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Contract No. DE-AC36-08GO28308

The Use of Solar and Wind as a 

Physical Hedge against Price 

Variability within a Generation 

Portfolio

Thomas Jenkin, Victor Diakov, Easan Drury, 

Brian Bush, Paul Denholm, James Milford, 

Doug Arent, and Robert Margolis

National Renewable Energy Laboratory

Ray Byrne

Sandia National Laboratories

Technical Report

NREL/TP-6A20-59065

August 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

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

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

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

The Use of Solar and Wind as 

a Physical Hedge against Price 

Variability within a Generation 

Portfolio

Thomas Jenkin, Victor Diakov, Easan Drury, 

Brian Bush, Paul Denholm, James Milford, 

Doug Arent, and Robert Margolis

National Renewable Energy Laboratory

Ray Byrne

Sandia National Laboratories

Prepared under Task No. SS13.1030

Technical Report

NREL/TP-6A20-59065

August 2013
NOTICE

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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

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Table of Contents

Executive Summary................................................................................................................................... vi

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

2 Study Methodology .............................................................................................................................. 5

2.1 Model Scenarios............................................................................................................................ 5

2.2 Study Region and Modeling Tools................................................................................................ 6

2.3 Natural Gas Price Distribution Modeling...................................................................................... 9

3 The Impact of RE Generation in Reducing the Uncertainty of Future Electricity Prices ............ 13

3.1 Impact of Increasing RE Generation on Annualized Variable Wholesale Electricity Cost and 

Cost Uncertainty—50:50 Contribution of Solar and Wind Energy ............................................ 13

3.2 Impact of Increasing RE generation on Variable Wholesale Electricity Cost and Cost 

Uncertainty—Variable Solar and Wind Energy Contributions................................................... 16

3.3 Impact of Coal-to-natural Gas Ratios on Annualized Variable Electricity Cost and Cost 

Uncertainty.................................................................................................................................. 18

3.4 Regulated vs. Restructured Markets and the Impact of Market Structure Assumptions on Cost 

and Cost Variance with Increasing RE........................................................................................ 19

4 Characterizing the Impact of RE Generation on the Annualized Variable Cost of Electricity and 

Cost Uncertainty using MC Simulations .......................................................................................... 22

4.1 The Stability of Optimal Production Cost Model Dispatch to a Range of Natural Gas Prices... 22

4.2 Annualized Electricity Cost Distribution .................................................................................... 25

5 The Potential Use and Value of Solar and Wind as a Physical Hedge against Cost Risk .......... 28

5.1 Economic Utility of Risk and Loss Aversion for Consumers..................................................... 28

5.2 The Cost of Hedging and Alternative Methods........................................................................... 31

6 Summary and Conclusions ............................................................................................................... 34

References................................................................................................................................................. 37

List of Tables

Table 1. Generation and Capacity by Technology for the 35% 50:50 Solar-and-wind Penetration 

Scenario......................................................................................................................................... 8

Table 2. Stochastic Model Parameters for Henry Hub, Colorado Utility, and Arizona Utility Prices. 12

Table 3. Impact of Alternative Dispatch Assumptions on the Annualized Cost of Electricity ............ 25

Table 4. Characteristics of the Natural Gas Price and Variable Electricity Cost Distributions ........... 27

List of Figures

Figure 1. Schematic of main elements of analysis................................................................................. 6

Figure 2. The Rocky Mountain Power Authority (RMPA) (FERC 2012) ............................................. 7

Figure 4. EIA forecasts of U.S. wellhead natural gas prices in various years (blue lines) compared 

with actual prices (red line)......................................................................................................... 10

Figure 5. Historical natural gas price data: Henry Hub (1997–2011), Colorado utility prices (2002–

2011), and Arizona utility prices (2002–2011) (EIA 2013b)...................................................... 11

Figure 6. 20 time-series simulations of 30 years of monthly Colorado utility prices........................... 12

Figure 7. Annualized variable cost of energy ($/MWh) for different natural gas prices under a range 

of 50:50 solar-wind generation penetration scenarios in RMPP (isolated)................................. 14

Figure 8. Daily average generation (GW) by technology for an equal mix of solar and wind 

generation (50:50) for different RE penetration (by percent of generation) scenarios in RMPP 

isolated ........................................................................................................................................ 15

Figure 9. Percentage of generation curtailed with increasing RE penetration in RMPP (isolated)...... 16
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Figure 10. Annual cost variance and cost variance reduction with RE penetration under high 

($9.2/MMBtu) and low ($3.9/MMBtu) natural gas prices for different solar-to-wind ratios in 

RMPP (isolated).......................................................................................................................... 17

Figure 11. Daily average generation (MW) by technology for 35% overall annual RE penetration for 

three cases with different solar-wind ratios in RMPP (isolated)................................................. 17

Figure 12. Annualized variable cost of electricity with RE penetration for the 50:50 solar-wind case 

and coal- and natural gas-dominated fossil scenarios ................................................................. 18

Figure 13. Annualized sensitivity of electricity prices (for restructured market) and annualized 

average variable cost (for regulated market) under different natural gas price scenarios for 

different RE penetration levels in RMPP (isolated).................................................................... 20

Figure 15. Hourly gas use over one year (2020) under different natural gas price and RE penetration 

scenarios, 50:50 solar-wind scenario in RMPP (isolated)........................................................... 23

Figure 16. Comparison of daily average dispatch for coal, CCGT, and CT technologies for different 

pairs of natural gas prices in RMPP (isolated)............................................................................ 24

Figure 17. Annualized variable electricity system costs for different RE penetrations based on MC 

simulations using underlying natural gas price distribution in RMPP (isolated)........................ 27

Figure 18. Utilities of various RE mixes for different attitudes towards risk and impact of time 

horizon......................................................................................................................................... 30

Figure 19. Premium for natural gas spot prices in the Northeast compared to other locations during 

winter 2012/13 (EIA 2013a)........................................................................................................ 33
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Acknowledgments

The authors would like to thank the following individuals for their input and comments: Venkat

Banunarayanan, Morgan Bazilian, Nate Blair, Mark Bolinger, Karlynn Cory, Hugh Li, Jeff 

Logan, Maggie Mann, Richard Tusing, and Jurgen Weiss. We also spoke informally to 

individuals from a number of utilities who provided many helpful comments. We would also like 

to thank Jarrett Zuboy and Kendra Palmer for their editorial support. This work was funded by 

the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy 

(EERE).
vi This report is available at no cost from the 

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Executive Summary

This study provides a framework to explore the potential use and incremental value of small- to 

large-scale penetration of solar and wind technologies as a physical hedge against the risk and 

uncertainty of electricity cost.

The idea that adding renewable energy (RE) to a conventional fossil portfolio generates 

diversity-related benefits is not new and has been discussed by many others (e.g., Bolinger et al. 

2002; Awerbuch and Berger 2003; Bazilian and Roques 2008; Roques et al. 2010). Similarly, 

there may be related benefits from combining RE and natural gas generation (Lee et al. 2012; 

Weiss et al. 2013) as well as from combining wind and solar within the RE component of the 

larger portfolio. The core idea behind the value of diversification, of not putting all the “eggs into 

one basket”--or in this case electric generation technologies--has widespread acceptance. In 

finance applications the value of diversification forms the foundation behind the application of 

mean-variance portfolio (MVP) theory to choose “efficient” portfolios of stocks and bonds 

(Markowitz 1952).1 The related concept of quantifying the value of diversity in the electric 

sector that may result from reducing the risk and uncertainty of the overall system costs over 

multi-year to multi-decade time horizons is less well understood or accepted (Stirling 1994;

Awerbuch and Berger 2003). Adding RE can be expected to reduce the variability of the overall 

electric system costs over a variety of timescales as natural gas-fired generation is displaced.

However, the direct application of MVP theory to “optimize” the mix of generation assets within 

a generation portfolio is problematic for a number of reasons, including the fact that the 

operational characteristics of some types of generation assets are dissimilar.

Earlier studies characterizing the impacts of adding RE to portfolios of electricity generators 

have often used a levelized cost of energy (LCOE) or simplified net cash flow approach. In this 

study, we expand on previous work by using an hourly production cost model (PLEXOS) to 

analyze the incremental impact of solar and wind penetration under a wide range of deployment

scenarios for a region in the western U.S. We do not attempt to “optimize” the portfolio in any of 

these cases. Rather, we consider different RE penetration scenarios that might, for example,

result from the implementation of a Renewable Portfolio Standard (RPS) to explore the 

dynamics, risk mitigation characteristics, and incremental value that RE might add to the 

system.

2

For our reference case, in which solar and wind make equal contributions (1:1) to total 

generation on an annual basis, we varied the annual RE generation from about 10% to more than

50% under a range of natural gas price scenarios. We then explored the impact of altering the 

annual solar-to-wind generation ratio to 3:1 and 1:3 and also varied the ratio of natural gas to 

coal generation in the fossil generation mix for the 1:1 reference case. We also simulated the 

variation in electricity costs using a Monte Carlo (MC) simulation approach. This allowed us to 

characterize the value of variance reduction for customers with different levels of risk and loss 

aversion and to compare this, at least in the near term, with the use of alternative mechanisms for 

 1 “Efficient” refers to portfolios of assets that lie on a curve (the “efficient frontier”) where each point represents a 

portfolio with lowest risk for a given return (over a range of returns). It is the lack of correlation of outcomes 

(returns in the case of financial assets) that reduces the risk (as measured by the variance of returns) for a given 

expected portfolio return. 

2 This approach was suggested in Bush et al. (2012).
vii This report is available at no cost from the 

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partially hedging against future price uncertainty. Some market structure issues were also 

considered.

Some key findings of our analysis include the following:

• Solar and wind generation significantly reduce the exposure of electricity costs to natural 

gas price uncertainty in fossil-based generation portfolios on a multi-year to multi-decade 

time horizon.

o The incremental impact, and any associated marginal value of RE in decreasing 

electricity cost volatility, declines with increasing RE penetration.

o The reduction in volatility of electricity costs with increased RE penetration is 

greater for natural gas-dominated portfolios than for coal-dominated portfolios.

• At low RE penetrations (e.g., 10%–15% annual RE generation) the annualized variable 

system costs vary widely with the price of natural gas in both our coal-dominated and 

natural gas-dominated fossil portfolios. For the modified region studied in this report:

o At 15% RE penetration in the coal-dominated system,

3 a $5/MMBtu variation in 

natural gas prices (between $4/MMBtu and $9/MMBtu) translates to 

approximately a $8/MWh range in the variable cost of electricity.

o For similar RE penetration (15%) in the gas-dominated portfolio, a $5/MMBtu 

variation in natural gas prices changes the variable cost of electricity by about 

$35/MWh--a more than three-fold difference compared to the coal-dominant 

portfolio.4

o In the coal-dominated fossil portfolio the incremental impact of further solar and 

wind penetration decreases with increasing RE penetration with only small 

incremental benefits achievable beyond 35% penetration. This is largely because,

at these higher levels of RE penetration, very little natural gas generation remains 

to be displaced.

o In contrast to the natural gas-dominated portfolio, the saturation effect in 

electricity cost variance reduction is not observed even at higher RE penetration 

levels (of over 40%) because a large amount of natural gas generation remains to 

be displaced.

• In the region studied, a mix of wind and solar provides a better physical hedge against 

uncertain fuel prices than either wind or solar alone because of the observed anticorrelation in solar and wind generation profiles at time scales ranging from intra-day to 

seasons.

 3 Where the ratio of coal thermal to natural gas combined cycle gas turbine (CCGT) capacity was approximately 2:1. 

For the natural gas-dominated portfolio, all coal thermal units were switched out with CCGTs. 4 The relative ratio of price variation depends not only on the ratio of coal thermal to natural gas plants but also on 

the cost of coal. Coal prices, even on an energy equivalent basis, vary significantly by location. The cost of coal per 

MMBtu for Colorado used in the study is amongst the lowest in the U.S.
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• Market structure choices are important. Adding RE reduces uncertainty in cost to 

consumers5 much more in restructured markets than in regulated markets since natural 

gas often sets the marginal price in a given hour in restructured markets (particularly 

during higher-priced peak periods), and this price is then paid to all generators

dispatched. 

