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.

Contract No. DE-AC36-08GO28308

Sensitivity of Utility-Scale Solar 

Deployment Projections in the 

SunShot Vision Study to Market 

and Performance Assumptions

Kelly Eurek, Paul Denholm, Robert Margolis, 

and Matthew Mowers

Technical Report

NREL/TP-6A20-55836

April 2013
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.

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Sensitivity of Utility-Scale Solar 

Deployment Projections in the 

SunShot Vision Study to Market 

and Performance Assumptions

Kelly Eurek, Paul Denholm, Robert Margolis, 

and Matthew Mowers 

Prepared under Task No. SS12.2220

Technical Report

NREL/TP-6A20-55836

April 2013
NOTICE

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iii

Acknowledgments

The authors would like to thank the following individuals for their helpful reviews of draft 

versions of this report: Gwen Bredehoeft and Chis Namovicz at the U.S. Energy Information 

Administration; Venkat Banunarayanan at the U.S. Department of Energy; and Patrick Sullivan, 

Trieu Mai, Nate Blair, and Robin Newmark at the National Renewable Energy Laboratory. We 

would also like to thank Jarett Zuboy, independent consultant, for his expert editing and input. 

This work was supported by the U.S. Department of Energy under contract number DE-AC36-

08GO28308. 
iv

List of Abbreviations and Acronyms

AC alternating current

AEO Annual Energy Outlook

ALL combination of NG-EUR, NCR, WG-2ETI, and HI-VAR sensitivities 

(sensitivity scenario)

Btu British thermal unit

CC combined cycle

CF capacity factor

CR-80 80 GW of regionally based coal retirements by 2026 (sensitivity scenario)

CR-35 35 GW of regionally based coal retirements by 2020 (sensitivity scenario)

CSP concentrating solar power

CT combustion turbine

DC direct current

DOE U.S. Department of Energy

EGS enhanced geothermal system

EHS enhanced hydrothermal system

EIA Energy Information Administration

ETI evolutionary technology improvement

EUR

Gas-CC

Gas-CT

estimated ultimate recovery

natural gas combined cycle

natural gas combustion turbine

GW gigawatts

HI-VAR high solar variability (sensitivity scenario)

HT hydrothermal

ITC investment tax credit

LCOE levelized cost of energy

LD low electricity demand trajectory (sensitivity scenario)

LD ALL combination of LD, NG-EUR, NCR, WG-2ETI, and HI-VAR sensitivities 

(sensitivity scenario)

MMBtu million Btu

MW megawatts

NCR

NEMS

no coal retirements (sensitivity scenario)

National Energy Modeling System

NG lower natural gas price (sensitivity scenario)

NG-EUR lower natural gas price, high estimated ultimate recovery case 

(sensitivity scenario)

NoDPV no distributed PV (sensitivity scenario)

NREL National Renewable Energy Laboratory

OMB Office of Management and Budget

PV

RE

photovoltaic

renewable electricity

RE Futures Renewable Electricity Futures

ReEDS Regional Energy Deployment System

RSMG relaxed solar market growth (sensitivity scenario)

SolarDS Solar Deployment System

SP-62.5 62.5% solar price reduction (sensitivity scenario)
v 

SP-50 50% solar price reduction (sensitivity scenario)

SSVS SunShot Vision Study

TWh terawatt-hours

USD U.S. dollars

USMG unlimited solar market growth (sensitivity scenario)

W watts

WG-ETI ETI cost and performance metrics for wind and geothermal only 

(sensitivity scenario)

WG-2ETI twice the cost reduction of the WG-ETI scenario for wind and geothermal 

(sensitivity scenario)
vi 

Executive Summary

The U.S. Department of Energy (DOE) SunShot Vision Study (U.S. DOE 2012) explores the 

impact of reducing solar prices by about 75% between 2010 and 2020 on potential U.S. 

deployment of solar technologies. DOE’s SunShot Initiative envisions reaching, by 2020, an 

installed system price of $1/WDC for utility-scale photovoltaics (utility PV) and $3.60/WAC for 

concentrating solar power (CSP) with up to 14 hours of thermal storage capacity. The National 

Renewable Energy Laboratory’s Regional Energy Deployment System (ReEDS) model was used 

to simulate utility PV and CSP deployment based on these price projections and market 

assumptions. In the SunShot Vision Study, U.S. total utility PV and CSP deployment reaches 209 

GW by 2030 and 475 GW by 2050. 

Utility PV and CSP deployment is sensitive to several factors in addition to installed system 

prices. In this study, we explore the sensitivity of utility-scale solar deployment to eight key 

model assumptions: 

1. Solar technology prices: 50%, 62.5%, and 75% (SunShot scenario) solar price reductions 

by 2020 

2. Electricity demand: flat demand and 1% growth per year (SunShot scenario) through 

2050 

3. Lower natural gas prices: natural gas prices reaching $7.40 and $9.50 per MMBtu by 

2050 versus $10.20 per MMbtu in the SunShot scenario 

4. Coal retirements: no retirements, retire 35 GW by 2020, retire 76 GW by 2030 (SunShot 

scenario), and retire 80 GW by 2026 

5. Cost and performance of non-solar renewable technologies: 10% and 20% lower wind 

costs combined with 11%–17% and 22%–34% lower geothermal costs (relative to the 

SunShot scenario) 

6. PV resource variability: 50% lower and higher output variability and solar resource 

correlation (relative to the SunShot scenario) 

7. Distributed photovoltaic (PV) deployment: no distributed PV adoption versus 240 GW of 

distributed PV adoption by 2050 in the SunShot scenario 

8. Solar market supply growth limits: rapid growth cost penalty, maximum deployment 

level (SunShot scenario), and no annual growth limit

We generated a total set of 45 sensitivity scenarios by varying model assumptions both 

individually and in various combinations. We find that installed price is the most important 

driver behind utility-scale solar adoption. However, our analysis also indicates that non-price 

factors can have significant impacts. By 2030, assuming the SunShot solar price targets are met, 

ReEDS builds utility-scale solar capacity between 175–212 GW for most scenarios under the 

SunShot-electricity-demand assumptions and 86–154 GW for the scenarios under the lowelectricity-demand assumptions (compared with 209 GW in the SunShot scenario). By 2050, 

ReEDS builds utility-scale solar capacity between 229–567 GW for the scenarios under the 

SunShot-electricity-demand assumptions and 90–236 GW for the scenarios under the lowelectricity-demand assumptions (compared with 475 GW in the SunShot scenario). 
vii 

With solar prices being equal, low electricity demand suppresses solar deployment the most, 

followed by lower natural gas prices. The other individual factors generally have a smaller 

impact, although relaxed solar market supply growth restrictions can boost utility-scale solar 

deployment substantially beyond SunShot levels by 2030 (up to 342 GW in one instance). Some 

combinations of factors have a major impact on solar deployment and generation, especially 

under the low-electricity-demand assumption. For example, there is a six-fold increase in 2050 

utility-scale solar capacity from the combination scenario with the lowest deployment to the 

scenario with the highest deployment. In fact, the worst-case combination scenario examined 

here results in utility-scale solar deployment that is lower even than the deployment in the 

sensitivity scenario where solar prices are two times higher than the SunShot scenario. Most 

combinations under the SunShot-electricity-demand assumptions, however, are in line with 

SunShot solar generation results through 2030, with discrepancies growing larger between 2030 

and 2050. 
viii

Table of Contents

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

2 Modeling Framework............................................................................................................................ 3

2.1 Scenario Development ..................................................................................................................... 3

2.2 Reference and SunShot Main Scenario ............................................................................................ 3

2.3 Solar Growth Results ....................................................................................................................... 5

3 Defining Sensitivity Scenarios............................................................................................................ 7

3.1 Solar Technology Prices (SP-62.5, SP-50) ...................................................................................... 9

3.2 Low Electricity Demand Growth (LD) .......................................................................................... 10

3.3 Lower Natural Gas Prices (NG, NG-EUR) .................................................................................... 10

3.4 Coal Retirements (CR-35, CR-80, NCR)....................................................................................... 11

3.5 Cost and Performance of Non-Solar Renewable Technologies (WG-ETI, WG-2ETI) ................. 13

3.6 PV Resource Variability (0.5σ, 1.5σ, ρ = 0, 0.5ρ, 1.5ρ, HI-VAR) ................................................ 15

3.7 Distributed PV Deployment Assumptions (NoDPV) .................................................................... 17

3.8 Solar Market Supply Growth Assumptions (RSMG, USMG) ....................................................... 17

4 Sensitivity Scenario Results ............................................................................................................. 19

4.1 Solar Technology Prices (SP-62.5, SP-50) .................................................................................... 20

4.2 Low Electricity Demand Growth (LD) .......................................................................................... 22

4.3 Lower Natural Gas Prices (NG, NG-EUR) .................................................................................... 23

4.4 Coal Retirements (CR-35, CR-80, NCR)....................................................................................... 25

4.5 Cost and Performance of Non-Solar Renewable Technologies (WG-ETI, WG-2ETI) ................. 27

4.6 PV Resource Variability (0.5σ, 1.5σ, ρ = 0, 0.5ρ, 1.5ρ, HI-VAR) ................................................ 29

4.7 Distributed PV Deployment Assumptions (NoDPV) .................................................................... 31

4.8 Solar Market Supply Growth Assumptions (RSMG, USMG) ....................................................... 32

4.9 Combination Scenarios .................................................................................................................. 34

4.10 Summary of Scenarios ............................................................................................................. 37

5 Conclusions ........................................................................................................................................ 40

References................................................................................................................................................. 43
ix

List of Figures

Figure 1. Total solar capacity under the SunShot and reference scenarios .....................................5

Figure 2. Evolution of electricity generation in the reference and SunShot scenarios ....................6

Figure 3. Comparison of end-use demand projections between the SunShot and low-demand 

scenarios ...................................................................................................................................10

Figure 4. Comparison of AEO natural gas price projections through 2035 ..................................11

Figure 5. Comparison of coal retirement trajectories through 2050 ..............................................12

Figure 6. Geographic differences in coal retirements between the CR-80 and SunShot scenarios13

Figure 7. Capital cost for land-based and offshore wind for SunShot, WG-ETI, and WG-2ETI 

scenarios ...................................................................................................................................14

Figure 8. Geothermal resource supply curves in 2010 and 2050 for SunShot, WG-ETI, and WG2ETI scenarios .........................................................................................................................15

Figure 9. Standard deviations of expected utility PV plant output for each time-slice .................16

Figure 10. Inter-region correlations of expected power output between PV resource Regions ....16

Figure 11. Utility PV growth penalty curve ...................................................................................18

Figure 12. Change in capacity from SSVS ReEDS code to updated ReEDS code .......................19

Figure 13. Utility-scale solar capacity under a range of SunShot solar price-reduction scenarios21

Figure 14. Utility-scale solar generation under a range of solar price-reduction scenarios ..........21

Figure 15. Capacity and generation comparisons between LD and SunShot scenarios ................22

Figure 16. Capacity and generation comparisons between the NG-EUR and SunShot scenarios 24

Figure 17. National average electric-sector natural gas prices and consumption outputs from 

ReEDS through 2050 ...............................................................................................................25

Figure 18. Capacity and generation comparisons between the NCR and SunShot scenarios .......26

Figure 19. Annual utility-scale solar generation fraction in 2030 and 2050 for all coal retirement 

scenarios ...................................................................................................................................27

