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Contract No. DE-AC36-08GO28308

Technical Report

NREL/TP-6A20-71916

February 2019

Historical Comparison of Capacity 

Build Decisions from the Regional 

Energy Deployment System 

(ReEDS) Model

Wesley Cole and Nina Vincent

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

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303-275-3000 • www.nrel.gov

Technical Report

NREL/TP-6A20-71916

February 2019

Historical Comparison of Capacity 

Build Decisions from the Regional 

Energy Deployment System 

(ReEDS) Model

Wesley Cole and Nina Vincent

National Renewable Energy Laboratory

Suggested Citation

Cole, Wesley and Nina Vincent. 2019. Historical Comparison of Capacity Build Decisions from 

the Regional Energy Deployment System (ReEDS) Model. Golden, CO: National Renewable 

Energy Laboratory. NREL/TP-6A20-71916. https://www.nrel.gov/docs/fy19osti/71916.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 Solar Energy 

Technologies Office. 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 are grateful to ReEDS modeling team for their input on this work. We also thank Kelly 

Eurek (NREL), Rebecca Jones-Albertus (EERE), Trieu Mai (NREL), Ryan Sims (EPA), and 

Dan Steinberg (NREL) for providing feedback on this report. This report was funded by the 

EERE Solar Energy Technologies Office under contract number DE-AC36-08GO28308. All 

errors and omissions are the sole responsibility of the authors.
iv

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List of Acronyms

AEO Annual Energy Outlook

CAES compressed-air energy storage

CSP concentrating solar power

dGen Distributed Generation model

DOE Department of Energy

NERC North American Electric Reliability Corporation

NG-CC natural gas combined cycle

NG-CT natural gas combustion turbine

NREL National Renewable Energy Laboratory

OGS oil/gas steam

PRM planning reserve margin

PV photovoltaic

REC renewable energy credit

ReEDS Regional Energy Deployment System

RPS renewable portfolio standard

WACC weighted-average cost of capital
v

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Executive Summary

The Regional Energy Deployment System (ReEDS) capacity expansion model is frequently used 

to project the evolution of the U.S. power sector under a wide range of conditions. However, the 

model’s capacity expansion decisions have never been evaluated against historical capacity 

expansion decisions. In this work, we perform a model validation exercise, comparing capacity 

expansion decision from ReEDS for 2010-2016 against the actual buildout of the electricity 

sector. We find that with three key adjustments ReEDS is able to reproduce national-level results 

with reasonably high accuracy (see Figure ES-1). Those three adjustments are to use historical 

average planning reserve margins instead of the NERC-recommended reserve margin levels, 

using actual financing costs rather than long-term average financing costs, and disallowing the 

lowest-cost hydropower upgrades, geothermal sites, and compress-air energy storage sites. We 

also compare the ReEDS results at a state-level and find that state-level results can be better

represented by including a hurdle rate (cost penalty) for transferring power between regions. The 

improved results with that hurdle rate indicate that the ReEDS transmission representation is 

likely not sufficiently stringent and should be improved. Overall these results show that with the 

improvements described above, ReEDS can effectively reproduce historical capacity additions, 

which increases our confidence in using the model in making forward-looking projections.

Figure ES-1. Capacity installed from 2010-2016. “Prescribed” uses historical builds, “Default” uses the 

ReEDS version 2017 default inputs, “PRM” adjusts the reserve margins to more closely match historical 

levels, “Finance” adjusts the financing inputs to better match historical levels, “Restricted” disallows new 

hydropower, geothermal, and compressed air energy storage, “Revised” combines the PRM, Finance, 

and Restricted changes, and “Revised+Hurdle” adds a hurdle rate to the Revised scenario. Additional 

details are in Section 3.
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Table of Contents

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

2 ReEDS Model Description ................................................................................................................... 2

3 Overview of Model Testing.................................................................................................................. 4

4 Results................................................................................................................................................... 6

5 Discussion........................................................................................................................................... 14

6 Summary and Conclusions ............................................................................................................... 15

7 References .......................................................................................................................................... 17

8 Appendix ............................................................................................................................................. 20
1

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1 Introduction

The Regional Energy Deployment System (ReEDS) model is a large-scale capacity expansion 

model of the electricity sector. ReEDS has been used for a wide variety of electricity-sector studies 

including the SunShot Vision Study (DOE 2012) and subsequent SunShot 2030 analysis work 

(Cole, Frew, et al. 2017), the Wind Vision Study (DOE 2015), the Hydropower Vision Study 

(DOE 2016), analysis of tax credit extensions (Mai, Cole, et al. 2016), renewable portfolio 

standards analysis (Mai, Wiser, et al. 2016), Clean Power Plan analysis (Cole et al. 2015), crossborder trade analysis (Beiter, Cole, and Steinberg 2017), high penetration renewable energy 

analysis (Mai et al. 2012), rooftop PV tariff and deployment impacts (Cole et al. 2016; Gagnon et 

al. 2017), and assessing the impact of clean energy research and development on power sector 

evolution (Donohoo-Vallett et al. 2017).

