Planning Policy Committee (TEPPC) 2024 Common Case (WECC 2013, 2015). 

0

5,000

10,000

15,000

20,000

25,000

0 2 4 6 8

Capital Costs (2012$/kW)

Identified Hydrothermal Resource 

Potential (GW)

0

5,000

10,000

15,000

20,000

25,000

0 2 4 6 8

Capital Costs (2012$/kW)

Near-field EGS Resource Potential 

(GW)
25

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Seasonal capacity adjustments allow more realistic seasonal variations in maximum capacity due 

to changes in water availability and operating constraints. These data are not available for nonWECC regions. Energy availability for the existing fleet for 2016 and beyond is defined 

seasonally using region-specific seasonal hydropower capacity factors averaged for 2006–2015 

as reported by the National Hydropower Asset Assessment Program (NHAAP),

25 totaling 270 

terawatt hours per year (TWh/yr). Capacity factors for 2010–2014 model years are calibrated so 

that modeled generation matches historical generation. PSH, both existing and new, is discussed 

in Section 2.3 on storage technologies.

There are three categories of new hydropower resource potential represented in the model.

1. Upgrade and expansion potential for existing hydropower

2. Potential for powering non-powered dams (NPD)

3. New stream-reach development potential (NSD)

The supply curves for each are discussed in detail in the Hydropower Vision report (DOE 

2016b), particularly Chapter 3 and Appendix B. 

ReEDS does not currently distinguish between different types of hydropower upgrades, so 

upgrade potential is represented generically as a potential for capacity growth that is assumed to 

have the same energy production potential per capacity (i.e., capacity factor) as the 

corresponding existing hydropower capacity in the region. The quantity of available upgrades is 

derived from a combination of limited resource assessments and case studies by the U.S. Bureau 

of Reclamation Hydropower Modernization Initiative (HMI), U.S. Army Corps of Engineers 

(Corps), and NHAAP Hydropower Advancement Project (Montgomery, Watson, and Harza 

2009; Bureau of Reclamation 2011). Upgrade availability at federal facilities not included in the 

HMI is assumed to be the HMI average of 8% of the rated capacity, and upgrade availability at 

non-federal facilities is assumed to be the NHAAP average of 10% of the rated capacity. Rather 

than making all upgrade potential available immediately, upgrade potential is made available 

over time at the earlier of either (1) the Federal Energy Regulatory Commission (FERC) license 

expiration (if applicable) or (2) the turbine age reaching 50 years. This feature better reflects 

institutional barriers and industry practices surrounding hydropower facility upgrades. The total 

upgrade potential from this methodology is 6.9 GW (27 TWh/yr). 

 25 http://nhaap.ornl.gov/content/nhaap-data-sources
26

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Figure 14. Modeled hydropower upgrade resource potential (DOE 2016b)

NPD resource is derived from the 2012 NHAAP NPD resource assessment (Hadjerioua, Wei, 

and Kao 2012; Hadjerioua et al. 2013), where the modeled resource of 5.0 GW (27 TWh/yr) 

reflects an updated site sizing methodology, data corrections, and an exclusion of sites under 500 

kW to allow better model resolution for more economic sites. 
27

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Figure 15. Modeled non-powered dam resource potential (DOE 2016b)

NSD resource is based on the 2014 NHAAP NSD resource assessment (Kao et al. 2014), where 

the modeled resource of 30.7 GW (176 TWh/yr) reflects the same sizing methodology as NPD 

and a sub-1 MW site exclusion, again to improve model resolution for lower-cost resource. The 

NSD resource assumes “low head” sites inundating no more than the 100-year flood plain and

excludes sites within areas statutorily barred from development—national parks, wild and scenic 

rivers, and wilderness areas.
28

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Figure 16. Modeled new stream-reach development resource potential (DOE 2016b)

The combined hydropower capacity coupled with the costs from the ATB (NREL 2018a) results 

in the supply curve shown in Figure 16. 

Figure 17. National hydropower supply curve of capital cost versus cumulative capacity potential
29

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The hydropower operating parameters and constraints included in ReEDS do not fully reflect the 

complex set of operating constraints on hydropower in the real world. Detailed site-specific 

considerations involving a full set of water management challenges are not easily represented in 

a model with the scale and scope of ReEDS, but several available parameters allow a stylized 

representation of actual hydropower operating constraints (Stoll et al. 2017).

Each hydropower category can be differentiated into “dispatchable” or “non-dispatchable” 

capacity, with “dispatchable” defined in ReEDS as the ability to provide the following services:

1. Diurnal load following within the capacity and average daily energy limits for 

each season

2. Planning (adequacy) reserves with full rated capacity

3. Operating reserves up to a specified fraction of rated capacity if the capacity is not 

currently being utilized for energy production.

“Non-dispatchable” capacity, on the other hand, provides:

1. Constant energy output in each season such that all available energy is utilized

2. Planning reserves equal to the output power for each season

3. No operating reserves.

Dispatchable capacity is also parameterized by a fractional minimum load, with the maximum 

fractional capacity available for operating reserves as one minus the fractional minimum load. 

The existing fleet and its corresponding upgrade potential are differentiated by dispatchability 

using data from the TEPPC 2024 Common Case for WECC and the NHAAP Hydropower 

Market Report for other regions (WECC 2013, 2015; Uria-Martinez, O’Connor, and Johnson 

2015). All WECC facilities that do not follow load are assumed non-dispatchable, and all nonWECC facilities in the following NHAAP categories are non-dispatchable: run-of-river, run-ofriver/peaking (downstream), and regulating. This methodology chooses only the most obvious 

facilities as non-dispatchable while leaving all other existing and upgrade capacity as 

dispatchable. In total, 43% of existing capacity and 49% of upgrade potential is assumed nondispatchable.

The same TEPPC database is used to define region-specific fractional minimum capacity for 

dispatchable existing and upgrade hydropower in WECC. Lacking minimum capacity data for 

non-WECC regions, 0.5 is chosen as a reasonable fractional minimum capacity.

Both the NPD and NSD resource assessments implicitly assume inflexible, run-of-river 

hydropower, so all NPD and NSD resource potential is assumed non-dispatchable. Additional

site-specific analysis could allow re-categorizing portions of these resources as dispatchable, but 

100% non-dispatchable remains the default assumption. 
30

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2.1.7 Biopower

ReEDS can generate electricity from biomass either in dedicated biomass integrated gasification 

combined cycle (IGCC) plants or cofired with coal in facilities that have been retrofitted with an 

auxiliary fuel feed. These cofire-ready coal plants can use biomass in place of coal to supply the 

fuel for up to 15% of the plant’s electricity generation. A cofire retrofit costs 305 $2017/kW 

based on EIA’s Electricity Market Module assumptions (EIA 2017, 101).

Dedicated and cofired plants source feedstock from the same biomass supply curves, which are 

derived from the U.S. Billion-Ton Update report (DOE 2011). The biomass types from this study 

included in ReEDS supply curves are crop and forest residues (including forest residues from 

federal lands) and energy crops. Figure 17 shows the national supply curve for biomass 

feedstock through the 2012 solve year, after which both supply and prices are adjusted each year 

through 2030 to follow projections in the U.S. Billion-Ton Update report (DOE 2011).

Figure 18. National supply curve for biomass feedstock through the 2012 solve year

2.1.8 Marine Hydrokinetic Wave

ReEDS does have a representation of marine hydrokinetic wave technologies, but this capability 

is not utilized in any of the recent or current ReEDS modeling work.

2.2 Conventional Energy Technologies

ReEDS includes all major categories of conventional generation technologies within its 

operating fleet or its investment choices. In the context of ReEDS, “conventional” is defined as 

thermal generating technologies driven by coal, gas, oil, or nuclear fuel. Coal technologies are 

subdivided into pulverized and gasified (IGCC) categories, with the pulverized plants further 

distinguished by 1) whether or not SO2 scrubbers are installed and 2) their vintage26 as pre- or 

post-1995. Pulverized coal plants have the option of adding a second fuel feed for biomass. New 

 26 While differentiating pre- and post-1995 is somewhat arbitrary, it allows the model to better represent 

performance differences between relatively old and new coal technologies.

0

4

8

12

16

20

0 2000 4000 6000

Fuel Costs (2016$/MMBtu)

Trillion Btu
31

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IGCC plants can only be constructed with CCS technology.27 Existing coal units built after 1995 

with SO2 scrubbers installed also have the option of retrofitting CCS capability.

Natural gas generators are categorized as combustion turbine (CT), combined cycle (CC), or gasCC with CCS.28 There are also nuclear (steam) generators, landfill gas generators,29 and oil/gas 

steam generators, though the latter two are not offered as options for new construction unless 

prescribed as a known unit expected to come online. The model distinguishes each conventionalgenerating technology by costs, efficiency, and operational constraints.

Where renewable energy technologies have many unique characteristics, ReEDS conventional 

technologies are characterized more generally by the following parameters: 

• Capital cost ($/MW) 

• Fixed and variable operating costs (dollars per megawatt-hour [$/MWh]) 

• Fuel costs (dollars per million British thermal units [$/MMBtu])

• Heat rate (MMBtu/MWh) 

• Construction period (years) and expenses 

• Equipment lifetime (years) 

• Financing costs (such as interest rate, loan period, debt fraction, and debt-servicecoverage ratio) 

• Tax credits (investment or production) 

• Minimum turndown ratio (%) 

• Quick-start capability and cost (%, $/MW) 

• Spinning reserve capability 

• Planned and unplanned outage rates (%).

Cost and performance assumptions for all new conventional technologies are taken from the 

ATB (NREL 2018a), which are based on the projections from the AEO2018 Reference 

scenario.30 The National Energy Modeling System (NEMS) Electricity Market Module, which is 

used for AEO analyses, represents technological learning endogenously, with costs falling as 

more capacity of that type is built. Because ReEDS does not include endogenous learning, the 

 27 New coal plants without CCS plants are not allowed by default due the U.S. Environmental Protection Agency’s 

proposed New Source Performance Standard. There is currently no option for partial CO2 capture technologies in 

ReEDS, so CCS plants in ReEDS are assumed to be 90% capture plants, meaning that they capture 90% of the CO2

emissions in the flue gas. Thus the technology representation is more conservative than allowed under proposed 

EPA regulations. Transportation and sequestration of the CO2 is not modeled, but an $11/tonne (2011$) costs is 

assumed based on (Timothy Fout 2015). No revenue from the potential use of CO2 is currently captured in ReEDS. 28 Retrofits from Gas-CC to Gas-CC-CCS are not currently an option in the model. 29 Landfill gas generators are classified as conventional generators but can count toward renewable portfolio 

standard requirements. 30 Where AEO includes two separate projections (advanced and conventional) for any single ReEDS technology, 

ReEDS uses an average of those figures to represent a medium level of technological advancement.
32

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technology cost projections used in ReEDS reflect technology growth in the AEO Reference 

scenario. The AEO assumptions include a projected materials price index (MPI) that also 

influences capital costs over time; we remove the MPI from our projections for better 

consistency of ReEDS technology representations across both renewable and conventional

energy sources. Regional variations and adjustments are included and described in Section 2.4.

In addition to the performance parameters listed above, technologies are differentiated by their

ability to provide operating reserves. In general, natural gas plants, especially combustion 

turbines, are better suited for ramping and reserve provision, while coal and nuclear plants are 

designed for steady, baseload operation. 

The existing fleet in ReEDS is extracted from the ABB unit database. In particular, ReEDS uses

summer nameplate capacity, location, and heat rate to characterize the existing fleet. ReEDS uses 

a modified “average” heat rate for existing stock: a small, technology-specific increase on the 

full-load heat rate is applied to accommodate for units not always operating at their design point. 

The modifiers, shown in Table 5, are based on the relationship between full-load and average 

heat rates for generators in the Ventyx database. Coal adjustment factors also take into account 

the historical observed fleet-wide heat-rates, which are calculated using EIA form 923.

Table 7. Multipliers Applied to Full-Load Heat Rates to Approximate Heat Rates for PartLoad Operation

Technology Adjustment Factor

Coal (all) 1.0674

Gas-CC 1.0545

Gas-CT 1.1502

OGS 1.1704

Emissions rates from conventional plants are a function of the fuel emission rate and the plant 

heat rate. Burner-tip emissions rates are shown in Table 6. Because ReEDS does not differentiate 

coal types, the coal CO2 emissions rate in the model is the average of the bituminous and 

subbituminous emissions rate.31

 31 See https://www.eia.gov/tools/faqs/faq.cfm?id=74&t=11, accessed November 11, 2016.
33

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Table 8. Emissions Rate by Generator Type in Pounds per MMBtua

Generator SO2

Emissions 

Rate

NOx

Emissions 

Rate

Mercury 

Emissions 

Rate

CO2

Emissions 

Rate

Hydropower 0 0 0 0

Gas-CT 0.0098 0.15 0 117.00

Gas-CC 0.0033 0.02 0 117.00

Gas-CC-CCS 0.0033 0.02 0 11.70

Pulverized Coal with

Scrubbers (pre-1995) 0.2 0.19 4.3612E-06 210.55

Pulverized Coal with

Scrubbers (post-1995) 0.1 0.08 4.3612E-06 210.55

Pulverized Coal 

without Scrubbers 1.11 0.19 5.30785E-06 210.55

IGCC Coal 0.0555 0.085 4.3612E-07 210.55

Coal-CCS 0.0555 0.085 4.3612E-07 21.06

Oil/Gas Steam 0.299 0.1723 0 137.00

Nuclear 0 0 0 0

Geothermal 0 0 0 0

Biopower 0.08 0 0 0

a The assumed CO2 pollutant rate for land-fill gas is zero, so the optimization does not see 

the emissions benefits of land-fill gas. However, ReEDS can track land-fill gas emissions

and the associated benefits as a post-processing calculation. Land-fill gas is assumed to 

have negative effective carbon emissions because the methane gas would be flared 

otherwise, thereby it produces the less potent greenhouse gas.

Not all parameter data are given in this document. For those values not included here, see the 

NREL ATB (NREL 2018a) as well as Short et al. (2011). Financing parameters and calculations 

are discussed in Section 9.1.

2.3 Storage and Demand-ide Technologies

2.3.1 Storage

ReEDS includes three utility-scale energy storage options: PSH, batteries, and CAES. All three 

storage options are capable of load shifting (arbitrage), providing planning and operating 

reserves, and reducing curtailment of VRE. Load shifting can be done only within a season’s 

representative day, and it is accomplished by charging the reservoir during inexpensive lowdemand time-slices and discharging at peak times. The nameplate capacity of storage is assumed 

to contribute to planning reserves, and capacity not otherwise committed can be utilized to 

provide any of the operating reserves products represented in ReEDS (see Section 6 on how 

reserves are differentiated in ReEDS).
34

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The ReEDS framework also allows for standalone thermal energy storage, though this 

technology is inactive by default because its site-specific nature makes it difficult to include in a 

nationwide optimization framework. This technology is representative of chilled water and ice 

storage units in buildings where, during the summer, cold water or ice is produced during cooler 

hours when loads are lower and used to replace or supplement the air conditioning during the 

warmer hours. Only units for commercial buildings are considered. A supply curve for thermal 

energy storage units was developed at the NERC subregions level. The model restricts the use of 

thermal energy storage devices by the regional cooling load profile, with power delivered from

thermal energy storage available only during times of high cooling load (e.g., summer 

afternoons). Thermal energy storage technologies can contribute to operating and planning 

reserves and reduce curtailment.

Although storage is neither directly linked nor assumed co-located with renewable energy 

technologies in ReEDS, it can play an important role in reducing curtailed electricity from 

variable generation resources by charging during time-slices with excess renewable generation. 

The ability of storage to reduce curtailment is calibrated using the REFlex model (Denholm et al. 

2010). For batteries, the curtailment calculation in ReEDS takes into account a finite storage 

duration32, while CAES and PSH are assumed to have sufficient storage to operate as needed 

within the diurnal variations represented in the model. Similarly, CAES and PSH are not energylimited in performing energy arbitrage across time-slices within a season; thus, there is no 

explicit storage duration defined for these technologies.

Existing PSH totals 22.2 GW, and ReEDS includes the existing 100 MW CAES facility in 

Alabama. New PSH and CAES are location-restricted due to hydrology and topography (for 

PSH) and geology (for CAES). In contrast, utility-scale batteries are not restricted to any subset 

of regions. New PSH potential is derived from FERC license applications since 198033 with an 

additional 750 MW of high-cost “artificial” resource placed in each region to prevent overconstraining PSH expansion in regions without FERC applications while remaining consistent 

across all regions. In total, there is 108.7 GW FERC-based PSH resource and 100.5 GW artificial 

PSH resource in ReEDS. FERC-based PSH capital costs are based on O’Connor et al. (2015), 

and artificial capacity is assumed to be $3,500/kW,34 which is near the upper bound of greenfield 

PSH resource sized at 750 MW (Figure 19). PSH fixed O&M costs are also based on O’Connor 

et al. 2015, and round-trip efficiency is assumed to be 80% for all existing and new capacity. 

CAES site development costs are estimated based on the underground geology, where domal 

salt is the least costly resource at $1170/kW (22.6 GW available), bedded salt is the next most 

costly resource at $1,420/kW (37.0 GW), and aquifers (porous rock) are the most costly resource 

at $1,680/kW (61.6 GW) (Black & Veatch 2012; Lazard 2016). 35 CAES requires a natural gas 

fuel input when supplying power output, and its heat rate is assumed to be 4.91 MMBtu/MWh. 

This additional fuel input (to the electrical power input during compression) results in a roundtrip efficiency of 125%. 

 32 The default storage duration is 4 hours but can be specified by the user. 33 See “Preliminary Permits,” FERC, updated October 11, 2016, http://www.ferc.gov/industries/hydropower/geninfo/licensing/pre-permits.asp. 34 In 2015$ 35 Values in 2016$
35

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Figure 19. Modeled new PSH resource potential (DOE 2016b)

Figure 20. National PSH supply curve of capital cost versus cumulative capacity potential
36

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Battery cost and performance assumptions are based on lithium-ion battery systems, with costs 

taken from Cole, Marcy, et al. (2016). Low, mid, and high cost projections are available and will 

scale with the user-defined battery duration. In contrast to all other generator technologies in 

ReEDS, which outlast the 20-year evaluation window of the model, the battery is assumed to last 

15 years. As a result, its capital cost is uprated by 1.14 to account for the equipment lifetime 

shortfall. The 1.14 uprate is based on a cash-flow calculation using the default ReEDS financing 

assumptions. The batteries are assumed to have a round-trip efficiency of 85% (Lazard 2017). 

The contribution of storage toward the reserve margin requirement is discussed in Section 6.2.

2.3.2 Demand Response

Demand response is not typically active in ReEDS due to the difficulty defining cost and 

availability for demand response technologies at the resolution available in ReEDS. When active, 

however, demand response in ReEDS allows shifting of electricity use during hours with high 

electricity prices or peak load. Demand response is limited by a fraction of electricity load that 

grows from grows from 2% in 2018 to 10% in 2050. In addition to load shifting, demand 

response can also provide operating reserves.

2.3.3 Plug-In Electric Vehicles

Because ReEDS does not include vehicle choice or transportation sector modeling, the 

deployment of plug-in electric vehicles or plug-in hybrid electric vehicles (and their associated 

charging infrastructure) is input into ReEDS.36 The number of plug-in vehicles is simply 

translated in the model into additional annual demand for electricity. This annual demand can be 

met through either a fixed charging profile over the 17 time-slices within a year in ReEDS, an 

endogenously determined dynamic profile, or some combination of the two. The endogenous 

profile allows for time-of-day flexibility within seasons but does not allow for load shifting 

across seasons. Ultimately, this profile is driven by the net load and is determined simultaneously 

with investment and operation of dispatchable and non-dispatchable resources within each timeslice of ReEDS. 