• MC analysis of the impact of natural gas price variations over multi-decade time horizons

complements scenario analysis by generating electricity cost distributions that show the 

likelihood (or “density”) of outcomes. These distributions also show that the electricity 

costs are positively skewed.

o While the upside risk (lower electricity prices) is largely capped by physical 

constraints on fuel costs, the downside risk (higher electricity prices) is not. RE 

may be important to both reduce the overall variance of system costs, as well as 

provide insurance future price increases, which may be particularly important

given the current low natural gas prices (Bolinger 2013).

o Inter-annual variability in generation is also important since it can lead to 

deviations from average annual generation of ±10% or more in any year for solar 

and wind generation. However, while year to year variation in RE generation was 

not explicitly integrated into the production cost runs used in this study, the 

impact of such resource variation may be expected to be mitigated over long time 

horizons as year to year variations will tend to offset each other (Drury et al. 

2013).

• We find that much of the MC analysis of natural gas price uncertainty impacts can be 

done outside of the production cost model by recognizing the stability of the simulated 

hourly system dispatch for a wide range of natural gas prices. This greatly enhances our 

ability to perform many simulations which otherwise would be limited by model run 

times.6

The potential benefits of diversified portfolios containing significant solar and wind generation 

will depend on two main factors. One factor is how much consumers’ values lower price 

uncertainty due to risk aversion, loss aversion, scarcity, or other characteristics. The second 

factor is the potential cost and effectiveness of alternative financial or physical hedging methods, 

such as forward contracts, swaps, or physical supply contracts7

, and the timeframe over which 

these are available; this includes the degree to which price uncertainty risks are mitigated and the 

 5 Bilateral contracts within a restructured market, which are common for solar and wind, may mitigate this leverage

and have an asymmetrical effect on consumers. This and other market structure-related issues are a focus of our 

follow-on research. 6 The wide range that this stability effect was due in part is due to the low coal prices found in the region studied (on 

a $/MMBtu basis), and so the effect is likely to be less pronounced in many other regions of the U.S. with higher 

coal prices.

7 A buyer (or seller) of natural gas (or electricity) can protect itself, or hedge against future price uncertainty by 

agreeing to an over the counter (OTC) forward contract to buy (or sell) a commodity at some time. The price to be 

paid at delivery is specified in advance when the contract is made. An alternative way for a buyer to hedge is to buy 

gas at spot market prices, but also have an arrangement where the buyer pays the third party a fixed price for natural 

gas and in return receives (or swaps) payments linked to the market price of natural gas (Eydeland and Wolyniec 

2003).
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extent to which new risks may be introduced (e.g., associated with natural gas transportation 

constraints, counterparty risks, market liquidity, and others). 

The cost of using financial instruments to hedge against future price uncertainty depends in part 

on whether long-term forward contracts (for natural gas or electricity) contain a premium over 

expected future prices. Electricity sellers and buyers may both be risk averse, and there is no 

consensus about the net impact of this on the existence of a forward premium for eliminating 

price volatility in the United States. Some studies that suggest, at least in the short-term, it may 

be more cost effective to use financial-hedging instruments often assume (either implicitly or 

explicitly) there is no risk aversion or other premium in the forward price over the expected 

futures price. On the other hand, some studies have suggested there may a positive premium 

over the expected future price due to risk aversion (Bolinger et al. 2002) or due to scarcity or 

other factors (Borenstein et al. 2007),

8 while others suggest a negative premium (Modjtahedi and 

Movassagh 2005). The answer may be “all of the above”, with the existence and magnitude of a 

premium (positive or negative) likely to vary with location, commodity, and timescale, while 

changing over time.

Of particular relevance to RE, it is difficult and rare to be able to lock in financial or physical 

supply contracts of 10 years or more for natural gas. Such contracts may include premiums that 

reflect lack of liquidity and counterparty risk (Bolinger 2013).9 Because of these and other 

issues, in the longer term solar and wind may be able to provide a physical hedge that is not 

easily replicated in the financial and physical commodity markets.10 It also provides insurance 

value against rising electricity prices in futures where natural gas prices rise or carbon emissions 

are priced via a tax or some other mechanism. Even in the shorter term, RE may be the better 

choice for some consumers. While most of this report deals with the system wide effect on the 

average consumer at a multi-utility level, the preference for cost mitigation and over what 

timeframes may vary widely by customer type. Size also matters where some residential and 

commercial customers may decide to install distributed RE in part if their ability to hedge using 

financial or physical instruments is limited by a lack of knowledge, high transaction costs, or a 

lack of availability of such instruments.

 8 Graves and Levine (2010) make the interesting observation about how the positively skewed nature of the price 

distribution for natural gas could explain observed differences between the expected forward price and the observed 

prices--even if there is no meaningful premium simply due to the expected value of the distribution lying above the 

mostly likely and the median values.

9 “Passive” hedging with RE could also provide benefits by affecting a wide range of buyers in a similar manner. 

This may be helpful because many firms have trouble knowing how to hedge appropriately (possibly overreacting to 

a crisis and locking in high prices), and this can bring business risks. Alternatively, a firm could hedge in a smart 

way—while many of its competitors do not—and get “unlucky” if, for example, the prices of inputs fall sharply for 

the industry. Passive or natural hedging with RE in this way may provide a “cushioning” effect to help mitigate

these types of business risks. 10 The use of rolling, short-term hedging over longer time horizons provides a hedge against evolving market 

conditions and prices. It does not provide a long-term hedge against future price changes (as might a hedge due to 

RE).
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1 Introduction

The United States has experienced rapid growth in renewable energy (RE) over the last decade,

with 47 GW of wind capacity, 4 GW of photovoltaics (PV), and 0.5 GW of concentrating solar 

power (CSP) at the end of 2011 (Gelman 2012). This growth has been driven by a variety of 

factors, including technology cost reductions, performance improvements, and federal incentives 

and state mandates such as the production tax credit (PTC) and Renewable Portfolio Standards

(RPSs), respectively. More recently, increased U.S. shale gas production and associated 

relatively low natural gas prices have increased electric-sector natural gas use and reduced the 

economic attractiveness of RE relative to natural gas combined cycle gas turbines (CCGTs)

(FERC 2012a).

The decision whether to invest in RE, natural gas generation, or a combination of these and other 

technologies is complex. Arguments in favor of RE, including some level of support, include the

failure of the market to internalize many of the external costs associated with conventional

generation, the presence and history of subsidies for other energy forms, imperfect market 

structures, regulatory barriers, and the need to support a diverse range of RE technologies (whose 

future improvements in cost and performance remain unknown) to prevent lock-in of 

conventional generation.

11 The purpose of this study is much narrower: it provides a framework 

to explore the potential use and incremental value of small- to large-scale penetration of solar 

and wind technologies as a physical hedge against the risk and uncertainty of electricity cost.12

The idea that adding RE to a conventional fossil portfolio generates diversity-related benefits is 

not new and has been discussed by many others (e.g., Bolinger et al. 2002; Awerbuch and Berger 

2003; Bazilian and Roques 2008; Roques et al. 2010). Similarly, there may be related benefits 

from combining RE and natural gas generation (Lee et al. 2012; Weiss et al 2013)

13, as well as 

from combining wind and solar within the RE component of the larger portfolio. The core idea 

behind the value of diversification, of not putting all the “eggs into one basket”--or in this case,

electric generation technologies--has widespread acceptance. In finance applications the value of 

diversification forms the foundation behind the application of mean-variance portfolio (MVP) 

theory to choose “efficient” portfolios of stocks and bonds (Markowitz 1952).14 Less well 

understood or accepted is the related concept of quantifying the value of diversity in the electric 

sector that may result from reducing the risk and uncertainty of the overall system costs over 

multi-year to multi-decade time horizons (Stirling 1994; Awerbuch and Berger 2003). Adding 

RE can be expected to reduce the variability (and variance) of the overall system cost over a 

variety of timescales as natural gas generation is displaced. However, the direct application of 

MVP theory to “optimize” the mix of generation assets within a generation portfolio is 

problematic for a number of reasons, including the fact that the operational characteristics of 

 11 See Weiss and Marin (2012) and references contained within for a more comprehensive discussion of this topic. 12 RE can provide physical asset-backed protection against future price uncertainty. 13 Lee et al. (2012) discuss how natural gas and RE can complement each other from a system perspective due both 

to their similarities (e.g., a low carbon source relative to coal) and their differences (e.g., likely impact on the 

volatility of electricity costs). The dispatch flexibility of natural gas also better mitigates the intermittency issues 

associated with wind and solar than less flexible coal thermal units (Weiss et al. 2013). 

14 “Efficient” portfolio refers to portfolios of assets that lie on a curve (the “efficient frontier”) where each point 

represents a portfolio with lowest risk for a given return (over a range of returns). It is the lack of correlation of 

outcomes (returns in the case of financial assets) that reduces the risk (as measured by the variance of returns) for a 

given expected portfolio return. 
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at www.nrel.gov/publications.

some types of generation assets are dissimilar.15 Awerbuch and others recognized the limitations 

of the use of levelized cost of energy (LCOE) for MVP optimization for any real system, noting 

that the approach does not “point to a specific capacity-expansion plan” and that the results are 

“largely expositional” (Awerbuch and Yang 2007). A related problem with the LCOE-based 

approach is that the load factors for different technologies are not typically fixed for different 

portfolios.

16 In this study, we do not attempt to “optimize” the generation portfolio in any of the

cases we study. Rather, we consider different RE penetration scenarios, which might, for 

example, result from the implementation of an RPS, to explore the dynamics, risk mitigation 

characteristics, and incremental value that RE might add to the system.

A second issue that has been raised related to the incremental value of adding RE is whether 

alternative options could provide a similar benefit more effectively. Even if electricity buyers are 

risk averse and value limiting their exposure to price fluctuations, it may be less expensive to use 

financial instruments, such as forward contracts or swaps17 (at least over short time horizons)

than to install RE. This argument tends to assume there is no risk premium (over the expected 

future price) paid by the buyer to hold a forward contract for natural gas or power.

18 Whether 

this is true appears to be unsettled,19 particularly since the existence and size of any forward 

premium may vary by location, timescale, electricity market structure, and financial market 

liquidity.

Diversity is important, even if valuing it is difficult. For example, in the electric sector too little

diversity can create reliability and security concerns. Bazilian and Roques (2008), for instance,

note the case of the UK electric sector, whose overdependence on domestically abundant coal 

made it vulnerable to strike action by coal miners. Similarly in the United States, constraints in 

pipeline capacity for transporting natural gas may pose significant risks from an overreliance on 

natural gas for electricity generation; for example, extreme weather and limited pipeline capacity 

in New England early in 2013 led to natural gas prices and wholesale electricity prices tripling 

 15 If risk is ignored the use of LCOE under this approach fails to properly optimize the electricity portfolio because it 

suggests the use of a single technology with the lowest LCOE. This selection of the single lowest LCOE technology 

does not reflect the realistic “mix” of technologies for any electric system that has to serve real load profiles. It 

follows that adapting this approach to include risk will also not result in an optimal portfolio. For this reason and 

others, more recent studies often restrict such “optimization” analysis to baseload generation (see, e.g., Roques et al. 

2008).

16 Because of this, the LCOE for each technology for specified fossil fuel prices will generally not be constant across 

different portfolios, which is contrary to the assumption often used in this type of analysis. Delarue et al. (2011) 

recently used a refined optimization algorithm that distinguished between installed power capacity and generated 

electric energy where the model itself determined the load factors of the different technologies installed. 17 A buyer (or seller) of natural gas (or electricity) can protect itself against future price uncertainty by agreeing to an 

over the counter (OTC) forward contract to buy (or sell) a commodity at some time. The price to be paid at delivery 

is specified in advance when the contract is made. An alternative way for a buyer to hedge is to buy gas at spot 

market prices, but also have an arrangement where the buyer pays the third party a fixed price for natural gas and in 

return receives (or swaps) payments linked to the market price of natural gas (Eydeland and Wolyniec 2003).