1

The ReEDS model has been under continuous development at NREL for over 15 years (Short et al. 

2003; Eurek et al. 2016). One key piece of model development and use is model verification and 

validation. Verification and validation are used to ensure a model has sufficient accuracy that it can 

be used to analyze the real system it attempts to represent. Because capacity expansion decisions 

happen over long time scales, it is challenging to validate planning models such as ReEDS, and to 

date model validation has not been feasible because the ReEDS model has not been used over a 

sufficient number of historical years to perform a validation exercise. However, with ReEDS

currently solving from 2010-2050, enough time has now passed that we have sufficient historical 

data to attempt a comparison between ReEDS model outputs and actual historical builds.

Validating large-scale, long-term planning models is challenging for a number of reasons. First, 

models such as ReEDS are very large. For example, in any given solve year ReEDS typically has 

3-4 million decision variables, and attempting to validate results across all variables would not be 

practical. Second, compiling robust data for historical years can be challenging, making 

comparisons with model outputs an imperfect exercise. Differences in reporting requirements 

coupled with the wide variety of entities or organizations who interact with the power sector make 

it difficult to create fully accurate historical data sets of simple capacity expansion model inputs 

such as electricity demand, electricity generator costs, and policy requirements. Third, investment 

decisions in the power sector are made both on actual realized values as well as perceptions or 

anticipation of how those values might change over time. For example, a utility or investor may 

consider expected (but not yet enacted) policies when evaluating alternative investment options.

The purpose of this paper is to evaluate the performance of ReEDS in making capacity build 

decisions. We focus here on only new build decisions, and do not explicitly consider operational 

decisions, though any new build decisions are obviously impacted by how the model chooses to 

operate the generators. There is separate ongoing work that is evaluating the dispatch and 

operational decisions within ReEDS. We first provide a brief description of the ReEDS model 

(Section 2), then outline our model testing procedure (Section 3) and show the ReEDS results 

compared to historical results (Section 4). We conclude with a summary and set of 

recommendations for future model improvements (Section 5).

 1 See https://www.nrel.gov/analysis/reeds/publications.html for a complete list of publications using ReEDS. Over 

50 publications were supported using the ReEDS model from 2014-2017.
2

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2 ReEDS Model Description

ReEDS simulates the expansion and operation of generation and transmission capacity from 

present day to 2050 across the contiguous United States (see Eurek et al. (2016) and Cole et al. 

(2017) for detailed descriptions of ReEDS).2 ReEDS selects the regional mix of technologies that 

meets physical and policy requirements of the electric sector at least cost. Model results are 

based on total system costs, which account for the type and location of fossil, nuclear, renewable, 

and storage resource development; the transmission infrastructure expansion requirements of 

those installations; and the generator dispatch and fuel needed to satisfy regional electricity 

consumption requirements and maintain grid system adequacy. ReEDS also accounts for 

technology, resource, and policy considerations such as state renewable portfolio standards 

(RPS). Because ReEDS does not explicitly model distributed generation, we also use the 

Distributed Generation (dGen) model (Sigrin et al. 2016), a consumer adoption model for the 

contiguous U.S. rooftop, distributed PV market.

The primary outputs from ReEDS include the amount, type, year, and location of generator 

capacity; generation from each technology; storage capacity expansion; and transmission 

capacity expansion needed to satisfy regional electricity consumption requirements and maintain 

grid system adequacy. The generation and storage technologies modeled in ReEDS include coalfired (pulverized coal with and without scrubbers, biomass cofiring, and integrated gasification 

combined cycle with and without carbon capture and storage), natural-gas-fired combined cycle 

(NG-CC) and combustion turbine (NG-CT), oil/gas steam (OGS), nuclear, wind (land-based and 

offshore), biopower, geothermal, hydropower, utility PV, concentrating solar power (CSP) with 

and without thermal energy storage, pumped-hydropower storage, compressed-air energy storage 

(CAES), and utility-scale batteries. 

ReEDS represents the electric sector with high spatial resolution to enable comparative 

electricity sector cost evaluation based on local costs, regional pricing, and the relative value of 

geographically and temporally constrained renewable power sources. The model divides the 

contiguous United States into 134 “balancing area” regions, wherein electricity supply and 

consumption are balanced and planning reserves are enforced. ReEDS also characterizes the 

quality, variability, uncertainty, and geographic resource constraints of renewable resources 

across these 134 regions; wind and CSP technologies are further characterized into more 

resolved sub-regions (see Figure 1). Long-distance transmission is represented as corridors

between most adjacent or near-adjacent modeling balancing area regions, and ReEDS models 

both existing and new transmission capacity on these inter-region corridors. ReEDS also models 

the intra-region “spur line” transmission costs required to connect renewable capacity to 

substations or load centers. 