In addition to not modeling vehicle deployment, the ReEDS model does not consider vehicle-togrid or reserve services from vehicles. Plug-in vehicles cannot be used to meet the planning 

reserve requirements, and, in fact, can increase the reserve requirements through increasing peak 

demand. Likewise, these vehicles cannot contribute to meeting operating reserve requirements. 

However, dynamically charged plug-in vehicles are allowed one operational benefit in ReEDS—

adjusting load to better match generation profiles.

2.3.4 Energy Efficiency

Energy efficiency investment decisions are not modeled endogenously, although, past efforts

have used exogenously specified trajectories (Bird et al. 2011).

 36 For details and an example of how transportation electrification can be represented in ReEDS, see Appendix K 

of (NREL 2012).
37

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2.4 Capital Stock

2.4.1 Initial Capital Stock, Prescribed Builds and Restrictions

Existing electricity generation capacity is taken from the ABB Velocity Suite unit database

(ABB 2018), unless specific unit information has been determined to be more accurate. Units are 

mapped to ReEDS technologies based on a combination of fuel source and prime mover of the 

generation technology, and they are then aggregated to a total capacity for each modeled region37

based on net summer capacity of the units. For conventional technologies, cooling technologies 

are also assigned as described in Section 4.1. Hydropower has additional subcategories to 

differentiate dispatchability as discussed in Section 2.1.6. In addition, any plants that are listed as 

under construction or in testing phases in the unit database become prescribed builds. In other 

words, ReEDS builds any under-construction units, with the units coming online in the 

anticipated online year listed in the database.

For wind technologies, there are near-term regional growth restrictions to reflect the difficulty of 

immediately scaling the wind industry. Specifically, regional wind plant construction in the 2018 

solve year (which represents 2017–2018) is limited to plants that are planned to be installed 

before the end of 2018 and are currently designated in the ABB database as “under 

construction”, “permitted”, “testing”, “undergoing feasibility studies”, or “on standby”. Wind 

plant construction in the 2020 solve year (which represents 2019–2020) is limited similarly, 

except plants designated as “proposed” are allowed in addition to the other categories. The result 

of these restrictions is that no more than 21 GW of cumulative wind capacity is permitted in the 

contiguous US in the 2018 solve year, and no more than 83 GW is permitted in the 2018 plus 

2020 solve years. This treatment is intended to bound model decisions due to the complex factors 

and tax incentive rules associated with the wind production tax credit (PTC) (see Section 8.2). 

After 2020, no wind capacity limits are implemented.

2.4.2 Retirements

Renewable energy generator and battery retirements are based on assumed lifetimes. Once a 

generator has reached its lifetime, it is retired. Renewable energy and battery lifetime 

assumptions are shown in Table 7. When renewable energy capacity is retired, the resource 

associated with that capacity is made available, and ReEDS can choose to rebuild a renewable 

energy generator using the newly available resource, without the need to rebuild the grid 

interconnection infrastructure. A consequence of this assumption is that retired renewable 

capacity can be replaced without incurring interconnection costs and, with all other 

considerations being equal, re-powered or re-built renewable capacity has lower cost than new 

“green-field” capacity of the same type.38 One exception to this procedure is hydropower, which 

due to assumed non-power requirements is never retired unless there is an announced 

hydropower capacity retirement listed in the ABB Velocity Suite database (ABB 2018).

 37 Plant locations are determined by the county specified in the ABB database or, when county is unavailable, the 

intersection of the plant location with GIS shapefiles for the ReEDS regions. 38 ReEDS does not account for any decommissioning costs for renewable or any other capacity type.
38

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Table 9. Lifetimes of Renewable Energy Generators and Batteries

Technology Lifetime 

(Years)

Source

Land-based Wind 24 Wind Vision (DOE 2015)

Offshore Wind 24 Wind Vision (DOE 2015)

Solar Photovoltaic 30 SunShot Vision (DOE 2012)

Concentrating Solar Power 30 SunShot Vision (DOE 2012)

Geothermal 30 Renewable Electricity Futures Study, Vol. 1 (Mai et 

al. 2012)

Hydropower 100 Hydropower Vision (DOE 2016b)

Biopower 50 ABB (2010)

Marine Hydrokinetic 20 Previsic et al. (2012)

Battery 15 (Cole, Marcy, et al. 2016)

Retirements of existing conventional energy generators in ReEDS are primarily a function of 

announced retirement dates and technology-specific estimated lifetimes, taken from the ABB 

Velocity Suite database that seeds ReEDS (ABB 2018). Estimated retirement dates depend on 

the size of the unit, and the most common lifetimes are shown in Table 8 for plants that are 

smaller or larger than 100 MW. Nuclear plants are assumed to have a mix of 60 and 80 year 

lifetimes as explained below. All conventional generators that are economically built in ReEDS 

are given the lifetime of plants greater than 100 MW, and these lifetimes are used as necessary 

when the solution period extends beyond 2050. No refurbishment costs or increased O&M costs 

can be applied endogenously to extend the life of nuclear or fossil facilities. 

Table 10. Most Common Estimated Lifetimes of Conventional Energy Generators (ABB 2018)

Technology Lifetime less than 100 MW

(Years)

Lifetime greater or equal 

to 100 MW (Years)

Gas Combustion Turbine 50 50

Gas Combined Cycle and CCS 60 60

Coal, all techs, including cofired 65 75

Oil-Gas-Steam 50 75

Biopower 50 50

Compressed-Air Energy Storage 100 100

In addition to age-based retirements, additional retirements can occur for coal technologies based 

on plant utilization. ReEDS does not have a generalized framework for endogenous retirements, 

so modeled utilization-based coal retirements represent a proxy for economic considerations that 

might accelerate coal retirements. Utilization-based retirements are implemented by comparing 

each BA’s coal annual capacity factor to a minimum utilization threshold. If the capacity factor 

is beneath the threshold in a given year, capacity is retired such that the remaining BA capacity, 

assuming the same annual production, would operate at the capacity factor threshold.
39

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The utilization-based retirement is not active until 2020 and becomes increasingly stringent over 

time.39 The oldest and least efficient extant units are retired preferentially in this scheme.

ReEDS includes four exogenous nuclear retirement scenario settings. The four settings are

defined by first dividing the currently operating reactors into two bins. Any plants participating 

in a restructured market and all single-reactor plants are assigned to Bin 1. The remaining plants, 

which are all multi-reactor plants in a traditional regulated environment, are assigned to Bin 2. 

The only exception to these categorizations is that plants that have announced their intent to seek 

a second operating license renewal from the Nuclear Regulatory Commission (NRC) are 

included in Bin 2. Table 9 breaks down the bins and shows total capacity in each case. These 

bins are categorizations that reflect the current discussion pointing to more economic pressure on

restructured and single-reactor units (Haratyk 2017; Steckler 2017). 

Table 11. Amount of Nuclear Power Plant Capacity (in GW) in Each Bin

Plant Category Bin 1 Bin 2

Restructured, Single Reactor 8.7

Restructured, Multi Reactor 27.5 2.0a

Regulated, Single Reactor 15.7

Regulated, Multi Reactor 42.1

Total 51.9 44.1

a The Peach Bottom plant (2.0 GW) has announced its intent to seek a second license renewal. 

Therefore, it is moved from Bin 1 to Bin 2 even though it is in a restructured market.

The four nuclear retirement scenarios are: (1) Early Retirement, (2) 60-Year Lifetime, (3) MidCase (mix of 60 and 80-year lifetimes), and (4) 80-Year Lifetime (see Table 10). The Early 

Retirement scenario retires nuclear capacity in Bin 1 when its lifetime reaches 50 years, and 

capacity in Bin 2 at 60 years. The 50-year lifetime emulates the retirements of recent plants that 

did have a renewed operating license but retired before they reached the end of their license. The 

60-Year Lifetime scenario retires all plants at 60 years, which would be at the end of their first 

operating license renewal. The 80-Year Lifetime scenario retires all plants at 80 years, 

simulating a successful completion of a second operating license renewal from the NRC. The 

Mid-Case scenario serves as the default setting in ReEDS and retires capacity in Bin 1 at 60 

years and capacity in Bin 2 at 80 years.

Table 12. Nuclear Power Plant Lifetime for Each Scenario by Bin (years)

Scenario Name Bin 1 Bin 2

Early Retirement 50 60

60-Year Lifetime 60 60

Mid-Case 60 80

80-Year Lifetime 80 80

 39 The capacity factor threshold starts at 0.01 in 2020, increases linearly to 0.50 in 2040, and stays at that value 

until 2050.
40

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2.4.3 Growth Penalties

We impose penalties on (1) component capital costs for all technologies based on excessive

national growth and (2) installation costs for wind, CSP, and utility-scale PV based on excessive

regional growth.40 The growth penalties are functions of the nominal installed capital cost and 

the increase in new construction over previous years. 

Similar to EIA (EIA 2017), we use a step-wise growth penalty cost function shown in Figure 20. 

The rate of installations can increase by 44% of the base capacity in every model year before 

growth cost penalties apply. The base capacity is defined as the maximum of (1) 90% of the 

previous base, (2) the minimum base, and (3) installations and upgrades from the previous model 

year. Table 11 summarizes the minimum base values used.

Figure 21. National and regional growth penalty cost curve

 40 For wind, CSP, and UPV, we assume the breakdown of total cost is 75% for components and 25% for installation.
41

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Table 13. Minimum Base Values for Assessing Excessive Growth

Technology Region Minimum Base (MW)

Conventionala National 400: Geothermal

500: Natural gas combustion turbine,

natural gas combined cycle with

CCS, coal with CCS, and marine 

hydrokinetic

1,000: natural gas combined cycle, 

hydro, biopower

2,000: Nuclear 

Wind National 1,000b

Resource 

region

200c

CSP National 1,000d

Resource 

region

200e

Utility-scale PV National 1,000d

BA 600f

a Minimum base for conventional plants based on the approximate size of a new plant

b Roughly based on the average size of annual wind installations in the United States from 1999 to 

2005

c Assuming one wind farm built in a single wind resource region is typically about 200 MW

d Using the same value as wind

e Assuming one CSP system built in a single CSP resource region is typically about 200 MW

f Assuming three PV systems, about 200 MW each, built in a single BA

2.5 Regional Parameter Variations and Adjustments

For most generation technologies, regional cost multipliers are applied to reflect variations in 

installation costs across the United States (see Figure 22). These regional multipliers are applied 

to the base overnight capital cost presented in earlier sections. The regional multipliers are 

technology-specific and are derived primarily from the EIA/Leidos Engineering report (EIA 

2016) that is the source of capital cost assumptions for the NEMS model. While the regional 

costs presented in the EIA/Leidos Engineering report are based on particular cities, the regional 

multipliers for ReEDS are calculated by interpolating between these cities and using the average 

value over the ReEDS regions for each technology. For technologies such as CSP that are not 

included in the newer report, we rely on the older EIA/Science Applications International 

Corporation report (EIA 2013).
42

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(a) (b)

(c) (d)

(e) (f)
43

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(g) (h)

(i) (j)

Figure 22. Maps of regional capital cost multipliers for the various technology types

Regions shown in white in (j) do not apply to offshore wind.
44

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3 Fuel Prices

Natural gas, coal, and uranium prices in ReEDS are based on the most recent AEO. Coal prices 

are provided for each of the nine EIA census divisions. Low and high natural gas price 

alternatives are taken from the Low and High Oil and Gas Resource and Technology scenarios.

ReEDS includes only a single national uranium price trajectory. Base fuel price trajectories are 

shown in Figure 22 for the AEO2018 (EIA 2018). Biomass fuel prices are represented using 

supply curves with five bins in each region. The costs and resource availability are based on the 

U.S. Billion-Ton Update study (DOE 2011). Biomass costs range from $1.91/MMBtu to 

$18.33/MMBtu (in 2014$).

Figure 23. Fuel price assumptions

Coal and uranium are assumed to be perfectly inelastic; the price is pre-determined and insensitive 

to the ReEDS demand for the fuel. With natural gas, however, the price and demand are linked.

Actual natural gas prices in ReEDS are based on the AEO scenario prices but are not exactly the 

same; instead, they are price-responsive to ReEDS natural gas demand. In each year, each census 

division is characterized by a price-demand “set point” taken from the AEO Reference scenario but 

also by two elasticity coefficients: regional (βr) and national (βn) elasticity coefficients for the rate 

of regional price change with respect to (1) the change in the regional gas demand from its setpoint and (2) the overall change in the national gas demand from the national price-demand set 

point respectively. The set of regional and national elasticity coefficients are developed through a 

linear regression analysis across an ensemble of AEO scenarios41,42 to estimate changes in fuel 

prices driven solely by electric sector natural gas demand (as described in Logan et al. (2013) and 

Cole, Medlock III, and Jani (2016), though the coefficients have since been updated for the latest 

AEO data). Though there is no explicit representation of natural gas demand beyond the electricity 

sector, the regional supply curves reflect natural gas resource, infrastructure, and nonelectric sector 

demand assumptions embedded within the AEO modeling. For details, see the section in the 

appendix titled, Natural Gas Supply Curves.

 41 Supply curves are nonlinear in practice, but a linear regression approximation has been observed to be satisfactory 

under most conditions.

42 The elasticity coefficients are derived from all scenarios of AEO 2018, but the price-demand set points are taken 

from any one single scenario of the AEO.

0

2

4

6

8

10

12

2010 2020 2030 2040 2050

Natural Gas Fuel Prices 

(2017$/MMBtu)

Low Resource

Reference

High Resource

0.0

0.5

1.0

1.5

2.0

2.5

3.0

2010 2020 2030 2040 2050

Fuel Prices (2017$/MMBtu)

Coal

Uranium
45

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In addition to the natural gas supply curve representation, ReEDS includes targeted fuel price 

foresight for new natural gas capacity investments. Specifically, the effective investment cost for 

new natural gas combined cycle capacity includes an extra term representing the present value of 

the difference between flat natural gas prices and expected future natural gas prices.

Finally, the natural gas fuel prices include a seasonal price adjustor, making winter prices higher 

than the natural gas prices seen during the other seasons of the year. For details, see the section 

in the appendix titled, Seasonal Natural Gas Price Adjustments.
46

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4 Thermal Cooling Water

In addition to representing a full suite of power generation technologies, ReEDS further 

differentiates power technologies by cooling system type to better characterize and constrain 

electric sector water requirements. The cooling system impacts the cost and performance of the 

thermal generators, and water withdrawal and consumption rates are used in conjunction with 

water availability and cost to appropriately constrain and track the water requirements of new 

capacity expansion. The ReEDS cooling water formulation is discussed in Macknick et al.

(2015), which forms the basis of this section.

4.1 Cooling System Characteristics

Cooling systems implemented in the ReEDS model fall into one of four categories: oncethrough, pond, recirculating, and dry cooling systems.43 ReEDS can then select the optimal 

cooling technology amongst allowable power-cooling technology combinations for any new 

thermal generating capacity it chooses to build. Individual power plant cooling systems are 

derived from a database developed by the (Union of Concerned Scientists 2012). Within each 

ReEDS BA, existing generation capacity by power generation technology is disaggregated into 

power-cooling system technology categories based on 2008 generation data from the database. 

When the database does not contain information on cooling systems for certain fuel types within 

a particular BA, cooling technology is assumed using the following rules:

1. If there are other units of the same fuel type at the same plant with assigned cooling 

technology, assume the same cooling technology.

2. If (1) does not apply and there are other thermal units at the same plant with assigned 

cooling technology, assume the same cooling technology.

3. If neither (1) nor (2) apply, and no additional information is available, assume 

recirculating cooling.

Table 12 lists the allowable power-cooling technology combinations under the current cooling 

technology representation. Allowed combinations must be consistent with the existing fleet and

reflect allowed cooling technologies for new builds. New builds in ReEDS are not permitted to 

use once-through cooling due to pending U.S. Environmental Protection Agency (EPA)

regulations and industry trends, so technologies that do not exist using once-through cooling 

(e.g., CCS technologies) are not allowed to use once-through cooling. In addition, new capacity 

cannot choose to use cooling ponds because designs are too site-specific for a reasonable general 

model representation. Only existing pond cooling facilities are characterized. Dry cooling is not 

allowed for nuclear or CCS due to their exceptionally large cooling requirements.

 43 Once-through cooling uses water as a medium to exchange heat without the water undergoing a phase change 

(i.e., evaporation), with most withdrawn water returned to the water source. Pond cooling use a similar process but 

with a static cooling pond as a thermal reservoir instead of a flowing water source. Recirculating cooling uses 

evaporative cooling with water to reduce water withdrawal needs, but most withdrawn water is “consumed”, as it 

evaporates to the atmosphere without being returned directly to the water source. Dry cooling uses air instead of 

water as a heat exchange medium.
47

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Table 14. Allowed Power and Cooling Technology Combinations in ReEDS

Power Technology

Cooling Technology Allowed

OnceThrough Recirculating Dry

Cooling 

Pond

(existing 

fleet only)

None

Hydropower No No No No Yes

Gas-CT No No No No Yes

Gas-CC Yes Yes Yes Yes No

Gas-CC-CCS No Yes Yes No No

Pulverized coal 

with scrubbers

(pre-1995)

Yes Yes Yes Yes No

Pulverized coal 

without scrubbers Yes Yes Yes Yes No

Pulverized coal 

with scrubbers (post1995)

Yes Yes Yes Yes No

IGCC coal Yes Yes Yes Yes No

Coal-CCS Yes Yes No No No

Oil/gas steam Yes Yes Yes Yes No

Nuclear Yes Yes No Yes No

Geothermal44 No No No No Yes

Biopower Yes Yes Yes Yes No

Cofired coal

(pre-1995) Yes Yes Yes Yes No

Cofired coal

(post-1995) Yes Yes Yes Yes No

Landfill gas No No No No Yes

PV No No No No Yes

Wind No No No No Yes

CSP No Yes Yes No No

 4444 Geothermal capacity uses the ‘none’ cooling technology designation because its cooling requirements are 

defined instead by the geothermal resource subcategory (see Section 2.1.5) and whether the resource is assumed to 

use a binary or flash system configuration. 
48

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The cooling system affects capital and O&M costs through cost multipliers, and performance 

effects are implemented using heat rate multipliers. Cost and performance characteristics are 

derived from Woldeyesus, Macknick, and Colman (forthcoming), and apply to baseline input 

data assuming recirculating cooling (Black & Veatch 2012). In general, once-through cooling 

systems are the least expensive and have the highest efficiencies. Recirculating cooling systems 

generally have higher capital and operating costs and are less efficient than once-through cooling 

systems, but they are less expensive and more efficient than dry cooling systems. Pond-cooled 

systems have various configurations that can resemble either recirculating or once-through

cooling systems; thus, they generally have characteristics that fall between those of recirculating 

and once-through systems. Table 13– Table 17 summarize the cooling technology 

parameterization by generation technology type.