Figure 20. Capacity and generation comparisons between the WG-ETI and SunShot scenarios .28

Figure 21. Capacity and generation comparisons between the HI-VAR and SunShot scenarios..30

Figure 22. Estimated utility PV curtailments for the HI-VAR and SunShot scenarios .................31

Figure 23. Capacity and generation comparisons between the NoDPV and SunShot scenarios ...32

Figure 24. Capacity and generation comparisons between the USMG and SunShot scenarios ....33

Figure 25. Cumulative and annual utility PV capacity deployment for all solar market supply 

growth scenarios ......................................................................................................................34

Figure 26. Capacity and generation comparisons between the ALL and SunShot scenarios ........35

Figure 27. Capacity and generation comparisons between the LD ALL and SunShot scenarios .36

Figure 28. Cumulative utility-scale solar capacity through 2050 for selected sensitivity scenarios 

in Section 4 ..............................................................................................................................37

Figure 29. Utility-scale solar generation through 2050 for selected sensitivity scenarios in 

Section 4...................................................................................................................................38

Figure 30. Annual utility-scale solar generation fraction in 2030 and 2050 for combination 

scenarios – low demand ...........................................................................................................39

Figure 31. Annual utility-scale solar generation fraction in 2030 and 2050 for combination 

scenarios – AEO 2010 demand ................................................................................................39
x 

List of Tables

Table 1. Projected PV and CSP Installed System Prices and Performance (2010 U.S. Dollars/W)4

Table 2. Overview of SunShot Scenario Results .............................................................................5

Table 3. Descriptions of Single-Factor Sensitivity Scenarios .........................................................7

Table 4. Sensitivity Combinations Using the LD, NG-EUR, NCR, WG-2ETI, and HI-VAR 

Single-Factor Scenario Assumptions .........................................................................................8

Table 5. Price Inputs for SunShot and Solar Price Sensitivity Scenarios ........................................9

Table 6. Descriptions of PV Resource Variability Scenarios ........................................................17

Table 7. 2030 and 2050 Annual Generation from Utility-Scale Solar and Coal ...........................27

Table 8. Marginal Utility PV Curtailment Fractions at Various Levels of Deployed Utility PV 

Capacity – Variability Scenarios .............................................................................................29
1 

1 Introduction

The U.S. Department of Energy (DOE) SunShot Initiative aims to reduce the price of solar 

energy systems by about 75% between 2010 and 2020. Achieving this target is expected to make 

the unsubsidized cost of solar energy competitive with the cost of other currently operating 

energy sources, paving the way for rapid, large-scale adoption of solar electricity across the 

United States.

To assess the potential impacts of achieving the SunShot targets, DOE’s Solar Energy 

Technologies Program produced the SunShot Vision Study (SSVS) (U.S. DOE 2012). The SSVS

assumes that the SunShot price targets are achieved by 2020 and models the resulting penetration 

of solar technologies in the United States using two models: the National Renewable Energy 

Laboratory’s (NREL’s) Regional Energy Deployment System (ReEDS) model is used to 

simulate utility-scale solar deployment, and NREL’s Solar Deployment Systems (SolarDS) 

model is used to simulate distributed solar deployment. The results suggest that solar energy 

could satisfy roughly 14% of U.S. electricity demand by 2030 and 27% by 2050.1

 Solar growth 

based on non-cost factors (e.g., greenhouse gas reduction and energy security benefits) is not 

considered in the analysis but could result in additional solar market penetration. 

The actual deployment of solar will be driven by a large number of highly uncertain factors, 

including the cost of all generation technologies and fuels, future characteristics of the electric 

sector, and challenges associated with integrating variable energy sources. Thus, it is important 

to examine the sensitivity of the projected solar deployment in the SSVS to these types of 

factors. In this report, we explore the sensitivity of projected utility-scale solar deployment using 

the ReEDS model; a companion paper explores the sensitivity of projected distributed solar 

deployment using the SolarDS model (Drury et al. 2013). 

To examine the sensitivity of utility-scale solar2

 penetration in ReEDS and explore variations on 

the SSVS assumptions, this report analyzes the sensitivity of utility-scale solar deployment to a 

variety of factors, both individually and in combination: 

1. Solar technology prices: 50%, 62.5%, and 75% (SunShot scenario) solar price reductions 

by 2020 

2. Electricity demand: flat demand and 1% growth per year (SunShot scenario) through 

2050 

3. Lower natural gas prices: natural gas prices reaching $7.40 and $9.50 per MMBtu by 

2050 versus $10.20 per MMBtu in the SunShot scenario 

4. Coal retirements: no retirements, retire 35 GW by 2020, retire 76 GW by 2030 (SunShot 

scenario), and retire 80 GW by 2026 

 

1 All results in this report refer to the contiguous United States (excluding Alaska and Hawaii). The contribution of 

solar to total demand does not include the allocation of curtailment among various renewable energy sources (wind 

and solar). As a result, the actual contribution of solar could be slightly lower. This issue is discussed in detail in 

Section 4.

2 Throughout this report, utility-scale solar refers to utility-scale PV and CSP.
2 

5. Cost and performance of non-solar renewable technologies: 10% and 20% lower wind 

costs combined with 11%–17% and 22%–34% lower geothermal costs (relative to the 

SunShot scenario) 

6. PV resource variability: 50% lower and higher output variability and solar resource 

correlation (relative to the SunShot scenario) 

7. Distributed photovoltaic (PV) deployment: no distributed PV adoption versus 240 GW of 

distributed PV adoption by 2050 in the SunShot scenario 

8. Solar market supply growth limits: rapid growth cost penalty, maximum deployment 

level (SunShot scenario), and no annual growth limit 

As observed in the SSVS, installed price is the most important driver behind solar adoption. 

However, our analysis also indicates that non-price factors have significant impacts. By 2030, 

assuming the SunShot solar price targets are met, ReEDS builds utility-scale solar capacity 

between 175–212 GW for most scenarios under the SunShot-electricity-demand assumptions and 

86–154 GW for the scenarios under the low-electricity-demand assumptions (compared with 

209 GW in the SunShot scenario). By 2050, ReEDS builds utility-scale solar capacity between

229–567 GW for the scenarios under the SunShot-electricity-demand assumptions and 90–

236 GW for the scenarios under the low-electricity-demand assumptions (compared with 

475 GW in the SunShot scenario). 

With solar prices being equal, low electricity demand suppresses solar deployment the most, 

followed by lower natural gas prices. The other individual factors generally have a smaller 

impact, although relaxed solar market supply growth restrictions can boost utility-scale solar 

deployment substantially beyond SunShot levels by 2030 (up to 342 GW in one instance). Some 

combinations of factors have a major impact on solar deployment and generation, especially 

under the low-electricity-demand assumption. For example, there is a six-fold increase in 2050 

utility-scale solar capacity from the combination scenario with the lowest deployment to the 

scenario with the highest deployment. In fact, this worst-case combination scenario examined 

here results in utility-scale solar deployment that is lower even than the deployment in the 

sensitivity scenario where solar prices are two times higher than the SunShot scenario. Most 

combinations under the SunShot-electricity-demand assumptions, however, are in line with 

SunShot solar generation results through 2030, with discrepancies growing larger between 2030 

and 2050. 

The remainder of this report is structured as follows. Section 2 summarizes the modeling 

framework used in the SSVS, including a discussion of the capacity-expansion model and base 

assumptions as well as the SSVS results. Section 3 describes the sensitivity analyses performed 

in this study and details the various modified input assumptions. Section 4 presents the results of 

the individual and combined sensitivity analyses and the range of projected solar deployment. 

Section 5 summarizes the results and provides conclusions. 
3 

2 Modeling Framework

2.1 Scenario Development 

Two models were used to develop the SSVS scenarios: ReEDS (Short et al. 2011) and the 

SolarDS model (Denholm et al. 2009). ReEDS was the primary model used to simulate utilityscale solar deployment. Developed at NREL, it is a linear-optimization, capacity-expansion 

model that simulates the least-cost deployment and dispatch of generation resources. ReEDS 

determines the geographic deployment of PV, concentrating solar power (CSP), and other 

generation technologies based on a number of factors: future technology and fuel price 

projections, future U.S. electricity demand projections, regional solar resource quality and 

resource limitations, impacts of variability in renewable generation, transmission requirements, 

and grid reliability requirements. The model does not choose technologies based simply on their 

levelized cost of energy (LCOE). Rather, ReEDS co-optimizes energy, capacity, and ancillary 

service requirements—along with the temporal characteristics of variable generators among 

other factors—to produce an overall mix of generators that minimizes the system cost. 

Additionally, ReEDS optimizes transmission capacity expansion to accommodate the regional 

deployment of technologies. Through this economic optimization, ReEDS was used to examine

one possible set of impacts on the U.S. electric sector resulting from achieving the SunShot price 

targets. Major impacts include regional solar deployment levels, additional transmission capacity 

expansion requirements, additional firm and flexible resource requirements, emissions 

reductions, and electricity price and overall system cost impacts.

Because ReEDS is not designed to account for distributed rooftop PV generation, the penetration 

of distributed rooftop PV was exogenously specified in ReEDS from the SolarDS model for the 

SSVS. SolarDS—also developed at NREL—is a market-penetration model for commercial and 

residential rooftop PV. It simulates rooftop PV deployment based on regional electricity prices, 

financial incentives, regional solar resource quality, rooftop availability, and market-diffusion 

characteristics. 

A third model, ABB’s GridView (ABB Inc. 2008) was used to determine the operational 

feasibility of the SunShot results by performing hourly simulations of the ReEDS system, subject 

to more rigorous treatment of power-flow transmission constraints than can be captured 

by ReEDS.

2.2 Reference and SunShot Main Scenario

The SSVS includes a reference scenario with moderate solar energy price reductions to compare 

with the costs, benefits, and challenges of achieving the SunShot price targets. Future installed 

system price estimates for all technologies, including solar, are based on Black & Veatch (2012) 

in the reference scenario. In both the SunShot and reference scenarios, electricity demand is 

assumed to increase by about 1% per year through 2050. This assumption is consistent with U.S. 

Energy Information Administration (EIA) projections through 2035 (U.S. EIA 2010) and an 

extension of EIA’s projected trend through 2050. At this rate, demand reaches about 4,400 TWh 

by 2030 and 5,100 TWh by 2050. 
4 

For the SunShot scenario, solar technology installed system prices are assumed to reach the 

SunShot price targets by 2020: $1/WDC

3 for utility PV systems, $1.25/WDC for commercial 

rooftop PV, $1.50/WDC for residential rooftop PV, and $3.60/WAC for CSP systems with up to 

14 hours of thermal storage capacity. Table 1 provides a breakdown of PV and CSP prices 

including benchmarked prices in 2010 and projected prices in 2020 for the SunShot and 

reference scenarios. The SunShot prices represent a set of very aggressive, but technically 

possible, targets that would give solar technology a similar LCOE as competing electricity 

sources in each market segment. Pathways to achieving these cost and performance targets are 

discussed in detail in the SSVS (U.S. DOE 2012). 

Table 1. Projected PV and CSP Installed System Prices and Performance (2010 U.S. Dollars/W) 

Utility PV Residential 

Rooftop PV

Commercial 

Rooftop PV CSP

SunShot Ref. SunShot Ref. SunShot Ref. SunShot Ref.