18 It also assumes the transactions costs of buying and managing such contracts are low. While this is may be true 

for many utilities, it may not be the case for all consumers, especially those with low electricity use. 19 While some studies have suggested there may be a positive premium on the forward price of natural gas (or 

electricity) over the expected future price due to risk aversion or other factors (Bolinger et al. 2002; Bolinger and 

Wiser 2008), others studies have suggested the premium is zero or even negative.
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at www.nrel.gov/publications.

compared with their usual levels (Wald 2013).20 Furthermore, uncertainties over future carbon

emission policies and criteria pollutant regulation add risk and uncertainty to fossil fueldominated generation portfolios, which could lead to significant economic and social costs due 

to locking in portfolios dominated by fossil fuel technologies.21

Earlier studies that focused on characterizing the impacts of adding RE to portfolios of electricity 

generators often used an LCOE or simplified net cash flow approach (e.g., Awerbuch and Berger 

2003; Lesser et al. 2007; Roques et al. 2008).

22 In this study, we expand on this previous work 

and a suggested approach by Bush et al. (2012) to demonstrate the use of an 8760 hourly 

production cost model (PLEXOS) to analyze the incremental impact of solar and wind 

penetration under a wide range of penetration scenarios. We studied the Rocky Mountain Power 

Pool (RMPP) region, which covers the state of Colorado and parts of Wyoming and South 

Dakota, because it has good wind and solar resources as well as significant thermal coal and 

natural gas generation and because the datasets used in our production cost model analysis were 

readily available as a result of the Western Wind and Solar Integration Studies (WWSIS) (GE 

Energy 2010; Lee et al. 2013). The region is also large enough to investigate many of the 

impacts on the cost of electricity caused by integrating large amounts of RE into existing fossilbased portfolios, while small enough to allow us to run many cases, including Monte Carlo (MC) 

simulations to investigate the impact of natural gas price generation uncertainty on the cost of 

electricity.

For the reference case, in which solar and wind make equal contributions to generation on an 

annual basis (i.e., a solar-to-wind generation ratio of 50:50), we varied the annual RE generation 

from about 10% to more than 50%.23 We then explored the impact of altering the proportion of 

solar-to-wind generation in high-solar and high-wind cases with annual solar-to-wind generation 

ratios of 3:1 and 1:3, respectively. The impact of changing the ratio of natural gas generation to 

coal generation in the fossil generation mix was also considered by switching out coal thermal 

generation units with CCGTs in some scenarios. Some market structure issues were also 

considered. Further, we simulated the variation in annualized variable electricity costs using an

MC simulation approach. This allowed us to characterize the value of variance reduction to a 

range of customers with different levels of risk and loss aversion and to compare this, at least in 

the near term, with the use of different financial products as hedges against future price 

uncertainty.

The remainder of the report is organized into five sections. Section 2 outlines the study 

methodology and describes the modeling tools, solar and wind data, and natural gas price 

 20 For example, for the week ending March 13, 2013, natural gas prices for delivery in Boston were more than 

$8.50/MMBtu, while natural gas at the Henry Hub cost less than $4/MMBtu (EIA 2013a). 21 An interagency working group (IWG) recently recommended the use of an expected value of $43/ton in 2020 for 

the social cost of carbon (SCC) (as well as a number of other scenarios) for inclusion in regulatory impact analysis 

(assuming a 3% social discount rate) (IWG 2013). Using this value for the SCC leads to an external value for 

avoided generation by a natural gas-fired CCGT in line with the current PTC for wind. In contrast, the estimated 

cost of abatement for an advanced natural gas-fired CCGT exceeds the cost of the PTC with a recent estimate by 

EIA, which puts the incremental cost for CCS at about $25/MWh (EIA 2013c).

22 See also Bazilian and Roques (2008) for further examples of analysis using this type of approach. 23 We used 2006 wind and solar data from the WWSIS study (GE Energy 2010) to determine the installed capacity.

The generation is, therefore, only approximate over time due to inter-annual solar and wind variability (Wan 2012; 

Drury et al. 2013). The implications of this assumption are discussed in Section 4.2.
4 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

projections used in the analysis. Section 3 characterizes how varying levels of wind and solar 

generation impact annualized variable wholesale electricity costs for a range of future natural gas 

prices and the impacts of varying the mix of natural gas to coal generation capacity and market 

structure. Section 4 uses MC simulations to characterize distributions of future variable 

electricity costs for a range of future natural gas prices. Section 5 explores the value of reducing 

the uncertainty of future electricity costs to various types of customers with different risk- and 

loss aversion profiles. It also discusses potential price hedging alternatives to RE, such as the use 

of financial instruments or physical supply contracts, and the potential limitations of these 

alternatives (e.g., in terms of coverage, availability on longer time horizons, and potential 

locational, delivery, credit, and other risk-related issues). Section 6 provides a brief review of the 

study’s key findings.
5 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

2 Study Methodology

This section describes the scenarios explored in this analysis, the study region, the hourly 

production cost-modeling tool (PLEXOS), and how portfolios with varying levels of renewable 

and fossil generation were constructed. We also describe the methodology used to characterize 

distributions of future natural gas prices, which were used in the MC simulations.

2.1 Model Scenarios

The range of future electricity prices and price uncertainty is sensitive to several factors 

including future natural gas prices, RE penetration, the mix of wind and solar generation for a 

given penetration level, the amount and mix of conventional generation (e.g., coal thermal, 

natural gas CCGTs and combustion turbines [CTs], nuclear, and hydroelectric), market structure, 

and other factors. We quantified the sensitivity of the future variable cost of electricity and cost 

uncertainty to several of these factors using PLEXOS over a wide range of natural gas prices

(from $1.5/MMBtu to $9.2/MMBtu) which represents over 90% of the historical distribution. 

We evaluated the impact on cost and cost variance of: 

1) Increasing the fraction of RE generation (50% wind and 50% solar (or 1:1 ratio) for the 

reference case on an annualized energy basis) from 10% to 55% in the study region 

(Section 3.1)

2) Varying the relative contributions of wind and solar generation to the total RE generation 

mix (Section 3.2) from 3:1 to 1:3 over a similar RE range

3) Varying the mix of conventional generators from a coal-dominated system in the 

reference scenarios to a natural gas-dominated system (Section 3.3). 

For the 50:50 solar-wind reference case, we also investigated (4) the impact of market structure 

on wholesale variable electricity prices and price uncertainty (Section 3.4). These tasks are 

outlined in Figure 1.
6 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 1. Schematic of main elements of analysis

We also investigated electricity price distributions using an MC simulation approach, which is 

described in Section 4. This allowed us to characterize the value of variance reduction to a range 

of customers with different levels or risk and loss aversion and to compare this, at least in the 

near term, with the potential use of financial products as hedges against future price uncertainty

(Section 5).

2.2 Study Region and Modeling Tools 

The geographic area for this study is the RMPP, with 2020 projections of the electric load 

profile, power production utilities, and transmission grid based on projections by the Western 

Electricity Coordination Council (WECC)

24. The RMPP region shown in Figure 2 corresponds 

to all of Colorado and parts of Wyoming and South Dakota. The region includes two balancing 

areas, Western Area Colorado Missouri (WACM) and PSC, with projected annual loads in 2020 

of 28,100 GWh and 50,400 GWh, respectively. Peak loads for the same year are assumed to be 

4.4 GW in WACM and 10.1 GW in PSC; the total load for RMPP corresponds to about 2% of 

overall projected U.S. load in 2020. We chose the RMPP region because it (i) has abundant solar 

and wind resources (with average PV and wind capacity factors of 20% and 34%, respectively), 

 24 The assumptions for the year 2020 are summarized in the Assumption Matrix for the 2020 TEPPC Dataset

(WECC 2013); the dataset builds on 2017 forecasts from the 2008 Annual Report of the Western Electricity 

Coordinating Council’s Transmission Expansion Policy Committee, Appendix B (WECC 2008). 
7 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

and (ii) has a mix of coal thermal and natural gas electric generation. The region was large 

enough to allow meaningful analysis using a production cost model and yet small enough for us 

to run many scenarios. We modified the solar, wind, and fossil generation characteristics of this 

area to explore various RE penetration scenarios.

Figure 2. The Rocky Mountain Power Authority (RMPA) (FERC 2012)

We used the power market modeling software PLEXOS25 to simulate a year of the optimal 

hourly (8,760) dispatch of fossil and non-fossil generation with different levels of PV and wind 

generation. The optimal hourly dispatch for any scenario run represents the lowest overall 

variable system costs for an entire year, calculated based on hourly generation and electricity 

demand and subject to several system characteristics, including startup and shut-down costs, 

startup times, and minimum operating windows. Model inputs were based on detailed 

information representing current and alternative hypothetical scenarios for future power 

generation facilities, transmission lines, and electric load. The input database of wind and solar 

resource data (from 2006) was developed for the WWSIS Phase II (WWSIS2) (Potter et al. 2008; 

Lew et al. 2013). Electricity transmission resources and transmission utilization in the production 

cost model were aggregated to the Balancing Area level (which corresponds to two regions in 

RMPP). We simulated dispatch in the RMPP system by assuming that it is isolated from the rest 

of WECC to suppress large-scale regional changes in power distribution caused by varying RE

participation levels. This likely resulted in a conservative estimate of the impact of RE

penetration because access to wider geographic resources reduces variability in wind and solar 

 25 See e.g.: “PLEXOS for Power Systems.” Energy Exemplar, http://energyexemplar.com/wpcontent/uploads/brochures/PLEXOSBrochure_Web.pdf.
8 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

generation. The impact of broadening the geographic footprint to a much larger area is a focus of 

ongoing research.

Table 1 shows 2020 generation and capacity by technology for the scenario with 35% RE

penetration by generation with equal shares of solar and wind by generation. The PV generation 

capacity shown in Table 1 is composed of 80% utility (37% single-axis tracking, 43% fixed-axis) 

and 20% rooftop. Table 1 also shows that the ratio of baseload and intermediate coal-to-naturalgas capacity (using CCGTs) is roughly 2/3 to 1/3. The capacity of gas-fired CTs used for 

peaking generation and CCGTs are approximately equal.

Table 1. Generation and Capacity by Technology for the 35% 50:50 Solar-and-wind Penetration 

Scenario 

Category Generation 

(GWh/year)

Rating 

(MW)

Capacity 

factor Units % Fossil capacity

Combined cycle gas turbine 3,678 2,579 16.3% 13 22%

Combustion turbine 213 2,735 0.9% 48 23%

Coal 42,996 6,455 76.0% 43 55%

Hydro (including pumped hydro) 4,583 1,412 37.1% 47 —

Wind 13,801 4,590 34.0% 43 —

Photovoltaics 9,543 5,362 20.3% 85 —

Concentrating solar power 4,347 1,504 33.0% 1 —

Other 290 569 5.8% — —

In addition to simulating hypothetical expansion scenarios based largely on the existing RMPP 

system (Table 1), we also explored a significantly modified RMPP system in which coal 

generation is replaced by CCGTs. To create these natural gas generation-dominated scenarios,

each coal plant from the input database was replaced with a CCGT plant of equal capacity.

Typical heat rate and startup costs for that size of CCGT plant were then estimated using a 

simple regression fit (Figure 3). The capacity of CTs remained the same in all scenarios at 

approximately one-quarter of the overall fossil generation capacity. We did not retire any 

conventional generation capacity with the addition (i.e., scale-up) of wind or PV generation. For 

this reason, actual utilization of natural gas CCGT will be artificially low since in practice,

capacity of CCGT would be lower (and hence utilization would be higher than shown in Table 

1). In the next phase of this study, which likely will involve capital cost issues, we anticipate 

making retirement decisions as needed. It matters less, however, for this study because the focus 

is on the variable system costs (and not capital recovery) and because, in meeting demand in any 

given hour, available RE resources usually displace fossil generation.
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at www.nrel.gov/publications.

Figure 3. Comparative plots of heat rate capacity for coal and CCGT generators in RMPP

The line represents a linear regression fit used to determine the heat rates of CCGT plants replacing coal generation 

in several hypothetical scenarios.

Our WWSIS (GE Energy 2010) solar and wind data were from a single year (2006) projected to

2020 while assuming 2006 weather patterns (see Section 5 for a discussion of this assumption’s 

impacts). The data originated from detailed weather simulations (at 10-minute intervals over an 

approximately 4 km x 4 km spatial grid) that interpolated meteorological measurements over the 

WECC area, and they were converted to wind, PV (fixed or single-axis tracking), or CSP electric 

output based on choices of geographic locations and generation technologies made in the 

WWSIS. The generators (wind and PV) were aggregated geographically to the bus level (Table 

1) and to a 1-hour frequency to facilitate computations.