Table 15. Capital Cost Multipliers for Power-Cooling Technology Combinations

OnceThrough Recirculating Dry Cooling 

Pond

Gas-CC 0.978 1.000 1.102 0.978

Gas-CC-CCS n/a 1.000 n/a n/a

Pulverized coal with scrubbers (pre-1995) 0.981 1.000 1.045 0.981

Pulverized coal without scrubbers 0.981 1.000 1.045 0.981

Pulverized coal with scrubbers

(post-1995) 0.981 1.000 1.045 0.981

IGCC coal 0.988 1.000 1.033 0.988

Coal-CCS 0.982 1.000 n/a n/a

Oil/gas steam 0.981 1.000 1.045 0.981

Nuclear 0.981 1.000 n/a 0.981

Biopower 0.981 1.000 1.045 0.981

Cofired coal (pre-1995) 0.981 1.000 1.045 0.981

Cofired coal (post-1995) 0.981 1.000 1.045 0.981

CSP n/a 1.000 1.050 n/a
49

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Table 16. Operations and Maintenance Cost Multipliers for Power-Cooling 

Technology Combinations

OnceThrough Recirculating Dry Cooling 

Pond

Gas-CC 0.996 1.000 1.021 0.996

Gas-CC-CCS n/a 1.000 n/a n/a

Pulverized coal with scrubbers (pre-1995) 0.989 1.000 1.051 0.989

Pulverized coal without scrubbers 0.989 1.000 1.051 0.989

Pulverized coal without scrubbers (post-1995) 0.989 1.000 1.051 0.989

IGCC coal 0.996 1.000 1.021 0.996

Coal-CCS 0.993 1.000 n/a n/a

Oil/gas steam 0.989 1.000 1.051 0.989

Nuclear 0.989 1.000 n/a 0.989

Biopower 0.989 1.000 1.051 0.989

Cofired coal (pre-1995) 0.989 1.000 1.051 0.989

Cofired coal (post-1995) 0.989 1.000 1.051 0.989

CSP n/a 1.000 1.050 n/a

Table 17. Heat Rate Multipliers for Power-Cooling Technology Combinations

OnceThrough Recirculating Dry Cooling 

Pond

Gas-CC 0.980 1.000 1.050 0.980

Gas-CC-CCS n/a 1.000 n/a n/a

Pulverized coal with scrubbers (pre-1995) 0.985 1.000 1.050 0.985

Pulverized coal without scrubbers 0.985 1.000 1.050 0.985

Pulverized coal with scrubbers (post-1995) 0.985 1.000 1.050 0.985

IGCC Coal 0.980 1.000 1.050 0.98

Coal-CCS 0.800 1.000 n/a n/a

Oil/Gas Steam 0.985 1.000 1.050 0.985

Nuclear 0.973 1.000 n/a 0.973

Biopower 0.985 1.000 1.050 0.985

Cofired Coal (pre-1995) 0.985 1.000 1.050 0.985

Cofired Coal (post-1995) 0.985 1.000 1.050 0.985

CSP n/a 1.000 1.000a n/a

a There are currently no data to inform a heat rate multiplier for CSP.

Water withdrawal and consumption rates for power technology-cooling system combinations are 

defined by volumes of water withdrawn and consumed per unit of electricity generation (i.e., 

gallons per MWh of electricity generated) (Macknick et al. 2012). 
50

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Table 18. Water Withdrawal Rates for Power-Cooling Technology Combinations (gal/MWh)

OnceThrough Recirculating Dry Cooling 

Pond

Gas-CC 11,380 255 2 5950

Gas-CC-CCS n/a 506 n/a n/a

Pulverized coal with scrubbers (pre-1995) 36,350 1,005 0 12,225

Pulverized coal without scrubbers 36,350 1,005 0 12,225

Pulverized coal with scrubbers (post-1995) 27,088 587 0 17,914

IGCC Coal 18,136 393 0 9,635

Coal-CCS 56,483 1,224 n/a n/a

Oil/gas steam 35,000 1,203 0 5,950

Nuclear 44,350 1,101 n/a 7,050

Biopower 35,000 878 0 450

Cofired coal (pre-1995) 35,000 878 0 450

Cofired coal (post-1995) 35,000 878 0 450

CSP trough/tower n/a 906/786 78/26 n/a

Table 19. Water Consumption Rates for Power-Cooling Technology Combinations (gal/MWh)

OnceThrough Recirculating Dry Cooling 

Pond

Gas-CC 100 205 2 240

Gas-CC-CCS n/a 378 n/a n/a

Pulverized coal with scrubbers (pre-1995) 250 687 0 545

Pulverized coal without scrubbers 250 687 0 545

Pulverized coal with scrubbers (post-1995) 113 479 0 545

IGCC coal 90 380 0 32

Coal-CCS 217 921 n/a n/a

Oil/gas steam 240 826 0 240

Nuclear 269 672 n/a 610

Biopower 300 553 0 390

Cofired coal (pre-1995) 300 553 0 390

Cofired coal (post-1995) 300 553 0 390

CSP trough/tower n/a 906/786 78/26 n/a

ReEDS also allows capacity to upgrade from more withdrawal-intensive cooling technologies to 

less withdrawal-intensive technologies. Doing so could be advantageous if the value of reducing

water needs in water-constrained regions offsets any cost and performance penalty.
51

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4.2 Water Access Constraints

Water availability is constrained in each BA through a requirement for new capacity to purchase 

sufficient water access to satisfy the withdrawal requirements of the power technology-cooling 

system combination and the size of the facility (Macknick et al. 2015). Sufficient water access is 

defined as enough water for a power plant to operate at full capacity at any time throughout the 

year. This conservative method ensures new power plant builds have secured more than enough 

water than they would realistically use in a year so that they can operate at maximum output 

during the annual low-flow condition. The ReEDS model calculates actual water withdrawals 

and consumption separately from the purchases of water access based on the actual operation of 

the power fleet. It is important to distinguish between constraining water access, which affects 

capacity expansion only, and constraining operational water availability, which would influence 

operation. Additional constraints described below are designed to represent operational water 

availability limits.

All water constraints in ReEDS are implemented on withdrawals but not consumption. Though 

consumption is important to physical water availability downstream and overall impacts on the 

water system, water withdrawals are used to define legal availability of water to the power 

sector, which is assumed to be an important driver of capacity expansion decisions. Furthermore, 

baseline water availability data are defined as water available for future power sector water 

withdrawal, so enforcing constraints on withdrawal is the most internally consistent use of this 

data set. Future analysis could explore constraints on consumption, but doing so 

comprehensively and consistently would require new data development.

4.3 Cooling Water Availability and Cost

Each BA has unique water access availability and cost for up to five resource categories: 

unappropriated freshwater, appropriated freshwater (western United States only), fresh 

groundwater, brackish groundwater, and wastewater.45 Multiple types of water resources can be 

purchased to meet the demand for new generation capacity. Water access availability of retiring 

power plants is returned to the pool of available water access for new power plants at the price of 

appropriated water access.

Water availability and cost are defined using supply curves developed by Tidwell, Zemlick, and 

Klise (2013). Resource assessment methodologies are unique to each water category as detailed 

in that report as well as by Macknick et al. (2015). Water access costs include capital and O&M 

costs necessary to procure each water type. Where available, unappropriated freshwater is the 

lowest-cost resource, typically followed by fresh groundwater, appropriated freshwater, 

wastewater, and brackish groundwater. Many regions in the West have no unappropriated 

freshwater and limited fresh groundwater and appropriated freshwater, making these regions 

more likely to require higher-cost wastewater or brackish groundwater resources. Figure 23

shows regional water availability and cost for each type of water represented in ReEDS.

 45 Unappropriated freshwater is fresh surface water not previously assigned by a prior rights or access agreement. 

Appropriated freshwater is fresh surface water where water rights are presently owned, but the resource has been 

identified by Tidwell et al. (2013) as low-value and thus potentially available for sale to the power sector. 

Groundwater is defined as fresh or brackish based on salinity, with brackish groundwater typically deeper 

underground and more expensive to access. Wastewater resource is the identified portion of wastewater treatment 

plant discharge that is available for power plant cooling.
52

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Fresh Groundwater Availability Fresh Groundwater Cost

Unappropriated Freshwater Availability
53

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Appropriated Freshwater Availability Appropriated Freshwater Cost
54

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Figure 24. Water availability and cost for each water type

AFY = acre-feet per year

Source: Tidwell, Zemlick, and Klise 2013
55

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4.4 Operational Water Constraints

ReEDS also constrains physical quantities of water withdrawn in each season and balancing area, 

with a less restrictive option that constrains annual water withdraws. The water availability data 

described in Section 4.3 apply to new capacity only, and because there is no comprehensive 

water resource data set for the existing fleet, total available water is initialized in 2010 solve as 

the quantity of water withdrawn in that year when operational water constraints are inactive. This 

method implicitly assumes that known historical generation accounts for any historical 

constraints on generation due to limited water availability. When new capacity and associated 

water access are purchased, total water availability is updated by adding purchased amounts from 

the Tidwell et al. (2013) supply curves. Annual quantities of unappropriated freshwater are 

distributed seasonally using the 2010 seasonal distribution, and all other water categories are 

assumed available uniformly throughout the year (Cohen et al. [in review]). This framework can 

create disincentives for purchasing unappropriated water if it is unavailable in certain seasons 

(and thus does not support generation in that season). There could also be an added incentive for 

less water-intensive technologies that avoid operational water constraints. Though operational 

water availability is linked to water access purchased for specific generating capacity, ReEDS 

aggregates capacity of a given generating technology across a BA, so operational water in a BA 

is not constrained for each generation technology. Available operational water of all types within 

a BA is available to all generating capacity in that BA. Additional discussion of operational 

water constraints and their application can be found in (Cohen et al. [in review]).
56

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5 Transmission

5.1 Transmission System

ReEDS uses a reduced network with 134 nodes (at the centroids of ReEDS BAs) connected by

roughly 300 aggregate lines, as shown in Figure 24. Each line has a nominal carrying capacity 

limit that is determined for the start-year (2010) based on power-flow analysis using ABB’s 

GridView model and NERC-reported line limits (NERC 2010). In later years, ReEDS can

expand these carrying capacities, though the model cannot build new node-to-node pathways. 

Transmission expansion is limited before 2020 to lines for which new construction is already 

planned (EEI 2010). After 2020, that limitation is dropped. ReEDS constrains transmission flows

in each of the 17 time-slices when dispatching generation and contracting operating reserves, and 

available transmission capacity can also be used for firm capacity to meet system adequacy 

needs.

Figure 25. Existing long-distance transmission infrastructure as represented in ReEDS

Transmission network flows in ReEDS are determined by a linearized DC power-flow model

(Stott, Jardim, and Alsaç 2009). The power-flow model is a linear approximation of DC power 

flow using effective line susceptance46 to distribute power injected at a node, with susceptance

approximated as transmission capacity over distance. As in a real power system, flows are 

interdependent and determined by the topology of the network, including the generators, loads, 

and lines. Changing the pattern of generation affects the flows on all loop-forming alternating 

 46 Susceptances are approximated as a proportionality of line capacity divided by distance. Specifically in ReEDS, 

susceptances are (10*Line Capacity in MW)/Distance (as measured using a Cartesian distance approximation).
57

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current (AC) lines within an interconnect. A few notable DC transmission connections in the 

U.S. power system are distinguished from the AC networks in ReEDS by granting them flow 

control, as they are not subject to Kirchhoff’s voltage laws. 47

ReEDS can choose to build additional transmission capacity on the existing network to reduce 

congestion.48 Adding capacity on a transmission corridor will also increase that line’s 

susceptance in subsequent years and thus increase the proportion of a power injection that takes 

that route.49

ReEDS does not represent reactive power and does not address AC-power-flow issues of 

voltage, frequency, or limiting phase angle differences. Intra-BA transmission and distribution 

networks are similarly ignored, effectively ignoring the effects of transmission congestion within 

each region.

Transmission and distribution losses are considered in the model. There are bulk transmission 

losses of 1% per 100 miles for power that flows between BAs. In addition, distribution losses of 

5.3% are assumed and thus added to the end-use demand (Section 6.1) to scale end-use demand 

to busbar load. Distribution losses do not apply to rooftop PV, as they are assumed to be 

downstream within distribution networks, but they do apply at a lower rate to DUPV systems, 

which are assumed to connect directly to low-voltage distribution substations (Section 6.1).

Wind, CSP, and PV technologies are highly sensitive to location and often require spur lines to 

connect remote locations to the bulk transmission system. The GIS supply curve algorithm 

described in Section 5.3 estimates spur line distances and costs for potential development sites

for these technologies, which are then grouped into supply curve bins. These spur lines, which 

are distinct from the inter-BA bulk transmission system for ReEDS, are assumed to link remote 

sites to the larger transmission system and to load centers. All other technologies are assumed to 

be built close to existing transmission infrastructure, and they therefore need only a nominaldistance spur line. Because the plant envelope used to determine technology capital cost 

assumptions includes the onsite switchyard, a short spur line, and relevant upgrades at the 

substation (EIA 2013), those technologies incur no additional grid interconnection cost.

5.2 Transmission Cost

The long-distance and spur-line transmission costs in ReEDS are based on regional line voltage 

and cost premium assumptions. For long-distance interregional transmission lines, an assumed 

voltage (345 kV, 500 kV, or 765 kV) is applied for each region. The voltage assumption in each 

BA for long-distance transmission is taken from the highest voltage line currently operating in 

the BA from the Homeland Security Infrastructure Project (HSIP 2012). For BAs where the 

highest voltage of currently operating transmission lines is less than 500 kV, the voltage in the 

future is assumed to be 765 kV, and the associated costs for 765-kV lines are used for all years. 

For BAs where the highest voltage of currently operating transmission lines is 500 kV, the costs 

 47 Those DC connections include the nine AC-DC-AC interconnect interties, the Pacific DC Intertie that links 

Columbia River hydropower to Southern California loads, the Intermountain HVDC line between Utah and 

Southern California, as well as future DC projects along defined corridors. 48 The Renewable Electricity Futures Study includes sample ReEDS transmission flows. (NREL 2012) 49 ReEDS includes scenario options that make transmission more expensive and/or increase transmission losses, 

with both options aimed at representing additional unmodeled barriers to transmission. See Cole, Mai, et al. (2016).
58

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for 500-kV lines are used. The only exception to these rules for voltages in the Eastern 

Interconnection are for BAs in New England (Connecticut, Maine, Massachusetts, New 

Hampshire, Rhode Island, and Vermont), which are assumed to use 345-kV transmission lines 

for all years.

Each voltage class is associated with a base capital cost sourced from the Phase II Eastern 

Interconnection Planning Collaborative (EIPC) report: $2,333/MW-mile, $1,347/MW-mile, and 

$1,400/MW-mile for 345-kV, 500-kV, and 765-kV transmission lines respectively (EIPC 

2012).

50 All wind and solar spur line costs are based on 230-kV line costs, assumed to be 

$3,667/MW-mile (ibid).

51 For DC transmission lines, an additional cost of $253,000/MW (in 

2015$) is applied to account for the cost of the converter stations.

In addition to the base transmission costs, regional multipliers are also applied and are largely 

based on assumptions from EIPC (2012). Regional transmission cost multipliers are the average 

of the EIPC report’s high and low multipliers in each North American Electricity and 

Environmental Model region, and they are associated with the assumed voltage within the 

region. BAs in ERCOT and the Western Interconnection (excluding Canada and California) are 

assumed to have a regional transmission multiplier of one (1). Long-distance transmission costs 

in BAs in the California Independent System Operator are 2.25 times the cost of the other 

baseline costs for the rest of the Western Interconnection. For long-distance transmission 

between BAs with different transmission costs, the average cost is used. The same process is 

applied to wind and solar spur line costs.

Figure 25 and Figure 26 show the regional long-distance and spur-line transmission costs, 

respectively, that result from the previously described steps and assumptions.

 50 The base transmission costs for ReEDS are converted to $/MW-mile according to new transmission line cost and 

capacity assumptions for single circuit conductors for each voltage in EIPC (2012). The costs reported are in 2010$ 

as used by the EIPC.

51 Wind and solar spur line costs are applied within the development of the resource supply curves. 
59

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Figure 26. Map of long-distance transmission costs

Figure 27. Map of spur-line transmission costs
60

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5.3 Spur Lines

To supplement the resource assessments that seed ReEDS with renewable potential, GIS analysis 

adds an accessibility dimension to the supply curves for wind, PV, and CSP technologies based 

on the expected cost of linking renewable resource sites to the transmission network. Coupled 

with supply curves for the renewable resource itself, the resulting two-dimensional supply curves 

allow ReEDS to make internal decisions about tradeoffs between resource quality and 

interconnection cost.

The geospatial site-connection algorithm evaluates each potential link from a wind or solar

resource site to an electricity infrastructure feature, and the model assigns to the link a cost of 

development. For the algorithm, wind resource assessed at 2-km resolution are downscaled to 90 

m resolution for the purpose of identifying developable and non-developable locations based on 

numerous land cover, land use, and protected areas GIS data sets. The 90 m resolution pixels are 

then aggregated to a grid of roughly 5.7 km resolution to reduce the number of points to be used 

in additional analyses downstream. CSP sites are similarly grouped into 1.35 km x 1.35 km cells, 

and PV sites are grouped into 3.15 km x 3.15 km cells. Each resource site and each link is 

characterized by a LCOE that includes the expected cost of interconnection. 

The resource supply curves are the result of successively selecting the least-cost site-toinfrastructure link and allocating that site to the infrastructure component until the transmission 

lines are saturated. At the start, each transmission line is assumed to have 10% of its nominal 

carrying capacity available for renewable connections, and each time a link is added, the 

remaining headroom is decremented by the capacity of the site being tied in. As the available 

capacity at transmission infrastructure features decreases, resource sites have to look farther 

afield for available linkages.

The resource supply curves are described in detail in (Galen Maclaurin et al. [in review]) (DOE

2015), Previsic et al. (2012), and (C. Murphy et al. 2019).

5.4 International Electricity Trade

ReEDS is capable of endogenously representing Canada and Mexico (Section 10), but our 

default model configuration only covers the contiguous United States and represents electricity 

trade with Canada exogenously. In the default configuration, imports and exports are specified 

by Canadian province based on the National Energy Board’s (NEB) Canadian Electricity Futures 

Reference Scenario (NEB 2016), with net exports across all regions shown in Figure 27 (values 

beyond NEB projections to 2040 are held constant at the 2040 value). Each province is required 

to send electricity to or receive electricity from any of the ReEDS BAs that have connecting 

transmission lines to that province. Thus, while the total amount of energy imported from and 

exported to each Canadian province is exogenous, the model can choose which BAs supply or 

receive that electricity. Electricity flows in 2010–2014 are taken from historical data (Canadian 

Electricity Association 2015). Canadian imports are assumed to be from hydropower and are 

counted toward RPS requirements where allowed by state RPS regulations. Canadian imports 

also count toward reserve margin requirements.
61

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Figure 28. Net exports from Canada to the United States

A limited amount of trade with Mexico is represented endogenously in that several Mexican 

plants along the Mexico-California border are included in ReEDS with transmission lines that 

connect to the southern California BAs. These plants are operated using the same framework as 

all other plants in ReEDS and can send power into California. ReEDS is not allowed to build 

new plants in Mexico to serve U.S. load except for plants that are already under construction and 

are under contract to send their power to the United States. Renewable energy generated in 

Mexico and sent to California is eligible for the California RPS. Other than these interties 

between Mexico and California, all other U.S.-Mexico interties are not currently included in the 

U.S.-only model. In Section 10, we discuss separate model versions that explicitly include the 

Canadian and Mexican electricity systems.

0

10

20

30

40

50

60

2010 2015 2020 2025 2030 2035 2040 2045 2050

Net Exports from Canada to U.S. 

(TWh)
62 

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6 Electricity System Operation and Reliability

ReEDS finds the least-cost way of building and operating the electricity system while meeting 

certain requirements that are dominated by the need to meet electricity load while maintaining 

system adequacy and operational reliability.

6.1 Electricity Load

The primary constraint in ReEDS is to serve electricity load in each BA and time-slice. The enduse electricity load projection used in ReEDS is exogenously defined, based on scenarios from 

AEO2018 (EIA 2018) and calibrated in 2010 to load data from Ventyx (2014). Hourly load profiles 

from Ventyx transmission zones are summarized and averaged to the 17 time-slice load profiles 

for the model BAs. These 2010 profiles are then scaled to ensure a match with the state-level 

annual load data from EIA’s “Electricity Data Browser” (EIA 2015). The regional growth factors 

for years after 2010 are calculated from the AEO scenario electricity consumption by census 

division.52 For each model year in ReEDS, the regional load profiles are scaled by regional growth 

factors, but the shape of the load profile is assumed constant throughout the study period.53 

The end-use load, described in the previous paragraph, is defined at the meter level. ReEDS 

includes inter-BA transmission system losses in the optimization but does not represent 

distribution losses, so the end-use load must be scaled up to busbar load to account for 

distribution losses. The 5.3% distribution loss factor used for this conversion is estimated based 

on a combination of EIA and ReEDS numbers. ReEDS is required to generate sufficient power 

in each time-slice and BA (allowing for transmission of power but accounting for losses) to meet 

this busbar load. 54

6.2 Resource Adequacy

Resource adequacy is “the ability of supply- and demand-side resources to meet the aggregate 

electrical demand” (NERC 2016). Planning reserve requirements in ReEDS ensure adequate 

resource is available at all times, within an acceptable probability of failing to do so. In practice, 

this constraint is enforced by requiring the system to have sufficient firm capacity to meet the 

forecasted peak demand plus a reserve margin. This constraint is enforced for each season to 

accommodate the potential for peak net load to shift seasons as renewable penetration increases.