$/WDC $/WDC $/WDC $/WDC $/WDC $/WDC $/WAC

Hours 

Storagea 

CFb

(%) $/WAC

Hours 

Storagea

CFb

(%)

2010 4.00 4.00 6.00 6.00 5.00 5.00 7.20 6 43 7.20 6 43

2020 1.00 2.51 1.50 3.78 1.25 3.36 3.60 14 67 6.64 6 43

2030 1.00 2.31 1.50 3.32 1.25 2.98 3.60 14 67 5.40 6 43

2040 1.00 2.16 1.50 3.13 1.25 2.79 3.60 14 67 4.78 6 43

2050 1.00 2.03 1.50 2.96 1.25 2.64 3.60 14 67 4.78 6 43

a The number of hours of thermal energy storage for CSP is optimized in ReEDS and is slightly different than 

the numbers in this table due to restrictions on the solar multiple within ReEDS (see Appendix A of the SSVS).

b Capacity factor (CF)

For the purposes of modeling the SunShot scenario, the SunShot system prices are assumed to 

remain constant through the 2020–2050 timeframe (i.e., no further price reductions are modeled 

for solar technologies beyond 2020). Installed system price estimates for all conventional and 

other renewable (including wind) electricity-generating technologies are based on Black & 

Veatch (2012). Fuel prices and price elasticities are based on EIA’s Annual Energy Outlook

(AEO) 2010 and are extrapolated through 2050 based on modeled fuel demand (U.S. EIA 2010). 

Future costs are discounted using a 7% real discount rate, per guidance from the U.S. Office of 

Management and Budget (OMB 2003). Additional details on model assumptions are provided in 

the SSVS (U.S. DOE 2012). 

Both scenarios assume the federal investment tax credit (ITC) and production tax credit run 

through their currently established expiration dates4

—end of 20165 and 2012, respectively—but 

that existing supports for conventional technologies that are embedded in the tax code or through 

other provisions continue indefinitely. Further, the scenarios do not incorporate any pending or 

proposed regulations associated with criteria pollutants or carbon emissions. 

 

3 Unless otherwise noted, all prices and values in the study are given in 2010 U.S. dollars. 4 These assumptions are based on policies that existed at the time the SSVS was published in February of 2012.

5 The SSVS assumes the ITC ends in 2016 even though current U.S. law maintains a 10% ITC after 2016. This 

choice is made to be consistent with the SunShot Initiative’s goal of making large-scale solar energy systems cost 

competitive without subsidies by the end of the decade (U.S. DOE 2012).
5 

2.3 Solar Growth Results

Achieving the SunShot targets is projected to result in the cumulative installation of 

approximately 302 GW of PV and 28 GW of CSP by 2030. By 2050, the cumulative installed 

capacities are projected to increase to 632 GW of PV and 83 GW of CSP. To achieve this level 

of cumulative installed capacity, annual installations would need to reach about 25–30 GW for 

PV and about 3–4 GW for CSP. Solar grows much more slowly in the reference scenario 

(Figure 1). 

Figure 1. Total solar capacity under the SunShot and reference scenarios

By 2030, the SunShot scenario levels of installations translate into PV generating 505 TWh of 

electricity, or 11% of total electricity demand, and CSP generating 137 TWh, or 3% of total 

demand. By 2050, PV is projected to generate 1,036 TWh, or 19% of total demand, and CSP is 

projected to generate 412 TWh, or 8% of total demand. Table 2 summarizes the electricity 

generation and installed capacity of PV and CSP in 2030 and 2050. 

Table 2. Overview of SunShot Scenario Results

Year Technology

Electricity

Generation 

(TWh)

Installed 

Capacity

(GW)

2030

PV 505 302

CSP 137 28

2050

PV 1,036 632

CSP 412 83
6 

The SunShot scenario results in a reduction in the need for new conventional generation capacity 

and the use of fossil fuels—primarily natural gas and coal. Figure 2 shows the evolution of the 

electric sector in the SunShot and reference scenarios. Before 2030, solar generation6 primarily 

offsets natural gas generation in the SunShot scenario. This is because midday solar generation 

corresponds well with times of peak midday electricity demand, and solar electricity frequently 

offsets more expensive peaking generation resources, like natural gas combustion turbines (CTs). 

However, once a large amount of solar generation has been added to the system (14% of demand 

by 2030), the “net load” of the system, defined as electricity demand minus solar and wind 

generation, shifts from midday to evening. Once this happens, solar generation offsets the new 

build-out of coal capacity seen in the reference scenario, and solar begins to significantly offset 

coal use after 2030. Additional natural gas resources developed after 2030 satisfy the evening 

peak in net load, and CSP resources deployed with several hours of storage provide a 

dispatchable solar generation resource.

Figure 2. Evolution of electricity generation in the reference and SunShot scenarios 

PV (Photovoltaic), CSP (Concentrating Solar Power), Other RE (Other Renewable Electricity), Gas-CT 

(Natural Gas Combustion Turbine), Gas-CC (Natural Gas Combined Cycle). 

 

6

 Note that generation numbers are reported at the bus. Therefore, generation from distributed rooftop PV has been 

multiplied by 1.053 (U.S. DOE 2012) to account for distribution losses that centralized generation systems incur.

This adjustment allows for a more equal comparison of generation from distributed PV systems with that from other 

technologies.
7 

3 Defining Sensitivity Scenarios

Here we present the assumptions behind a set of 45 sensitivity scenarios created in order to 

examine key drivers of potential solar market adoption. This analysis also provides greater 

understanding of basic model performance, which will help to improve the representation of 

solar technologies in models in the future. 

Table 3. Descriptions of Single-Factor Sensitivity Scenarios 

Sensitivity Category Scenario 

Abbreviation Brief Description

Baseline SunShot SunShot main scenario 

(baseline scenario for this sensitivity analysis)

1. Solar Technology 

Pricesa

SP-62.5 62.5% reduction in SSVS reference scenariob solar prices

SP-50 50% reduction in SSVS reference scenariob solar prices

2. Electricity Demanda LD Low electricity demand trajectory

3. Lower Natural Gas 

Pricesa

NG Natural gas prices based on AEO 2011 reference case

NG-EUR Natural gas prices based on AEO 2011 high estimated 

ultimate recovery case

4. Coal Retirementsa

NCR No coal retirements

CR-80 80 GW of regionally based coal retirements by 2026

CR-35 35 GW of regionally based coal retirements by 2020

5. Cost and 

Performance of NonSolar Renewable

Technologiesa

WG-ETI Evolutionary technology improvement (ETI) cost and 

performance metrics for wind and geothermal only

WG-2ETI Twice the cost reduction of the WG-ETI scenario for wind and 

geothermal

6. PV Resource 

Variabilitya

0.5σ

Technical ReEDS parameter analysis scenarios examining the 

treatment of PV resource variability

1.5σ

ρ = 0

0.5ρ

1.5ρ

HI-VAR

7. Distributed PV 

Deploymentc,d NoDPV No distributed rooftop PV systems are allowed

8. Solar Market Supply 

Growth Limitsc,e

USMG Unlimited solar market growth; solar growth penalties applied 

as cost

RSMG Relaxed solar market growth (remove 15-GW annual growth 

cap); utility PV and CSP growth can double every 2 years a Sensitivity scenario is based on exogenous model parameter assumptions (light blue). b The SSVS reference scenario is different than the baseline scenario (SunShot main scenario) for these 

sensitivities (see Section 2.2). c Sensitivity scenario is based on endogenous model assumptions (dark blue). d Rooftop PV deployment results from the SSVS are held constant in all these scenarios except for the 

NoDPV scenario that eliminates all rooftop PV.

e The growth of utility PV and CSP is limited in all of these scenarios except for the two scenarios that 

relax these limits.
8 

Nineteen single-factor sensitivity scenarios are summarized in Table 3 and detailed in 

Sections 3.1–3.8. As shown in Table 3, the scenarios in this analysis are designed around eight 

sensitivity categories, including a mix of exogenous data inputs to the model (light blue) and 

endogenous model assumptions (dark blue): solar prices, electricity demand projection, natural 

gas prices, coal retirements, cost and performance of non-solar renewable technologies, PV

resource variability, distributed PV deployment, and limits on solar market supply growth. 

In addition to the 19 single-factor sensitivity scenarios explored in this analysis (Table 3),

26 sensitivity scenarios are generated through a combination of five of the factors shown in 

Table 3: (1) low demand (LD), (2) lower natural gas prices (NG-EUR), (3) no coal retirements 

(NCR), (4) improved economic performance of wind and geothermal (WG-2ETI), and (5) high 

variability of PV (HI-VAR). These five factors are chosen as the basis for the 26 combination 

sensitivities because each factor represents the worst-case scenario (with respect to solar 

deployment) for the sensitivity categories examined here related to exogenous model inputs 

(Table 3). All of the combination scenarios assume the SunShot solar prices (Table 1). The 26 

combination scenarios (Table 4) explored in this analysis represent only a subset of the possible 

sensitivity combinations that could be explored using the single-factor sensitivities from Table 3. 

The combinations were chosen to provide the widest possible range of impacts due to the factors

examined here. 

Table 4. Sensitivity Combinations Using the LD, NG-EUR, NCR, WG-2ETI, and HI-VAR SingleFactor Scenario Assumptions

Low-Demand Scenarios AEO 2010 Demand Scenarios

1 LD NG-EUR NCR WG-2ETI HI-VAR 1 NG-EUR NCR WG-2ETI HI-VAR

2 LD NCR WG-2ETI HI-VAR 2 NCR WG-2ETI HI-VAR

3 LD NG-EUR NCR HI-VAR 3 NG-EUR NCR HI-VAR

4 LD NG-EUR WG-2ETI HI-VAR 4 NG-EUR WG-2ETI HI-VAR

5 LD NG-EUR NCR WG-2ETI 5 NG-EUR NCR WG-2ETI

6 LD NCR HI-VAR 6 NCR HI-VAR

7 LD WG-2ETI HI-VAR 7 WG-2ETI HI-VAR

8 LD NG-EUR HI-VAR 8 NG-EUR HI-VAR

9 LD NCR WG-2ETI 9 NCR WG-2ETI

10 LD NG-EUR NCR 10 NG-EUR NCR

11 LD NG-EUR WG-2ETI 11 NG-EUR WG-2ETI

12 LD HI-VAR

13 LD NCR

14 LD WG-2ETI

15 LD NG-EUR
9 

3.1 Solar Technology Prices (SP-62.5, SP-50)

To explore the sensitivity of solar deployment to solar technology prices, two additional price 

scenarios were modeled, each less aggressive than the SunShot targets: (1) PV prices decline 

50% (the SP-50 scenario) between 2010 and 2020, and (2) PV prices decline 62.5% (the SP-62.5 

scenario) between 2010 and 2020. Both sensitivity scenarios include comparable price declines 

for CSP. Table 5 shows the SunShot and sensitivity scenario prices for all solar technologies and 

applications. The SunShot scenario’s 2010 utility PV benchmarked price is $4/WDC; thus, the 

sensitivity scenarios’ 2020 utility PV prices are $2/WDC (SP-50) and $1.50/WDC (SP-62.5). 