The amount of PV and wind generation capacity needed to meet varying RE generation-fraction

targets was achieved by scaling capacity up or down at the site locations on a proportional basis 

to bring wind and solar generation to the desired level. We did not consider how increases in RE 

would change the location of installations, and used the approximation that created all the RE

penetration scenarios using the same set of solar and wind generation locations.

2.3 Natural Gas Price Distribution Modeling

Accurately estimating future natural gas prices is impossible. One common estimation method 

employs historical data. The recent largely unforeseen increase in U.S. shale-gas supply,

however, has driven natural gas prices below historically based forecasts (to $4/MMBtu or less

in 2012).

26 Clearly, the impact of this “recent” information about shale gas is not reflected in 

much of the historical data –which in turn raises some legitimate concerns about the usefulness 

 26 With forward prices 5 years out typically trading somewhat higher at $5/MMBtu or more (EIA 2013).
10 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

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of past trends for projecting future prices, even more so owing to the recent disconnection of a

relatively strong historical correlation between natural gas and oil prices. The inherent difficulty 

in estimating future natural gas prices is illustrated in Figure 4, which shows that the U.S. Energy 

Information Administration’s (EIA’s) natural gas price projections have tended to vary widely 

over time to be biased toward and trail changes in recent prices, with a lag.

Figure 4. EIA forecasts of U.S. wellhead natural gas prices in various years (blue lines) compared 

with actual prices (red line)

Source: www.eia.gov.

Natural gas prices in early 2013 might not accurately indicate future trends either. Several factors 

could put upward pressure on prices, which are currently at or near historical lows. This

possibility has been at least partly factored into short-term (5 to 10 years out) forward prices, 

which are significantly higher than spot prices. In the longer term, natural gas prices could 

increase for a variety of reasons, such as a greater demand for natural gas (as technologies shift 

to natural gas or natural gas is converted into liquid fuels), increased U.S. liquefied natural gas

(LNG) exports, and tighter environmental or regulatory controls, and other reasons.

This study uses historical natural gas prices to explore the potential impact of future natural gas 

prices on variable electricity costs for a range of RE penetrations. However, based on preceding 

discussion we make no claims about the likely accuracy of these projections. Since Colorado 

utility prices are only available from EIA from 2002 forward, we first looked at Henry Hub data, 

which are available on a monthly basis from 1997. Figure 5 compares monthly Henry Hub prices 

to Colorado and Arizona utility prices over the period 1997 to 2011. The prices are strongly 

correlated, although there are significant differences in certain months. Figure 6 shows a 

histogram of the same Henry Hub data compared to a simulated log-normal distribution.
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at www.nrel.gov/publications.

Figure 5. Historical natural gas price data: Henry Hub (1997–2011), Colorado utility prices (2002–

2011), and Arizona utility prices (2002–2011) 

Source: EIA 2013b

To help assess the impact of adding RE over multi-decade time horizons (to reflect the lifetime 

of RE technologies), we generated 30 years of simulated monthly natural gas prices using the 

following methodology: 

Define Xi = log(Historical price data)

Seasonally adjust the data by defining Yi = Xi − (monthly means of Xi)

Employ a maximum likelihood estimator to determine the parameters (α, γ, σ)

dYi = α(γ − Yi)dt + σdzi, where dzi = ε�Δti, ε ∼ N(0,1)

Simulate 30 years of monthly data using the mean-reverting, seasonally adjusted 

stochastic model

Table 2 summarizes the parameters estimated for Henry Hub, Colorado, and Arizona. The 

parameters under “actual dt” were estimated using the actual time periods (e.g., the number of 

days in a month divided by the number of days in the year). The parameters under “dt = 1/12” 

were estimated with the approximation of 1/12 for the time period of each month. The 

parameters estimated using both approaches agree to two significant digits.27

 27 The mean reversion parameter for the Henry Hub data is different than the Colorado or Arizona parameter. This 

illustrates one of the difficulties in modeling price data. In general, many price time series are non-stationary (e.g.,

the mean and variance change over time).
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Table 2. Stochastic Model Parameters for Henry Hub, Colorado Utility, and Arizona Utility Prices

Actual dt dt = 1/12

Location α γ σ α γ σ

Colorado 1.532185 0.038892 0.556404 1.539787 0.038675 0.557913

Arizona 1.220383 0.052217 0.423223 1.219653 0.052198 0.423343

Henry Hub 0.444511 -0.036365 0.471478 0.443914 -0.036400 0.471295

Figure 6 shows twenty 30-year monthly MC simulations of natural gas prices (strips of 360 data 

points: 30 years x 12 months); these are equivalent from a data perspective to 600 (20 x 30) 

annual 12-month price simulations except that, in the former case, the last price in December for 

any given year influences the price in January for the next year.28

Figure 6. 20 time-series simulations of 30 years of monthly Colorado utility prices

 28 Further discussion of the approach used in modeling natural gas prices can be found in Byrne et al. (2013).
13 This report is available at no cost from the 

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at www.nrel.gov/publications.

3 The Impact of RE Generation in Reducing the 

Uncertainty of Future Electricity Prices

The range of future electricity prices and price uncertainty are sensitive to future natural gas 

prices, RE generation fractions, the relative mix of wind and solar generation, the mix of 

conventional fossil generators (e.g., coal generators, natural gas CCGTs and CTs), market 

structure, and other factors. In this section, we quantify the sensitivity of future variable 

wholesale electricity cost and cost uncertainty to several of these factors using the hourly 

production cost model (PLEXOS). For a wide range of natural gas prices we evaluate the impact 

of: increasing the fraction of RE generation (50% wind and 50% solar on an annualized energy 

basis) from 10% to 55% in the study region (Section 3.1), varying the relative contributions of 

wind and solar generation to the total RE generation mix (Section 3.2), and varying the mix of 

conventional generators from a coal-dominated system in the reference scenarios to a natural 

gas-dominated system (Section 3.3). Section 3.4 explores the impact of market structure on the 

uncertainty of costs or prices faced by the consumer.

3.1 Impact of Increasing RE Generation on Annualized Variable 

Wholesale Electricity Cost and Cost Uncertainty—50:50 

Contribution of Solar and Wind Energy

To explore the impact of increasing RE generation on the annualized variable wholesale cost of 

electricity and cost uncertainty, we scaled solar and wind capacity in the RMPP region to reach 

10%–55% of total annual generation while maintaining a 50:50 contribution of wind and solar 

energy. Because of differences in solar and wind capacity factors, the installed capacities for 

solar and wind in the reference case are significantly different, and the capacity proportion

changes with overall penetration to compensate for different curtailment levels.

29 For example,

Table 1 shows that average annual wind and solar capacity factors are approximately 20% and 

34%, respectively, for the 35% RE generation 50:50 generation scenario. Reaching the RE 

generation targets requires 6,866 MW of solar capacity and 4,590 MW of wind capacity. In a 

different year, these generation percentages will vary due to changes in weather.

Figure 7 shows the range of annualized variable cost of electricity ($/MWh) estimated using an 

hourly production cost model for a range of representative natural gas prices and for RE 

generation levels increasing from about 10% to 55%.30 This figure shows that, at low RE

penetrations (e.g., 10% to 15% RE), the annualized variable system costs vary widely with the 

price of natural gas. For 10% RE penetration, a very low natural gas price of $2/MMBtu leads to 

an annualized variable cost of electricity of approximately $15/MWh. This cost increases to 

 29 Curtailment is defined as excess RE generation not used to meet load. Typically it occurs when demand is low and 

there are constraints and/or related costs associated with turning down the output of baseload units. It can also arise 

from other constraints such as transmission limits or environmental requirements to hydro generation, although these 

constraints were not explicitly considered in this study. 30 Each simulation (represented by a data point on the figure) was conducted by scaling wind and solar generation to 

achieve the desired amount of generation from these resources. Because curtailed energy is not taken into account 

(the horizontal axis on the figure includes only generation toward meeting the load), choosing the right scaling 

factors for wind and solar sometimes involved several iterations. In this, we paid more attention to keeping the ratio 

between wind and solar generation (1:1 for the data represented on the figure) than achieving the exact desired 

amount of renewable generation. This allows (the low gas price) data points on the graph to deviate from the exact 

(15%, 25%, 35%, and 45%) values. 
14 This report is available at no cost from the 

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more than $27/MWh at a natural gas price of $9.2/MMBtu, more than $12/MWh higher.

Increasing the natural gas price $5/MMBtu, from about $4/MMBtu to $9/MMBtu, increases the 

variable electricity cost by approximately $10/MWh.

Figure 7. Annualized variable cost of energy ($/MWh) for different natural gas prices under a range 

of 50:50 solar-wind generation penetration scenarios in RMPP (isolated)

Adding RE generation to the RMPP system significantly reduces the range of variable electricity 

costs. In addition, the variable electricity cost for any natural gas price declines with increasing 

RE penetration, which is to be expected since solar and wind have no fuel costs. The changing 

slope of the variable cost curves shows, however, that the incremental impact of further RE

penetration decreases with increasing penetration, and that only small incremental benefits are 

achievable beyond 35% penetration. Note that these results say little about total system costs,

which would include capital-recovery costs for renewable and conventional generator 

investments and fixed operations and maintenance costs. Whether total system costs would 

increase or decrease with increasing RE penetration would depend on future cost projections for 

each generation technology, including technology-specific subsidies, which are outside the scope 

of this analysis.

The reduction in the range of variable electricity costs with increasing RE penetration can be 

understood in the following manner. In the low-RE cases, substantial natural gas generation is

used throughout the year, only some of which is displaced by solar and wind generation. Because 

of this, variations in natural gas prices significantly impact annualized system costs. As RE

penetration increases, natural gas generation is more frequently displaced by RE, which 

decreases both the overall variable system costs and the sensitivity of variable system costs to 

natural gas prices; this displacement is reflected by large drops in utilization for both gas 

generation technologies when RE penetration increases from 15% to 35% (from 45.5% to 16.9% 

for CCGTs and from 6.0% to 0.9% for CTs). At 35% RE penetration, the range in variable 

electricity costs is reduced to about $2- $3/MWh for the natural gas prices explored. Adding RE 

generation beyond 35% becomes increasingly ineffective at eliminating the remaining natural 

gas generation because the wind and solar generation occur at the wrong times. This effect can 

be seen in Figure 8 and Figure 9.

0

5

10

15

20

25

30

0% 10% 20% 30% 40% 50% 60%

Annualized variable electricity cost, $/MWh

RE penetration (energy) for 50:50 solar-wind scenarios

NG $9.2/MMBtu

NG $6.0/MMBtu

NG $3.9/MMBtu

NG $2.2/MMBtu

NG $1.5/MMBtu
15 This report is available at no cost from the 

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at www.nrel.gov/publications.

Figure 8. Daily average generation (GW) by technology for an equal mix of solar and wind 

generation (50:50) for different RE penetration (by percent of generation) scenarios in RMPP 

isolated

Figure 8 shows the daily generation supply by technology over a one-year period under 15%, 

35%, and 50% 50:50 solar-and-wind by generation penetration scenarios. Electricity demand in 

the RMPP region is represented by the top of the generation stack, wind and solar generation are 

shown by the two bottom layers, and curtailments are shown by unfilled red lines above the solid 

generation stack. The three figures reveal information about the daily operation of the system, 

how it varies over the year, and the impacts of integrating increasing amounts of RE generation. 

Electricity demand peaks in both the summer and winter. Wind generation tends to be 

significantly higher than solar generation in winter, while the reverse is true in summer. Overall 

RE generation is lowest in summer when electricity demand is highest. There is curtailment in 

the 35% RE penetration scenario during spring and fall, when demand is low and most (but not 

all) of the CCGT generation is displaced. This curtailment effect, coupled with the inability of 

RE to eliminate natural gas generation, becomes more apparent in the 50% penetration case.

This curtailment effect can also be seen in Figure 9, which shows the overall generation curtailed 

as a percentage of total load and how this varies with increasing RE penetration by generation. 