52 The demand growth factors from AEO’s census divisions are applied to the ReEDS NERC-level regions. Due to 

differences in AEO’s census divisions and the similarly sized NERC regions in ReEDS, the projected national load 

in ReEDS does not agree exactly with AEO’s demand projections, but the differences are small. 53 Demand profiles and annual consumption can be altered with different plug-in hybrid electric vehicle and rooftop 

PV scenarios; however, absent these demand-side technologies, the profiles are assumed to be the same as those 

from 2010.

54 Load balancing is implemented with equality constraints, so there is no physical representation of lost load and an 

associated cost.
63

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Each technology is assigned a capacity value (CV)55 reflecting its expected availability when 

power is needed, typically during the highest-risk hours, which are ideally identified as the hours 

with highest loss of load probability (LOLP)56. For conventional generators in ReEDS, the CV

matches the nameplate capacity. 

6.2.1 VRE Capacity Value

For VRE technologies (i.e., wind and solar), ReEDS estimates a seasonal CV for each 

region/class bin via an hourly LDC approximation of expected load carrying capability (ELCC)57

performed between solve years. ELCC can be described as the amount of additional load that can 

be accommodated by adding those generators while maintaining a constant reliability level. Prior 

to ReEDS v.2017, a statistical approximation was used to calculate this ELCC by time-slice, 

region, and bin (Walter Short et al. 2011). The new 8760-based methodology is better able to 

capture the highest load and net load hours, which typically represent the highest risk hours, 

thereby supporting a more accurate representation of CV. Details of this new LDC approach, as 

well as a comparison against the former statistical method, can be found in Frew et al. (2017).

The LDC approach for calculating CV is based on explicit hourly (8760) tracking of timesynchronous load and VRE resources. The CV method uses a capacity factor proxy that is 

applied to top hours in load and net load-duration curves (LDCs and NLDCs) to estimate ELCC.

Figure 28 is a graphic representation of the ReEDS CV methodology. The LDC reflects the total 

load in a given modeling region, which is sorted from the hours of highest load to lowest load 

and is shown by the blue line. The NLDC represents the total load minus the time-synchronous 

contribution from VRE, where the resulting net load is then sorted from highest to lowest, as 

shown by the solid red line.58 The NLDC(δ), which represents further addition of VRE

resources, can be created by subtracting the time-synchronous generation of an incremental 

capacity addition from the NLDC, where the resulting time series is again sorted from highest to 

lowest; this is shown by the dashed red line. 

 55 In ReEDS, CV is defined as the fraction of nameplate capacity that contributes to the top peak net load hours. 56 LOLP is defined as the probability of a loss-of-load event in which the system load is greater than available 

generating capacity during a given period.

57 ELCC is the contribution (units of MW that can then be reported as a fraction of the installed capacity to represent 

CV) that an additional resource provides toward meeting the system’s load while maintaining a fixed system-wide 

reliability level.

58 Residual LDC (RLDC) is an equivalent term to NLDC used in the literature.
64

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Figure 29. LDC-based approach to calculating CV

ReEDS calculates the ELCC as the difference in the areas between the LDC and NLDC during 

the top 10 hours of the duration curves in each season, as represented by the dark blue shaded 

area in Figure 28. These 10 hours are a proxy for the hours with the highest risk for loss of load, 

i.e., the LOLP.59 Similarly, the contribution of an additional unit of capacity to meeting peak 

load is the difference in the areas between the NLDC and the NLDC(δ), as shown by the light 

blue shaded area in Figure 28. To ensure resource adequacy, ReEDS calculates CV based on a

1,000-MW incremental capacity size of new solar and wind builds. These areas are then divided 

by the corresponding installed capacity and number of top hours (10 hours per season in this 

case, although the number of hours can be adjusted by the user) to obtain a fractional seasonalbased CV. 

The resulting existing and marginal CV60 values then feed into ReEDS to quantify each VRE

resource’s capacity contribution to the planning reserve requirement. Existing VRE CV 

calculations are performed by region and technology. For all candidate VRE resources that might 

be built in the coming year, the marginal CV is calculated by region, technology, and resource 

class. In all cases, the VRE profile is compared against the Regional Transmission Organization 

(RTO)-level load profile for determining the CV (Figure 29). We use the RTO-level load profile 

to simplify the challenge of representing the ability of transmission to wheel VRE capacity from 

one BA to another. In essence we have assumed a copper plate within each RTO for the purpose 

of sharing VRE capacity. We use RTO regions rather than NERC regions for this assumption 

because transmission and trading tend to be closely related to RTO regions than NERC regions.

 59 We currently use only a single year of wind, solar, and load data to calculate CV. Expansion of this method to use multiple 

years of data would increase the robustness of this calculation.

60 We refer to “existing” CV as the reliable capacity contribution from resources that have already been deployed in the model 

prior to the buildout of additional “marginal” resources.
65

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Figure 30. Map of ReEDS 134 BA regions and 18 RTO subregions

6.2.2 Storage Capacity Value

For storage, the CV is assumed to be 1 for PSH and CAES. For battery storage, ReEDS estimates 

CVs based on curves fitted to results from NREL’s REFlex model using multiple years of input 

load and solar data for California (Denholm and Margolis 2018). This approach approximates the 

CV of storage as the potential of storage to reduce the peak net load. The battery storage CV

curves assume a piecewise nonlinear fit as a function of storage penetration, PV penetration, and 

storage duration. To be consistent with the method used in REFlex, storage penetration is 

calculated as the battery power capacity divided by the instantaneous peak load, and PV 

penetration is calculated as the total annual PV generation energy divided by the total annual 

load. Thus, a storage penetration of 2 means that the total storage power capacity is double the 

peak load, and a PV penetration of 0.3 means that the annual generation from PV that serves load 

is 30% of the total annual load. A detailed description of these curves and the implementation in 

ReEDS is provided in (B. A. Frew et al. 2018).

The battery storage CV is calculated for battery storage capacity that is already deployed along 

with marginal battery storage available for new deployment in the subsequent ReEDS solve 

period. Existing storage CV is calculated by integrating along the given PV penetration curve 

from 0 to the current storage penetration (purple area in Figure 30). This integration value is then 

divided by the storage penetration to return an average existing storage CV that is then applied to 

all existing storage (dashed red line in Figure 30). The marginal storage CV is the rightmost 

value along the storage CV curve (red point in Figure 30). Both the existing and marginal battery 

storage CV are calculated at the ReEDS RTO level and then mapped to the BA level for 

implementation within ReEDS.
66

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Figure 31. Illustration of the calculation of marginal and existing storage CV with the functional 

form curve fit for PV penetration of 7% and storage duration of four hours

The battery storage CVs estimated by the fitted curves are then multiplied by an availability 

factor, which accounts for uncertainty in electricity demand, storage dispatch, and generator 

outages that could all lead to supply scarcity. These availability factors are a function of 

duration, as shown in Figure 31, which was derived from Sioshansi, Madaeni, and Denholm

(2014). The resulting adjusted battery storage CV parameters are included in the planning 

reserve constraint, similar to the treatment for VRE technologies.

Figure 32. Battery capacity value availability factor as a function of battery duration

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12

Battery Capacity Value 

Contribution (Fraction)

Hours of Storage
67 

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6.2.3 Planning Reserve Margins

Figure 33. Map of ReEDS 134 BA regions and 18 NERC reliability subregions.

The reserve margin fractions applied in ReEDS are based on reserve margin requirements for 

NERC reliability subregions (NERC 2010). Each ReEDS BA must meet the requirement, but 

regions can engage in bilateral contracts for firm capacity subject to transmission limits on AC or 

DC corridors. 

The minimum reserve margin required in each ReEDS region varies over time. The 2018 reserve 

margin is set by taking the minimum of the average actual long-term interconnect-wide reserve 

margin from 1990 to 2016 and the average actual short-term regional reserve margin from 2010 

to 2016.61 However, if the minimum of those two values is lower than the NERC-recommended 

level, the NERC-recommended level is taken instead. Figure 33 shows the resulting planning 

reserve margins used in 2018 (under the “Combined” label). The minimum reserve margin 

requirement in 2025 in ReEDS is then set to the NERC-recommended level, and intermediate 

years between 2018 and 2025 are a linear interpolation between the two values. This dynamic 

treatment gives the model some incentive to maintain reserve margins above the NERCrecommended levels until 2025. 

61 A long-term regional value could not be calculated because of how often the NERC region definitions have 

changed.
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Figure 34. Planning reserve margin by NERC region.

NERC Rec. are the NERC-recommended levels, Long-term Interconnect Avg. is the average by 

interconnect from 1990 to 2016, Short-term Region Avg. is the average by NERC region from 2010

to 2016, and combined is the minimum of the short-term and long-term average, unless that values 

falls below the NERC-recommended level, in which case it is set to the NERC-recommended level.

6.3 Operational Reliability

In addition to ensuring adequate capacity to satisfy long-term planning reserve requirements, 

ReEDS requires operational reliability—that is, the ability to continue operating the bulk-power 

system in the event of a sudden disturbance (NERC 2016). In practice, ancillary reserve 

requirements ensure there is sufficient flexibility from supply-side and demand-side technologies 

to rebalance fluctuations in generation and demand. 

ReEDS represents three type of operating reserve products, including, spinning, regulation, and 

flexibility reserves. The requirement specified for each product in each time-slice is a function of 

load, wind generation, and photovoltaic capacity (during daytime hours).62 Technologies

providing these reserve products must be able to ramp their output within a certain amount of 

time (Table 18).

 62 The PV reserve requirement is only valid during daytime hours when the PV systems are operating. In addition, 

the requirement is a function of capacity rather than generation because reserves are especially important around 

sunrise and sunset when PV generation is low.
69

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Table 20. Summary of Operating Reserve Requirements

Reserve 

Product

Load Requirement 

(% of load) a

Wind Requirement 

(% of generation) b

PV Requirement 

(% of capacity) b

Time Requirement 

to Ramp (minutes)

Spinning 3% 10

Regulation 1% 0.5% c 0.3% c 5

Flexibility 10% 4% 60

a See Lew et al. 2013, Section 5.3.4.

b Reserve requirements for wind and PV were derived from the outcomes from (Lew et al. 2013).

The flexibility requirement for wind was estimated as the ratio of the change in the reserve 

requirement to the change in wind generation from the Lew et al. “High Wind” scenario; the 

requirement was estimated similarly for PV using the Lew et al. “High Solar” scenario. c The estimated regulation requirements (0.5% wind generation and 0.3% PV capacity) are based 

on incremental increases in regulation reserves across all scenarios in (Lew et al. 2013).

All ancillary reserve requirements must be satisfied in each BA for each time-slice; however, 

reserve provision can be traded between BAs using AC transmission corridors. Trades are only 

allowed within an RTO and not across RTO boundaries. The amount of reserves that can be 

traded is limited by the amount of carrying capacity of an AC transmission corridor that is not 

already being used for trading energy.

The ability of technologies to contribute to reserves is limited by the ramping requirement for a 

given reserve product, the plant ramp rate, and online capacity (Table 19). Online capacity is 

approximated in ReEDS as the maximum generation from all time-slices within a modeled day. 

Reserves can be provided by generation and storage technologies that are turned on but not fully 

dispatched in a time-slice. In addition, demand-side interruptible load can also contribute to 

reserve requirements, if enabled in a scenario. Neither nuclear, PV, nor wind can contribute 

toward the supply of reserves. 

The cost for providing regulation reserves is represented in ReEDS using data from (Hummon et 

al. 2013); see Table 20. Because ReEDS does not clearly distinguish between coal fuel types, 

$12.5/MWh is the assumed regulation cost for all coal technologies. The cost of providing 

regulation reserves from Gas-CT, geothermal, biopower, land-fill gas, and CAES is assumed to 

be the same as oil/gas steam.
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Table 21. Flexibility Parameters of the ReEDS Generation Technologies

Assumed 

Ramp 

Rate 

(%/min)

Upper Bound (% of online capacity) = Ramp Rate (%/min) ×

Ramp Requirement (min)

Spinning Regulation Flexibility

Gas-CTa 8 8×10=80 8×5=40 8×60=480, so 100

Gas-CCa 5 5×10=50 5×5=-25 5×60=300, so 100

Coala 4 4×10=40 4×5=20 4×60=240, so 100

Geothermalb 4 4×10=40 4×5=20 4×60=240, so 100

CSP with Storagec 10 10×10=100 10×5=50 10×60=600, so 100

Biopowerb 4 4×10=40 4×5=20 4×60=240, so 100

Oil/Gas Steama 4 4×10=40 4×5=20 4×60=240, so 100

Hydro 100

No Upper Bound Storage 100

a See (Bloom et al. 2016b). b Geothermal and biopower values are assumed to be the same as oil/gas steam units. In practice, 

geothermal plants typically do not ramp given their zero or near-zero variable costs, and therefore 

only provide energy and not operating reserves.

c. See (Jorgenson et al. 2013).

Table 22. Cost of Regulation Reserves

Generator Type Cost of Regulation

Reserves (2013$/MWh)

Supercritical Coal 15

Subcritical Coal 10

Combined Cycle 6

Gas/Oil Steam 4

Hydro 2

Pumped Storage Hydropower 2

6.4 Curtailment

Curtailment is a reduction in the output of a generator from what it could otherwise produce 

given available resources. Most renewable generators, being resource dependent and lacking fuel 

costs, are subject to curtailment that reduces plant revenue, in particular, when there is ample 

generating capacity committed, insufficient load to absorb it locally, and limited options to 

export surplus power via the transmission network. The economics of investment in renewable 

generators can be impacted by the amount of curtailment to which a plant will be subjected, so 

ReEDS estimates curtailment fractions for both existing renewable generators and candidate 

sites.
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The ReEDS curtailment calculation is a statistical estimate of expected surplus generation in 

each balancing area and time-slice given the expected load level, expected VRE output, expected 

minimum turndown level for committed thermal units (see Table 19), and the variances and 

correlations of the preceding factors. Recommitting thermal capacity from one year to the next 

can change curtailment patterns by changing the minimum stable output level of the thermal 

fleet. Similarly, adding new storage capacity can reduce curtailment levels by effectively 

increasing the available load. 

Short et al. (2011) describe the calculation details for curtailment in ReEDS. The only difference 

in the current version of ReEDS is that storage is no longer explicitly included in the statistical 

calculations for the existing and marginal VRE curtailments. Instead, the contribution of all 

storage toward reducing curtailment via charging by region and time-slice is captured within a 

storage effectiveness parameter. This parameter is nominally set to 1, which assumes full 

reduction in curtailment for energy placed into storage, for all load and renewable energy 

generation levels. Details about this parameter can be found in Cole, Marcy, et al. (2016).
72

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7 Climate Impacts

ReEDS nominally assumes a constant climate throughout the study period; however, it has the 

capability to represent several impacts, both combined and isolated, that climate change could 

have on the electricity system. These include temperature impacts on electricity load and power 

system performance, changes to thermal cooling water availability, and changes to hydropower 

availability. The effect of changing water temperature on operation and reliability is not 

included, but we have explored ways to incorporate such effects through linking with detailed 

hydrological models (Vorosmarty et al. 2017). There is also no representation of climate impacts 

on future wind and solar resource availability, though ongoing work is exploring these effects.

7.1 Temperature Impacts on Load

Temperature changes influence electricity load primarily through their effects on heating and 

cooling demands. Modeled climate impacts on load are based on projected changes in heating 

and cooling degree days from the Coupled Model Intercomparison Project Phase 5 (CMIP5)

climate model results (Wood 2014). Degree days are calculated directly from temperature data. 

Data are either derived from a single simulation of a general circulation model (GCM) for a 

specific representative concentration pathway (RCP), or scenario data are aggregated across 

several GCM-RCP scenarios with features relevant to the present analysis. Because ReEDS is 

not inter-temporally optimized and has limited foresight, raw degree day data with a high degree 

of inter-annual variability must be smoothed before use in the model to avoid skewing decisions. 

Smoothed degree day data are aggregated for each BA and season, and the resulting impacts on 

electricity load are based on temperature-load relationships developed by Sullivan et al. for each 

ReEDS time slice (Sullivan, Colman, and Kalendra 2015). 

7.2 Temperature Impacts on Power System Performance

Temperature change can also affect the efficiency of thermodynamic cycles used in power 

generation. Higher air temperature reduces cycle efficiency and maximum power output 

capacity. ReEDS imposes temperature-performance relationships during summer time slices that 

increase thermal power plant heat rate by 0.1–0.2% and reduce available capacity by 0.5–0.75%

per 1°C increase (ICF 1995; Jaglom et al. 2014). Heat rate and generation capacity impacts in 

other time slices are assumed negligible.

Air temperature changes also affect cooling system performance. The relationships between 

temperature and water withdrawal and consumption rates in Rutberg et al. (2011) are assumed 

valid for all cooling technologies despite their derivation only for recirculating cooling (Rutberg 

et al. 2011). Though performance of once-through and pond cooling technologies will not 

necessarily respond to temperature in the same way as recirculating cooling, we use air 

temperature change as a proxy for water temperature change for these cooling systems. The 

result is the following functional relationship between temperature T (°C) and fractional change 

in withdrawal or consumption rate y. This equation allows cooling system performance to be 

adjusted for temperature in a consistent manner with power plant capacity and efficiency.

yy = −1.04 × 10−7TT3 + 1.19 × 10−5TT2 − 3.62 × 10−5TT + 4.46 × 10−3
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Higher temperatures can also reduce transmission capacity by increasing electrical resistance. 

This effect is simulated for summer afternoon time slices with a 0.55% reduction in transmission 

capacity per 1°C increase. 

7.3 Precipitation Impacts on Cooling Water Availability

Climate change can also influence both how much water is available and when it is available. To 

assess climate impacts on unappropriated freshwater supplies, external collaborators in 

partnership with NREL have developed data specifying the changes across solve years to 

unappropriated freshwater available in each BA and season, accounting for changes both to total 

precipitation and its seasonal distribution. This procedure requires downscaling CMIP5 climate 

model precipitation projections, modeling the resultant effects on surface water runoff in 

hydrological modeling frameworks, and relating runoff changes to power sector water 

availability. As with temperature data, inter-annual smoothing is required to avoid biasing 

optimal decisions. Ongoing work has utilized multiple approaches for projecting changes to 

unappropriated freshwater availability, and these approaches are discussed in greater detail by

Cohen et al. and EPA (Cohen et al. [in review]; EPA 2017).

Climate impacts are not imposed on any other water resource types. Appropriated surface water 

availability is assumed static with climate due to its legally binding nature. Climate change could 

also affect groundwater resources, but there are no robust methods for characterizing how 

precipitation and surface water runoff under alternative climate regimes affect groundwater 

resources across the entire United States. Applying climate impacts to other water resource 

categories requires new methodology and data.

7.4 Precipitation Impacts on Hydropower

Climate impacts on hydropower energy availability are determined using similar methodology as 

with thermal cooling water, where precipitation-runoff relationships are assumed applicable to 

water available to hydropower facilities. Impacts are represented as fractional changes to 

hydropower capacity factors, which are applied differently for dispatchable and non-dispatchable 

resources. Dispatchable hydropower is assumed to have sufficient reservoir size to maintain the 

same seasonal distribution of energy availability, so changes are applied on an annual basis. 