Similarly, the sensitivity scenarios’ 2020 distributed PV (residential and commercial) prices are 

50% and 62.5% lower in relation to their 2010 benchmarked prices. For CSP, the decline in 

installed capital cost was set to yield a similar level of relative cost reduction on an LCOE basis, 

including a shift to increased storage. The increased levels of storage assumed for CSP are 

reflected in Table 1, with higher values for capacity factors assumed in all scenarios. All 

conventional and non-solar renewable technology prices are the same for the SunShot and the 

solar price sensitivity scenarios. For all other parameters, the sensitivity analysis matches the 

SunShot analysis. 

Table 5. Price Inputs for SunShot and Solar Price Sensitivity Scenarios

Technology/Application

SunShot 

Scenario 2020

 -75% Price

Sensitivity 

Scenario 2020 

-62.5% Price 

(SP-62.5)

Sensitivity 

Scenario 2020 

-50% Price

(SP-50)

Reference 

Scenario 

2020

PV – Residential ($/WDC) 1.50 2.25 3.00 3.78

PV – Commercial ($/WDC) 1.25 1.88 2.50 3.36

PV – Utility Scale ($/WDC) 1.00 1.50 2.00 2.51

CSP, 6/14 hour Storage 

($/WAC)

a 3.60 4.87 6.14 6.64

a All values are for CSP systems with 14 hours of thermal storage except for the reference scenario value, 

which is for 6 hours of thermal storage.
10 

3.2 Low Electricity Demand Growth (LD) 

Adapted from Renewable Electricity Futures (RE Futures), the low-demand (LD) scenario

assumes a high level of energy efficiency within the buildings and industrial sectors, resulting in 

essentially flat load growth, despite substantial growth in electric vehicle deployment. Figure 3

provides the demand profile. Additional details are in RE Futures Volume 3 (NREL 2012). 

Figure 3. Comparison of end-use demand projections between the SunShot and low-demand 

scenarios 

3.3 Lower Natural Gas Prices (NG, NG-EUR)

Natural gas prices for the SunShot scenario are based on AEO 2010 natural gas price projections

(U.S. EIA 2010). ReEDS adjusts these projections upward or downward based on estimated 

electric-sector consumption and a demand-elasticity estimate. This analysis considers two lower

natural gas price trajectories: (1) the AEO 2011 natural gas price reference case trajectory (the 

NG scenario) and (2) the AEO 2011 high estimated ultimate recovery case trajectory (the NGEUR scenario), which projects high shale gas recovery (U.S. EIA 2011). Figure 4 compares 

natural gas price projections from the AEO 2010–2012 reference cases as well as the AEO 2011 

high EUR case through 2035. The AEO 2012 reference case projections are not considered in the 

sensitivity analysis because of their similarity with the AEO 2011 reference case projections

(U.S. EIA 2012). 
11 

Figure 4. Comparison of AEO natural gas price projections through 2035

AEO 2010 base case (baseline for the SunShot analysis), AEO 2011 base case (intermediate lower

natural gas, NG for this analysis), AEO 2011 high EUR case (NG-EUR for this analysis), AEO 2012 base 

case (not considered for this analysis). 

3.4 Coal Retirements (CR-35, CR-80, NCR) 

Under the assumption that coal plants have an 80-year lifetime, the SunShot scenario requires 

1/80th of the existing coal fleet from 20107 to retire each year beginning in 2012. The 1/80th

retirement percentage is applied to every plant across all regions irrespective of plant vintage. 

Furthermore, this particular approach does not consider retirements on an economic basis. 

Approximately 76 GW of the 2010 coal fleet are retired by 2030, and approximately half 

(152 GW) are retired by 2050. 

Coal fleet retirement is a potentially important driver for the construction of new generation 

capacity. To explore the impact of a range of retirements, this analysis includes a scenario

without any retirements (the NCR scenario) as well as two additional retirement scenarios. The 

two additional scenarios consider near-term retirements due to various existing and pending 

regulations8

 and decreased competitiveness due to low natural gas prices. The first of these 

scenarios (CR-80) assumes 80 GW of coal retirement by 2026. The second scenario (CR-35) 

assumes 35 GW of coal retirements by 2020. Both CR-80 and CR-35 retirement schedules use 

geographically specified9 retirements loosely based on Logan et al. (2012). 

 

7 The existing coal fleet totaled approximately 305 GW in 2010.

8 Pending regulations include the Cross States Air Pollution Rule, Mercury and Air Toxic Standard (aka Utility 

MACT), 316(b) water intake rule, and Coal Combustion Residual rule. 9 The retirements in the SSVS are not geographically specified.
12 

Figure 5 compares cumulative coal retirements through 2050 among the base SunShot, CR-35, 

and CR-80 scenarios. Beyond 2020 and 2026, coal retirements continue out to 2050 according to 

regionally based coal retirement projections from Ventyx (2010). The Ventyx retirements are 

based on lifetime estimate data for power plants.

Figure 5. Comparison of coal retirement trajectories through 2050 

SunShot (1/80th of existing coal fleet retired each year), CR-35 (35 GW retired by 2020 and Ventyx

retirements to 2050), CR-80 (80 GW retired by 2026 and Ventyx retirements to 2050). 

The CR-35 and CR-80 scenarios have coal retirements that differ from the baseline SunShot 

scenario in both location and timing. These coal retirement scenarios account for expected 

retirements in the Ohio River Valley and eastern states that have many old coal plants. By 2030, 

the CR-80 scenario retires more coal in the Ohio River Valley than the SunShot scenario; the 

largest difference is about 5.5 GW, in both Pennsylvania and Kentucky. By 2050, CR-80 retires 

more coal in most of the Southeast (Figure 6); the largest differences are in Illinois (4.5 GW) and 

Tennessee (4.11 GW). Conversely, the SunShot scenario has more retirements in the West 

(where the best CSP resource exists) than the CR-80 scenario; the largest difference is in Texas, 

where the SunShot scenario retires 4.25 GW more coal by 2030 and 7.55 GW more by 2050 

(Figure 6). 
13 

Figure 6. Geographic differences in coal retirements between the CR-80 and SunShot scenarios 

(+) More coal retirements in CR-80 scenario; (-) more coal retirements in SunShot scenario

3.5 Cost and Performance of Non-Solar Renewable Technologies 

(WG-ETI, WG-2ETI)

The SunShot scenario assumes limited cost and performance improvements for wind and 

geothermal technologies. Competitive costs for these technologies could enable their projected 

capacity to increase, displacing capacity previously assigned to solar or conventional generators. 

Two capital cost reduction trajectory scenarios are based on the evolutionary technology 

improvement (ETI) costs in RE Futures (NREL 2012).10 The first (the WG-ETI scenario) adapts 

capital cost trajectories for wind and geothermal11 directly from RE Futures ETI scenarios. The 

second (the WG-2ETI scenario) assumes capital cost reductions equal to twice the difference 

between the wind and geothermal capital costs in the SunShot and RE Futures ETI scenarios. 

Figure 7 compares offshore and land-based wind capital costs among the SunShot, WG-ETI, and 

WG-2ETI scenarios. 

 

10 Capacity factors and operations and maintenance costs for wind are adapted from RE Futures for these scenarios 

as well.

11 There is a large range of estimates for possible cost reductions of wind and other renewable energy technologies. 

These are discussed in more detail in the technology chapters within RE Futures. While RE Futures considers cost 

and performance improvements for biomass and hydropower, these changes are not considered in this analysis.
14 

Figure 7. Capital cost for land-based and offshore wind for SunShot, WG-ETI, and WG-2ETI

scenarios

Geothermal technologies modeled include hydrothermal (HT), enhanced hydrothermal systems 

(EHS), and enhanced geothermal systems (EGS). In addition to capital cost reductions, HT

includes additional undiscovered resources as defined in RE Futures. Figure 8 shows the 

geothermal resource supply curves from the SunShot scenario in 2010 as well as the cost 

improvements for the WG-ETI and WG-2ETI scenarios by 2050. The SunShot scenario assumes 

no cost reductions through 2050 for all three geothermal technologies. The WG-ETI and WG2ETI scenarios assume cost reductions through 2050 for all three geothermal technologies 

(Figure 8). Note that Figure 8 only shows the first 20,000 MW of resources less than $8,000/kW 

for each geothermal type. 
15 

Figure 8. Geothermal resource supply curves in 2010 and 2050 for SunShot, WG-ETI, and WG-2ETI

scenarios

3.6 PV Resource Variability (0.5σ, 1.5σ, ρ = 0, 0.5ρ, 1.5ρ, HI-VAR) 

ReEDS considers the seasonal and diurnal variability of expected output of wind and solar 

plants. For variable resource renewable technologies like wind and solar, resource variability 

impacts the evaluation of capacity value, operating reserve requirements, and curtailments 

associated with these technologies. ReEDS statistically calculates these metrics by considering 

the current system characteristics including electricity demand, existing capacity mix, variance in 

expected output, and inter-region correlations of expected output. To determine the sensitivity of 

ReEDS to these factors, several parametric scenarios are evaluated. 

Hourly wind and solar resource data are used to calculate average production profiles and the 

standard deviation of expected plant output for each of the 17 ReEDS times-slices12 for these 

technologies. Variable resource technologies with a large variance in expected plant output for a 

typical hour in a time-slice exhibit lower capacity value and higher curtailments. Furthermore, 

variable resource technologies with a large variance in expected plant output from one hour to 

the next within a time-slice exhibit high expected forecast error reserve requirements. For this 

analysis, the effects of solar variability on solar deployment are addressed by varying these 

standard deviations (symbolized as σ) up and down by a multiplier (1.5σ or 0.5σ). Figure 9

shows these changes in standard deviations for each time-slice.

 

12 The 17 time-slices in ReEDS consist of four representative time segments (morning, afternoon, evening, night) in 

a typical day for each season (summer, fall, winter, spring) as well as a peak-load summer time-slice. 
16 

Figure 9. Standard deviations of expected utility PV plant output for each time-slice 

All else being equal, ReEDS will select wind and solar resource sites based on geographic 

diversification of resource (i.e., preferentially selecting an untapped solar resource site that is less 

correlated with existing solar capacity). Resource sites that are less correlated (i.e., two sites with 

correlation coefficients near zero) in a time-slice will exhibit higher capacity value and lower 

curtailments. Furthermore, resource sites that are less correlated from one hour to the next within 

a time-slice will exhibit lower expected forecast error reserve requirements. To this effect, 

additional sensitivity scenarios include varying geographic solar resource correlations

(symbolized as ρ) up and down by a multiplier (1.5ρ or 0.5ρ). Figure 10 shows these changes in 

correlation coefficients. Table 6 summarizes all of the solar variability scenarios considered. 

Figure 10. Inter-region correlations of expected power output between PV resource Regions
17 

Table 6. Descriptions of PV Resource Variability Scenarios 

PV Resource

Variability

Scenarios 

Brief Description

0.5σ Standard deviation in expected PV output is reduced by 50% (i.e., less uncertainty)

1.5σ Standard deviation in expected PV output is increased by 50% (i.e., more 

uncertainty) 

ρ = 0 PV resource is assumed to be uncorrelated between different resource regionsa

0.5ρ All inter-regional correlation coefficients for PV are decreased by 50% (i.e., PV

resource becomes more geographically uncorrelated)

1.5ρ All inter-regional correlation coefficients for PV are increased by 50% (i.e., PV

resource becomes more geographically correlatedb)

HI-VAR Combination of 1.5σ and 1.5ρ PV resource variability scenarios (i.e., more 

uncertainty and more correlated) a Correlation coefficients for resource sites that are more correlated (i.e., ρ is near -1 or 1) change more in 

absolute terms than those for resource site that are nearly uncorrelated (i.e., ρ is near 0) in the ρ = 0 

scenario. b Correlation coefficients must range from -1 to 1. Therefore, correlation coefficients less than -0.67 or 

greater than 0.67 will increase (i.e., approach -1 or 1) less than 50% in the 1.5ρ scenario. 