The curtailed generation increases with RE penetration. As the RE generation fraction increases

from 25% to 35%, curtailment increases from near-zero to about 0.2% of total demand. This 

results in a marginal curtailment of about 2% of new RE generation resources added to the 

system. As RE generation fraction increases further, total and marginal curtailments increase 

significantly. For example, increasing RE generation from 35% to 45% increases total 

curtailment to 1% of demand and marginal curtailment to 8%. Increasing RE generation from 

45% to 55% increases total curtailment to 5% of demand and marginal curtailment to 40%. 

0

4

8

12 Curtailment

CT(MW)

CC (MW)

Hydro

Coal

Other

Wind

Solar

0

4

8

12

0

4

8

12

15% RE

35% RE

50% RE

Daily average generation (GW)
16 This report is available at no cost from the 

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at www.nrel.gov/publications.

Curtailment at higher RE penetrations is slightly lower for lower natural gas prices, reflecting

increased use of more flexible natural gas generators and decreased use of coal.

Figure 9. Percentage of generation curtailed with increasing RE penetration in RMPP (isolated)

3.2 Impact of Increasing RE generation on Variable Wholesale 

Electricity Cost and Cost Uncertainty—Variable Solar and Wind 

Energy Contributions 

This subsection explores the impact of varying the relative contribution of solar and wind energy

for meeting the overall RE generation target. Figure 10 shows how the range in variable 

annualized electricity cost is reduced for different total RE penetration levels as the proportion of 

solar-to-wind generation is varied. The figure represents the difference in the annualized variable 

electricity prices for high ($9.2/MMBtu) minus low ($3.9/MMBtu) natural gas prices. In addition 

to the 50:50 solar-wind reference case, two alternate solar-wind scenarios are shown. The high 

solar case (High Solar) corresponds to 75% solar and 25% wind (or 3:1 ratio) generation on an 

annualized energy basis while the high wind case (Low Solar) corresponds to a 25% solar and 

75% wind (or 1:3) generation ratio.

Figure 10 shows that the variable cost difference curves for each of the three cases are similar in 

terms of overall magnitude and rate of decline with increasing RE penetration. However, the 

50:50 solar-wind case reduces cost variance the most with increasing RE penetration, and the 

High Solar case reduces it the least. This suggests that the ability of increased solar capacity to 

displace natural gas generation during peak periods is more than offset by the impact of 

combining wind and solar to complement seasonal differences in generation (wind generation 

peaks in winter and spring, and solar generation peaks in summer) for the isolated region used in 

this study.

0%

1%

2%

3%

4%

5%

6%

0% 10% 20% 30% 40% 50% 60%

Curtailments, % of load

RE penetration (energy) for 50:50 solar-wind scenarios

$9.2/MMBtu NG

$6.0/MMBtu NG

$3.9/MMBtu NG

$2.2/MMBtu NG

$1.5/MMBtu NG
17 This report is available at no cost from the 

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at www.nrel.gov/publications.

Figure 10. Annual cost variance and cost variance reduction with RE penetration under high 

($9.2/MMBtu) and low ($3.9/MMBtu) natural gas prices for different solar-to-wind ratios in RMPP 

(isolated)

Figure 11. Daily average generation (MW) by technology for 35% overall annual RE penetration for 

three cases with different solar-wind ratios in RMPP (isolated)

Figure 11 shows the daily energy supply over the year by technology for the three different ratios 

of solar-to-wind generation (3:1, 1:1, and 1:3). While the total annual RE generation is set to

35% in each case, there is considerable seasonal variation in the daily wind, solar, and overall 

0

2

4

6

8

10

0% 10% 20% 30% 40% 50% 60%

Electricity cost difference, $/MWh

RE penetration (energy)

25% solar, 75% wind

50% solar, 50% wind

75% solar, 25% wind

0

4

8

12 High solar – low wind (75% - 25%)

0

4

8

12 Curtailment

CT(MW)

CC (MW)

Hydro

Coal

Other

Wind

Solar

50% solar – 50% wind

Low solar – high wind (25% - 75%)

0

4

8

12

Daily average generation (GW)
18 This report is available at no cost from the 

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at www.nrel.gov/publications.

RE generation. Wind and solar generation are anti-correlated over several timescales–on a 

monthly basis (R = -0.64) reflecting more wind during the winter and more solar in the summer, 

and on a daily (R = -0.36) basis reflecting the fact that solar output peaks during the day, whereas 

wind can blow both day and night. The impact of these anti-correlations can be seen in Figure 

11. In the 50:50 (or 1:1) scenario, the different daily generation characteristics of wind and solar

offset each other over the year, so annual variability of daily RE generation is lower than for the 

high and low solar cases. Adding similar amounts of solar and wind (by annual generation) 

appears to provide a more effective natural hedge against RE generation variability than either 

the solar- or wind-dominated cases, which are less effective at displacing natural gas generation.

3.3 Impact of Coal-to-natural Gas Ratios on Annualized Variable 

Electricity Cost and Cost Uncertainty

In the previous two subsections, we explored the impact of varying the amount and type of RE 

generation in the coal-dominated RMPP system, finding that the incremental reduction of 

variable annual system cost and cost uncertainty decreased significantly with increasing RE 

penetration. These diminishing returns were caused by the limited amount of natural gas 

generation in the RMPP system that was available to be displaced and the hourly mismatch

between RE supply and electricity demand. Here, we investigate the sensitivity of these results to 

the mix of fossil fuel generators by analyzing a scenario in which all the coal generators in 

RMPP are replaced by natural gas CCGTs. In this analysis, CCGTs have more ramping 

flexibility than coal in addition to different marginal cost structures. The methodology and 

assumptions used to do this were discussed in Section 2.2.

Figure 12. Annualized variable cost of electricity with RE penetration for the 50:50 solar-wind case 

and coal- and natural gas-dominated fossil scenarios

Figure 12 shows the variable electricity system costs with increasing RE penetration for the

reference 50:50 solar-wind case for two scenarios. The original coal-dominant scenario with both 

coal thermal units and natural gas generation (see Table 1) and a second scenario where all the 

coal generators have been replaced with CCGTs (“No coal” in the figure legend). Figure 12

0

10

20

30

40

50

60

70

0% 10% 20% 30% 40% 50% 60%

Annualized variable electricity cost, $/MWh

RE penetration for 50:50 solar-wind scenario

No coal, $9.2/MMBtu NG

No coal, $6.0/MMBtu NG

No coal, $3.9/MMBtu NG

With coal, $9.2/MMBtu NG

With coal, $6.0/MMBtu NG

With coal, $3.9/MMBtu NG
19 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

shows that the impact of natural gas price uncertainty on variable system costs is much greater in 

the natural gas-dominated portfolio for all RE penetrations because larger amounts of natural gas 

generation are present. Because of this, coal generation, even though less flexible operationally 

than natural gas, also provides a partial hedge against natural prices changes. For example, at low 

RE penetration (15%), a $5.3/MMBtu variation in natural gas prices increases the variable cost 

of electricity by about $35/MWh in the natural gas-dominated portfolio but only by about 

$8/MWh in the coal-dominated portfolio–a factor of 4 difference. The magnitude of this 

difference in part is due to the low coal prices found in the region studied (on a $/MMBtu basis), 

and so the effect is likely to be lower in many other regions of the United States.

31

The effectiveness of RE in reducing the variance of annualized system costs is also less in the 

natural gas-dominated portfolio because, even at high RE penetration, much natural gas remains 

to be displaced, and differences in the timing of solar and wind generation matter less. RE

displaces some, but not all, flexible natural gas generation even at the highest RE generation 

fractions, and curtailment is significantly lower. This suggests that the magnitude of the physical 

hedging effect for any level of RE penetration increases with the share of natural gas generation 

in the fossil mix.

3.4 Regulated vs. Restructured Markets and the Impact of Market

Structure Assumptions on Cost and Cost Variance with 

Increasing RE

The production cost model used in this study solves for the optimal hourly dispatch of electricity 

generation resources, which minimizes annualized variable system costs. Because of this, the 

optimal hourly dispatch is largely independent of market structure assumptions, since in both a 

regulated and restructured market the generation units should be dispatched in a way that

minimizes overall system costs.

On the other hand, the price of power to consumers and the revenues earned by owners of 

generation assets may be very different in regulated and restructured markets. In a regulated 

market, the annualized cost of electricity ($/MWh) is calculated to provide generators with 

payments for incurred fuel costs plus a reasonable return on capital (with adjustments for actual 

vs. planned fuel costs if necessary). In contrast, capital recovery is not guaranteed in a 

restructured market where the wholesale electricity price in any given hour is set by the marginal 

unit, which in turn sets the price paid to all generators dispatched in that hour.

32 Because the 

marginal hourly price is frequently set by natural gas generators (especially during hours of peak 

demand), the revenue received by all generators in a restructured market (which in turn affects 

the cost of energy to consumers) is often much more sensitive to the price of natural gas than in 

regulated markets.

 31 The EIA estimates the U.S. average cost of fuel and variable O&M for conventional coal thermal plants to be 

$29.2/MWh, but with a range from $18.6/MWh to $47.4/MWh (EIA 2013c). The region and coal prices used in this 

study are consistent with the low end of these estimates. 

32 There are some exceptions to this. For example, in energy and capacity markets, capacity payments are available 

to encourage investment for some peaking units. In addition, in a multi-nodal market with transmission constraints, 

there may be an array of prices at different locations, known as locational marginal prices (or LMPs).
20 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 13 demonstrates this point by comparing the annualized variable cost of power in a 

regulated market to the estimated annualized average market price for wholesale electricity in a 

restructured market. The average market price for the year was estimated by assuming that the 

price in any given hour was the variable cost of the most expensive unit dispatched to meet 

demand, and then estimating the load-weighted average price for the year. As discussed in 

Section 3.1, in a regulated market with 15% RE penetration, a $5/MMBtu variation in natural gas 

prices can translate into an approximately $8/MWh variation in the variable cost of electricity.

For a restructured market, a similar price difference for natural gas leads to an electricity price

difference of more than $50/MWh–an increase by a factor greater than 5.

Adding RE in a restructured market also leads to a greater reduction in both the overall variable 

price of electricity and the uncertainty in electricity prices. For example, increasing RE 

penetration from 15% to 35% by generation in the restructured case reduces the variation in price 

by more than $30/MWh (from more than $50/MWh to $20/MWh), which is also greater than a 

factor of 5 (the decrease observed for the regulated case, about $5 to $6/MMBtu). The variable 

cost of electricity in the regulated market is also much lower overall than the price of power in 

the restructured market because it does not contain an adder to allow for capital recovery and a 

reasonable rate of return (not included in Figure 13). For an “apples-to-apples” comparison, it 

would be necessary in the regulated case to include such a fixed cost. Even so, while such an 

adder would shift annualized system costs higher in a regulated market, it should not increase 

electricity cost sensitivity to natural gas price variation significantly.

Figure 13. Annualized sensitivity of electricity prices (for restructured market) and annualized 

average variable cost (for regulated market) under different natural gas price scenarios for 

different RE penetration levels in RMPP (isolated)

Figure 14 shows this effect on a much smaller timescale and at greater resolution. The figure 

compares the hourly marginal price for power in a restructured market with the hourly variable 

cost of electricity in a regulated market over a one-week period for two different natural gas 

prices ($3.9/MMBtu and $9.2/MMBtu). This is for the 15% RE penetration 50:50 solar-wind 

reference scenario. Similar to Figure 13, the difference in the observed electricity prices in the 

0

20

40

60

80

100

120

0% 10% 20% 30% 40% 50% 60%

Annualized electricity price (restructured) 

or cost (regulated), $/MWh

RE penetration for 50:50 solar-wind scenario

Restructured, $9.2/MMBtu NG

Restructured, $3.9/MMBtu NG

Regulated, $9.2/MMBtu NG

Regulated, $3.9/MMBtu NG
21 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

restructured market under the two gas prices is more than $40/MWh, while the difference in 

annualized variable cost in the regulated market is much lower ($5/MWh).