 2 This work uses the 2017 version of the ReEDS model, consistent with Cole et al. (2017).
3

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Figure 1. Map of the ReEDS balancing areas including wind and CSP resource subregions

ReEDS is temporally resolved into 17 “time slices” that each reflect a set of hours in each day 

within a season. For each two-year solution interval from 2010 to 2050, ReEDS dispatches all 

generation in each of these 17 time slices to capture seasonal and diurnal electricity load and 

renewable generation profiles. ReEDS represents generation, operating reserves, and curtailment 

at this 17 time slice resolution. Additionally, ReEDS uses hourly wind, PV and load profiles to 

estimate the contribution of wind and PV to meet peak demand requirements (Frew et al. 2017).

Specific elements of ReEDS that are especially relevant for this model validation exercise 

include:

• ReEDS is a sequential model that only solves for even years. Although ReEDS solves 

from 2010-2050, we can only compare years 2010, 2012, 2014, and 2016 to historical 

data.

• ReEDS has limited foresight to future conditions. ReEDS has foresight into future natural 

gas or carbon prices (if applicable), but not into load growth, policy timelines (such as the 

production or investment tax credit), or technology cost changes.

• ReEDS is a purely linear model.

• ReEDS uses system-wide optimization based on minimizing overall system costs. It does 

not model individual actors or profit-maximizing agents.

• ReEDS does not currently have the capability to convert units from one type to another 

(e.g., convert a coal plant to run on natural gas) or to uprate units.
4

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3 Overview of Model Testing

For historical years (2010-2016), we prescribe all capacity additions in ReEDS according to the 

online dates, technology types, locations, and capacities from the ABB Velocity Suite Database 

(ABB 2017). Thus, ReEDS is not allowed to make investment decisions until the 2018 solve 

year.3 In order to perform this model testing exercise, we do not enforce those prescriptive 

capacity additions and gave ReEDS the ability to begin making investment decisions in 2010. By 

doing this, all capacity additions are selected through the ReEDS optimization. However, several

other model features were left unchanged as described below:

Retirements: ReEDS retirements are prescribed based on actual retirement dates, announced 

retirements, or on lifetime. Because retirements are not model decisions, we do not 

change our treatment of retirements for our test scenarios, and therefore retirements will 

be represented with full accuracy.

Transmission: Transmission capacity additions are prescribed through 2016. Those 

prescriptive capacity additions were left unchanged for these model tests.

Historical data: Historical data for conventional generator cost and performance, load 

growth, and fuel costs were taken from EIA Annual Energy Outlooks (AEO) 2012-2017 

(each AEO has historical data from the two years prior) at the national (for generator cost 

and performance) or census region level. No attempt was made to gather more granular 

data (e.g., state-level demand data, hub-level fuel prices), and data were not 

disaggregated below the annual levels reported in the AEOs.

Rooftop PV: Rooftop PV is input exogenously into ReEDS based on projections from the 

dGen model (Sigrin et al. 2016). Because this model testing only focuses on the ReEDS 

model and not on dGen, we leave the rooftop PV projections as exogenous.

Historical cost and performance data for wind were taken from Wiser and Bolinger (2017) and 

for Solar PV from Fu et al. (2017) and Bolinger, Seel, and LaCommare (2017). The production 

and investment tax credits were implemented for eligible technologies for all years from 2010-

2016. Because ReEDS only has limited foresight, no attempt was made to capture anticipated 

changes in the market, such as the expiration of tax credit policies or that load might grow at a 

greater rate than what was observed empirically.

To perform the model testing, we ran 7 scenarios in ReEDS, solving only for years 2010-2016:

Prescribed. This scenario uses the prescribed new builds from the ABB Velocity Suite 

Database (ABB 2017) and does not allow ReEDS to build new generators. This scenario 

serves as the scenario with the “true” buildout of capacity, and it is the only scenario in 

which historical builds are enforced.

Default. This scenario uses the default settings in ReEDS as described above. In other words, 

this scenario shows the ReEDS buildout when ReEDS makes all the investment decisions 

from 2010-2016 without changing any model inputs or functionality.

 3 For model feasibility purposes, new natural gas combustion turbine plants are allowed in historical years, but nonprescribed combustion turbine capacity is small.
5

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PRM. This scenario uses the observed long-term average (1990-2016) planning reserve 

margin (PRM) instead of the NERC recommended levels for PRM. Figure 2 shows how 

the PRMs differ when using this long-term average PRM.