3.7 Distributed PV Deployment Assumptions (NoDPV) 

Because ReEDS is not designed to account for distributed rooftop PV generation, the 

deployment of distributed (residential and commercial) PV capacity is exogenously specified in

ReEDS from the SolarDS model (see Denholm et al. 2009 for a description of SolarDS).

Distributed PV deployment totals 121 GW by 2030 and 240 GW by 2050. This specification of 

distributed PV capacity effectively changes the net load shape in ReEDS. To assess the tradeoff 

between distributed PV and central utility PV, the NoDPV scenario sets distributed generation

deployment to zero through 2050. Note that Drury et al. (2013) examine sensitivities of

distributed generation adoption in the SSVS to market assumptions. 

3.8 Solar Market Supply Growth Assumptions (RSMG, USMG)

The SSVS limits the annual installed capacity growth of CSP and utility PV to no more than 

double in each 2-year model period, and it limits U.S. annual demand to no more than 15 GW of 

utility PV and no more than 15 GW of CSP.13 These constraints were added to avoid boom-bust 

cycles in supply and demand in which demand rises to a very high level for a few model periods 

and then collapses. The constraints could also suggest the fact that manufacturers would consider 

longer-term market sustainability before developing manufacturing capacity and that market 

distribution and installation infrastructure takes time to develop. For the purposes of defining the 

initial allowable solar growth (to no more than double in each 2-year model period), a starting 

capacity of 500 MW14 per year is used as a reference point in the SSVS and all scenarios 

discussed here. Using this 500-MW starting capacity means that, until the existing system has

 

13 For the solar market supply growth sensitivities, there is no interaction with the distributed PV market supply 

growth.

14 The 500-MW starting capacity does not reflect the actual installed utility PV or CSP capacity at the beginning of 

the ReEDS optimization.
18 

built more than 500 MW of utility PV or CSP, the model allows each of these solar technologies 

to grow by up to 1,000 MW over a 2-year model period. 

For the present sensitivity analysis, the solar growth limits are relaxed in two different scenarios. 

The first growth relaxation scenario (relaxed solar market growth, or RSMG) simply removes the 

15 GW per year cap on utility PV and CSP installations. The second (unlimited solar market 

growth, or USMG) also removes the 15-GW per-year cap but also replaces the doubling-every-2-

years constraint with a growth penalty cost curve. The growth penalty costs are a function of 

installed capital cost and the increase in new construction over previous years. 

Figure 11. Utility PV growth penalty curve

Using the growth penalty cost curve shown in Figure 11, ReEDS allows the rate of new utility 

PV installations to increase by 44% of the base utility PV capacity in every 2-year model period 

on both a national and regional level before growth cost penalties apply. The base capacity is 

reset in every 2-year model period. On the national level, the base utility PV capacity is defined 

as the highest of 1,000 MW or the national total of new utility PV installations from the previous 

2-year period or 90% of the base capacity from the previous 2-year period. On the regional level, 

the base utility PV capacity is defined as the highest of 600 MW or the new regional utility PV

installations from the previous 2-year model period or 90% of the base capacity from the 

previous 2-year period. Therefore, as a minimum, ReEDS can add up to 1,440 (1.44 x 1,000) 

MW and 864 (1.44 x 600) MW of new capacity nationally and regionally, respectively, in each 

2-year model period before incurring a growth penalty. 

Other models have used similar constructs to control market dynamics. For example, the 

RenewMarket model from the Union of Concerned Scientist has historically used growth 

penalties applied as cost when the average growth in installed capacity over previous years 

exceeds some growth rate threshold (UCS 1999). The National Energy Modeling System 

(NEMS) from EIA has historically used growth constraints and penalties applied as cost to 

represent capacity growth limitations on a national and regional level (U.S. EIA 1998). 
19 

4 Sensitivity Scenario Results

A slightly updated version of ReEDS was used for these sensitivity analyses than was used for

the SSVS. Therefore, the SunShot main scenario results presented here as the sensitivity baseline 

scenario deviate slightly from those in the SSVS. Figure 12 shows the difference in capacity 

build-out between the updated ReEDS model and the ReEDS model used in the SSVS. In 

general, there are no significant capacity changes between the scenarios. The updated ReEDS 

model produces a modest 6.7 GW (1.7%) increase in utility PV, to 398 GW by 2050. 

Figure 12. Change in capacity from SSVS ReEDS code to updated ReEDS code 

Gas-CC (Natural Gas Combined Cycle), Gas-CT (Natural Gas Combustion Turbine), CSP (Concentrating 

Solar Power), Utility PV (Utility-Scale Photovoltaic).

The results presented in the following sections focus on changes in CSP and utility PV

deployment rather than changes in solar deployment as a whole. Most of the scenarios explored 

in this analysis maintain the SunShot solar price assumptions described in Section 2.2. 

Distributed PV adoption is determined by SolarDS based on these price assumptions (among 

other inputs) and then specified exogenously in ReEDS.15 By considering changes in solar 

deployment as a whole for these sensitivities, the fixed deployment schedule for distributed PV 

dilutes the impacts of changes in market and performance assumptions on deployment of solar 

technologies that can vary endogenously in ReEDS (i.e., CSP and utility PV); therefore, the 

results for this analysis focus on changes in solar technologies over which ReEDS has 

endogenous control. 

In the SunShot scenario, ReEDS simulates 80 TWh of curtailments for variable resource 

technologies (wind and solar), representing 1.6% of demand and 4.6% of wind and solar 

generation. ReEDS accounts for curtailments of variable resource technologies as a whole but 

does not assign specific curtailment levels to particular technologies. Therefore, generation

 

15 For the solar price sensitivities, the SolarDS model was rerun to provide deployment trajectories for distributed 

PV based on the adjusted distributed PV prices.
20 

numbers presented in the SSVS reflect the potential generation that could contribute to electricity 

demand assuming the generation from wind and solar is not curtailed (see Section 2.3). The 

actual contributions of solar generation to electricity demand would be slightly lower than 

reported in the SSVS after considering curtailments.

16

For some of the sensitivity scenarios in this analysis, total curtailments can be significantly larger 

than in the SSVS scenarios. Thus, for all generation results presented in the following sections, 

we have developed a method for allocating curtailments in ReEDS to wind and PV.

17 In this 

approach, bulk curtailments for each time-slice modeled in ReEDS are allocated to wind and PV 

based on the fraction of generation from these technologies in each time-slice. As a result, most 

of the curtailments in night time-slices (10 p.m. to 6 a.m.) are assigned to wind because PV 

generation will be minimal during these hours. Most curtailments during afternoon time-slices 

(1 p.m. to 5 p.m.) are assigned to PV because of both high PV deployment compared with wind 

deployment and, in general, peak PV generation compared with typically non-peak wind

(specifically onshore wind) generation during these hours. 

The following subsections discuss the sensitivity results by category. First we discuss the 19 

single-factor sensitivity cases (Sections 4.1–4.8). Then we discuss two of the multi-factor 

sensitivity cases (Section 4.9). Finally we examine the full range of multi- and single-factor 

sensitivity cases together (Section 4.10). Note that some figures have different scaling to 

improve readability. Also, note that generation numbers are reported at the bus. Therefore, 

generation from distributed rooftop PV has been multiplied by 1.053 (U.S. DOE 2012) to 

account for distribution losses that centralized generation systems incur. This adjustment allows 

for a more equal comparison of generation from distributed PV systems with that from 

other technologies.

4.1 Solar Technology Prices (SP-62.5, SP-50)

Figure 13 shows the strong dependence of CSP and utility PV deployment on solar prices. In the 

SunShot scenario, total installed CSP and utility PV capacity reaches 209 GW in 2030 and 475 

GW in 2050. In the 62.5% price-decline scenario (SP-62.5), total CSP and utility PV capacity 

reaches 176 GW in 2030 (16% lower than in the SunShot scenario) and 331 GW in 2050 (30% 

lower). In the 50% price-decline scenario (SP-50), total CSP and utility PV deployment drops 

dramatically: to 85 GW in 2030 (59% lower than in the SunShot scenario) and 117 GW in 2050 

(75% lower). In the reference scenario, total CSP and utility PV capacity reaches 11 GW in 2030 

(95% lower than in the SunShot scenario) and 35 GW in 2050 (93% lower).

Figure 14 shows similar results for total generation from CSP and utility PV. Solar market 

penetration is sensitive to the projected level of PV and CSP price reductions. These results 

indicate a threshold at which solar deployment increases non-linearly as price decreases. This 

threshold is somewhere between $2.00/WDC (SP-50) and $1.50/WDC (SP-62.5) for utility PV

(and an equivalent level of price reduction for CSP). 

 

16 From the SSVS, solar technologies are projected to satisfy roughly 14% of contiguous U.S. electricity demand by 

2030 and 27% by 2050. 17 Because ReEDS mainly deploys CSP technology with storage, no curtailments are assigned to CSP in these 

estimates.
21 

Figure 13. Utility-scale solar capacity under a range of SunShot solar price-reduction scenarios

Figure 14. Utility-scale solar generation under a range of solar price-reduction scenarios
22 

4.2 Low Electricity Demand Growth (LD)

Compared with the SunShot scenario, by 2030 the LD scenario yields changes in capacity, 

including 4 GW less wind (5% change), 40 GW less gas-CC (22% change), 12 GW less gas-CT

(5% change), 11 GW less CSP (down to 16 GW, 41% change), and 43 GW less utility PV (down 

to 138 GW, 24% change). Generation from CSP and utility PV declines 139 TWh (down to 

321 TWh, 30% change) to 8% of total generation (versus 10% in the SunShot scenario). With 

distributed PV, this corresponds to a total solar generation fraction of 11% by 2030 (versus 13% 

in the SunShot scenario). 

By 2050, changes in capacity include 13 GW less wind (14% change), 46 GW less coal (22% 

change), 79 GW less gas-CC (29% change), 63 GW less gas-CT (20% change), 35 GW less CSP 

(down to 47 GW, 43% change), and 210 GW less utility PV (down to 189 GW, 53% change). 

Generation from CSP and utility PV declines 531 TWh (down to 565 TWh, 48% change) to 14% 

of total generation (versus 20% in the SunShot scenario). With distributed PV, this corresponds 

to a total solar generation fraction of 21% by 2050 (versus 26% in the SunShot scenario). 

Figure 15 compares the LD and SunShot scenario results. 

 

Figure 15. Capacity and generation comparisons between LD and SunShot scenarios

Low electricity demand (essentially flat electric load growth through 2050) substantially reduces 

future capacity and generation from all generating technologies compared with the SunShot 

scenario. The deployment of CSP and utility PV is cut approximately in half by 2050. Because 

distributed PV deployment levels are left constant between the SunShot and LD scenarios, 

distributed PV contributes a larger portion of demand in the LD scenario. Therefore, there is less 

room for growth in utility PV and CSP in general. 
23 

4.3 Lower Natural Gas Prices (NG, NG-EUR) 

Using AEO 2011 natural gas prices (the NG scenario) produces 400 TWh more electricity 

generation from natural gas CC plants—7% of total electricity generation—compared with the 

SunShot scenario in 2050. However, 350 TWh of this additional gas generation merely replaces 

coal generation, mostly from new coal plants that are built in the SunShot scenario after 2030. 