Season-specific changes to energy availability are applied to non-dispatchable hydropower. This 

capability was first exercised in the DOE Hydropower Vision report, with subsequent use for the 

4th National Climate Assessment (DOE 2016b; EPA 2017).
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7.5 Climate Scenarios

Several sets of climate impacts data have been developed for use in ReEDS. These include the 

following GCM-RCP combinations:

GCM RCP(s)

BCC-CSM-1.1(m) 4.5

CanESM2 4.5, 8.5

CCSM4 4.5, 8.5

GFDL-CM3 4.5

GFDL-ESM2M 2.6, 4.5, 6.0, 8.5

GISS-E2-R 4.5, 8.5

HadGEM2-ES 2.6, 4.5, 6.0, 8.5

IPSL-CM5A-LR 2.6, 4.5, 6.0, 8.5

MIROC5 4.5, 8.5

MIROC-ESM-CHEM 2.6, 4.5, 6.0, 8.5

NorESM1-M 2.6, 4.5, 6.0, 8.5

In addition to climate scenarios generated from specific GCM-RCP combinations, three 

aggregate scenarios represent temperature-precipitation futures based on summer-fall conditions 

in the Southwestern United States. The Southwestern United States is already water-stressed and 

at risk of becoming more so in hotter, dryer climate conditions, so focusing on the Southwest in 

the summer-fall allows exploration of particularly challenging future climate conditions. The 

three scenarios defined in this way include (1) ModNone: moderate warming, no precipitation 

change, (2) HotDry: significant warming, less precipitation, and (3) HotWet: significant 

warming, more precipitation. 
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8 Policy Descriptions

Policies modeled in ReEDS include federal and state-level emission regulations, tax incentives, 

and portfolio standards. This section primarily focuses on existing policies, but Section 8.4 does 

discuss additional frameworks that exist in the model. 

8.1 Federal and State Emission Standards

8.1.1 New Source Performance Standards

The U.S. EPA’s New Source Performance Standards (promulgated through Section 111(b) of the 

Clean Air Act) require that new capacity have annual average CO2 emissions below 1,400 

pounds CO2/MWh (at least as low as those of new natural gas combined cycle technology). 

Therefore, we do not allow ReEDS to build new coal without CCS because ReEDS does not 

have any non-CCS coal technologies represented that can meet this emission limit.

The existing source performance standard under the Clean Power Plan and the associated Clean 

Energy Incentive Program are excluded by default but can be modeled as discussed in the 

Appendix.

8.1.2 Cross-State Air Pollution Rule

ReEDS applies the Clean Air Interstate Rule (CAIR) and Cross-State Air Pollution Rule 

(CSAPR) using caps on power plant emissions to the states in the eastern half of the United 

States over which the rules are imposed. CAIR annual emission caps for SO2 and NOx are 

applied for 27 states in the entire eastern United States from 2010 to 2014. From 2016 onward, 

CSAPR annual emission allowance budgets for SO2 and NOx are applied at the state level, with 

CSAPR Phase 1 caps applied in 2016 and relatively more stringent Phase 2 caps applied from 

2018 (EPA 2016). In addition to the annual NOx caps, CSAPR also applies an ozone-seasonspecific cap for few states. ReEDS applies an annual estimate of these ozone season caps.

ReEDS also models the interstate cap and trade for SO2 and NOx allowance budgets within the 

respective SO2 and NOx groups. States can only purchase additional emission credits up to their 

assurance limits, which is fixed at 18% of state allowance budget. Even though states can go up 

to 118% of their allowance budget, the total emissions allowance at the group level must be 

respected. 

8.1.3 Mercury and Air Toxic Standards

In addition to these air pollution control policies, ReEDS also considers the impact of the EPA’s 

Mercury and Air Toxic Standards (MATS) that limit pollutants from individual power plants. In 

contrast to the CAIR and CSAPR implementations that apply at regional scale, MATS is 

implemented by prescribing compliance retrofits or retirements of each unit in the coal fleet in 

the 2016 solve year. All these retrofit decisions are taken outside the ReEDS and are 

appropriately represented by:

• Re-estimating the available coal fleet capacity in each category after accounting for the 

retrofits (i.e., coal-unscrubbed and coal-scrubbed capacities post-MATS)

• Accounting for the appropriate retrofit costs and their impacts on the system planning 

costs and electricity prices
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• Accounting for the change in generation emission factors (i.e., estimating average coal 

fleet SO2 emission factors after plants comply with MATS limits [0.2 lb/MMBtu]).

Though MATS is primarily designed to limit mercury, hydrogen chloride and particulate matter, 

there are also alternate limits to other metallic/non-metallic pollutants, including SO2. Therefore,

ReEDS also models the change in SO2 emissions factor for units retrofitted to comply with 

MATS. Though ReEDS does not constrain mercury emissions, it does track them over the 

planning horizon, and the MATS compliance effects are incorporated by reducing mercury

emission factors for these generation technologies by 90% during post-MATS period.

The following logic is applied to each coal unit in the ABB Velocity Suite database for making 

exogenous retrofit decisions and fleet updates in the 2016 model year: 

• Low-cost option: Do not retrofit if (1) the coal plant is set to retire before or during 2016 

or (2) its SO2 emission factor is within 20% of the MATS limit (to accommodate coal 

switching or other low-cost strategies) 

• Mid-cost option: Retrofit with dry sorbent injection if (1) the plant is set to retire before 

2025 or is small in size (<200 MW), (2) the plant is <300 MW and its SO2 emission 

factor is within 200% of the MATS limit, (3) the plant already has a flue-gas 

desulfurization system, or (4) there is a planned dry sorbent injection retrofit as per the 

ABB Velocity Suite database.

• High-cost option: retrofit with flue-gas desulfurization if (1) the plant does not meet any 

of the criteria above (i.e., it is large and will retire after 2025) or (2) there is a planned 

flue-gas desulfurization retrofit as per the ABB Velocity Suite database. 

These upgrades are mandated in ReEDS. The appropriate capacity-weighted retrofit costs—

which are estimated based on the capital and operational costs63 of the flue-gas desulfurization 

and dry sorbent injection retrofits—are applied to the system cost and the calculated electricity 

price.64 The above assumptions and representation of MATS in ReEDS have at least three broad 

caveats:

1. The national-scale SO2 emissions could be lower than in practice, as the alternative SO2

limit of MATS has been targeted for compliance. In reality, units may comply with 

primary mercury and hydrogen chloride limits while still having SO2 above the MATS 

alternate limits.

2. Alternative low-cost compliance strategies, such as adopting low-sulfur or low- hydrogen 

chloride coal switching, are not considered.

3. The retrofit decisions are not based on a full-fledged economic analysis considering unit 

size, lifetime, O&M cost, or other factors.

 63 O&M costs are estimated for individual retrofits as a function of unit size (MW), heat rate, pre-MATS emission 

rate, removal efficiency, limestone or sorbent injection rate, waste rate, limestone cost, waste disposal cost, auxiliary 

power cost, water cost, labor rate, and other factors. 64 The O&M costs were taken from documentation of EPA’s Integrated Planning Model (IPM) Versions 5.1, 5.2, 

and 5.5, which are no longer available online.
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8.1.4 California Carbon Cap

California’s Global Warming Solution Act of 2016 (referred to as Assembly Bill 398 or AB 398) 

established a program to reduce economy-wide greenhouse gas emissions to 1990 levels by 

2020. In 2016, legislation was passed that codified the 2030 greenhouse gas target to 40% below

1990 levels. In ReEDS, these state carbon caps are modeled as a cap on electricity-system CO2

emissions from generators either located in California or serving load in the state. Direct CO2

emissions from generators located in California count toward the cap. CO2 from imported 

electricity is estimated through an accounting of imported energy and the carbon intensity of the 

states from which that energy originated. The default representation in ReEDS estimates the 

imported carbon intensities at the state level and updates the intensities over time based on the 

prior year’s emissions. 

Because California’s greenhouse gas reduction targets are legislated for all economic sectors 

while ReEDS only models the electricity sector, we rely on published economy-wide modeling 

results to estimate electric sector-specific caps that are used in ReEDS. In particular, we apply 

power sector caps based on the annual CA electric sector emissions (from in-state and imported 

electricity) from California Public Utilities Commission (CPUC 2018), which provides guidance 

for a 42 million tCO2 cap by 2030. We enforce that cap from 2030 to 2050. The pre-2030 cap 

ramps linearly from 60 million tCO2 in 2020 to the 42 million tCO2 in 2030. Note that we also 

model California’s RPS policy.

8.1.5 Regional Greenhouse Gas Initiative

The Regional Greenhouse Gas Initiative cap-and-trade program limits the CO2 emissions for 

fossil fuel-fired power plants in nine states: Connecticut, Delaware, Maine, Maryland, 

Massachusetts, New Hampshire, New York, Rhode, Island, and, Vermont.

We enforce allowance budgets from the update model rule adopted in 2017.

65 We ignore the 

provision for privately banked allowances and therefore use the unadjusted budgets: 165 million 

short tons in 2012 declining to 91 million by 2014, then declining 2.5% per year from 2015 to

2020. According to the 2017 Model Rule, the 2021 cap is set at 75 million short tons and 

decreases by 2.275 million tons per year until 2030. We assume the budget remains constant 

beyond 2030. We do not model banking of allowances, emissions offsets, or recycling of 

initiative allowance revenues. A price floor is implemented starting in 2014 at $2 per short ton 

and increases 2.5% per year thereafter. 

 65 “2017 Model Rule,” accessed April 26, 2018, https://www.rggi.org/program-overview-and-design/programreview. For more information, see:

• “About the Regional Greenhouse Gas Initiative (RGGI),” fact sheet updated August 2016, 

https://www.rggi.org/docs/Documents/RGGI_Fact_Sheet.pdf

• “The RGGI CO2 Cap,” https://www.rggi.org/design/overview/cap

• “Regional Greenhouse Gas Initiative,” December 2013, http://www.c2es.org/docUploads/rggi-brief-12-18-

13-updated.pdf.
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8.2 Federal and State Tax Incentives

8.2.1 Renewable Production and Investment Tax Credits

Existing federal tax incentives for renewable energy are included in ReEDS. These include the 

PTC and the ITC—and the Modified Accelerated Cost Recovery System (MACRS) depreciation 

schedules. 66 The most recent renewable tax credit extensions were passed in December 2015 as 

part of the Consolidated Appropriations Act, 2016. Our representations of the PTC and ITC are 

based on this act, including tax credit values, schedules, expirations, and technology eligibility 

(Mai et al. 2016). Current technology-specific depreciation schedules are modeled for all years, 

as we assume they are permanent parts of the tax code.

Model representations of the latest tax credit extensions are complicated by the “commencedconstruction” provision in these policies, the annual ramp-down in tax credit value, and the twoyear modeling intervals used in ReEDS. Because ReEDS accounts for when plants come online 

rather than when they begin construction, we represent the commenced-construction provision 

by assuming the tax credit received by facilities corresponds to the value of the tax credits at the 

time the plant would typically start construction. For most purposes, we assume construction 

starts two years prior to the commercial operation date for utility-scale solar and wind and one 

year prior for commercial rooftop PV. However, guidance from the Internal Revenue Service

(IRS)67 allows for a longer construction period and equipment warehousing for wind projects to 

qualify for the PTC. It is unclear how the wind industry and wind deployment will react to this 

unprecedented IRS guidance. As a result, we conservatively represent a three-year period for 

wind in our PTC representation, meaning a project with a commercial online date of 2019 would 

qualify for the PTC value in 2016, for example.

For utility solar, facilities must be placed in service before the end of 2023 to qualify for a tax 

credit greater than 10%. To simplify representation in the model, we assume a two-year 

construction period.68 For residential PV, the ITC is a placed-in-service tax credit, meaning the 

commenced-construction provision does not apply. Furthermore, the effective value of the tax 

credit during each two-year model period is based on the average value of the tax credit available 

after accounting for these under construction periods. For example, an effective 28% ITC is 

modeled for utility-scale solar during the 2022 ReEDS model year (representing 2021–2022) 

based on the average of the 30% ITC available in 2019 and the 26% ITC available in 2020.69

Table 21 presents the effective wind PTC and solar ITC schedules modeled in ReEDS compared 

with the nominal schedule listed by the policy.

 66 Note that the eligible cost basis for MACRS is reduced by one-half the value of the tax credit. 67 “Beginning of Construction for Sections 45 and 48: Notice 2016-31,” IRS 2016, https://www.irs.gov/pub/irsdrop/n-16-31.pdf. 68 This is justified by the typically shorter construction periods for utility PV. Also, these assumptions are largely 

consistent with IRS guidance for commenced-construction provisions in earlier tax credit policies; see “Beginning 

of Construction for Sections 45 and 48: Notice 2015-25,” Internal Revenue Service, U.S. Dept. of the Treasury, 

accessed January 28, 2016, https://www.irs.gov/pub/irs-drop/n-15-25.pdf. 69 This representation may underestimate the impact of the tax credit on near-term deployment, as more projects 

may be moved forward to gain the higher tax credit value in earlier years. However, we conducted test scenarios and 

found little impact in renewable energy capacity deployment results when shifting the effective tax credit schedule 

slightly to account for this behavior.
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Though they are not shown in Table 21, we also include tax credits for other renewable energy 

technologies in the modeled scenarios. The model representation of the tax credit extensions 

includes effective PTCs for biopower, geothermal, and hydropower lasting through the 2017–

2018 model period to account for the commenced-construction deadline at the end of 2016. 

A PTC value of 2.3 ¢/kWh is applied to biopower and geothermal, and a PTC of 1.2 ¢/kWh70 is 

applied to new hydropower during this period. No tax credits are assumed for biopower and 

hydropower after 2018. A geothermal ITC of 10% is modeled for all years after 2018. Another 

nuance not shown on Table 21 is the ITC option in lieu of the PTC for certain qualifying 

facilities, including offshore wind. For offshore wind, we include an effective ITC through the 

2020 model year.

71

Due to these complexities and the required simplifications, we also limit near-term wind capacity 

deployment as discussed in section 2.4.1.

 70 These values in implemented in ReEDS in 2015$. 71 The current legislation specifies a ramp-down schedule for the ITC that is available to offshore wind wherein a 

30% ITC is available in 2016 and reduces by six percentage points annually through 2019. However, we have not 

implemented the ability to model ITC ramp-downs for offshore wind. We include a full 30% offshore wind ITC for 

all model years from 2016 to 2020, but in most scenarios, no new offshore wind capacity is endogenously added 

during this period by the model. For this reason, we conclude that a more accurate representation of the ramp-down 

schedule would yield the same result.
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Table 23. Actual and Modeled Schedule of Wind and Solar Tax Credits from the Consolidated Appropriations Act, 2016

Consolidated Appropriations Act, 2016 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 Future

Wind PTC Full Full 80% 60% 40% 0% 0% 0% 0% 0% 0% 0%

Solar ITC

Utility 30% 30% 30% 30% 30% 26% 22% 10% 10% 10% 10% 10%

Commercial/Third-Party-Owned 30% 30% 30% 30% 30% 26% 22% 10% 10% 10% 10% 10%

Residential Host-Owned 30% 30% 30% 30% 30% 26% 22% 0% 0% 0% 0% 0%

Model Representation 2015–16 2017–18 2019–20 2021–22 2023–24 Future

Wind PTC Full Full 90% 50% 0% 0%

Solar ITC

Utility 30% 30% 30% 28% 16% 10%

Commercial/Third-Party-Owned 30% 30% 30% 24% 10% 10%

Residential Host-Owned 30% 30% 28% 11% 0% 0%

Table is adapted from Mai et al. (2016) but is updated to reflect the longer construction periods allowed for qualifying wind facilities.
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Several states also have production and investment incentives for renewable energy sources. The 

default values used in these states are listed in Table 22. Though other states have tax credit 

policies in place, those programs are for small system installations and have dollar amount limits 

that render them of limited value for ReEDS utility-scale investments.

Table 24. Default State Tax Incentives

State PTC 

(2013$/MWh)

ITC (%) End Year

North Dakota72 — 12.6 2017

Oklahoma 5.0 — 2023

Utah 3.5 — —

8.2.2 Carbon Oxide Sequestration Tax Credit

The Bipartisan Budget Act of 2018 included expanded the IRS Tax Code Section 45Q Credit for 

Carbon Oxide Sequestration (26 USC 45Q: Credit for Carbon Oxide Sequestration 2018). 

Though this policy existed prior to the 2018 Budget Act, it was not previously included in 

ReEDS because it had a limited impact on the electricity system due to a stipulation that only 75 

million tons of CO2 was eligible for the tax credit across all facilities and time. The 2018 policy 

update removes this restriction and raises the credit value73 to $22.66/tCO2 in 2016 increasing 

linearly to $50/tCO2 in 2026 for CO2 sequestered without previously being used for enhanced oil 

or gas recovery (EOR/EGR); CO2 sequestered after use in EOR/EGR has a value of $12.83/tCO2

in 2016 increasingly linearly to $35/tCO2 in 2026. In both cases, the credit value remains 

constant thereafter (in nominal terms) without a planned expiration date for the policy. Any 

qualified facility beginning construction after January 1, 2018 is eligible to receive the credit for 

the first 12 years of operation.

ReEDS implements a simplified version of this policy given its current representation of CCS 

technologies. CCS capacity is made eligible for the policy beginning in the 2018 solve year. GasCC-CCS constructed through 2028 is eligible based on its 3-year construction time, and CoalCCS constructed through 2030 is eligible based on a 6-year construction time. ReEDS does not 

characterize EOR/EGR opportunities, so all captured CO2 is assumed sequestered without 

EOR/EGR. The $/tCO2 credit is converted to a $/MWh value using fuel CO2 content, heat rate, 

and CO2 capture rate, and this $/MWh value is then applied in a similar manner as the renewable

PTC. To account for the difference in marginal generating costs beyond the 12-year availability 

period, O&M costs for CCS technologies are increased 12 years after any new CCS construction 

so that operating costs of CCS capacity no longer receiving the credit are properly reflected in 

ReEDS dispatch decisions.

8.3 State Renewable Portfolio Standards

ReEDS models state RPSs, including technology set-asides and renewable energy certificate

(RECs) that can count toward RPS compliance. RPS rules are complex and can vary 

significantly between states. The RPS representation in ReEDS attempts to model the primary 

 72 The policy allows a credit of 3% per year for five years, which is discounted to 12.6% in ReEDS. 73 All CO2 credit values in this section are in 2008 U.S. dollars.
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impacts of these RPS rules but includes many simplifying assumptions. In addition, in recent 

years there have been numerous changes to RPS legislation. We periodically update our 

representation to capture the recent changes to the legislation; however, the numerous and 

frequent changes to state laws create challenges to having a current representation of all RPS 

legislation.

Table 23 shows the respective RPS targets and technology set-asides for years 2020, 2025 and 

2030 as a percentage of state electricity sales as modeled within ReEDS. These values—along 

with many other data that we use to represent nuanced RPS rules—are based on data compiled 

by Lawrence Berkeley National Laboratory, which takes into account the in-state REC multiplier 

incentives and load adjustments (e.g., sales-weighted RPS targets considering different load 

serving entities subject to compliance, such as investor owned utilities, municipal utility, and cooperatives).

74 Solar includes UPV and rooftop PV, wind includes both land-based and offshore 

technologies, and DG includes rooftop PV and ground-mounted PV systems located within the 

distribution network.75 ReEDS also models alternative compliance payments for unmet RPS 

requirement for both main RPS targets and solar set-asides as is consistent with the available 

data.75

Technology eligibility for state RPS requirements is appropriately modeled for each state.