4

Figure 2. Planning reserve margin for the approximate NERC regions within ReEDS. See Figure 11

in the Appendix for a map showing the region definitions.

Financing. This scenario uses the current estimates for rate of return on equity and interest 

rates, taken from the 2017 Annual Technology Baseline (NREL 2017). These financing 

values result in a nominal weighted average cost of capital (WACC) of 5.4%. The 

Default scenario, on the other hand, uses long-term averages financing values that result 

in a nominal WACC of 8.1%.

Restricted. This scenario disallows the buildout of new geothermal plants, hydropower

upgrades, and CAES plants.

Revised. This scenario combines the PRM, Financing, and Restricted scenario settings into a 

single scenario. In other words, the Revised scenario uses the long-term average PRM, 

the current financing values with a WACC of 5.4%, and does not allow new geothermal, 

hydropower upgrades, and CAES.

Revised+Hurdle. This scenario is the same as the Revised scenario, except that it also 

includes a $10/MWh hurdle rate for transferring power between ReEDS balancing areas 

(see Figure 1). The hurdle rate increases the cost of moving power between regions.

The comparison of the results focuses on the Prescribed, Revised, and Revised+Hurdle 

scenarios, but all scenarios are shown. No attempt was made to further harmonize any of these 

inputs with actual historical data. For example, PRMs and financing rates varied each year from 

2010-2016, but we apply the same PRM and financing rates across all years. Similarly, we do 

not address specific changes in load growth, generator cost and performance, state and local 

incentives (other than those already included in the model), and other model input assumptions 

that could change from one year to the next.

 4 See Reimers, Cole, and Frew (under review) for more details.

0

0.05

0.1

0.15

0.2

0.25

0.3

BASN

CALN

CALS

DSW

ERCOT

FRCC

ISO-NE

MAPP

MISO-US

NORW

NYISO

PJM

ROCK

SERC-E

SERC-N

SERC-SE

SERC-W

SPP

Planning Reserve Margin

NERC Recommended Long-term Interconnect Average

National Average
6

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4 Results

The result of allowing ReEDS to make all generator investment decisions is shown in Figure 3

and Figure 4. On an aggregate basis (Figure 3) there is relatively little difference between the 

Prescribed scenario and the two Revised scenarios. That is because less than 15% of the total 

capacity at the end of 2016 was built between 2010 and 2016. Figure 4 shows just the new 

capacity additions from 2010-2016 in the Prescribed and Revised scenarios. The overall capacity 

added to the system is approximately 5% greater in the Revised scenarios.

Figure 3. Final installed capacity by technology and scenario
7

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Figure 4. Capacity installed from 2010-2016

Figure 5 and Figure 6 provide the same information as Figure 3 and Figure 4, respectively, but 

compare the results by technology. Again, on an aggregate basis (Figure 5) the technologies are 

well aligned, but one can begin to see differences more easily when comparing only the new 

builds (Figure 6). For example, coal and nuclear plant options are not chosen by ReEDS. The 

new nuclear capacity is a combination of plant uprates and the second unit of the Watts Barr 

nuclear power plant that came online in 2016. The Watt Barr plant began construction in 1973, 

but was placed on hold for many years, and thus the cost and incentives for completing the plant 

are different than the inputs assumed in ReEDS. And ReEDS does not currently have the 

capability to model nuclear power plant uprates, so that shortcoming is highlighted here. The 

coal plants that came online (and that ReEDS did not build in the Revised scenarios) reflect that 

ReEDS does not have hindsight of conditions before 2010 such as high natural gas prices and 

demand growth that could have made new coal units a cost-effective option. Instead of these coal 

and nuclear plants, ReEDS chooses natural gas combined cycle plants. From Figure 6, the sum of 

the NG-CC, coal, and nuclear capacity in the Prescribed scenario is approximately equal to the 

NG-CC capacity in the Revised scenarios.
8

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Figure 5. Final installed capacity in 2016 by technology

Figure 6. Capacity added in 2010-2016 by technology

Additionally, the results from Figure 6 highlight other aspects of ReEDS operation. The 10 GW 

of OGS plants are largely coal plants that were converted to run on natural gas, thus becoming
9

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natural gas steam plants. ReEDS does not currently have an option to convert coal-fired plants to 

gas-fired plants, and thus misses these builds. Additionally, ReEDS NG-CT capacity is nearly 

triple that of what was deployed historically. One reason for that is that ReEDS builds NG-CT 

capacity instead of the OGS capacity. Another reason is that ReEDS assumes all peak demands 

are coincident, and thus the peak demand is ReEDS is likely higher than actual peak demand, 

and therefore ReEDS builds additional NG-CT capacity to meet that higher peak demand 

requirement. ReEDS also underestimates the “Other” technology category, which includes 

biomass, land-fill gas, geothermal, and CSP. Geothermal was disallowed by definition in the 