The CSP and utility PV generation fraction for this sensitivity scenario is relatively unchanged 

from the SunShot scenario. These results suggest that, if solar prices reach the SunShot targets, 

solar will compete with natural gas generation at a range of natural gas fuel prices, and natural 

gas does not compete directly with solar at high solar penetrations because the net load 

shape shifts. 

Compared with the SunShot scenario, by 2030 the NG-EUR scenario yields changes in capacity, 

including 9 GW less wind (11% change), 10 GW less gas-CC (6% change),

18 31 GW additional 

gas-CT (14% change), and 20 GW less CSP (down to 7 GW, 75% change). Generation from 

CSP and utility PV declines 106 TWh (down to 355 TWh, 23% change) to 8% of total 

generation (versus 10% in the SunShot scenario). With distributed PV, this corresponds to a total 

solar generation fraction of 11% by 2030 (versus 13% in the SunShot scenario). 

By 2050, changes in capacity include 20 GW less wind (22% change), 53 GW less coal (25% 

change), 93 GW additional gas-CC (34% change), 16 GW additional gas-CT (5% change), 48 

GW less CSP (down to 35 GW, 58% change), and 84 GW less utility PV (down to 314 GW, 

21% change). Generation from CSP and utility PV declines 385 TWh (down to 711 TWh, 35% 

change) to 13% of total generation (versus 20% in the SunShot scenario). With distributed PV, 

this corresponds to a total solar generation fraction of 19% by 2050 (versus 26% in the SunShot 

scenario). Figure 16 compares the NG-EUR and SunShot scenario results. 

In the NG-EUR scenario, gas prices are low enough to suppress utility PV deployment such that 

the net load shape is not shifted as far into the evening as in the NG scenario whereby natural gas 

still competes directly with utility PV. Lower natural gas prices in the NG-EUR scenario 

increase electricity generation from natural gas and reduce the contribution of other generating 

technologies, including solar. Gas-CCs operate at a higher average capacity factor in the NGEUR scenario than in the SunShot scenario by reducing gas-CC use to provide operating reserves 

and increasing gas-CC use to serve load. Consequently, gas-CTs provide a higher portion of 

operating reserves. 

 

18 A 10-GW decrease of gas-CC capacity by 2030 in a lower natural gas price scenario is not intuitive. In this 

scenario, ReEDS operates gas-CCs at a higher average capacity factor (0.7) than that in the SunShot scenario (0.6), 

which accounts for the lower gas-CC capacity in the NG-EUR scenario. 
24 

Figure 16. Capacity and generation comparisons between the NG-EUR and SunShot scenarios

Figure 17 shows the national average natural gas price and consumption outputs from ReEDS 

through 2050 for scenarios using the AEO 2010 reference case (the natural gas cost trajectory 

used in the SunShot scenario), AEO 2011 reference case (NG), and AEO 2011 high EUR case 

(NG-EUR). As natural gas prices decrease, the electricity generation from natural gas increases.
25 

Figure 17. National average electric-sector natural gas prices and consumption outputs from 

ReEDS through 2050

4.4 Coal Retirements (CR-35, CR-80, NCR)

Compared with the SunShot scenario, by 2030 the 76 GW of unretired coal in the NCR scenario 

yield changes in capacity, including 4 GW less wind (4% change), 45 GW less gas-CC (25% 

change), 12 GW less gas-CT (6% change), and 15 GW less CSP (down to 11 GW, 57% change).

Generation from CSP and utility PV declines 82 TWh (down to 378 TWh, 18% change) to 8% of 

total generation (versus 10% in the SunShot scenario). With distributed PV, this corresponds to a 

total solar generation fraction of 11% by 2030 (versus 13% in the SunShot scenario). 

By 2050, the 152 GW of unretired coal yield changes in capacity, including 13 GW less wind 

(14% change), 54 GW less new coal,

19 64 GW less gas-CC (23% change), 30 GW less CSP 

(down to 52 GW, 37% change), and 50 GW less utility PV (down to 348 GW, 13% change). 

Generation from CSP and utility PV declines 234 TWh (down to 862 TWh, 21% change) to 16% 

of total generation (versus 20% in the SunShot scenario). With distributed PV, this corresponds 

to a total solar generation fraction of 21% by 2050 (versus 26% in the SunShot scenario). 

 

19 The main SunShot scenario builds 54 GW of new coal between 2030 and 2050, whereas the NCR scenario does 

not build any additional coal in the same timeframe. Therefore, there is a net difference in coal capacity of 98 GW 

(152 GW of unretired coal capacity from the NCR scenario minus 54 GW of new builds from the SunShot scenario) 

between the NCR and SunShot scenarios.
26 

Under the NCR scenario, the unretired coal capacity displaces natural gas that would have 

otherwise been built to cover the coal plant retirements. Figure 18 shows capacity and generation

comparisons between the NCR and SunShot scenarios. 

 

Figure 18. Capacity and generation comparisons between the NCR and SunShot scenarios

The cumulative deployment of utility PV does not change from the SunShot scenario until after 

2030 in the NCR scenario. Therefore, the lower requirement for new capacity to provide energy 

and firm power due to foregone coal retirements does not affect utility PV deployment in the 

near and mid-term horizons. Although coal plants are not retired in this scenario, gas-CC, gasCT, oil-gas-steam, and nuclear retirements in conjunction with load growth create a need for new 

capacity to provide energy and firm power. 

The assumed coal retirements in the SunShot scenario do not distinguish plant vintage nor 

incorporate near-term planned retirements. The CR-35 and CR-80 sensitivity scenarios consider 

retirements based on plant vintage and near-term retirements due to various existing and pending 

regulations. The CR-35 and CR-80 scenarios result in state-specific variation of utility PV

deployment, but aggregate utility PV deployment and generation is largely unaffected (Table 7). 

This is expected given the diversity of PV solar resource. Additionally, because the CR-35 and 

CR-80 scenarios retire less coal in the West where the best CSP solar resource exists (Section

3.4), these scenarios result in lower CSP generation in 2030 and 2050 than the SunShot scenario 

(Table 7). 
27 

By 2030, the CR-35 scenario enforces fewer coal retirements than the SunShot scenario, thus 

coal-fired generation is higher in the CR-35 scenario and solar generation is lower (Table 7). 

Conversely, the CR-80 scenario enforces slightly higher coal retirements than the SunShot 

scenario, thus coal-fired generation is lower. By 2050, total coal retirements in CR-35 and CR-80 

are nearly the same as in SunShot; therefore, the utility-scale solar generation fraction is nearly 

unchanged (Figure 19).

Table 7. 2030 and 2050 Annual Generation from Utility-Scale Solar and Coal

Generation (TWh) 2030 2050

Utility PV CSP Coal Utility PV CSP Coal

SunShot 326 134 1,684 684 412 1,532

CR-35 324 88 1,894 691 382 1,598

CR-80 323 116 1,600 692 384 1,535

NCR 322 56 2,188 596 266 2,214

Figure 19. Annual utility-scale solar generation fraction in 2030 and 2050 for all coal retirement 

scenarios

4.5 Cost and Performance of Non-Solar Renewable Technologies 

(WG-ETI, WG-2ETI)

Compared with the SunShot scenario, by 2030 the WG-ETI scenario yields changes in capacity, 

including 39 GW of additional wind (50% change), 10 GW of additional geothermal (up to 15 

GW), 17 GW less gas-CC (9% change), 9 GW additional gas-CT (4% change), and 12 GW less 

CSP (down to 14 GW, 47% change). Generation from CSP and utility PV declines 70 TWh 

(down to 390 TWh, 15% change) to 8% of total generation (versus 10% in the SunShot 

scenario). With distributed PV, this corresponds to a total solar generation fraction of 12% by 

2030 (versus 13% in the SunShot scenario). 
28 

By 2050, capacity changes include 152 GW of additional wind (up to 244 GW), 12 GW of 

additional geothermal (up to 16 GW), 33 GW less coal (16% change), 20 GW less gas-CC (7% 

change), 47 GW additional gas-CT (15% change), 26 GW less CSP (down to 56 GW, 31% 

change), and 31 GW less utility PV (down to 367 GW, 8% change). Generation from CSP and 

utility PV declines 192 TWh (down to 904 TWh, 18% change) to 17% of total generation (versus

20% in the SunShot scenario). With distributed PV, this corresponds to a total solar generation 

fraction of 22% by 2050 (versus 26% in the SunShot scenario). Figure 20 compares the WG-ETI

and SunShot scenarios. 

Figure 20. Capacity and generation comparisons between the WG-ETI and SunShot scenarios

From the WG-ETI scenario, the WG-2ETI scenario produces only a small additional decrease of

utility-scale solar capacity by 2050 (4 GW less CSP, 9 GW less utility PV). Wind capacity 

expands an additional 60 GW beyond the levels in the WG-ETI scenario at the expense of coal, 

gas-CC, and geothermal.20 Finally, 21 GW of additional gas-CTs are required beyond the WGETI capacity levels for operating reserves for the increased wind capacity. Note that bulk 

curtailments from wind and PV nearly double in this scenario (relative to the SunShot scenario). 

Most of these increased curtailments are associated with the wind increase. Although utility PV

generation declines by 77 TWh in 2050 (relative to the SunShot scenario) due to less utility PV

capacity in the system, estimated curtailments associated with utility PV increase slightly. Higher 

curtailments are expected with higher levels of variable resource renewable technology 

penetration. 

 

20 Although both wind and geothermal costs are lower in the WG-2ETI than in the WG-ETI scenario, the 

incremental decrease in wind costs makes wind more competitive than geothermal. 
29 

The results of the WG-ETI and WG-2ETI scenarios highlight the complementary nature of solar 

and wind resources both in geography and in generation profiles. Overall, decreasing the costs of 

wind and geothermal technologies reduces the contribution of fossil generators much more than 

solar generation, as wind generation is not highly correlated with solar generation. However, in 

some locations, wind replaces solar to add net system benefits. For example, decreased wind 

costs result in additional wind in the Southwest, where the system has extremely high solar 

penetration. This very high penetration of solar creates generation that exceeds local demand and 

requires export or curtailment. Replacing solar with wind in this area actually improves the 

coincidence of renewable energy supply with demand. 

4.6 PV Resource Variability (0.5σ, 1.5σ, ρ = 0, 0.5ρ, 1.5ρ, HI-VAR)

The following scenarios evaluate the impacts and potential benefits of the treatment of solar 

variability in ReEDS. ReEDS statistically calculates the capacity value, curtailments, and 

operating reserve requirements associated with variable generation technologies like solar. A 

detailed description of these calculations can be found in the ReEDS documentation (Short et 

al. 2011). 