75 For 

instance, California’s RPS does not allow in-state rooftop solar technologies to contribute toward 

its RPS.76 Additionally, every state has specific rules regarding hydropower generation’s 

eligibility toward contributing RECs, which are usually based on each unit’s vintage and size 

(e.g., small hydro with specific capacity cut-offs are eligible in some states). ReEDS models 

these as allowable capacity fractions (estimated from ABB Velocity Suite database (ABB 

2018)), which is imposed on each state’s total hydropower generation thereby limiting the 

amount of hydropower RECs that each state could generate. Additionally, ReEDS also imposes 

an upper limit on the total hydropower RECs that a state can use toward its RPS based on 

historical data as compiled and estimated by Lawrence Berkeley National Laboratory.

We also model restricted eligibility of existing renewable capacity to contribute to future RPS 

targets. In other words, while the amount of renewable generation in 2014 exceeds RPS 

requirements in that year, some of this renewable generation has contractual or other obligations 

that prevent it from being eligible to meet RPS requirements. This eligibility restriction, 

estimated by region and technology type, was estimated by Lawrence Berkeley National 

Laboratory. For the contiguous United States, we estimate that 25% of renewable generation in 

excess of RPS demand in 2014 is eligible to meet RPS demand after 2014. The eligibility 

restriction is also reduced when capacity is retired by an amount equal to the restricted 

generation multiplied by the ratio of retired capacity to the capacity operating in 2014. We do not 

model banking or borrowing of RECs.

 74 See Barbose (2017) and https://emp.lbl.gov/projects/renewables-portfolio. Spreadsheet accessed January, 2017. 75 See Database of State Incentives for Renewables & Efficiency (DSIRE) website at dsireusa.org . If data are

unavailable, ReEDS forces RPS target to be met by using a default alternative compliance payments and solar 

alternative compliance payments of $200/MWh and $400/MWh respectively. 76 This version of ReEDS does not include CA Senate Bill 100 requiring 100% carbon-free electricity by 2045, as 

this bill was passed too recently to be included in this model version. The CA Senate Bill 100 policy will be 

included in future ReEDS versions.
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Table 23 also lists the allowable states from which each state may import RECs; interstate REC 

transactions that are required to be bundled with energy are marked with an asterisk. With the 

exception of California, ReEDS enforces an upper limit on the total RECs (both bundled and 

unbundled) that can be imported for that state’s RPS compliance. For California alone, due to its 

unique out-of-state rules, ReEDS enforces two upper limits, one on the total unbundled REC 

imports and the other on the total bundled REC imports. There are a myriad of possibilities of 

interstate REC transactions, in terms of both which two states can transact and the quantity of 

those transactions. To constrain the solution space of ReEDS to credible values, the interstate 

REC trading modeling is based on historical observations (Holt 2016), as shown in the final two 

columns of Table 23. The out-of-state total REC import percentages for each state in are limited 

to those observed in 2012–2013 (Heeter 2015).

Three states (NJ, MA, MD) have implemented carveouts for offshore wind. These carveouts are 

fulfilled through planned projects. NJ and MA have capacity based carveouts of 3,500 and 1,600 

MW, respectively, by 2030. MD has a retail sales-based requirement (2.5%) that we assume will 

be fulfilled by previously planned projects. These projects are assumed to be rebuilt once they 

are retired. 
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Table 25. Effective State RPS Requirements in ReEDS

RPS target / Solar / DG / Wind set-asides (%) Allowable states for REC import, includes 

Canadian provinces (*bundling requirement)

REC import limit 

(% of RPS 

target) State 2020 2025 2030

AZ 5.9/-/1.8/- 8.8/-/2.6/- 8.8/-/2.6/- CA*, CO*, NM* 100

CA 32.2/-/-/- 39/-/-/- 48.8/-/-/- AZ*, ID, MT, NM, NV*, OR*, UT*, WA, WY*, MEX* 25 <2013

15<2016

10>2017

(only unbundled, 

100% for 

bundled)

COa 19.1/-/0.8/- 19.5/-/0.8/- 19.6/-/0.8/- WY 8

CT 23/-/-/- 23/-/-/- 23/-/-/- MA, ME, NH, NY*, RI, VT, NB*, NL*, NS*, QC*, 

NFI*, PEI*

59

DEb 14.4/-/1.9/- 19/-/3/- 19/-/3/- IL*, IN*, MD, PA*, VA*, WV* 94

ILb,c 13.6/-/1.5/1.5 20.3/-/2.2/2.2 21.1/-/3.1/3.1 IA, IN, MD, MN, MO, ND, NY, OH, PA, SD, VA, WI, 

WV

60

MAb 14.8/-/3.5/- 19.1/-/3.6/- 23.5/-/3.7/- CT, ME, NH, NY*, RI, VT, NB*, NL*, NS*, QC*, 

NFI*, PEI*

62

MDb,d 18/-/1.9/- 20.6/-/2.1/- 23.1/-/2.4/- DE, IA, IL, IN*, NC*, NY*, OH*, PA*, TN*, VA*, WI*, 

WV*

70

ME 33.5/-/-/- 33.5/-/-/- 33.5/-/-/- CT, MA, NH, RI, VT, NB*, NL*, NS*, QC*, NFI*, 

PEI*

18

MI 10.9/-/-/- 13.0/-/-/- 12.9/-/-/- IA, IN, MN, WI 0

MN 25.3/0.8/-/11.4 28.1/0.8/-/11.4 28.1/0.8/-/11.4 IA, MI, ND, SD, WI, MB, ON, SK 24

MOb 6.8/-/0.1/- 10.1/-/0.2/- 10.1/-/0.2/- IA, KS, 94

MT 10.7/-/-/- 10.7/-/-/- 10.7/-/-/- ND*, OR*, WA*, BC* 6

NC 4.1/0.2/-/- 5/0.2/-/- 4.8/0.2/-/- AL*, FL*, GA*, LA*, MS*, OH*, PA*, SC*, TN*, VA*, 

WV*

43
85

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RPS target / Solar / DG / Wind set-asides (%) Allowable states for REC import, includes 

Canadian provinces (*bundling requirement)

REC import limit 

(% of RPS 

target) State 2020 2025 2030

NHb 20/-/0.3/- 24.3/-/0.3/- 24.3/-/0.3/- CT, MA, ME, NY*, RI, VT, NB*, NL*, NS*, QC*, 

NFI*, PEI*

35

NJb 23.4/-/3.4/- 23.9/-/4.3/- 24.1/-/6.8/- DE, IL*, IN*, MD, OH*, PA*VA*, WV* 76

NM 16/2.8/0.4/4.1 16/2.8/0.4/4.1 16/2.8/0.4/4.1 - 0

NV 16.4/0.5/-/- 21.1/0.6/-/- 21.2/0.6/-/- - 100

NY 7.9/-/0.7/- 16.3/-/0.7/- 25.4/-/0.7/- RI*, ON*, QC* 4

OHb 5.7/-/0.2/- 10.1/-/0.4/- 11.1/-/0.4/- IN*, KY*, MI*, PA*, WV* 57

ORb 14.2/-/0/- 20.2/-/0/- 25.9/-/0/- CA*, ID*, MT*, UT*, WA*, WY*, BC* 42

PAb 7.8/-/0.5/- 7.8/-/0.5/- 7.8/-/0.5/- DE, IL, IN, MD, NC, NJ, OH, VA, WV 47

RI 15.9/-/-/- 23.3/-/-/- 30.8/-/-/- CT, MA, ME, NH, NY*, VT, NB*, NL*, NS*, QC*, 

NFI*, PEI* 

77

TX 4.4/-/-/- 4.2/-/-/- 4/-/-/- - 0

VT 59/-/2.8/- 63/-/5.8/- 71/-/8.8/- CT, MA, ME*, NH, RI 100

WA 12.2/-/-/- 12.2/-/-/- 12.2/-/-/- ID, MT, OR, BC* 43

WI 10/-/-/- 9.9/-/-/- 10/-/-/- IA*, MI*, MN*, ND*, SD*, MB*, SK* 41

* Interstate REC transactions that are required to be bundled with energy are marked with an asterisk.

a Based on historical observation, only 50% of the actual DG carve out for Colorado is assumed to be met using distributed solar technologies 

(DUPV and distributed PV).

b Based on historical observation, the solar carve out in these states are seen to be met only using distribution side solar technologies. 

Hence the solar carve out in these states are modeled as a DG carve out in ReEDS.

c Illinois allows for 50% of its RPS target to be met using alternative compliance payments, and hence ReEDS targets are adjusted to reflect this.

d Washington, D.C. RPS targets are rolled into Maryland (Washington D.C. load also rolled into Maryland).
86

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8.4 Storage Mandates

Three state storage mandates are represented in ReEDS.77 These include a 1,125 MW storage 

requirement in California by 2022, a 50 MW storage requirement in Massachusetts by 2020, and 

a 2.5 MW storage requirement in Oregon by 2020. These are enforced through prescribed builds 

of 4-hour battery storage in each of the states, and the mandates are required to be maintained 

throughout the model horizon (i.e., once a battery is retired it must be rebuilt if the retirement 

causes the capacity to fall below the mandate). 

8.5 Other Policy Capabilities

In addition to the existing policies described above, ReEDS also includes several optional policy 

implementations that are useful for exploring alternative futures or the impact of existing 

policies. These additional policy frameworks include

• National Clean Energy Standard: This framework allows the user to specify which 

technologies count as “clean energy” and enforce a minimum limit for the penetration of 

these clean energy technologies.

• National Renewable Portfolio Standard: This standard enforces a national RPS, with 

the RPS trajectory defined by the user.

• Carbon Tax: This feature implements a user-specified carbon tax on burner-tip 

emissions from the power sector.

• National Emissions Limit: This framework limits the total national emissions according 

to user-specified values. The limit is often referred to as a carbon cap or CO2 cap.

• Alternative ITC and PTC Schedules: In addition to the ITC and PTC schedules 

described in Section 8.2, the ITC and PTC can be modified to apply for any number of 

years and to any technology.

• Alternative Financing Measures: Policy-related financing impacts such as MACRS or 

the under-construction provisions for the ITC and PTC can be modified as specified by 

the user.

 77 Additional storage policies will be evaluated for inclusion in future model versions. 
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9 Capital Financing, System Cost, and 

Electricity Rates

9.1 Financing of Capital Stock

The financing assumptions used in ReEDS are taken directly from the 2018 ATB spreadsheet 

(NREL 2018b), using the “Market Factor Financials” and the 20-year capital recovery period 

options. The ATB has technology-specific and time-varying financing parameters, including 

interest rate, rate of return on equity, debt fraction, and tax rate. Other elements of the ATB 

included in ReEDS include construction schedules, MACRS depreciation schedules, and 

inflation rates. These values are further defined and explained in the ATB, with additional 

explanation of our financing implementation detailed in the Capital Cost Financial Multipliers 

appendix of this document. The only deviation in ReEDS from the ATB is associated with the 

MACRS implementation. Because of the bonus depreciation opportunities in the most recent tax 

law, the MACRS schedules for all technologies use the 5-year MACRS schedule through 2023, 

then those schedules ramp back up to the schedules specified in the ATB by 2026.

Tax credits are assumed to impact financing costs. We assume lower debt fractions are available 

to technologies receiving tax credits to reflect some of the higher costs associated with financing 

these projects. The result is a higher weighted average cost of capital (WACC) when a tax credit 

(ITC or PTC) is available. Bolinger (2015) summarizes these considerations, and Mai et al. 

(2015) describe this implementation in detail.

9.2 Electric Sector Costs

Two system-wide cost metrics are calculated from each ReEDS run: a present value of direct 

electric sector system costs and electricity price. These cost calculations are not part of the 

ReEDS optimization process; they are calculated after the ReEDS optimizations have been 

conducted. The cost metrics provided directly from ReEDS do not include any environmental or 

health externalities (e.g., social cost of carbon emissions).

9.2.1 Present Value of Direct Electric Sector Cost

The present value system cost metric accounts for capital and operating expenditures incurred 

over the entire study horizon for all technology types considered, including generation, 

transmission, and storage. The cost in each future year is discounted by a 3% social discount

rate. Not to be confused with the discount rate used in the optimization for investment decisions, 

the social discount rate is selected to represent private-sector investment decisions for electric 

system infrastructure and approximates the expected market rate of return of investors. All costs

incurred prior to the start of the specified economic horizon are assumed to be sunk. The costs 

incurred from investments made near the end of the economic horizon are prorated to account 

for only the years in which the investment is used.

9.2.2 Electricity Price

One electricity price metric is a cost-of-service price with a 30-year rate base or amortization of 

all investments to 30 equal annual payments. Investments include costs for new generation 

capacity, replaced generation capacity, and new transmission lines. The output electricity price—

reported by BA—is a weighted average of the electricity prices for each time-slice, which are 

calculated differently depending on whether the region is a net importer or net exporter.
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Electricity produced from rooftop PV systems are subtracted from the load for this calculation.

The methodology uses a calibration step to match historical (2010) retail rates to consider 

distribution costs and/or the markup between wholesale and retail rates for regions with 

competitive wholesale markets. This additional cost is assumed to be uniform across all years. 

Though this metric is not indicative of actual retail prices in all regions (e.g., price impacts for 

competitive markets are not evaluated with ReEDS), it provides an indication of the price 

impacts over time. Details about how the cost-of-service prices are calculated in ReEDS can be 

found in the appendix under the “Cost-of-Service Electricity Prices” section.

There is also another methodology within ReEDS to calculate a “competitive”78 electricity price

(F. H. Murphy and Smeers 2005; Ventosa et al. 2005; EIA 2017). This calculation takes 

advantage of the linear programming formulation of the model. Specifically, the marginal price 

on a model constraint represents how much the objective function would change given a change 

in the right-hand side of the constraint. Each constraint can be viewed as a market with a 

marginal price and quantity. At optimality, the total revenue (the product of price and quantity) 

across all constraints is equal to the objective function value.

The constraints within ReEDS are written such that the marginal values from the load constraints

can be used as a proxy for the competitive electricity price. The load constraints are linked to the 

supply-demand balance constraints, capacity constraints, operating reserve constraints, and 

others79 through load variables. Taking the marginal value from the load balance constraint, we 

can find the marginal value of an additional unit of load (e.g., MWh) to the system, accounting 

for the additional energy, capacity, and operating reserves required. In other words, the reported 

competitive prices in ReEDS capture energy and capacity value. It is assumed that this 

methodology mimics a competitive electricity market’s behavior better than the cost-of-service

electricity price. The user can choose to use the competitive price, the regulated price, or a 

blending of the two to calculate national or regional electricity prices.

 78 See Murphy and Smeers (2005), Ventosa et al. (2005), and EIA (2017). 79 Specifically, state RPS constraints
89

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10ReEDS North American Expansion

As discussed in Section 5.4, ReEDS includes an exogenous representation of Canada and a 

limited endogenous representation of the Baja California region of Mexico when ReEDS is run 

only for the continental United States. However, the ReEDS model also includes the capability to 

model both Canada and Mexico endogenously. This continental version of the ReEDS model, 

which is referred to as ReEDS-North America, is described here. Country specific models can be 

run separate from the continental model as ReEDS-Canada and ReEDS-Mexico. The ReEDSCanada model includes 20 additional BAs, regions as shown in Figure 34, and 47 new wind/CSP 

resource. The Canadian BAs only cover areas where the grid is directly connected to the U.S. 

system and therefore do not include the Yukon, Northwest Territories, and Nunavut. The 

ReEDS-Mexico model includes 49 additional BAs, which are also used for defining wind/CSP 

resource regions. This representation directly aligns with the transmission regions used by 

Secretaría de Energía for Mexico (SENER) in their annual report Programa de Desarrollo del 

Sistema Eléctrico Nacional (PRODESEN). The Mexican BAs cover all regions in Mexico but do 

not include exporting generators on the Mexico-California border, which are modeled 

endogenously when modeling the United States.
90

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Figure 35. Regions defined in the ReEDS-Canada model

10.1Fuel Price and Availability

Natural gas supply and demand dynamics are represented in ReEDS through regional supply 

curves along EIA Census Division boundaries (see the section titled, Natural Gas Supply Curves

in the appendix). For Canadian provinces, the U.S.-only regional supply curves are modified 

based on consumption projections from the National Energy Board’s Canada’s Energy Future 

2016 (NEB 2016). Natural gas supply curves are modified to capture the additional supply and 

demand for natural gas in Canadian regions; adjacent Canadian provinces are assigned to EIA 

census divisions. A map of this allocation is shown in Figure 35. 
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Figure 36. Map of EIA Census Division boundaries extended to Canadian provinces

A single Mexican natural gas curve is adapted from the WSC census division in the AEO. 

Mexico-specific natural gas consumption is taken from PRODESEN 2018 and incorporated in 

the new gas curve. Prices were inflated by 9% for Mexico relative to WSC to reflect the higher 

price of natural gas in Mexico, based on Henry Hub prices from 2010 to 2015.

Canadian coal prices are provincial and represented as inelastic to changes in consumption; they 

are consistent with Canadian provincial coal price projections from NEB (2016). The price of 

nuclear fuel for Canadian units is assumed to be same as those for nuclear units in the United 

States.

Mexican coal and uranium prices are matched with the WSC census division in the AEO and 

are inelastic.

10.2Electricity Demand

Canadian annual electricity demand projections are based on the NEB (2016) reference scenario, 

and they reflect annual provincial load reduced by customer-sited electricity cogeneration. 

Provincial and zonal load shapes are described in Martinez et al. (2013). For provinces composed 

of multiple ReEDS BAs (Alberta, British Columbia, Ontario, and Quebec), the spatial distribution 

of annual provincial electricity demand among BAs is also described in Martinez et al. (2013).

Mexican load shapes and annual demand projections are based on 2016 PRODESEN. The 

PRODESEN load projection extends through 2030. For projections beyond 2030 load growth is 

projected based on the average load growth for the years 2020–2030. Load shape data in 

PRODESEN are available for nine regions that were mapped to the ReEDS BA representation 

through provided load participation factors.
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10.3 Generation Fleet Representation

Consistent with the ReEDS representation of the generation fleet in the contiguous United States, 

the ABB Velocity Suite is used as a basis for Canadian and Mexican generation units (ABB 

2018). This data set is then cross-referenced with NEB (2016) for Canada, PRODESEN for 

Mexico, and any Canadian provincial energy plans to represent the Canadian fleet. The existing 

fleet, prescribed new builds, and prescribed retirements are based on the ABB Velocity Suite 

database. Additionally, any new hydropower or geothermal capacity projected in NEB (2016) 

and PRODESEN is also included in ReEDS (ReEDS does not include hydropower or geothermal 

resource supply curves for Canada or Mexico, and thus cannot represent new builds of those 

technologies endogenously).

There are currently 18,215 MW of existing transmission capacity between the United States and 

Canada from a combination of AC and DC lines dispersed across the border. Potential new 

transmission lines between the United States and Canada are derived from a list of pending 

applications as of January 2016 (DOE 2016a). Lines that are not yet under construction are 

allowed to be built in the ReEDS-Canada model, but they are not prescribed. 

There are currently 2,238 MW of existing transmission capacity between the United States and 

Mexico, which include DC lines connecting Texas and Mexico and the AC lines connecting 

southern California to Mexico. Potential new transmission lines between the United States and 

Mexico are limited to reinforcements of existing cross-border connections. New connections 

specified in PRODESEN, including the DC interconnection between the main Mexican system 

asynchronous Baja California system and the Baja California Sur system, are allowed but not 

forced to be built in the ReEDS-Mexico model.

Transmission lines in Canada and Mexico are assumed to have the same cost as their 

neighboring regions in the United States. Table 24 summarizes pending cross-border 

transmission lines as of January 2016.
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Table 26. Pending Cross-Border Transmission Lines as of January 2016 (DOE 2016a)

Name Sponsor State-Province Length

(miles)

Voltage and

Capacity

Purpose In-service 

Date

Permit Status

Champlain 

Hudson Power 

Express

Transmission 

Developers Inc.