Revised scenario, and the updated geothermal supply curves from the forthcoming Geothermal 

Vision Study should address these shortcomings. Land-fill gas generators are not a technology 

allowed in ReEDS beyond what is prescribed because we have no land-fill gas resource 

assessment in ReEDS that would say how much resource exits across the country. Biomass and 

CSP were found to be less cost-competitive than other options in ReEDS. Actual biomass plants 

that are built usually have site-specific benefits not captured in ReEDS (e.g., a biomass plant that 

uses waste fuel at low cost). In the case of CSP, new plants were built with higher expected 

performance or lower expected costs.

Figure 7 shows the buildout of the various technologies from 2012-2016. In general, the Revised 

scenario has wind capacity coming online later than in the Prescribed scenario. In 2012, there 

was a significant rush to complete wind projects before the expiration of the production tax 

credit. Because ReEDS does not have policy foresight, it could not see that expiration and 

choose to build capacity before the incentive expired. By 2016, however, the total wind capacity 

is nearly the same. This result demonstrates that ReEDS results are often more robust when 

aggregated over time rather than looking at a single year (e.g., cumulative builds through 2030 is 

a more robust result than what was built in 2030).
10

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Figure 7. New capacity additions by year, technology, and scenario5

Figure 8 show the state-level distribution of the ReEDS results. The model prefers to build wind 

in the upper Midwest (Montana, North Dakota, and Wyoming). These three states have very 

high-quality wind resources. In addition, North Dakota has an investment tax credit for new wind 

plants. Wind in Texas is underestimated, in part because of the over-building of natural gas 

plants (Figure 8) in ERCOT. ERCOT is essentially the only region whose reserve margin is near 

or below the NERC-recommended planning reserve margin level, so by using the long-term 

interconnect average reserve margin, there is additional incentive to add natural gas combinedcycle units, which in turn reduces the value of new wind capacity. In general, the state-level 

wind results improve with the addition of a $10/MWh hurdle rate, indicating that a stricter 

transmission implementation can improve the spatial accuracy of the model.

 5 ReEDS models 2010, but does so with the 2010 capacity already installed and thus does not have any new builds 

in the Prescribed scenario. The new builds occurring in the Revised scenarios are therefore above what was 

installed, and are driven in large part by the higher planning reserve margin.
11

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Figure 8. Wind (top), solar (middle) and natural gas capacity added from 2010-2016 by state for 

Prescribed (left), Revised (middle), and Revised+Hurdle (right) scenarios. Note differences in 

scales.

Solar capacity is underpredicted in California for two primary reasons: 1) California exceeded its 

RPS requirement by several percentage points, whereas the Revised scenarios only built out to 

the minimum PRS requirement, and 2) ReEDS did not find CSP to be a cost-effective option, 

building extra wind instead of the CSP. With respect to the overcompliance of the California 

RPS, ReEDS does not have policy foresight to see that the RPS is going to increase (coupled 

with the anticipated 2016 expiration of the solar investment tax credit), nor does ReEDS have 

REC banking as an option, so the model does not see any benefit in exceeding the California 

RPS through 2016. State-level solar deployments in the Southwest also improve with the 

inclusion of the $10/MWh hurdle rate.

Figure 9 and Figure 10 show the total installed capacity and the capacity installed from 2010-

2016 in all seven scenarios. These figures illustrate how the various changes we implemented for 

the Revised scenario impacted model results. With no changes (Default), ReEDS builds very 

little new capacity because the system is overbuilt relative to the NERC-recommended reserve 

margin levels. Updating the planning reserve margins in ReEDS to use the long-term 

interconnect average values (refer back to Figure 2) results in substantially more capacity getting 

built. Changes in finance rates (from a long-term average to a short-term average) and in the 

availability of low-cost geothermal, hydropower upgrades, and CAES result in more wind and 

PV development. The reduction of hydropower and geothermal increase the demand for

renewable energy credits (RECs) from wind and PV, and the lower financing costs increase the 

Wind

Solar

Natural Gas
12

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competitiveness of wind and PV relative to natural gas generators because wind and PV are more 

capital intensive than NG-CC units.