Increasing the uncertainty in power output of PV resource (1.5σ) or increasing the resource 

correlation among PV sites (1.5ρ) causes capacity value to decline and curtailments to increase, 

both of which reduce the value of PV and thus reduce PV market penetration. The combination 

of more uncertainty in the expected output from PV (1.5σ) and more correlated inter-region PV

resource (1.5ρ) defines the HI-VAR scenario. The HI-VAR scenario reduces capacity value and 

increases curtailments more than either the 1.5σ or the 1.5ρ scenarios. This section uses 

curtailment level as a proxy for the value of PV to illustrate the effect of changing these 

parameters. Table 8 compares marginal utility PV curtailments at various levels of deployed 

utility PV capacity21 for the variability scenarios.

Table 8. Marginal Utility PV Curtailment Fractions at Various Levels of Deployed Utility PV

Capacity – Variability Scenarios 

Description 90 GW 210 GW 300 GW 385 GW

0.5 σ Less Uncertainty 4% 5% 8% 14%

ρ = 0 Uncorrelated 3% 6% 10% 14%

0.5ρ Less Correlated 3% 7% 13% 15%

SunShot Baseline 3% 8% 15% 22%

1.5ρ More Correlated 3% 7% 16% 24%

1.5σ More Uncertainty 2% 9% 24% 28%

HI-VAR

(1.5σ & 1.5ρ)

More Correlated and 

More Uncertainty 2% 12% 25% 28%

 

21 The different levels are based on the approximate deployment points from the model in different years.
30 

Compared with the SunShot scenario, by 2030 the HI-VAR scenario yields changes in capacity, 

including 2 GW additional storage (5% change), 11 GW additional gas-CT (5% change) and 2.5 

GW additional CSP22 (up to 29 GW, 9.5% change). Generation from CSP and utility PV is 

nearly unchanged at 9.8% of total generation (versus 10% in the SunShot scenario). With 

distributed PV, this corresponds to a total solar generation fraction of 13% by 2030 (versus 13% 

in the SunShot scenario). 

By 2050, capacity changes include 4 GW additional storage (10% change), modest increases in 

coal (12 GW, 6% change) and gas-CTs (12 GW, 4% change) and 13 GW less utility PV (down 

to 385 GW, 3% change). Generation from CSP and utility PV remains nearly unchanged at 19% 

of total generation (versus 20% in the SunShot scenario). With distributed PV, this corresponds 

to a total solar generation fraction of 24% by 2050 (versus 26% in the SunShot scenario). Figure 

21 shows capacity and generation comparisons between the HI-VAR and SunShot scenarios. 

Figure 21. Capacity and generation comparisons between the HI-VAR and SunShot scenarios

Note: The total generation change between the HI-VAR and SunShot scenarios does not sum to zero due 

to differences in transmission losses.

Although increasing the uncertainty in power output of the PV resource and increasing the 

resource correlation among PV sites reduce the value of PV, a high-variability (HI-VAR) 

scenario that increases both of these factors by 50% has a minimal effect on CSP and utility PV 

deployment and generation. The deployment of coal and natural gas increases modestly to 

 

22 The 2.5 GW of additional CSP is not unexpected because CSP is not directly affected by the changes in input PV 

variability parameters.
31 

compensate for the firm capacity deficits created by the higher solar variability. The additional 

gas-CTs also help provide the additional operating reserves required as a result of more variable 

output from utility PV. Even with approximately the same utility PV deployment in the HI-VAR 

and SunShot scenarios, the HI-VAR scenario shows an additional 27 TWh of estimated 

curtailments for utility PV. At a 12% generation fraction for utility PV, this increase in 

curtailments corresponds to an increase in the curtailment fraction of utility PV generation from 

6% to 10% (Figure 22).

Figure 22. Estimated utility PV curtailments for the HI-VAR and SunShot scenarios

4.7 Distributed PV Deployment Assumptions (NoDPV)

Distributed PV deployment in the SunShot scenario totals 121 GW by 2030 and 240 GW by 

2050. The NoDPV scenario sets distributed generation deployment to zero through 2050. 

Compared with the SunShot scenario, by 2030 the 121 GW of foregone distributed PV capacity 

deployment in the NoDPV scenario yield changes in capacity, including 2 GW additional wind 

(2% change), 13 GW additional gas-CC (7% change), 6 GW additional gas-CT (3% change), and 

4 GW additional CSP (up to 31 GW, 16% change). Generation from CSP and utility PV

increases by 43 TWh (up to 503 TWh, 9% change) to 11% of total generation (versus 10% in the 

SunShot scenario). This corresponds to a total solar generation fraction of 11% by 2030 (versus

13% in the SunShot scenario). 

By 2050, the 240 GW of foregone distributed PV capacity deployment in the NoDPV scenario 

yield changes in capacity, including 3 GW additional wind (3% change), 12 GW additional coal 

(6% change), 7 GW less gas-CC (3% change), 11 GW less gas-CT (4% change), 4 GW 

additional CSP (up to 86 GW, 5% change), and 83 GW additional utility PV (up to 481 GW, 

21% change). Generation from CSP and utility PV increases by 193 TWh (up to 1,288 TWh, 

18% change) to 24% of total generation (versus 20% in the SunShot scenario). This corresponds 

to a total solar generation fraction of 24% by 2050 (versus 26% in the SunShot scenario). Figure 

23 shows capacity and generation comparisons between the NoDPV and SunShot scenarios. 
32 

Eliminating rooftop PV reduces total solar deployment and generation. However, much of this 

gap is filled by increased deployment of utility PV, CSP, wind and coal by 2050. Note that utility 

PV deployment is constrained by a market growth cap (see Section 3.8), and this cap limits 

annual utility PV growth in the SunShot scenario through 2030 (see Section 4.8); although utility 

PV growth slows between 2030 and 2050 as the market saturates.

23 For the SunShot scenario, 

this market saturation occurs at about 570 GW of total PV deployment. In the NoDPV scenario,

utility PV growth is constrained by the annual growth limit through 2050 because the market 

does not saturate (i.e., the total PV deployment by 2050 is only 481 GW, which is significantly 

lower than the 570-GW saturation level observed in the SunShot scenario). 

 

Figure 23. Capacity and generation comparisons between the NoDPV and SunShot scenarios

4.8 Solar Market Supply Growth Assumptions (RSMG, USMG)

The solar growth limits are relaxed in two different manners: the RSMG scenario assumes utility 

PV and CSP can each double every 2 years without a 15-GW annual limit, and the USMG 

scenario assumes solar capacity can grow according to successively higher growth penalty cost

bins. Although both the RSMG and USMG scenarios result in similar utility PV deployment 

trajectories, the USMG scenario is the more aggressive of the two. Therefore, the results in this 

section focus on the USMG scenario. 

 

23 In this analysis, market saturation refers to the point where utility PV no longer reaches the annual growth limit 

because the PV economics are less favorable due to curtailments and a shift in the net load from midday to evening.
33 

Compared with the SunShot scenario, by 2030 the USMG scenario yields changes in capacity, 

including 10 GW less wind (13% change), 13 GW less gas-CC (7% change), 17 GW less gas-CT

(8% change), 3 GW less CSP (down to 23 GW, 11% change), and 138 GW additional utility PV

(up to 319 GW, 76% change). Generation from CSP and utility PV increases 197 TWh (up to 

657 TWh, 43% change) to 14% of total generation (versus 10% in the SunShot scenario). With 

distributed PV, this corresponds to a total solar generation fraction of 17% by 2030 (versus 13% 

in the SunShot scenario). 

By 2050, capacity changes include 10 GW less wind (11% change); minor changes (less than 

2%) in coal, gas-CC, and gas-CT; 2 GW additional CSP (up to 84 GW, 2% change); and 29 GW 

additional utility PV (up to 427 GW, 7% change). Generation from CSP and utility PV increases

52 TWh (up to 1,148 TWh, 5% change) to 21% of total generation (versus 20% in the SunShot 

scenario). With distributed PV, this corresponds to a total solar generation fraction of 27% by 

2050 (versus 26% in the SunShot scenario). Figure 24 compares the USMG and SunShot 

scenarios. 

Figure 24. Capacity and generation comparisons between the USMG and SunShot scenarios

Figure 25 shows how annual and cumulative utility PV deployment changes under the RSMG 

and USMG scenarios. The USMG scenario uses relatively low growth cost penalties, and 

therefore utility PV grows faster than the doubling-every-2-years constraint. The additional 

supply-chain cost due to rapid market growth might be higher than the growth cost penalties 

used for the USMG scenario. Additional work is required to better understand the implications of 

scaling up manufacturing to accommodate high solar deployment scenarios. 
34 

In general, the main SunShot scenario attempts to avoid boom-bust cycles that occur in the 

RSMG and USMG scenarios. Although the deployment trajectories based on relaxing the growth 

constraints might not be sustainable from a manufacturing standpoint on an annual growth basis, 

they do suggest that utility PV could achieve higher deployment levels by 2030 than the SunShot 

levels depending on supply-chain costs. By 2050, the two solar growth relaxation scenarios reach 

deployment levels only marginally greater than in the SunShot scenario. 

Figure 25. Cumulative and annual utility PV capacity deployment for all solar market supply 

growth scenarios

4.9 Combination Scenarios

This subsection discusses results for two combination scenarios generated using a mix of five 

sensitivity factors discussed in Section 3, including: (1) low electricity demand (LD), (2) lower

natural gas prices (NG-EUR), (3) no coal retirements (NCR), (4) improved economic 

performance of wind and geothermal (WG-2ETI), and (5) high variability of PV (HI-VAR). 

These combination scenarios were chosen to provide the widest possible impact due to the 

factors examined in this report.
35 

4.9.1 AEO 2010 Demand Plus NG-EUR, NCR, WG-2ETI, and HI-VAR (ALL)

The first combination scenario (the ALL scenario), including a mix of factors (2)-(5) listed 

above, decreases the overall competitiveness of solar technologies.

Compared with the SunShot scenario, by 2030 the ALL scenario (NG-EUR, NCR,

24 WG-2ETI, 

and HI-VAR) yields changes in capacity, including 5 GW more wind (7% change), 49 GW less 

gas-CC (27% change), 15 GW more gas-CT (7% change), 24 GW less CSP (down to 2 GW, 

92% change), and 8 GW less utility PV (down to 173 GW, 5% change). Generation from CSP 

and utility PV declines 151 TWh (down to 309 TWh, 33% change) to 7% of total generation

(versus 10% in the SunShot scenario). With distributed PV, this corresponds to a total solar 

generation fraction of 10% by 2030 (versus 13% in the SunShot scenario). 

By 2050, changes in capacity include 13 GW more wind (14% change), 28 GW less gas-CC

(10% change), 53 GW more gas-CT (17% change), 77 GW less CSP (down to 5 GW, 93% 

change), and 174 GW less utility PV (down to 224 GW, 44% change). Generation from CSP and 

utility PV declines 692 TWh (down to 404 TWh, 63% change) to 7% of total generation (versus

20% in the SunShot scenario). With distributed PV, this corresponds to a total solar generation 

fraction of 13% by 2050 (versus 26% in the SunShot scenario). Figure 26 compares the ALL and 

SunShot scenarios. 

.

Figure 26. Capacity and generation comparisons between the ALL and SunShot scenarios

 

24 This scenario has 76 GW of unretired coal by 2030 and 152 GW by 2050. No new coal is built in this scenario.
36 

4.9.2 Low Demand Plus NG-EUR, NCR, WG-2ETI, and HI-VAR (LD ALL)

For the second combination scenario (the LD ALL scenario), adding a reduced demand to the 

previous combination scenario (the ALL scenario) further reduces PV penetration. 