Figure 14. Hourly sensitivity of marginal electricity price and average variable cost under different 

natural gas price scenarios under low (15%) RE penetration, 2020

The impact of RE on electricity price volatility is substantially different for restructured and 

regulated markets. This is important because both types of market structure have a substantial 

presence in the United States (where about 2/3 of electricity is sold in restructured markets and

1/3 is sold in regulated markets), and the continuation of such bifurcation cannot be ruled out in 

the future. In practice, even in restructured markets the vast majority of wind and solar 

generation is sold under long-term bilateral contracts with fixed-price (or index-linked) power 

purchase agreements (PPAs); this is not, however, reflected in Figure 13 and Figure 14. The 

presence of such bilateral contracts in restructured markets may significantly lower the impact of

gas price volatility on both consumer costs and investor risk. Our ongoing research is examining 

the risks and rewards faced by investors and consumers in restructured vs. regulated markets, 

including the relevance and role of bilateral contracts.
22 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

4 Characterizing the Impact of RE Generation on the 

Annualized Variable Cost of Electricity and Cost 

Uncertainty using MC Simulations

In this section, we extend the natural gas price sensitivity analysis discussed in Section 3 by 

characterizing the impact of RE penetration on the uncertainty of electricity costs using MC 

simulations of natural gas prices. The resultant shape and density of the different electricity cost 

distributions add detail to earlier discrete price sensitivity analyses, and this enables a better 

understanding of the likelihood and impact of adverse cost outcomes. This may be important 

because the historical distribution of natural gas prices is asymmetric and positively skewed 

toward higher prices (Section 2.3), which in turn will lead to similar effects for annualized 

variable electricity costs generated by the production cost model.

33

4.1 The Stability of Optimal Production Cost Model Dispatch to a 

Range of Natural Gas Prices

In principle, MC simulation analysis using a production cost model requires estimating the future 

distribution of natural gas prices, and then drawing from this distribution hundreds (or 

thousands) of monthly natural gas prices in sets of 30 or more years, to generate the associated 

electricity cost distributions. If computation time were not an issue, the impact of each future 

natural gas price scenario could be simulated directly using the production cost model, and this 

process would be repeated for all RE penetration fractions and other scenarios of interest.

However, we focus here on exploring the stability of optimal dispatch solutions calculated using 

the hourly production cost model for a range of natural gas prices. Dispatch stability observed 

over a wide range of gas prices allowed us to significantly reduce the number of runs that need to 

be done within the production cost model.

Figure 15 suggests that the optimal hourly dispatch solution for one year (calculated using the 

hourly production cost model) does not significantly vary over a wide range of natural gas prices.

The graphs in the left column show the hourly amount of natural gas used over the year for the 

50:50 solar-wind reference case under low (15% top row) and high (50% bottom row) RE

penetration scenarios, with the natural gas price set at $3.9/MMBtu. The graphs in the right 

column show the same scenarios but with a higher natural gas price set at $9.2/MMBtu. The

hourly natural gas use (or off-take) is a good proxy for the generation from natural gas units. In 

the high 50% RE penetration scenarios, the use of natural gas decreases substantially throughout 

the year (compared to the low RE cases), reflecting greater displacement of natural gas 

generation. While the use of natural gas—and the optimal dispatch of natural gas generators—is 

significantly different for the low and high RE penetration scenarios (top vs. bottom rows), each 

of these distinct dispatch profiles is similar for both high and low natural gas prices (right vs. left

columns).

 33 Again we stress the MC analysis is based on a proxy for the future distribution of natural gas prices since the true 

distribution of future prices is unknown. Structural regime changes that have shifted the price in the past may not be 

matched by similar events going forward. In the context of portfolio optimization--which we do not attempt in this 

paper--Stirling (1994) has raised concerns against the potential to misuse “risk and probabilities” estimates to 

replace ignorance or lack of knowledge about future outcomes. 
23 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 15. Hourly gas use over one year (2020) under different natural gas price and RE

penetration scenarios, 50:50 solar-wind scenario in RMPP (isolated)

Figure 16 contains three pairs of graphs that show the daily generation for coal thermal units and

natural gas CCGTs and CTs for a range of natural gas prices (from $1.5/MMBtu to 

$9.2/MMBtu). The scatter plots in the left column compare the daily generation from each type 

of technology at moderate and high natural gas prices ($3.9/MMBtu and $9.2/MMBtu). The 

scatter plots in the right column compare daily generation at lower natural gas prices

($1.5/MMBtu and $3.9/MMBtu).

Figure 16 shows that the average daily generation is greater for coal than for CCGTs, and both 

these technologies operate far more frequently than do CTs; this is consistent with earlier 

discussion and Figure 8. The daily dispatch over the year for coal, CCGTs, and CTs is similar at 

the two higher natural gas prices (left column), shown by the points lining up in 1:1 agreement 

(on a line that bisects the plane at 45°). Consistent with this, we find very strong correlations in 

generation output for coal, CCGTs, and CTs (1.0, 0.99, and 0.98, respectively) for these prices.

The operational relationship is clearly weaker for the lower pair of natural gas prices (right 

column). The points are more scattered, and the correlations are much lower, for coal, CCGTs,

and CTs (0.88, 0.74, and 0.77, respectively). The higher dispersion and reduced correlation is 

expected at very low gas prices because the variable cost of CCGT generation may become 

comparable to or lower than coal generation, resulting in coal and gas units switching places in 

the supply stack.

34 This analysis supports the idea of using a single PLEXOS run between 

$3.9/MMBtu and $9.2/MMBtu for all natural gas prices within this range, which leads to a huge 

reduction in the number of required runs. Broader examination of the impact of alternative 

 34In 2012, natural gas prices fell below $4/MMBtu, enabling a significant amount of natural gas to displace coal, 

although this trend is expected to reverse in 2013 as natural gas prices recover from these extreme lows and move 

closer to the forward price of $5–$6/MMBtu.

0

10

20

30

40

50

60

1/1 4/1 7/1 9/30 12/30

Hourly gas offtake, 

thousand MMBtu

0

10

20

30

40

50

60

1/1 4/1 7/1 9/30 12/30

0

10

20

30

40

50

60

1/1 4/1 7/1 9/30 12/30

Hourly gas offtake, 

thousand MMBtu

0

10

20

30

40

50

60

1/1 4/1 7/1 9/30 12/30

15% RE, $3.9/MMBtu NG 15% RE, $9.2/MMBtu NG

50% RE, $3.9/MMBtu NG 50% RE, $9.2/MMBtu NG
24 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

dispatch assumptions (Table 3) allowed us to use only five separate PLEXOS runs for each MC

simulation of natural gas prices.

Figure 16. Comparison of daily average dispatch for coal, CCGT, and CT technologies for different 

pairs of natural gas prices in RMPP (isolated)

Table 3 shows the annualized variable cost of electricity for a range of natural gas prices ($1.5–

$9.2/MMBtu) calculated using three dispatch strategies: 

1) Without any approximation using the optimal dispatch solved for in the hourly 

production cost model for each of the five natural gas price scenarios

2) An approximation that uses the optimal dispatch for $1.5/MMBtu for all five gas price 

scenarios 

3) An approximation that uses the optimal dispatch for $9.2/MMBtu for the five gas price 

scenarios. 

We find that the use of a single optimal hourly dispatch solution calculated for $9.2/MMBtu

natural gas can be used to estimate annual variable electricity prices to within 0.4% accuracy for 

natural gas prices (down to $2.2/MMBtu) and within 1.3% accuracy for natural gas prices (down 

to $1.5/MMBtu). The optimal dispatch solution calculated for $1.5/MMBtu is less accurate for 

representing variable electricity prices for higher natural gas prices; this is likely because, at very 

0

2

4

6

8

0 2 4 6 8

Coal 

0

1

2

3

4

0 1 2 3 4

CCGT

Daily average dispatch (GWh) for $9.2/MMBtu NG

$3.9/MMBtu NG daily average (GWh)

0

2

4

6

8

0 2 4 6 8

Coal 

0

1

2

3

4

0 1 2 3 4

CCGT

0

0.5

1

1.5

2

0 0.5 1 1.5 2

CT 

$1.5/MMBtu NG daily average (GWh)

0

0.5

1

1.5

2

0 0.5 1 1.5 2

CT 
25 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

low natural gas prices, the variable cost of CCGTs can be lower than that of coal generators, but 

this dispatch order does not hold for higher natural gas prices (e.g., $3.9/MMBtu and above). In 

general, the optimal dispatch solutions for the five natural gas prices shown in Table 3 can 

represent optimal dispatch for natural gas prices between those in the table (by interpolation) and 

beyond the upper and lower bounds of prices in the table (by extrapolation).

Table 3. Impact of Alternative Dispatch Assumptions on the Annualized Cost of Electricity

Annual Variable Electricity Prices ($/MWh) Error

Natural Gas 

Price ($/MMBtu)

Optimal 

dispatch

Dispatch using 

$1.5/MMBtu

Dispatch using 

$9.2/MMBtu

in $1.5/MMBtu 

dispatch

in $9.2/MMBtu 

dispatch

1.5 14.49 14.49 14.68 0.0% 1.3%

2.2 15.75 15.96 15.82 1.3% 0.4%

3.9 18.53 19.55 18.61 5.5% 0.4%

6 22.08 24.07 22.12 9.0% 0.2%

9.2 27.49 30.97 27.49 12.7% 0.0%

Our finding—that the optimal dispatch solutions generated using the hourly production cost 

model PLEXOS are relatively stable (less than 1.5% error in impact on annualized costs) over a 

wide range of natural gas prices—allows us to use only a limited number of PLEXOS runs for 

each scenario and to run most MC simulations outside the model.

35

4.2 Annualized Electricity Cost Distribution

Figure 17 shows the distributions of annualized variable electricity cost ($/MWh) generated 

using MC simulations of natural gas prices under different RE penetration levels for the 50:50 

solar-wind reference case. The results are similar in many ways to those shown for the scenario 

analysis in Figure 7 and discussed in Section 3. As RE penetration increases, the annualized 

variable system cost and the variance of this system cost decrease. These cost-probability 

distributions show the positively skewed asymmetric nature of the outcomes; the most likely 

outcome is often significantly below the mean (or expected value). This means that, while the 

upside risk (of lower costs) is largely capped, the downside risk (of higher costs) is not. This may 

be particularly important given the current low natural gas prices (Bolinger 2013).

36

For comparison, the natural gas input price distribution is also shown (in common $/MWh units).

The “width” of outcomes for natural gas prices is much greater than the variation in the system 

costs--even in the low RE penetration case. This reflects the fact that the system has much more 

coal thermal generation than natural gas CCGT, which will tend to be dispatched before natural 

gas. Table 4 shows some of the characteristics of the distributions. As the renewable energy 

penetration increases both the standard deviation (srdev) and coefficient of variation (coefvar) in 

electricity costs declines. This also leads to a significant reduction in the difference between the 

mean and the 95th percentile electricity costs. The distributions are all positively skewed with 

 35 These findings may, to some degree, be region dependent. To some extent, these finding may be dependent on 

the low cost for coal energy in the region used in this study. At higher coal prices, the natural gas price at which the 

order of the supply curve starts to change may be significantly higher than found in this study.

36In many ways, there is a much greater chance of upward rather than downward movement, although some of this is 

already reflected in the forward price being $1–$2/MMBtu higher than the recent spot price, which has fallen below 

$4/MMBtu. 
26 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

some variation with RE penetration. The greatest variation, seen in the 10% RE scenario, 

between the 95th and mean electricity prices remains under $5/MWh. However, this variation 

would be higher in portfolios with greater fractions of natural gas generation. 37 The difference 

may also be greater in the future years because regime changes – of unknown timing or impact -

in supply or demand or both could lead to much higher or much lower expected natural gas (and 

hence electricity cost) trajectory than is reflected by using the mean of the historical distribution. 

More generally, production models such as PLEXOS tend to underestimate price volatility that 

arises due to scarcity especially in restructured markets.

Figure 17 shows the uncertainty in variable system costs over a single year. Over a longer 

timeframe (e.g., 20 or 30 years), the distribution for the average annualized cost of electricity 

over the longer period will tend to be lower than for any given year, since lower cost “good 

years” will tend to offset higher cost “bad years.” This reduced variability may be mitigated to 

the degree that the future mean of natural gas price “moves” due to regime changing events and 

one ends up on a lower or higher price path for a sustained period.38 RE can provide insurance 

against future outcomes in the United States where supply and demand tighten and natural gas 

prices rise. 