6

Figure 9. Final installed capacity of all scenarios

 6 Because financing costs impact the capital cost of a plant and not the fuel cost of a plant, the total system cost of 

wind and PV are more sensitive to financing costs than NG-CC units because wind and PV have no fuel costs. 
13

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Figure 10. Capacity installed from 2010-2016
14

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5 Discussion

As seen in the results, one fundamental driver of a model being able to reproduce actual builds is 

whether or not the model is using the best inputs and assumptions. In its default configuration, 

ReEDS performed poorly in reproducing actual builds, but as key assumptions were modified, 

that gap was closed substantially. Additional refinement of assumption and inputs, such as the 

inclusion of region-specific foresight or nuclear uprates, would likely lead to improvements in 

accuracy. However, additional model features come with added cost and complexity including

the costs from the creation and maintenance of the capability, and the complexity from the 

increased difficulty in interpreting results while additional drivers are impacting the results.

The goal of this work was not perfect replication of historical grid buildout. ReEDS does not 

serve as a prediction tool, but rather as an analysis tool where scenarios are typically compared 

to one another rather than evaluated in isolation. This way of using the tools means that being 

able to capture the appropriate drivers rather than the exact place and time of the builds tends to 

be more important. Therefore, the goals of this exercise were to understand what features were 

missing from ReEDS that might impact long-term solutions, and to provide some quantitative 

understanding about whether the ReEDS model is capturing overall power sector investment 

behavior and trends. We identify additional potential drivers of power sector modeling in the 

Summary and Conclusions section below, but we did not pursue these additional drivers because 

1) the Revised scenario buildout was sufficiently close to the Prescribed scenario as to not give 

us cause for concern in the solution, and 2) the cost of adding and testing many of these features 

is high, and it is unclear how many of them would be incorporated into the ReEDS forwardlooking projections (as opposed to being used only to test historical years). 
15

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6 Summary and Conclusions

In this work we compared the capacity expansion decisions from the ReEDS model for 2010-

2016 against historical build decisions for the contiguous U.S. electricity sector. We found that 

with the changes implemented, ReEDS was able to reproduce the 2010-2016 buildout with 

reasonably high accuracy, especially given the large amount of assumptions that were not 

matched to historical data. The changes that were implemented highlight areas where the ReEDS 

model can and should be improved:

• Planning reserve margins. ReEDS has traditionally used the NERC-recommended 

planning reserve margin level for all years, but this validation exercise has demonstrated 

that using these planning reserve margin levels are insufficient to reproduce historical 

buildout. We have already implemented this change in ReEDS (Reimers, Cole, and Frew 

under review2019).

• Financing rates. ReEDS has always used long-term average financing rates for inputs, 

but these rates are significantly higher than current rates. We are currently working on 

incorporating annual financing rates into ReEDS that will allow the current low rates to 

ramp up to long-term average rates over time.

• Hydropower upgrade, geothermal, and CAES supply curves. The lowest cost 

resources on the hydropower upgrade, geothermal and CAES supply curves as so low 

cost that they get built as soon as the model is allowed to build them. These costs are not 

necessarily wrong; often there are other factors that restrict the timing of deploying what 

appears to be a cost-effective resource. The hydropower upgrades are addressed in 

ReEDS by staging in the available capacity gradually over time based on relicensing 

schedules (DOE 2016). The geothermal supply curves have been updated as part of the 

forthcoming Geothermal Vision Study, and those new supply curves address the too-lowcost issues seen here. And the CAES supply curve cost have been based on a Black & 

Veatch cost report (Black & Veatch 2012), but through this exercise we are updating

those CAES input costs based on a more recent study done by Lazard (Lazard 2016).

The state-level results showed that the ReEDS results were somewhat improved by adding a cost 

penalty for moving power between regions, indicating that the overall ReEDS transmission 

treatment is likely too generous. We have ongoing work to address the treatment of transmission 

in ReEDS and anticipate that that work will identify how ReEDS transmission should be updated 

to capture possible flows between regions.

In addition to these key areas of improvement listed above, we also found a number of additional

areas where the model could be improved:

• Inclusion of foresight, especially for policies, would lead to an improved solution, 

especially with respect to the timing of builds. For example, ReEDS underpredicts the 

wind deployment that occurred in 2012 due to the expiring tax credit, but it could capture 

that buildout with appropriate foresight.

• REC trading rules can have a large impact on state decisions, but ensuring that all rules 

are appropriately represented is challenging both in data collection and model 

implementation. Some resources exist, but because of the continuous evolution of the 

RPS policies, maintaining the rules over time is a challenge.
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• ReEDS gives all technologies the same financing costs, but in reality, each technology 

has different risk profiles and potential investors that lead to a range of financing costs.

• Several build decisions over the past few years, such as the Watts Bar nuclear power 

plant, are very site or situation specific. Insofar as these specific considerations can be 

identified, their incorporation can lead to improved results.

• Because ReEDS does not model endogenous retirements, we can only evaluate new build 

decisions in this exercise. However, retirement decisions are a key aspect of capacity 

expansion and inclusion of retirement decisions could lead to better model projections.