New YorkQuébec333 1,000 MW,

HVDCa

(underwater, 

underground, 

merchant)

Deliver hydro and 

wind energy from 

QC to New York 

City area

Fall 2017

(expected)

Permit issued 

October 2014

Great Northern 

Transmission 

Line

Minnesota 

Power (MP)

MinnesotaManitoba220 500 kV, 

750 MW, AC

Part of MP-MB 

Hydro PPA; 

supports building 

wind in North 

Dakota

June 2020

(expected)

Application filed 

April 2014

Lake Erie 

Connector

ITC PennsylvaniaOntario72.4 1,000 MW, 

HVDC 

(underwater, 

merchant)

Deliver surplus ON 

renewable energy, 

enhance service 

reliability

Q4 2019 Application filed 

May 2015

New England 

Clean Power 

Link

TDI-New 

England

VermontQuébec

154 1,000 MW, 

HVDC 

(underwater, 

underground, 

merchant) 

Deliver renewable 

energy from QC into 

VT and New 

England

2019

(expected)

Application filed 

May 2014

Northern Pass Northern Pass 

Transmission 

LLC

New HampshireQuébec187 1,200 MW, 

HVDC line with 

345 kV AC spur 

Deliver QC hydro 

into NH and New 

England

2017

(expected)

Application filed 

October 2010; 

re-filed with new 

route July 2013

Soule River 

Hydroelectric 

Project

Soule Hydro, 

LLC

Alaska-British 

Columbia

10 138 kV, HVAC

(submarine)

Support 77 MW 

hydro project in AK 

(sales to BC or 

Pacific NW)

To be 

determined

Application filed 

March 2013

a high-voltage, direct current
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10.4Renewable Energy Representation

10.4.1 Hydropower Generation Capacity

ReEDS is not allowed to endogenously build new hydropower capacity in Canada or Mexico due 

to a lack of available hydropower supply curves that are compatible with those developed for the 

United States. Instead, we rely on a schedule of expected builds from NEB (2016) for new 

Canadian hydropower units. Though annual hydropower output can fluctuate significantly from 

year to year based on precipitation and other weather patterns, we assume there is no inter-annual 

variation in available hydropower generation in ReEDS; rather, average capacity factors are 

assumed that reflect long-run historical averages. Seasonal hydropower capacity factors are 

specified for each Canadian province and are derived from NEB (2013).

In Mexico, existing hydropower capacity is represented, but no new prescribed capacity is added 

to the system. Operating characteristics are determined from the historical fleetwide capacity 

factor from PRODESEN.

10.4.2 Wind Resource

For Canada and Mexico the onshore-wind resource is represented using the same methodology 

described in Section 2.1.1. Using reV, the quality, capacity, and spur line costs are calculated and 

represented within the model at the wind resource region level. Wind turbine performance is 

modeled using results from the WIND Toolkit. Toolkit data include complete coverage of 

Mexico and Southern Canada. Meteorological data for northern Canada are supplemented using 

NASA’s MEERA-2 data set. Resource calculations are preformed using 2012 data; however, the 

methodology flexibly supports alternate weather years.

Offshore-wind technology is not represented as an available resource for Canada or Mexico

because a comprehensive resource assessment has not been completed.

10.4.3 Solar Resource

UPV and DUPV resources are calculated for Canada and Mexico using the same approach 

described in Section 2.1.3. The quality, capacity, and spur line costs are calculated using reV and 

represented within the model for BA regions. The performance of PV panels is based on

insolation data from the NSRDB 2.0 using a 2012 metrological year.

The deployment of residential and commercial rooftop PV systems is exogenously defined in for 

the United States using output from NREL’s dGen model (see Section 2.1.3). Because this model 

is not currently designed to capture consumer behavioral dynamics in Canada and Mexico, we 

assume no rooftop PV deployment outside the United States. Direct normal irradiance

throughout Canada is below the threshold necessary to produce economic CSP generation, and 

thus is not represented in Canada. Though the direct normal irradiance is high in Mexico, 

comprehensive resource data for CSP in Mexico have not been developed due to its expected 

competitive disadvantage relative to PV. Thus CSP is not represented as an available resource in 

Mexico.

10.4.4 Geothermal Resource

Geothermal technologies are not available for development outside the United States due to a 

lack of sufficient information on the location and potential of geothermal resources. Mexico does 
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currently produce electricity from geothermal technology, and any existing capacity is included 

in ReEDS-Mexico. 

10.4.5 Biomass Resource

The biomass feedstock supply curve for each Canadian BA is assumed to be the same as that of 

the contiguous BAs in the United States (scaling to account for differences in area). Biomass is 

not available as a resource or generation technology in Mexico because resource data are

unavailable. Future resource assessments for biomass feedstocks are needed to improve these 

representations. 

10.5Policy and Regulation

ReEDS represents existing RPS requirements for:

• New Brunswick: 40% by 202080

• Nova Scotia: 5% by 2011, 10% by 2013, 25% by 2015, and 40% by 2020 (Nova Scotia 

Department of Energy 2010)

• Prince Edward Island: 15% by 2010 (Department of Environment, Energy and Forestry, 

n.d.).

Canadian contributions to U.S. state RPS (and vice versa) are permitted in alignment with 

existing laws, regulations, and practices as of October 2014. We list the rules for U.S.-Canada 

REC trading in Table 25, focusing on U.S. states that are physically or electrically adjacent to a 

Canadian province, or where international REC trading activity has been observed in practice.

 80 “Provincial Government Strengthens its Commitment to Renewable Energy,” news release dated August 7, 2014 

http://www2.gnb.ca/content/gnb/en/news/news_release.2014.08.1007.html. 
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Table 27. International REC Trading Rules Implemented in ReEDS

State/Province Canadian RECs 

Accepted?

Notes

California Yes Power must be delivered to state

Colorado No —

Massachusetts Yes Power must be delivered to ISO-New Englanda

Maine No —

Michigan No —

Minnesota Yes Power must be delivered to state from Manitoba

Montana No —

Nevada No —

New Hampshire No —

New York No —

Nova Scotia Yes RECs accepted from New England Power Pool

Ohio No —

Oregon Yes Subject to same bundling rules as U.S. states

Pennsylvania No —

Rhode Island Yes Power must be delivered to ISO-New England

Washington Yes Power must be delivered to state

Wisconsin Yes Power must be delivered to state

a ISO = independent system operator

British Columbia has a carbon tax that is applied to in-province electricity generation (British 

Columbia Ministry of Finance 2015). This tax is also applied to imported electricity; in this case, 

the average carbon intensity of delivered electricity is endogenously calculated from the 

imported generation mix in the previous solve year81 and taxed appropriately. We also 

implement the British Columbia 2016 requirement for electricity self-sufficiency82 (Clean 

Energy Act of 2010 2010).

New Canadian coal units that do not have carbon capture and storage are not in allowed in the 

model beginning in the 2016 solve year. Any early retirements of existing coal units are reflected 

in the exogenous retirements of Canadian coal units.83

The Mexico clean energy standard of 35% of load by 2024 is also modeled. (Presidencia de la 

República 2016) Renewable energy and nuclear power are allowed to contribute to this policy, 

while CCS is excluded.

 81 This calculation uses a proportional sharing construct adapted from Bialek (1996). 82 The requirement is structured such that plants within British Columbia must generate as much energy as is 

required to serve load within the province.

83 For details about the electricity regulations for coal units in Canada, see “Coal-Fired Electricity Generation 

Regulations: Overview,” Government of Canada, modified December 16, 2013,

https://ec.gc.ca/cc/default.asp?lang=En&n=C94FABDA-1.
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11Model Linkages

11.1ReEDS-dGen

All ReEDS scenarios utilize dGen to project rooftop PV deployment, but the two models are also 

capable of operating in a more tightly coupled manner with two-way feedback in each solve 

year. Under this arrangement, ReEDS provides dGen with estimated curtailment of rooftop PV 

resources, which dGen uses to update its projected rooftop PV deployment for the following 

solve year. This coupled configuration improves rooftop PV projections by disincentivizing new 

capacity that would achieve low utilization within the interconnected bulk system modeled in 

ReEDS. Additional details on this linkage and its influence on results can be found in (Cole, 

Lewis, et al. 2016).

11.2ReEDS-PLEXOS

The ReEDS reduced-form dispatch and variable renewable parameterization aims to represent 

enough operational detail for realistic capacity expansion decisions, but the model cannot 

explicitly represent detailed power system operations. To verify the feasibility of ReEDS 

solutions and better inform its representation of system operation, NREL has developed utilities 

to implement a ReEDS capacity expansion solution for any solve year in the PLEXOS 

production-cost model (PCM).

PLEXOS is a commercial PCM tool capable of representing individual generating units and 

transmission nodes for least-cost dispatch optimization at hourly or subhourly time resolution. It 

can incorporate unit-commitment decisions and detailed operating constraints (e.g., ramp rates, 

minimum runtime) to simulate realistic power system operations. NREL has previously used 

PLEXOS in several analyses such as the Western Wind and Solar Renewable Integration Study 

and the Eastern Renewable Grid Integration Study (Lew et al. 2013; Bloom et al. 2016a). 

The ReEDS-PLEXOS linkage involves disaggregating the ReEDS solution and adding necessary 

parameters for to the resolution necessary for PLEXOS. To facilitate the translation, the existing 

linkage maintains the spatial resolution of ReEDS and operates PLEXOS as a zonal model 

matching ReEDS BAs to PLEXOS transmission zones. PLEXOS uses the ReEDS transmission 

line capacity, and reactance and resistance are calculated from ReEDS transmission properties to 

represent the aggregated transmission system. Generating capacity within each zone is, however, 

converted from aggregate ReEDS capacity to individual units in PLEXOS using a characteristic 

unit size for each technology. For consistency, ReEDS cost and performance parameters are used 

when possible and reasonable, but values are taken from the average across WECC data when

parameters are not available from ReEDS or are available but used inconsistently in ReEDS due 

to structural differences between the models.

84

Once the ReEDS solution is converted to a PLEXOS database, we can simulate hourly dispatch 

over a full year and compare results with ReEDS outcomes. A consistent solution builds 

confidence in the effectiveness of ReEDS capacity expansion decisions, while inconsistencies 

 84 Minimum load is an example of one such parameter. The aggregate representation of minimum load in ReEDS at 

the technology-BA level does not effectively reflect unit-level operating constraints used in PLEXOS, so PLEXOS 

uses native assumptions for minimum load.
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and reliability concerns such as load shedding indicate the need for improving capacity 

expansion model structures.

Additional discussion of this capability is forthcoming in ongoing analyses where detailed 

mechanics and results will be discussed.

11.3 ReEDS-JEDI

A linkage between ReEDS outputs and the Jobs and Economic Development Impact Models

(JEDI) allows the analysis of technology-specific economic results (jobs, earnings, value added, 

total output) to ReEDS scenarios (“NREL: Energy Analysis - Jobs and Economic Development 

Impact (JEDI) Models” n.d.). Currently, linkages have been built for the JEDI land-based wind, 

photovoltaics, natural gas, and coal models, so economic results are limited to these technologies 

alone. ReEDS outputs of capacity, generation, fuel use, capital cost, O&M cost, and fuel cost by 

BA are processed through the JEDI models to produce state-level economic results.
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12References

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Beiter, Philipp, and Tyler Stehly. 2016. “A Spatial-Economic Cost-Reduction Pathway Analysis 

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Appendix

Natural Gas Supply Curves

The ReEDS model does not explicitly model the U.S. natural gas (NG) system, which involves 

multiple sectors of the economy and includes complex infrastructure and markets. Rather, a 

regional supply curve representation is a used to approximate the NG system as it interacts with 

the electric sector. For more information on the impact of natural gas representation in ReEDS,

see Cole et al. (2016).

The premise of using regional supply curves is that the price in each region will be a function of 

both the regional and national NG demand. The supply curves are parameterized from AEO 

scenarios for each of the nine EIA census divisions (see Figure A-1). 

Figure A-1. The nine census divisions defined by EIA (EIA 2014)

A regional supply curve was created in ReEDS for each of these census divisions.

The AEO scenarios were used to estimate parameters for the following NG price-consumption 

model:

[1]

where Pi,j is the price of natural gas (in $/MMBtu) in region i and year j, the α parameters are the 

intercept terms of the supply curves with adjustments made based on region (αi), year (αj), and 

the region-year interaction (αi,j), βnat is the coefficient for the national NG demand (Qnat, in 

quads), βi is the coefficient for the regional NG demand (Qi,j) in region i. Note that the four α 

parameters in [1] can in practice be represented using only αi,j. 

The β terms are regressed from AEO2014 scenarios, with nine of the 31 AEO2014 scenarios 

removed as outliers (EIA 2014). These outlier scenarios typically include cases of very low or very 

high natural gas resource availability, which are useful for estimating NG price as a function of 

supply but not for estimating NG price as a function of demand—for given supply scenarios. The 

, ,, j Pi j =+ + + + + αα α α β β i j i j nat nat i i j Q Q
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national and regional β terms are reported in Figure A-2. We made a specific post-hoc adjustment 

to the regression model’s outputs for one region; the βi term for the West North Central division 

was originally an order of magnitude higher than the other βi values because the West North 

Central usage in the electricity sector is so low (0.05 quad85 in 2013, compared to ~0.5 quad or 

more in most regions). The overall natural gas usage (i.e., not just electricity sector usage) in West 

North Central is similar to the usage in East North Central, so intuitively it makes sense to have a βi

for West North Central relatively close to that of East North Central. We therefore manually 

adjusted the West North Central βi term to be 0.6 (in 2004$/MMBtu/quad) and recalculated the 

alpha terms with the new beta to achieve the AEO2014 target prices. The situation in West North 

Central whereby such a small fraction of NG demand goes to electricity is unique; we do not 

believe that the other regions warrant similar treatment.

Figure A-2. β values for the nine census divisions

The “National” value at the far left is βnat. A β of 0.2 means that if demand increases by one quad, 

the price will increase by $0.20/MMBtu (see Equation [1]).

The α terms are then regressed for each individual scenario assuming the same β values for all 

scenarios. Although the β terms are derived from AEO2014 data, α terms are regressed using 

AEO2018 data for the scenario they are intended to represent (EIA 2018). Thus, we assume 

natural gas price elasticity has remained constant while price projections shift over time as 

represented by the α values.

Comparison to Literature Values

Technical literature tends to report the price elasticity of supply and the price elasticity of 

demand, which are estimates of the supply and demand, respectively, of a good given a change 

in price. In the formulation given by Equation [1], we attempt to estimate a value that is similar 

to the price elasticity of demand—we estimate a change in price given a change in demand. 

Therefore we present here a comparison against the price elasticity of demand as the closest 

available proxy, noting however that it is not necessarily identical to estimates of β. Price 

elasticity of demand is typically negative but is reported here as a positive number for 

convenience.

 85 A quad is a quadrillion Btu, or 1015 Btu.
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External sources are varied and often vague in their estimates of price sensitivity of natural gas. 

Using the reported domestic NG market demand given for 2012 in AEO2014, the β values 

reported here yield an overall NG sector elasticity value of 0.36–0.92 (higher values of β 

correspond to lower elasticity values). Arora (2014) estimated the price elasticity of demand for 

NG to be 0.11–0.70, depending on the granularity and time horizon of the NG price data 

considered. Bernstein and Griffin (2006) examined the price elasticity of demand for residential 

NG usage, and they estimated the long-run elasticity to be 0.12–0.63 depending on the region. 

The Energy Modeling Forum at Stanford University reports NG price elasticity of demand for 13 

different energy models (Huntington 2013). The reported elasticity ranges from 0 to 2.20

depending on the year, model, and scenario considered. For the NEMS model, which is used for 

the AEO, the elasticity ranges from 0.22 to 0.81 depending on the year and scenario (Huntington 

2013).

The EPA’s proposed Clean Power Plan included a projection that natural gas usage will increase 

by 1.2 quads in 2020, resulting in an 8%–12% increase in NG prices for the electric sector 

(Smith 2014). This corresponds to a βnat of 0.38–0.51 in 2004$/MMBtu/quad.

Seasonal Natural Gas Price Adjustments

We use natural gas futures prices to estimate the ratio of winter to non-winter natural gas prices

to implement seasonal gas price differences in ReEDS. We chose futures prices for two reasons: 

(1) ReEDS represents a system with no unforeseen disturbances, which is similar to futures 

prices and (2) historical natural gas prices have fluctuated greatly since the deregulation of 

natural gas prices.

Figure A-3 shows the cyclical nature of the natural gas futures prices. Figure A-4 breaks the 

same prices out into seasons, showing that the non-winter seasons have nearly the same price 

while wintertime prices are consistently higher. Wintertime prices are on average 1.054 times 

higher than non-winter prices. The standard deviation of this price ratio is 0.004, indicating that 

the ratio shows very little year-to-year variation.

Figure A-3. Natural gas futures prices from the New York Mercantile Exchange for July 10, 2014

The prices show the higher wintertime prices and the cyclical nature of the prices.

0

1

2

3

4

5

6

7

2014 2016 2019 2022 2024 2027

Natural Gas Futures Price 

($/MMBtu)

Year
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Figure A-4. Natural gas futures prices from Figure A-3 separated by season

Non-winter prices are nearly the same while wintertime prices are consistently higher.

A seasonal natural gas price multiplier is calculated in ReEDS based on the natural gas price 

ratio such that wintertime prices are 1.054 times higher than non-winter prices without changing 

the year-round average price. Mathematically, this can be expressed as

PPyyyyyyyy− rrrrrrrr = WWwwww ww PPwwww ww + (1 − WWwwww ww )PP −wwww [2]

PPwwww ww = 1.054PP −wwww ww [3]

PPwwww ww = ρρPPyy − rrrrrrrr [4]

PP −wwww ww = σσPPyyyyyyyy− rrrrrrrr [5]

where P is the natural gas price for the period indicated by the subscript, Wwinter is the fraction of 

natural gas consumption that occurs in the winter months, and ρρ and σσ are the seasonal 

multipliers for winter and non-winter, respectively. The multipliers ρρ and σσ are determined by 

solving Equations [2] through [5].

Capital Cost Financial Multipliers

The following equations are used to calculate the financial multipliers applied to ReEDS capital 

costs. The parameters may vary by technology or year as applicable.

0

1

2

3

4

5

6

7

2014 2016 2018 2020 2022 2024 2026 2028

Natural Gas Futures Price 

($/MMBtu)

Year

Winter

Spring

Summer

Fall
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Parameters

• dd : nominal discount rate

• dddd: debt fraction

• rrrrrrrr : nominal rate of return on equity (RROE)

• TT: tax rate

• II : nominal interest rate on debt

• CCCCmm mm : construction cost multiplier

• IIcc cc : nominal interest rate on debt for construction financing

• cc : construction fraction in year t

• PPVV dd : present value of depreciation

• dd : depreciation fraction in year t

• II : investment tax credit

The financial multiplier represents the present value of revenue requirements necessary to

finance a new investment, including construction financing, return to equity holders, interest on 

debt, taxes, and depreciation. The formula is based on (W. Short, Packey, and Holt 1995).

FFFFFFFFFFFFFFFFFF MMMMMMMM MMMM = CCCCmm mm ∗

1 − TT ∗ PPVV dd ∗ �1 − II

2 � − II

1 − TT

The construction cost multiplier captures the cost to finance the construction of the plant. We use 

a mid-year discounting and account for the deduction of interest payments for taxes.

CCCCmm mm = �1 + (1 − TT) ∗ �IIcc cc +0.5

� ∗ cc cc

The present value of depreciation is computed based on the fraction of the plant value that is 

depreciable in each year. All investments use a MACRS depreciation schedule.

PPVV dd = � 1

(1 + dd ) ∗ dd

The following term accounts for the fact that the eligible cost basis for MACRS is reduced by 

one-half the value of the tax credit:

PPVV dd ∗ �1 − II

2 �

The nominal discount rate is represented as the WACC.

dd = (1 − dddd) ∗ rrrrrrrr + (1 − TT) ∗ dddd ∗ II
115

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Present Value of Direct Electric Sector Cost

The following equations are used to calculate the present value cost of building and operating the 

system for some defined economic analysis period. The cost in each future year is discounted to 

the initial year of the economic analysis period, , by a social discount rate, dd.