The uncertainty in the variable cost of generation in any given year will be somewhat greater 

than suggested in Figure 17 because of the inter-annual variability of solar and wind generation. 

Drury et al. (2013) found that simulated annual PV generation can naturally vary by up to ±15% 

from mean performance in any given year, with annual PV standard deviations ranging from 

±3% to ±7% depending on location. However, this year-to-year solar variability is likely to be 

smoothed over longer timeframes, and 20-year samples of inter-annual PV variation were found 

to be reduced by more than 80% relative to annual variability, leading to a 20-year standard 

deviation of about ±2%. Similar inter-annual variability of wind generation is also likely to 

occur. One recent study on the inter-annual variability of wind at U.S. wind farms over a 7- to 

10-year period found annual variations of up to ±15%–20% and standard deviations of ±8%–

13% (Wan 2012), although this variation may be significantly reduced when aggregated over a 

large number of wind farms. The plus or minus variation in RE generation in any given year 

compared to the “average” means that years of lower wind, which are less well hedged, will tend 

to be compensated largely by years in which the hedge is better than average.39

 37 By way of illustration, for a system with 100% natural gas CCGT and a conversion efficiency of 50%, the 

variable cost distribution at low RE would be about double the “width” of the natural gas distribution in Figure 17 

(natural gas prices of $10 and $30/MWh would translate to variable costs of $20 and $60/MWh, or more).

38 Two important caveats apply to this idea. First, the mean-reverting nature of the distribution implies that the price 

in any given year is dependent on prior years’ values, and as a result over longer periods will tend to oscillate around 

the mean. In the real world the expected mean of the future natural gas prices has changed a number of times in the 

past, most recently with lower prices due to the availability of shale gas. In other words, the mean in non-stationary 

over time and changes to the mean going forward are also possible, and so 20 years out in the future the customer 

may actually be on the lower side or the upper side of the annual price distribution. It should also be emphasized that 

the customer will not experience the entire probability distribution (as a series of “trials”). Rather, a customer will 

experience 20 observations from the single year distribution over 20 year period and only one from the 20-year 

average distribution (see also Stirling 1994).

39 The impact of inter-annual variability of solar and wind is one of the focuses of our ongoing work. Unlike the 

natural gas price mean, the solar and wind means may be more stable and less dependent on prior years, though 

climate change may lead to shifts over longer periods.
27 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 17. Annualized variable electricity system costs for different RE penetrations based on MC

simulations using underlying natural gas price distribution in RMPP (isolated)

Table 4. Characteristics of the Natural Gas Price and Variable Electricity Cost Distributions

0

10

20

30

40

50

60

70

80

90

5 10 15 20 25 30 35

Frequency of occurrence, a.u.

Resource price or cost, $/MWh

NG price distribution x10

10% RE Electricity

15% RE Electricity

25% RE electricity

35% RE Electricity

45% RE Electricity

52% RE Electricity
28 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

5 The Potential Use and Value of Solar and Wind as a 

Physical Hedge against Cost Risk

The prior sections analyzed how increased RE penetration reduces the uncertainty of future 

electricity system costs—and how solar and wind together may provide a more stable hedge than 

either one alone. To understand the potential basis for the incremental value and cost

effectiveness of RE as a partial physical hedge against price volatility, it is useful to consider the 

following:

• How to estimate the incremental value of RE to consumers in reducing their price

exposure and how this marginal value varies with increasing penetration (e.g., due to 

saturation effects as more of the natural gas generation is displaced)

• Whether buyers interested in reducing their exposure to future price risk actually need to 

pay for such a hedging benefit or if they may find cheaper methods to replicate this 

effect, at least over some timescales at some locations.40

There are also some broader diversity-related security and other macroeconomic-related benefits 

of including RE within the generation portfolio. This includes the potential benefits from not 

relying on too few technologies and/or fuel sources. This issue is discussed in more detail in the 

concluding subsection 5.2.

5.1 Economic Utility of Risk and Loss Aversion for Consumers

If consumers are risk averse, then the real cost or economic utility (or disutility since it reflects a 

loss) paid for by the consumer depends not only on the expected annualized cost, but also on the 

uncertainty of annualized future costs. This loss in economic utility (U) can be represented by:

U[V] = E[V] − λ

2 Var[V]

where V represents the annualized variable cost of electricity. The reduction in utility to the 

consumer has two terms: the expected cost or E[V] (which will be negative for a payment by the 

consumer) together with a second negative term that increases with the uncertainty of these costs 

(as measured by the variance of the distribution). In practice, the overall loss in utility is, of 

course, at least offset by the benefits provided to the consumer through the purchase and use of 

the electricity. The λ (lambda) term reflects the level of risk aversion so that, in the limiting case 

where the consumer is risk neutral (and λ = 0), the utility is simply equal to the expected cost 

(the flat line in 18). For a given level of risk aversion, the larger the variance of the distribution, 

the greater the reduction in utility. This utility-based approach enables quantification of the 

impact of increasing RE penetration, as discussed by Awerbuch (Awerbuch 2006), and others, 

and is similar to ideas that have been proposed for integrated resource planning (Logan et al. 

1994). Figure 18 also shows how, for a given level of risk aversion, the marginal increase in

utility declines with penetration, which is consistent with the saturation effects described in 

Section 3.

 40 This is a little misleading in the sense that our paper focuses on the incremental value associated with adding RE 

to a system, and we have not dealt with the broader economic question about the optimal amount of RE to be added 

to a system when all relevant costs and benefits are considered.
29 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Another potential concern for consumers is that bad outcomes (which in this case would 

correspond to higher-than-expected costs) might be weighted more than “equivalent” benefits (or 

gains) associated with similar reductions in costs from the expected value. This phenomenon is 

known as “loss aversion” and has been the subject of considerable investigation in the field of 

behavioral economics (see, e.g., Kahneman and Tversky 1979). The loss aversion term modifies 

the utility function to consumers to include a downside risk measure (DSM) to become:41

U[V] = E[V] − λ

2 Var[V] − DSM[V]

where DSM[V] corresponds to the DSM

DSM[V] = �[θ+(V − E[V])+ − θ−(V − E[V])−]dV

∞

0

with the notation (V)± = �

V for V ≷ 0

0 otherwise

The value of the DSM term is obtained by integrating the product of the probability density 

function by the difference in the variable term, V, and the expected value over the entire 

distribution. This product is multiplied by a constant, θ, that changes value on either side of the 

expected value to reflect the fact that “losses” are valued more greatly than otherwise-equivalent 

gains. Unlike risk aversion, which treats variations about the expected value symmetrically, with 

loss aversion the value of DSM reflects the net difference in how outcomes to the left and right 

of the expected value are perceived (as well as the width of the distribution).42 The origin of loss 

aversion to input prices can be quite rational if wide swings in natural gas prices affect a firm 

profits asymmetrically. In such cases, hedging can generate economic value even if the cost of 

natural gas with and without the hedge is unchanged on an expected basis. This methodology 

may also provide some justification to those who view one reason for investing in RE as a way 

of providing some degree of insurance against future cost increases that might arise from 

changes in natural gas prices or the imposition of a carbon tax.

Figure 18 shows the gain in utility as a function of RE penetration under different assumptions of 

risk and loss aversion for the 50:50 solar-wind reference case discussed in Section 4. The figure 

shows that the marginal value of more RE in reducing price exposure declines rapidly, reflecting 

the displacement of existing gas generation assets. The value to specific customers or society 

more generally, which depends on assumptions about their level of risk and loss aversion, ranges 

from nearly $10/MWh down to zero in our illustrative example. In a natural gas-dominated fossil 

mix (as opposed to coal-dominated), the expected benefits of variance reduction in consumer 

costs would increase with the proportion of natural gas generation in the mix. As discussed 

earlier, if the mean remains stable over long time periods there may be a significant reduction in 

uncertainty over a multi-decade horizon. However, in practice structural changes have occurred 

 41 This approach to estimating the utility-based cost to consumers for electricity was outlined in Bush et al. (2012) 

and is similar to an approach for valuing portfolios of financial instruments suggested by Jarrow and Zhao (2006). 42 Loss aversion is not necessarily fixed and, for an individual, will depend on how losses or gains affect his or her

existing wealth (or some other reference point). 
30 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

in the past that have led to significant changes in natural gas prices, most recently associated

with the discovery of increased shale gas resources. RE can provide insurance to mitigate the 

impact of future scenarios where structural changes lead the production cost of natural gas to rise

significantly to meet supply.

Figure 18. Utilities of various RE mixes for different attitudes towards risk and impact of time 

horizon (illustrative)

While investors, such as electricity producers, are generally considered risk averse, the degree to 

which their customers are risk averse appears to be less certain. Customers who are also 

investors can be generally expected to be risk averse. For example, a merchant CCGT plant is an 

investor because it sells power but also a customer because it buys natural gas as an input, the 

price of which can have a substantial impact on the profitability of the plant, business, as well as

industry more generally.

43 Similar considerations might apply to many other companies for 

which the cost of fuel or electricity is a significant input to the cost of goods or services sold, 

such as aluminum smelters, data centers, or airlines. On a smaller scale, many owners who install 

PV systems may do so at least in part to protect themselves against future electricity price 

increases. On the other hand, being locked into fixed prices for long periods can also bring 

business risks if market prices change significantly, and many industries that hedge (e.g., airlines 

or biofuel manufacturers) tend to do so over shorter time horizons.

44

More generally, the acceptable level of electricity cost-cost risk and loss aversion applicable for 

a particular customer, firm, or investor will depend on many factors, including their overall 

exposure to other risks, the structure and degree of integration of the firm (e.g., 

 43 For a discussion of how a combination of events led to the collapse of the merchant natural gas industry in the 

early 2000s, see Rigby (2004).

44 In this way, these companies are hedged against changes in relatively short-term, multi-year situations, but not 

with long-term prices, since, when the time comes to periodically renew a multi-year hedge, the new forward price 

will reflect current market conditions and may be substantially different from the original hedge price. On the other 

hand, this may have some advantages in keeping costs in line with the market.
31 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

horizontal/vertical integration, geographic diversity), opportunities for risk pooling and intertemporal cross subsidies, cash-flow considerations, business models, timeframes considered (20 

years, 1 year, seasonal, or weekly), as well as the possible availability, cost, and effectiveness of 

alternative hedging opportunities provided by financial or physical supply instruments.

5.2 The Cost of Hedging and Alternative Methods

The value of a particular investment must be compared with the value of the next best 

alternative, not with the status quo of doing nothing. For this reason, the incremental economic 

value of displacing an old thermal coal plant with wind should be compared to the best 

alternative investment, which might be a natural gas CCGT. Similarly, at least for short to 

moderate timescales (1–10 years), utilities might hedge against price volatility at very low cost 

using financial instruments (such as forward contracts and swaps), other long-term contracts on 

natural gas, or forward electricity contracts.

45 In other words, there may be cheaper and better 

methods to replicate the hedging impact of RE by using financial or physical supply instruments

(Graves and Livinova 2009).

46 In the longer term, however, solar and wind may be able to 

provide a physical hedge that is not easily replicated in the financial or physical supply markets 

because of lack of availability, liquidity, and counterparty risk (Bolinger 2013; Graves and 

Litvinova 2009). It is worth noting, however, that the periodic (or rolling) renewal of shorter 

term hedges of few years provides protection against local market conditions, rather than the

longer term price changes that RE could mitigate.

47

In the short to moderate term, the cost effectiveness of using various types of financial or 

physical supply instruments will depend on a number of interrelated factors including: 1) 

whether or not the forward price includes a risk or other forward “premium” over the expected 

future price of the commodity; 2) the timeframe over which such instruments or contracts are 

available and their liquidity at different locations; 3) their effectiveness as a hedge for power 

(rather than just natural gas) prices, including delivery considerations; and 4) other risks such as 

counterparty risk. 