• Nuclear uprates and coal-to-gas switching are not endogenously modeled in ReEDS and 

thus can never be chosen as technology options. Inclusion of these technology options 

would lead to more robust solutions.

• ReEDS only includes “large-scale” state incentives that would apply to many large-scale 

plants. For example, an incentive that would only apply to plants of size 1 MW or smaller 

would be excluded from ReEDS on basis of scale. However, a number of states have 

smaller-scale incentives that impact decisions to build new generators. Representing 

these incentives would improve state-level results.

• ReEDS does not allow for REC banking to meet future RPS requirements, and therefore 

does not see value in exceeding RPS requirements from 2010-2016 in states like 

California.

Overall, we have found the model validation exercise performed here to be important for 

identifying areas of improvement for the ReEDS model and in giving us confidence in the 

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

7 References 

ABB. 2017. “ABB Velocity Suite.” 2017. http://new.abb.com/enterprise-software/energyportfolio-management/market-intelligence-services/velocity-suite.

Beiter, Philipp, Wesley J. Cole, and Daniel C. Steinberg. 2017. “Modeling the Value of 

Integrated U.S. and Canadian Power Sector Expansion.” The Electricity Journal 30 (2): 47–59. 

https://doi.org/10.1016/j.tej.2017.01.011.

Black & Veatch. 2012. “Cost and Performance Data for Power Generation Technologies.” 

Overland Park, KS: Black & Veatch Corporation. https://www.bv.com/docs/reports-studies/nrelcost-report.pdf.

Bolinger, Mark, Joachim Seel, and Kristina LaCommare. 2017. “Utility-Scale Solar 2016: An 

Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United States.” 

LBNL-2001055. Berkeley, CA: Lawrence Berkeley National Laboratory. 

https://emp.lbl.gov/sites/default/files/utility-scale_solar_2016_report.pdf.

Cole, Wesley, Bethany Frew, Pieter Gagnon, James Richards, Yinong Sun, Jarett Zuboy, and 

Robert Margolis. 2017. “SunShot 2030 for Photovoltaics (PV): Envisioning a Low-Cost PV 

Future.” NREL/TP-6A20-68105. Golden, CO: National Renewable Energy Laboratory. 

https://www.nrel.gov/docs/fy17osti/68105.pdf.

Cole, Wesley, Haley Lewis, Ben Sigrin, and Robert Margolis. 2016. “Interactions of Rooftop PV 

Deployment with the Capacity Expansion of the Bulk Power System.” Applied Energy 168 

(April): 473–81. https://doi.org/10.1016/j.apenergy.2016.02.004.

Cole, Wesley, Trieu Mai, Paul Donohoo-Vallett, James Richards, and Paritosh Das. 2017. “2017 

Standard Scenarios Report: A U.S. Electricity Sector Outlook.” Technical Report NREL/TP6A20-68548. Golden, CO: National Renewable Energy Laboratory. 

https://www.nrel.gov/docs/fy18osti/68548.pdf.

Cole, Wesley, Trieu Mai, Kelly Eurek, Daniel C. Steinberg, and Robert Margolis. 2015. 

“Considering the Role of Solar Generation under Rate-Based Targets in the EPA’s Proposed 

Clean Power Plan.” The Electricity Journal 28 (8): 20–28. 

https://doi.org/10.1016/j.tej.2015.09.002.

DOE. 2012. “SunShot Vision Study.” DOE/GO-102012-3037. Washington, D.C.: U.S. 

Department of Energy. http://www.nrel.gov/docs/fy12osti/47927.pdfDOE/GO-102012-3037.

———. 2015. “Wind Vision: A New Era for Wind Power in the United States.” DOE/GO102015-4557. Washington, D.C.: U.S. Department of Energy. 

http://www.energy.gov/sites/prod/files/WindVision_Report_final.pdf.

———. 2016. “Hydropower Vision: A New Chapter for America’s 1st Renewable Electricity 

Source.” Technical Report DOE/GO-102016-4869. Washington, D.C.: U. S. Department of 

Energy. http://energy.gov/eere/water/articles/hydropower-vision-new-chapter-america-s-1strenewable-electricity-source.
18

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

Donohoo-Vallett, Paul, Trieu Mai, Matthew Mowers, and Gian Porro. 2017. “Impact of Clean 

Energy R&D on the U.S. Power Sector.” NREL/TP--6A20-67691. Golden, CO: National 

Renewable Energy Laboratory. https://www.osti.gov/scitech/biblio/1340173.