86 In the 

equations, PPVV represents the present value cost to operate the system for the analysis period, 

including fixed and variable operating and maintenance costs, and fuel costs.87 The second term,

PPVVcc1, represents the cost of new capital investments made at least 20 years prior to the end of the 

analysis period, ff. The third term, PPVVcc2, represents the cost of new capital investments from the 

last 18 years of the analysis period. For investments that will last beyond the end of the model 

horizon, the cost is reduced by the weighting factor. 

“New capacity” in PPVVcc1 and PPVVcc2 includes the capital cost88 for new generation and storage 

capacity installed in each year, including rebuilt renewable capacity. Grid interconnection costs 

for new generators are also included. ReEDS does not consider any lifetime-based replacement 

of existing or new transmission infrastructure.

Parameters

• : initial year of economic analysis

• ff: final year of economic analysis

• dd: social discount rate = 0.03

• : economic lifetime = 20 years

• CCCC : capital recovery factor

• CC , : operational costs in year t

• CCcc , : capital costs in year t

• PPVV : present value of operational costs

• PPVVcc1: present value of capital investments made at least 20 years prior to tf

• PPVVcc2: present value of capital investments made within 18 years of tf

Sets

• TT: set of optimization years:{2010,2012,...,2100}

• TT ⊆ TT : � | ≥ ∩ ≤ ff�: set of years t between to and tf

• TTcc1 ⊆ TT :� | ≥ ∩ ≤ ff − 20� : set of years t that are at least 20 years prior to tf 

• TTcc2 ⊆ TT ∶ � | ≤ ff − 18 ∩ ≤ ff�: set of years t that are within 18 of tf

 86 The real discount rate used here represents the social discount rate and is not the same as the discount rate used in 

the investment decisions within the optimization. 

87 Operation costs include the marginal price of fuel. 88 The capital costs are not overnight capital costs; the costs account for capital financing (e.g., construction 

financing, interest on debt, and return to equity), but they do not include investment tax credits.
116

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We discount operational costs from model year t by 1

(1+ )tt−ttoo for the all model years in TT and 

we multiply by two to account for the operational costs of non-modeled years:

PPVV = 2 ⋅ �

ff

∈TToooo

CC , ⋅ 1

(1 + dd) − oo

We discount investments made at least 20 years prior to the ff by 1

(1+ )tt−ttoo:

PPVVcc1 = �

∈TTcc1

CCcc , ⋅ 1

(1 + dd) − oo

We scale investments made within 18 years of ff to only account for years that the investment is 

utilized. We then discount these scaled investments by 1

(1+ )tt−ttoo:

PPVVcc2 = �

∈TTcc2

CCcc , ⋅ ss ∙ 1

(1 + dd) − oo

We define the scaling factor as the ratio of the capital recovery factor for the full economic 

lifetime n to the capital recovery factor for the number of years that the investment is used (i.e., 

ff + 1 − ). Note that we add 1 to ff to correctly calculate the number of years that the capacity 

is used. For example, assuming a 20-year economic lifetime and an analysis period out to 2050, 

investments made in 2031 will be paid off by 2050, which is represented by the 2050 solve year. 

So, ff + 1 − = 2050 + 1 – 2031 = 20, gives the correct number of years the investments made 

in 2031 are utilized in the analysis horizon:

ss = CCCC (dd, )

CCCC (dd, ff + 1 − )

CCCC (dd, ) = dd

1 − 1

(1 + dd)

Cost-of-Service Electricity Prices

Sets

• n, n’: balancing area

• m, m’: time-slice

• q: technology

• t: year
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Parameters

• PP ,mm, ee : cost-of service for net exporter region ‘n’ in time-slice ‘m’ in year ‘t’ [$/MWh]

• ,mm,

gg : average cost of local generation in region’n’ in time-slice ‘m’ in year’t’ [$/MWh]

• , : non-generation transaction cost in year ‘t’ [$/MWh]

• PP ,mm, : cost-of service for net exporter region ‘n’ in year ‘t’ [$/MWh]

• ee ,mm, : exports from region ‘n’ in time-slice ‘m’ in year ‘t’ [MWh]

• qq, ,mm, : generation from technology ‘q’ in region ‘n’ in time-slice ‘m’ in year ‘t’ 

(note: generation from rooftop PV is not included in this term) [MWh]

• dd ,mm, : load in region ‘n’ in time-slice ‘m’ in year ‘t’ (note: loadn,m,t is the total load 

minus rooftop PV generation) [MWh]

• ,mm, : average of cost of imported generation in year ‘t’ [$/MWh]

• ,

rr : total rate base cost in region ‘n’ in year ‘t’ [$]

• , : total O&M cost in region ‘n’ in year ‘t’ [$]

• ,

ff : total fuel cost in region ‘n’ in year ‘t’ [$]

• , : total REC cost for state RPS compliance in region ‘n’ in year ‘t’ [$]

• ,

ww ww : total water cost in region ‘n’ in year ‘t’ [$]

• ,

cc : total allowance/emissions rate credit (ERC) cost for Clean Power Plan (CPP)

compliance in region ‘n’ in year ‘t’ [$]89

• n′, ,mm, := net transmission flow from Canada to region ‘n’ in year ‘t’ [$]

• n', , : cost of transmission from region n’ to region ‘n’ in year ‘t’ [$/MWh]

• rrbb , : ratebase owed in region ‘n’ in year ‘t’[$]

• rrbb , rr: ratebase remaining in region ‘n’ in year ‘t’[$]

• dd: discount rate

• rrbb : initial ratebase (in year to) [$]

• ii : investment made in region ‘n’ in year ‘t’ [$]

• ,mm,

wwh

: average wheel cost in region ‘n’ in time-slice ‘m’ in year ‘t’ [$/MWh]

• , ′,mm: upstream power fraction; fraction of power originating in region n’ that flows 

to region ‘n’ [unitless]

 89 This term is not included by default but contributes if the CPP policy representation is activated.
118

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The retail cost-of-service price for a net exporter region—where generation exceeds load—is the 

average cost of local generation, which is adjusted for non-generation transaction costs with a 

markup from wholesale to retail:90

PP ,mm, ee = ,mm,

gg + ,

Alternatively, the retail cost-of-service price for a net importer region—where load exceeds 

generation—is a weighted average of costs from local and imported generation, which is also 

adjusted for non-generation transaction costs:

PP ,mm, = ∑qq� qq, ,mm, � ∙ ,mm,

gg + ee ,mm, ∙ ,mm,

dd ,mm,

+ ,

The average cost of local generation is the total cost of local generation averaged over the total 

electricity generated. Total costs include the rate base cost (additional cost to consumers to cover 

a utility’s regulated rate of return), fixed and variable O&M (including PTC), fuel (including

CO2 tax), RPS shortfall compliance cost, water cost, Clean Power Plan compliance costs if 

applicable (allowances, ERCs), Canadian imports, and rooftop PV cost:

,mm,

gg = � ,

rr + , + ,

ff + , + ,

ww ww + ,

cc �

∑ � qq, ,mm′� qq,mm′

+ "can", ,mm, ∙ "can", ,

∑qq� qq, ,mm, �

The costs associated with the rate base include the annual payments owed and the “interest” on 

the remaining rate base, which is based on the discount rate, d, corresponding to the investment 

discount rate:

rr, , = rrbb , + rrbb ,

mm ∙ dd

The annual rate base payment owed includes the payment from initial rate base and payments 

from new investments since the initial year. We assume the investments for the initial rate base 

are spread equally from 1980 to 2009. Therefore, by 2010, the investment in 1980 is fully 

depreciated by 2010; by 2040, the entire initial rate base is depreciated. The initial rate base is 

scale by �1 − − oo

30 � to account for implicit payments made prior to 2010:

rrbb , = 1

30 ∙ rrbb ∙ �1 − −

30 � +

1

30 ∙ � ii ′

′

=max( , −29)

∀ ≤ + 30

 90 The non-generation transaction cost defined as the difference between the 2010 historical retail price and the 

estimated wholesale cost-of-service price in the 2010 model year.
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rrbb , = 1

30 ∗ � ii ′

′=max( oo, −29)

∀ > + 30

The remaining rate base is calculated from the remaining rate base in the prior year, adding in 

new investments, and deducting payment owed from initial rate base:

rrbb , rr = rrbb , −1 rr + − rrbb ,

The average cost of imports to region is the blending of the cost of Canadian power imports and 

the wheeling cost of all other imported generation, including the levelized cost of new 

transmission.

91 These costs are weighted by net transmission flows:

,mm, = "can", ,mm, ∙ "can", , + ∑ � ′, ,mm, ∙ � ,mm,

wwh

+ n′, , ′ ��

"can", ,mm, + ∑ � ′ ′ , ,mm, � + ,

The weighted average wheeling cost of imported generation is the blending of local generation 

costs from all regions where the imports originate. The local generation costs are weighted by 

the fraction of the total imports that was generated at the upstream origin (i.e., upstream 

power fraction:

,mm,

wwh = � ′,mm,gg ∙ , ′,mm

′

Marginal Electricity Prices

ReEDS marginal “competitive” electricity prices are derived from the linear programming 

formulation.

In standard form, the primal formulation of a linear program is:

(PP) min TT

s. t. AA = bb

≥ 0

The associated dual formulation of the primal is:

(DD) max yyTTbb

s.t. yyTTAA ≤ TT

Consider a simplified formulation of the ReEDS model with a subset of constraints: (1) resource

limits, (2) capacity limits, (3) supply/demand balance, (4) planning reserve margin requirement, 

and (5) operating reserve requirement. The primal formulation is:

 91 The levelized cost of new transmission connect two regions is the annualized cost of transmission (line + 

substation) connecting the regions averaged over the net energy flow across the line.
120

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Parameters

• : capital cost of model plant i ($/MW)

• mm : variable O&M cost of model plant i ($/MWh)

• ss : available supply of model plant i (MW)

• : electric load (MW)

• : capacity value of model plant i (MW)

• : planning reserve margin (unitless)

• : operating reserve requirement (unitless)

Variables

• CC : capacity of model plant i (MW)

• GG : generation of model plant i (MWh)

• OO : operating reserve allocation of plant i (MWh)

mm mm mm � ∙ CC + ∙ GG

Subject to:

CC ≤ ss ∀ [1]

GG

8760 +

OO

8760 − CC ≤ 0 ∀ [2]

�GG = [3]

� ∙ CC ≥ (1 + )

8760 ∙ [4]

�OO ≥ ∙ [5]

CC , GG ,OO ≥ 0 ∀ [6]

Constraints [1] define the resource limits for each model plant. Constraints [2] limit how 

capacity is allocated for each model plant (i.e., for energy or reserves). Constraint [3] requires 

the total generation supplied to equal the load. Constraint [4] ensures the total firm capacity 

meets the planning reserve margin requirement. Constraint [5] ensures the total operating 

reserves meet the operating reserve requirement.

From the dual formulation of the primal, the objective function is:

yyTTbb = yy1 ∙ ss + yy2 ∙ 0 + yy3 ∙ + yy4 ∙

(1 + )

8760 ∙ dd + yy5 ∙ ∙
121

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Reformulating the primal with Constraints [3], [4], and [5] “linked” with a “load” variable, L, an 

alternative, but equivalent, primal formulation is the following:

mm mm mm � ∙ CC + ∙ GG

Subject to:

CC ≤ ss ∀ [1]

GG

8760 +

OO

8760 − CC ≤ 0 ∀ [2]

�GG − LL ≥ 0 [3′

]

� ∙ CC − (1 + )

8760 ∙ LL ≥ 0 [4′

]

�OO − ∙ LL ≥ 0 [5]

CC , GG ,OO ≥ 0 [6]

LL = [7′]

From the dual formulation of the alternative primal, the objective function is:

yyTTbb = yy1 ∙ ss + yy2 ∙ 0 + yy3′ ∙ 0 + yy4′ ∙ 0 + yy5′ ∙ 0 + yy7′ ∙

Equating the dual objective functions from the two equivalent primal formulations, we find that 

the marginal off the linking constraint [7’] is a blending of all constraints containing the “load”

variable, including, constraints [3], [4], and [5]:

yy7′ ∙ = yy3 ∙ + yy4 ∙

(1 + )

8760 ∙ + yy5 ∙ ∙

yy7′ = yy3 + yy4 ∙

(1 + )

8760 + yy5 ∙

Therefore, we define the marginal off the linking constraint [y7′ ] as the “all-in” marginal price of 

electricity (i.e., change in total cost [objective function] given a small change in load). This 

marginal electricity price includes the energy price, capacity price and ancillary service price.

Clean Power Plan

As noted in Section 8.1.1, the Clean Power Plan and the associated Clean Energy Incentive 

Program are inactive by default. However, ReEDS is able to model these policies in detail if 

desired. 

Existing Source Performance Standard

The EPA’s Clean Power Plan, promulgated through Section 111(d) of the Clean Air Act, is 

designed to reduce CO2 emissions to 32% below 2005 levels by 2030 through rate- and massbased standards for existing sources. Existing sources are defined as any fossil fuel-fired steam-
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generating unit that commenced construction as of January 8, 2014. In ReEDS, we identify these 

affected fossil generators as existing source pollutant technologies. Generating units that 

commenced construction after January 8, 2014 (or are built endogenously by ReEDS) are 

considered new source pollutant technologies.

The CO2 emission targets for the interim (2022–2029) and final (2030) compliance periods are 

taken from the EPA technical support documents (U.S. Environmental Protection Agency 2015). 

For regulatory purposes, compliance with the Clean Power Plan with these emission targets must 

be met on the state level, though trading across states is permitted by the rule.92 No Clean Power 

Plan emissions limits are enforced in the pre-compliance years (prior to 2022); however, ReEDS

uses foresight of the future cost of CO2 emissions to inform decisions for new investment.

93

Beyond 2030, we assume the targets remain constant at the 2030 level (the Clean Power Plan 

does not specify what the targets will be post-2030).

For mass-based compliance94 in ReEDS, available allowances must meet or exceed CO2

emissions from sources that are covered by the Clean Power Plan. Total available allowances in 

a given year are the sum of the following allowance streams: (1) allowances initially allocated to 

generators (equal to the mass-based target for that year), (2) excess allowances from prior 

compliance years, and (3) early action allowances (described below).

95 We assume the 

allowances are purchased from an auction at the marginal CO2 price, which is equal to the 

shadow value of the model Clean Power Plan emissions constraint. The cost of allowances is 

incorporated into the electricity price.

For rate-based compliance in ReEDS, available ERC must offset the total ERCs required. 

Available ERCs are created from:

• Over-complying covered sources (i.e., sources with emission rates below their 

compliance targets)

• Shifts from coal to natural gas resources96

 92 The default representation of the Clean Power Plan in ReEDS allows for national trading so that some states can 

under-comply with the rule as long as other states over-comply (Cole, Mai, et al. 2016). Accordingly, this 

documentation focuses on that case. However, ReEDS has the capability to model any scope of compliance trading 

along spatial and technological dimensions. 93 These future CO2 emissions costs are exogenously specified but were calibrated based on several iterations of 

model runs.

94 Mass-based compliance is based on the mass of CO2 emitted, while rate-based compliance is based on the CO2

emission rate. 95 We define excess allowances in this context to consist only of allowances allocated through the mass-based 

targets. In practice, early-action allowances are also banked for use in future model years and are treated 

equivalently to excess allowances. Banked allowances (both excess and early-action) are allocated equally over 

future model years through 2030.

96 These ERCs are referred to as “gas-shift” ERCs in the EPA-proposed Federal Implementation Plan and are only 

applicable under subcategory rate compliance (U.S. Environmental Protection Agency 2016).
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• Qualifying zero-emitting resources97

• Energy efficiency in low-income communities (exogenously specific level of efficiency)

• Qualifying biomass98

• Biogenic portion of municipal solid waste99

• ERCs from prior years

• Early action credits.

We do not capture ERCs from non-affected combined heat and power plants, ERCs from 

transmission and distribution improvements, or ERCs from other sources. The total ERCs 

required is defined by the EPA as:

EE ′dd = σσii− ii

ii

· ,

where 

• σσ : emission rate of source i, (tons CO2 per MWh)

• ss : emission rate standard of source i, (tons CO2 per MWh)

• : generation of source i, (MWh).

For under-complying sources (i.e., σσ > ss ), the amount of ERCs required is a positive number, 

meaning these sources must purchase ERCs. For over-complying sources (i.e., σσ < ss ), the 

amount of ERCs required is a negative number, meaning these sources generate ERCs that can 

be sold. Gas-shift ERCs are defined as:

EE CCgg = sscc − σσ

sscc

· · II ∀ ∈ IIgg ss. . σσ < sscc ,

where

• σσ : emission rate of source i, (tons CO2 per MWh)

• sscc : emission rate standard of coal, (tons CO2 per MWh)

• : generation of source i, (MWh)

• II : incremental generation factor as specified by EPA.

We include the cost of ERCs in the electricity price, represented as tax and subsidy components 

(Fischer 2001), as follows:

 97 Qualifying zero-emitting resources include renewable and nuclear that was installed after 2012. For renewable 

resources, we do not track vintage; therefore, we exclude generation from those sources in 2012. Currently, we do 

not adjust the exclusions for retirements. 98 We assume every megawatt-hour generated from biomass qualifies as an ERC. 99 We represent the combustion of the biogenic portion of municipal solid waste as the landfill gas technology.
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• Tax[$/MWh] = (PPeeee ∙ σσ ∙ ) = PP ∙ σσ

• Subsidy[$/MWh] = − (PPeeee ∙ ∙ ) = −PP ∙ ss

Clean Energy Incentive Program

The Clean Energy Incentive Program is a voluntary program under the Clean Power Plan that is 

intended to incentivize the early adoption of energy efficiency in low-income communities and 

the deployment of renewable energy. Early adoption of eligible projects100 creates early action 

credits (EACs) which are drawn from the pool of state allowances/ERCs from future compliance 

periods and matched by an EPA pool up to an equivalent of 300 million tons of CO2.

We convert EACs to allowances assuming a conversion rate of 0.8 ton/MWh as described in the 

EPA Federal Implementation Plan. This means that one megawatt-hour of generation/savings 

from eligible renewable energy/energy efficiency equals 0.8 ton of EACs for mass-based policy; 

and one megawatt hour of renewable energy/energy efficiency equals one megawatt-hour of 

EACs for rate-based policy. In total, the national EAC budget for a rate-based policy (in MWh) 

equals 300 million tons divided by 0.8 ton/MWh, or 375 million MWh.

Eligible energy efficiency/renewable energy projects earn EACs for energy saved and renewable 

energy generated in 2020 and 2021,

101 which can be banked and used for compliance in any 

future year. Each megawatt-hour of energy generated from renewable energy gets one-half of an 

EAC each from the state pool and the EPA match pool. Each megawatt-hour of energy saved

from energy efficiency gets one EAC each from the state pool and the EPA match pool.

To incentivize wind and solar under the Clean Energy Incentive Program, we apply a CO2 tax on 

new capacity that is not wind or solar to represent the expected advantage that wind and solar 

enjoy from acquiring (and eventually selling) early-action credits or allowances. Note that this 

tax does not incentivize the displacement of existing sources by wind or solar in the earlyaction period.

 100 Projects eligible for EACs include energy efficiency, wind, and solar projects constructed after the submittal of a 

state compliance plan. Assuming all states submit a state compliance plan by the end of 2016, renewable energy and 

energy efficiency projects built in the 2018 and 2020 model years are eligible for EACs. 101 By convention, the 2022 model year represents 2022 and 2021. Because we need to determine the amount of 

generation from eligible renewable energy from 2021 prior to the 2022 model year, we assume the dispatch in 2020 

is representative of the 2021 dispatch.