Compared with the SunShot scenario, by 2030 the LD ALL scenario (LD, NG-EUR, NCR, WG2ETI, and HI-VAR) yields changes in capacity, including 6 GW additional wind (8% change), 

50 GW less gas-CC (27% change), 50 GW less gas-CT (23% change), 24 GW less CSP (down to 

2 GW, 92% change), and 97 GW less utility PV (down to 84 GW, 54% change). Generation 

from CSP and utility PV declines 308 TWh (down to 153 TWh, 67% change) to 4% of total 

generation (versus 10% in the SunShot scenario). With distributed PV, this corresponds to a total 

solar generation fraction of 7% by 2030 (versus 13% in the SunShot scenario). 

By 2050, changes in capacity include 3 GW less wind (3% change), 148 GW less gas-CC (54% 

change), 83 GW less gas-CT (26% change), 80 GW less CSP (down to 2 GW, 97% change), and 

310 GW less utility PV (down to 88 GW, 78% change). Generation from CSP and utility PV

declines 940 TWh (down to 155 TWh, 86% change) to 4% of total generation (versus 20% in the 

SunShot scenario). With distributed PV, this corresponds to a total solar generation fraction of 

10% by 2050 (versus 26% in the SunShot scenario). Figure 27 compares the LD ALL and 

SunShot scenarios. 

 

Figure 27. Capacity and generation comparisons between the LD ALL and SunShot scenarios
37 

4.10 Summary of Scenarios

Figure 28 and Figure 29 summarize the range of utility-scale solar deployment and generation 

results, respectively, for the sensitivity scenarios in Section 4 that use the SunShot solar price 

assumptions.25 These figures are intended to provide context for the range of results in this 

analysis. Showing all 45 sensitivity scenarios (Section 3) in these figures would make the results 

difficult to interpret, so only a subset of these scenarios are showcased here. All of the 45 

sensitivity scenarios presented in this report produce CSP and utility PV deployment trajectories

in between the best-case (NoDPV) scenario and the worst-case (LD ALL) scenario examined 

here (with respect to CSP and utility PV penetration). Note that there is a six-fold increase in 

utility-scale solar capacity from the LD ALL to the NoDPV scenario. The total CSP and utility 

PV deployment in the LD ALL scenario is comparable to the SP-50 (50% solar price reduction) 

scenario (Section 4.1).

Figure 28. Cumulative utility-scale solar capacity through 2050 for selected sensitivity scenarios 

in Section 4 

Note: These scenarios all use the SunShot solar price assumptions. 

 

25 Section 4.1 includes similar capacity and generation figures for the solar price sensitivities.
38 

Figure 29. Utility-scale solar generation through 2050 for selected sensitivity scenarios in 

Section 4 

Note: These scenarios all use the SunShot solar price assumptions. 

Figure 30 (low-demand scenarios) and Figure 31 (AEO 2010 demand scenarios) show the 

generation fraction for utility-scale solar technologies in 2030 and 2050 in order of decreasing 

generation by 2050 for the combination scenarios of Table 4 (as well as the single-factor 

scenarios used to generate the combination scenarios). In most low-demand scenarios, the total 

CSP and utility PV generation fraction is below 7% in 2030 (versus 10% in the SunShot 

scenario) and below 10% in 2050 (versus 20% in the SunShot scenario). In most AEO 2010 

demand scenarios, the total CSP and utility PV generation fraction is 7%–10% in 2030 (versus

10% in the SunShot scenario) and 7%–20% in 2050 (versus 20% in the SunShot scenario). The 

utility PV generation fraction is nearly insensitive to changes in input assumptions by 2030, but 

variations in CSP are more noticeable (Figure 31). Changes in both utility PV and CSP 

generation fractions are larger in 2050. 
39 

Figure 30. Annual utility-scale solar generation fraction in 2030 and 2050 for combination 

scenarios – low demand 

Note: The single-factor scenarios used to generate these combination scenarios are included.

Figure 31. Annual utility-scale solar generation fraction in 2030 and 2050 for combination 

scenarios – AEO 2010 demand 

Note: The single-factor scenarios used to generate these combination scenarios are included.
40 

5 Conclusions 

The SSVS (U.S. DOE 2012) projected U.S. utility PV and CSP solar deployment using the 

ReEDS capacity-expansion model (with inputs from the SolarDS model for rooftop PV). Under 

the SunShot scenario, 302 GW of PV (181 GW of utility PV, 121 GW of rooftop PV) and 28 

GW of CSP capacity are installed by 2030 with solar contributing 14% of total electricity 

generation. By 2050, 632 GW of PV (391 GW of utility PV, 240 GW of rooftop PV) and 83 GW 

of CSP capacity are installed with solar contributing 27% of total electricity generation. 

In this study, we expand on these SSVS results by analyzing the sensitivity of ReEDS-generated 

utility PV and CSP deployment projections to combinations of additional variables: different 

solar prices, low electricity demand, lower natural gas prices, different coal retirement

assumptions, low-cost wind and geothermal technologies, different levels of PV variability, no 

distributed PV deployment, and different solar market supply growth assumptions. The rooftop 

PV deployment results from the SSVS are held constant in all these scenarios except for one 

sensitivity case: the NoDPV scenario. The growth of utility PV and CSP is limited in all of these 

scenarios except for the two scenarios that relax theses limits: the USMG and RSMG scenarios. 

The main insights from our analysis include the following: 

• Varying the SunShot solar price targets indicates a threshold somewhere between

$2.00/WDC (SP-50) and $1.50/WDC (SP-62.5) for utility PV (and an equivalent reduction 

for CSP) at which utility-scale solar deployment increases non-linearly as price 

decreases. When solar prices decline only 50% by 2020 (compared with a 75% decline in 

the SunShot scenario), total CSP and utility PV deployment is 75% lower by 2050 than in 

the SunShot scenario. 

• Low electricity demand (essentially flat electric-load growth through 2050) substantially 

reduces future capacity and generation from all generating technologies compared with 

the SunShot scenario. The deployment of CSP and utility PV is cut approximately in half 

by 2050. 

• Lower natural gas prices increase electricity generation from natural gas and reduce the 

contribution of other generating technologies, including solar. By 2050, CSP deployment 

is 58% lower and utility PV deployment is 21% lower under the lowest natural gas price 

scenario (NG-EUR), compared with the SunShot scenario. Total CSP and utility PV

deployment is 27% lower. If natural gas prices are low enough (somewhere between 

prices in the NG and NG-EUR scenarios), they will suppress utility PV deployment such 

that the net load shape is not shifted as far into the evening whereby natural gas still 

competes directly with utility PV. 

• Changing assumptions about coal plant retirements has a relatively small effect on solar 

deployment. Assuming no coal retirements (NCR) reduces total CSP and utility PV

deployment in 2050 by 17% compared with the SunShot scenario. The contribution from 

natural gas decreases substantially as the contribution from coal increases. Results from 

two scenarios (CR-35 and CR-80) show that changing the total number of retirements as 

well as the location and timing of those retirements causes state-specific variations of 

utility PV deployment, but aggregate utility PV deployment is largely unaffected due to 
41 

the diversity of PV solar resource. CSP deployment declines due to less geographic 

diversity of CSP solar resource. 

• Doubling the capital cost reductions for wind and geothermal technologies (compared 

with the SunShot and RE Futures ETI scenarios) reduces total CSP and utility PV

deployment in 2050 by 14% (WG-2ETI). At the same time, total renewable capacity and 

generation, especially from wind, increase substantially. Because of the complementary 

nature of solar and wind resources (both in geography and in generation profiles), fossil 

generation is reduced much more than solar generation. 

• Although increasing the uncertainty in power output of the PV resource and increasing 

the resource correlation among PV sites reduce the value of PV, a high-variability (HIVAR) scenario that increases both of these factors by 50% has a minimal effect on 

utility-scale solar deployment and generation. However, the curtailment fraction for 

utility PV increases with increasing utility PV deployment; for example, at a 12% 

generation fraction for utility PV, the curtailment fraction of utility PV generation 

increases from 6% (SunShot scenario) to 10% (HI-VAR). The deployment of coal and 

natural gas increases modestly to compensate for the firm capacity deficits created by the 

higher solar variability. 

• Eliminating rooftop PV (NoDPV) reduces total solar deployment and generation. 

However, much of this gap is filled by increased deployment of utility PV, CSP, wind, 

and coal by 2050. 

• Different solar market supply growth assumptions produce a large effect by 2030, but this 

effect tapers off by 2050. Under the unlimited growth (USMG) scenario, total CSP and 

utility PV deployment increases 65% by 2030 but only 6% by 2050 compared with the 

SunShot scenario. The boom-bust trajectories associated with this relaxed growthrestriction assumption might not be sustainable from an annual manufacturing standpoint. 

Additional work is required to better understand the implications of scaling up 

manufacturing to accommodate high solar deployment scenarios. 

• Combining factors from the individual sensitivity scenarios can affect solar deployment 

dramatically. For example, combining low electricity demand with lower natural gas 

prices, no coal retirements, more competitive wind and geothermal technologies, and a 

highly variable PV resource reduces total CSP and utility PV deployment 81% and total 

CSP and utility PV generation 86% by 2050. This combined sensitivity case (LD ALL) is

the most extreme scenario analyzed here. Using the SunShot electricity-demand 

assumptions, most combinations of sensitivity factors result in total CSP and utility PV

generation fractions between 7% and 10% in 2030 (compared with 10% in the SunShot 

scenario) and between 7% and 20% in 2050 (compared with 20% in the SunShot 

scenario).

Although not included in this sensitivity analysis, additional scenarios could consider the 

implications of time delays in SunShot solar price reductions. 

In conclusion, installed price is the most important driver behind utility-scale solar adoption. 

However, our analysis also shows that non-price factors have significant impacts. This analysis 

projects by 2030, assuming the SunShot solar price targets are met, utility-scale solar 

deployment of 175–212 GW for most scenarios under the SunShot-electricity-demand 
42 

assumptions and 86–154 GW for the scenarios under the low-electricity-demand assumptions 

(compared with 209 GW in the SunShot scenario). By 2050, utility-scale solar deployment 

projections are 229–567 GW for the scenarios under the SunShot-electricity-demand 

assumptions and 90–236 GW for the scenarios under the low-electricity-demand assumptions 

(compared with 475 GW in the SunShot scenario). 

With solar prices being equal, low electricity demand suppresses solar deployment the most, 

followed by lower natural gas prices. The other individual factors generally have a smaller 

impact, although relaxed solar market supply growth restrictions can boost utility-scale solar 

deployment substantially beyond SunShot levels by 2030 (up to 342 GW in the USMG 

scenario). Some combinations of factors have a major impact on solar deployment and 

generation, especially under the low-electricity-demand assumption. For example, there is a sixfold increase in 2050 utility-scale solar capacity from the combination scenario with the lowest 

deployment (LD ALL) to the scenario with the highest deployment (NoDPV). In fact, this lowdemand combination (LD ALL) scenario results in utility-scale solar deployment that is lower

even than the deployment in the sensitivity scenario where solar prices are two times higher than 

the SunShot scenario (SP-50). However, most combinations under the SunShot electricity 

demand assumptions are in line with SunShot solar generation results through 2030, with 

discrepancies growing larger between 2030 and 2050. 
43 

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