Eurek, Kelly, Wesley Cole, David Bielen, Stuart Cohen, Bethany Frew, Jonathan Ho, Venkat 

Krishnan, Trieu Mai, and Daniel Steinberg. 2016. “Regional Energy Deployment System 

(ReEDS) Model Documentation: Version 2016.” NREL/TP-6A20-67067. Golden, CO: National 

Renewable Energy Laboratory. http://www.nrel.gov/docs/fy17osti/67067.pdf.

Frew, Bethany, Wesley Cole, Yinong Sun, James Richards, and Trieu Mai. 2017. “8760-Based 

Method for Representing Variable Generation Capacity Value in Capacity Expansion Models.” 

NREL/CP-6A20-68869. Golden, CO: National Renewable Energy Laboratory. 

https://www.nrel.gov/docs/fy17osti/68869.pdf.

Fu, Ran, David J. Feldman, Robert M. Margolis, Michael A. Woodhouse, and Kristen B. Ardani. 

2017. “U.S. Solar Photovoltaic System Cost Benchmark: Q1 2017.” NREL/TP-6A20-68925. 

Golden, CO: National Renewable Energy Laboratory. https://doi.org/10.2172/1390776.

Gagnon, Pieter, Wesley J. Cole, Bethany Frew, and Robert Margolis. 2017. “The Impact of 

Retail Electricity Tariff Evolution on Solar Photovoltaic Deployment.” The Electricity Journal, 

Energy Policy Institute’s Seventh Annual Energy Policy Research Conference, 30 (9): 22–28. 

https://doi.org/10.1016/j.tej.2017.10.003.

Lazard. 2016. “Lazard’s Levelized Cost of Storage - Version 2.0.” 

https://www.lazard.com/media/438042/lazard-levelized-cost-of-storage-v20.pdf.

Mai, Trieu, Wesley Cole, Eric Lantz, Cara Marcy, and Benjamin Sigrin. 2016. “Impacts of 

Federal Tax Credit Extensions on Renewable Deployment and Power Sector Emissions.” 

NREL/TP-6A20-65571. Golden, CO: National Renewable Energy Laboratory. 

http://www.nrel.gov/docs/fy16osti/65571.pdf.

Mai, Trieu, R. Wiser, D. Sandor, G. Brinkman, G. Heath, P. Denholm, D. J. Hostick, N. 

Darghouth, A. Schlosser, and K. Strzepek. 2012. “Exploration of High-Penetration Renewable 

Electricity Futures. Vol. 1 of Renewable Electricity Futures Study.” NREL/TP-6A20-52409-1. 

Golden, CO: National Renewable Energy Laboratory.

https://www.nrel.gov/docs/fy12osti/52409-1.pdf.

Mai, Trieu, Ryan Wiser, Galen Barbose, Lori Bird, Jenny Heeter, David Keyser, Venkat 

Krishnan, Jordan Macknick, and Dev Millstein. 2016. “A Prospective Analysis of the Costs, 

Benefits, and Impacts of U.S. Renewable Portfolio Standards.” NREL/TP--6A20-67455; LBNL-

-1006962. Golden, CO: National Renewable Energy Laboratory. 

https://www.osti.gov/scitech/biblio/1337612-prospective-analysis-costs-benefits-impactsrenewable-portfolio-standards.

NREL. 2017. “2017 Annual Technology Baseline.” Golden, CO: National Renewable Energy 

Laboratory. http://www.nrel.gov/analysis/data_tech_baseline.html.
19

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

Reimers, Andrew, Wesley J. Cole, and Bethany A. Frew. under review2019. “The Impact of 

Planning Reserve Margins in Long-Term Planning Models for the Electricity Sector.” Energy 

Policy 135: 1–8. https://doi.org/10.1016/j.enpol.2018.10.025.

Short, Walter, Nate Blair, Donna Heimiller, and Vikram Singh. 2003. “Modeling the Long-Term 

Market Penetration of Wind in the United States.” NREL/CP-620-34469. Golden, CO: National 

Renewable Energy Laboratory. http://www.nrel.gov/docs/fy03osti/34469.pdf.

Sigrin, Benjamin, Michael Gleason, Robert Preus, Ian Baring-Gould, and Robert Margolis. 2016. 

“The Distributed Generation Market Demand Model (DGen): Documentation.” NREL/TP-6A20-

65231. Golden, CO: National Renewable Energy Laboratory. 

http://www.nrel.gov/docs/fy16osti/65231.pdf.

Wiser, Ryan, and Mark Bolinger. 2017. “2016 Wind Technologies Market Report.” Department 

of Energy. 

https://energy.gov/sites/prod/files/2017/08/f35/2016_Wind_Technologies_Market_Report_0.pdf.
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8 Appendix

Figure 11. Map of the “NERC” regions at which the planning reserve margin is specified.

Figure 12. Difference between the selected scenarios and the Prescribed Scenario.