Origin of the forward price premium? Whether or not the forward prices for natural gas or 

electric power have positive or negative premiums over the expected future prices is somewhat 

controversial and may depend on a number of factors, including those listed above (Bolinger and 

Wiser 2008; Borenstein et al. 2007; Bolinger 2013). In terms of a potential risk premium, if the 

buyers of natural gas (or electricity) are more risk averse than the sellers, they may be “more 

willing” to pay a premium for a fixed price over the expected future price (compared to the seller 

being willing to sell at a discount for a fixed price), in which case the net risk premium may be 

positive. On the other hand, if the sellers are more risk averse than the buyers, the forward 

premium may be negative. If the buyers and sellers are both equally risk averse, then there may 

be no risk premium. In this case, while the risk premium may have been competed away, both

parties still find the fixed-price contract valuable (and gain utility from it). Whether or not there 

 45 This view typically assumes there is no risk premium in the forward price over the future expected price. 46 This is a little more nuanced than it sounds because our paper discusses incremental benefits of RE, so there are

no additional costs involved. Whether the RE hedge is more or less effective than the use of financial contracts is a 

somewhat different question. 47 And that of course may be an appropriate risk mitigation strategy for many businesses where mitigating cost 

swings with respect to near term market conditions to their goal. 
32 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

is a positive or negative premium due to risk or other factors does not appear to have a single 

definitive answer.

Is there a premium over the expert future price due to risk aversion or other factors? Bolinger 

and Wiser (2008) compared natural gas price forecasts to the prices of various futures contracts 

for natural gas over a 5- to 10-year period and suggested there may be a positive premium 

ranging from $0.5–$2.4/MMBtu, equivalent to $4–$17/MWh assuming a highly efficient gasfired plant. Borenstein et al. (2007) support the idea of a positive forward premium for natural 

gas in at least some U.S. locations, but they argue this is due to lack of liquidity in delivered 

natural gas rather than “traditional” risk aversion.48 On the other hand studies have suggested the 

U.S. forward premium for natural gas may be zero or even negative (Modjtahedi and Movassagh 

2005). Graves and Levine (2010) note that the shape of the distribution matters and the expected 

future price of natural gas could be greater than the most likely observed future price (that is,

have a “positive difference” of sorts) without implying any risk (or other meaningful) premium. 

Rather, this positive difference would reflect the fact that price distribution is positively skewed

and so the mean value of the distribution (the expected value) will be greater and lie to the right 

of the peak of the distribution (which corresponds to the most likely value).

Recent work also suggests that there may be a premium for electricity forward prices (Redl and 

Bunn 2011; DeBenedictis et al. 2011). Redl and Bunn (2011) listed a number of factors that may 

lead to positive premiums for fixed electricity prices, including natural gas price forward 

premiums, price volatility, reserve margins, scarcity, and skewedness of the distribution and 

market power.49

How effective are financial instruments? The effectiveness of using forward contracts for natural 

gas or power as a hedge against price exposure is also relevant. A natural gas futures or forward 

contract is an indirect hedge on one of the main drivers of power prices, but it is not a perfect 

hedge because the price of power for the system also depends on other factors (such as the cost 

of other fuels [including coal], the timeframe over which the futures contract is available, and 

market structure). In addition, for utilities that generate a large of amount of their electricity 

using natural gas, it is not enough to purchase the natural gas; the gas also must be delivered, and 

there may be significant risk or costs associated with ensuring delivery, particularly at periods of 

high demand when the system may be stressed.50 Figure 19 shows the impact of pipeline 

transmission constraints in the Northeast (from the South and West) coupled with bad weather

(and future expectations of such weather). These factors led both spot and near-term forward 

natural gas prices to effectively disconnect from the “shale driven” prices of less than $4/MMBtu 

 48 For our purposes, the value of RE does not depend on the source. 49 DeBenedictis et al. (2011) estimated an average 5% risk premium in the electricity forward price for delivery in 

the Mid-Columbia hub in the Pacific Northwest—although the monthly value varied widely, including changing its 

sign. Some of the difficulty in knowing the sign or magnitude of a risk premium, or perhaps reflecting difficulties in 

estimating it, is that this 5% premium was an average over 12 months, whereas the estimated monthly risk premium 

varied from less than -5% to more than 20%. 50 Take, for example, the case of a peaking unit that is rarely run but needs to run when the system is stressed. To 

ensure that gas is available, such a unit would need to purchase fixed transmission capacity, which introduces a 

considerable cost if the unit is rarely run. Alternatively, there is real risk under interruptible transmission that it will 

not be able to run when most needed.
33 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

seen around much of the rest of the United States while also inducing significant price volatility 

(from $4 to more than $12/MMBtu near Boston).

51

Figure 19. Premium for natural gas spot prices in the Northeast compared to other locations

during winter 2012/13 (EIA 2013a)

Hedging using electricity forward prices is more direct, although again the effect of these 

instruments may still be approximate because they may only be available at limited locations

with limited resolution for limited time horizons that only approximately reflect the more 

granular hourly price profile characteristic of electricity. In addition, many of the potential issues 

associated with the use of natural gas forward contracts, including both liquidity and 

counterparty risk issues, may have a significant influence on the electricity forward price (Redl 

and Bunn 2011).

On the question of whether forward premiums for natural gas or electricity are positive, zero, or 

negative, the answer may be “all of the above.” The existence of a forward premium (positive or 

negative) may vary by location, timescale, market structure, and weather. This area requires 

additional research, particularly focused on the actual behavior of buyers and sellers.

 51 At the end of December 2012, the natural gas future prices for delivery to Boston in January, February, and March 

were approximately $10, $8, and $6/MMBtu. During this time, the Henry Hub price (Louisiana) remained roughly 

flat at less than $4/MMBtu. The premium in Boston of $6/MMBtu for January fell to about $2/MMBtu for March.

Over this same period, New York City had a smaller premium over the Henry Hub of about $2/MMBtu in January,

with prices becoming comparable in March. Other factors contributing to this price increase were lower LNG 

imports and reduced production from the Canadian Sable field (EIA 2013a).
34 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

6 Summary and Conclusions

This study investigated the impact on the variable cost of generation of adding RE generation to 

fossil portfolios under a number of scenarios. For the reference (50:50 solar-wind) case, we 

varied the annual RE generation from about 10% to over 50%, with solar and wind contributing 

equally to generation on an annual basis. We then explored the impact of altering the annual 

solar-to-wind generation ratio to 3:1 and 1:3. The impact of changing the ratio of natural gas to 

coal generation was also considered by switching out coal thermal generation units with CCGTs 

in some scenarios. Some market structure issues were also considered. Some key observations

are outlined below

Solar and wind generation significantly reduces the exposure of electricity costs to natural gas 

price uncertainty in fossil-based generation portfolios on a multi-year to multi-decade time 

horizon. The incremental impact, and any associated marginal value of RE in decreasing 

electricity-cost volatility, declines with increasing RE penetration. The reduction in volatility of 

electricity costs with increased RE penetration is greater for natural gas-dominated portfolios 

than for coal-dominated portfolios.

At low RE penetrations (e.g., 10%–15% annual RE generation), the annualized variable system 

costs vary widely with the price of natural gas in both our coal-dominated and natural gasdominated fossil portfolios. For similar RE penetration (15%) in the gas-dominated portfolio in 

the region studied a $5/MMBtu variation in natural gas prices changes the variable cost of 

electricity by about $35/MWh in the gas-dominant portfolio--a more than three-fold difference 

compared to the coal-dominant portfolio.52

In the coal-dominated fossil portfolio, the incremental impact of further solar and wind 

penetration decreases with increasing RE penetration, with only small incremental benefits being

achievable beyond 35% penetration. In contrast in the natural gas-dominated portfolio, the 

saturation effect in electricity cost variance reduction is not observed even at higher RE 

penetration levels (of over 40%) because a large amount of natural gas generation remains to be 

displaced.

In the region studied, a mix of wind and solar provides a better physical hedge against uncertain 

fuel prices than either wind or solar alone, because of the observed anti-correlation in solar and 

wind generation profiles at time scales ranging from intra-day to seasons.

Market structure choices are important. Adding RE reduces uncertainty in cost to consumers53

much more in restructured markets than in regulated markets since natural gas often sets the 

marginal price in a given hour in restructured markets (particularly during higher-priced peak 

periods), and this price is then paid to all generators dispatched. 

 52 The relative ratio of price variation depends not only on the ratio of coal thermal to natural gas plants but also on 

the cost of coal. Coal prices vary significantly by location and the cost of coal per MMBtu for CO and used in the 

study is amongst the lowest in the United States.

53 Bilateral contracts within a restructured market, which are common for solar and wind, may mitigate this leverage

and have an asymmetrical effect on consumers. This and other market structure-related issues are a focus of our 

follow-on research.
35 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

MC analysis of the impact of natural gas price variations over multi-decade time horizons

complements scenario analysis by generating electricity cost distributions that show the 

“density” of outcomes and how the electricity distribution is positively skewed. We find that 

much of the MC analysis of natural gas price uncertainty impacts can be done outside of the 

production cost model by recognizing the stability of the simulated hourly system dispatch for a 

wide range of natural gas prices. This greatly enhances our ability to perform many simulations, 

which otherwise would be limited by model run times.54

The potential benefits of diversified portfolios containing significant solar and wind generation 

will depend on two main factors. One factor is how much consumers value lower price 

uncertainty due to risk aversion, loss aversion, scarcity, or other characteristics. The second 

factor is the potential cost and effectiveness of alternative financial or physical hedging methods, 

such as forward contracts, swaps, or physical supply contracts, and the timeframe over which 

these are available; this includes the degree to which price uncertainty risks are mitigated and the 

extent to which new risks may be introduced (e.g., associated with natural gas transportation 

constraints, counterparty risks, market liquidity, and others). 

The cost of using financial instruments to hedge against future price uncertainty depends in part 

on whether long-term forward contracts (for natural gas or electricity) contain a premium over 

expected future prices. Electricity sellers and buyers may both be risk averse, and there is no 

consensus about the net impact this has on the existence of a forward premium for eliminating 

price volatility in the United States. Some studies suggest that, at least in the short term, it may 

be more cost effective to use financial hedging instruments; these often assume (either implicitly 

or explicitly) that there is no risk aversion or other premium in the forward price over the 

expected futures price. On the other hand some studies have suggested there may a positive 

premium over the expected future price due to risk aversion (Bolinger et al. 2002) or due to 

scarcity or other factors (Borenstein et al. 2007), while others suggest a negative premium 

(Modjtahedi and Movassagh 2005). The answer may be “all of the above”, with the existence 

and magnitude of a premium (positive or negative) likely to vary with location, commodity, and 

timescale, while changing over time.

Of particular relevance to RE, it is difficult and rare to be able to lock in financial or physical 

supply contracts of 10 years or more for natural gas, and such contracts may include premiums 

that reflect lack of liquidity and counterparty risk (Bolinger 2013).55 Because of these and other 

issues, in the longer term solar and wind may be able provide a physical hedge that is not easily 

replicated in the financial and physical commodity markets.56 It also provides insurance value 

against rising electricity prices in futures where natural gas prices rise or carbon emissions are 

priced via a tax or some other mechanism. Even in the shorter term, RE may be the better choice 

 54 This range of stability of dispatch is partly due to the low coal prices found in the region studied (on a $/MMBtu 

basis), and so the effect is likely to be less pronounced in many other regions of the United States.

55 “Passive” hedging with RE could also provide benefits by affecting a wide range of buyers in a similar manner. 

This may be helpful because many firms have trouble knowing how to hedge appropriately (possibly overreacting to 

a crisis and locking in high prices), and this can bring business risks. Alternatively, a firm could hedge in a smart 

way—while many of its competitors do not—and get “unlucky” if, for example, the prices of inputs fall sharply for 

the industry. Passive or natural hedging with RE may provide a “cushioning” effect to these types of business risk. 56 The use of rolling, short-term hedging over longer time horizons provides a hedge against evolving market 

conditions and prices. It does not provide a long-term hedge against future price changes (as might a natural hedge 

due to RE).
36 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

for some consumers. While most of this report deals with the system wide effect to the average 

consumer at a multi-utility level, the preference for cost mitigation and over what timeframes 

may vary widely by customer type. Size also matters where some residential and commercial 

customers may be more likely to decide to install distributed RE if their ability to hedge using 

financial or physical instruments is limited by a lack of knowledge, high transaction costs, or a 

lack of availability of such instruments.
37 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

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