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

Operation of Concentrating 

Solar Power Plants in the 

Western Wind and Solar 

Integration Phase 2 Study

Paul Denholm, Gregory Brinkman, Debra Lew, 

and Marissa Hummon

Technical Report

NREL/TP-6A20-61782

May 2014
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

Operation of Concentrating 

Solar Power Plants in the 

Western Wind and Solar 

Integration Phase 2 Study 

Paul Denholm, Gregory Brinkman, Debra Lew, 

and Marissa Hummon

Prepared under Task No. SS13.3060

Technical Report

NREL/TP-6A20-61782

May 2014
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

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

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

Available electronically at http://www.osti.gov/scitech

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

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

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

 Printed on paper containing at least 50% wastepaper, including 10% post consumer waste. 
iii

Acknowledgments

This project was funded by the U.S. Department of Energy Solar Energy Technologies Program. 

The following individuals provided valuable input during the analysis and publication process: 

Aaron Bloom, Adam Green, Udi Helman, Jennie Jorgenson, Mary Lukkonen, Maggie Mann, 

Mark Mehos, David Schlosberg, and Frank Wilkins. Any errors or omissions are the sole 

responsibility of the authors. 

Abstract

The Western Wind and Solar Integration Study (WWSIS) explores various aspects of the 

challenges and impacts of integrating large amounts of wind and solar energy into the electric 

power system of the West. The phase 2 study (WWSIS-2) is one of the first to include 

dispatchable concentrating solar power (CSP) with thermal energy storage (TES) in multiple 

scenarios of renewable penetration and mix. As a result, WWSIS-2 provides unique insights into 

CSP plant operation, grid benefits, and how CSP operation and configuration might need to 

change under scenarios of increased renewable penetration. Examination of the WWSIS-2 results 

indicates that in all scenarios CSP plants with TES provide firm system capacity, reducing the 

net demand and the need for conventional thermal capacity. The plants also reduced demand 

during periods of short-duration, high-ramping requirements that often require use of lower 

efficiency peaking units. Changes in CSP operation are driven largely by the presence of other 

solar generation, particularly photovoltaics (PV). Use of storage by the CSP plants increases in 

the higher solar scenarios, with operation of the plant often shifted to later in the day. CSP 

operation also becomes more variable, including more frequent starts. Finally, CSP output is 

often very low during the day in scenarios with significant PV, which helps decrease overall 

renewable curtailment (overgeneration). However, the CSP plant configuration studied was not 

designed to minimize curtailment, implying further analysis of configuration is needed to 

understand the role of CSP in enabling high renewable scenarios in the western United States. 

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

Table of Contents

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

2 Overview of WWSIS-2 .......................................................................................................................... 3

3 CSP Operation ...................................................................................................................................... 7

3.1 Overview of CSP Operation ............................................................................................................ 7

3.2 Operation of CSP Plants to Provide Peak Capacity ......................................................................... 7

3.3 CSP Operation to Reduce Ramping Requirements ........................................................................ 15

3.4 CSP Operation to Reduce Renewable Curtailment and Overgeneration ....................................... 20

4 Conclusions ........................................................................................................................................ 27

References................................................................................................................................................. 28

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
1 

1 Introduction

The Western Wind and Solar Integration Study (WWSIS), one of the largest regional solar and 

wind integration studies to date, explores some of the challenges and impacts of integrating large 

amounts of wind and solar energy into the electric power system of the West. The first phase 

(WWSIS-1) examined the hourly operation of the Western Interconnection with wind and solar 

penetrations up to 35% (GE Energy 2010). Phase 2 (WWSIS-2) (Lew et al. 2013) added several 

analysis components, including 5-minute dispatch simulations and analysis of wear-and-tear 

costs and emissions impacts of cycling the fossil-fueled fleet. WWSIS-2 also analyzed additional 

wind and solar scenarios, including larger amounts of solar. In addition, WWSIS-2 added 

significant amounts of concentrating solar power (CSP) with thermal energy storage (TES). 

CSP with TES is a dispatchable source of renewable energy and can provide valuable grid 

flexibility services, including the ability to shift energy in time, rapidly change output, and 

provide firm capacity. The ability to store energy for later use can be particularly valuable in 

high renewable scenarios during periods when there is limited correlation between the natural 

supply of solar or wind energy and electricity demand. The WWSIS-2 report provides some 

indication of the value of dispatchable CSP, but it does not provide detailed insights into how 

CSP plants are operated in the various scenarios. This includes both the plant operational 

characteristics, such as start frequency and use of storage, and potential benefits, such as 

provision of reliable capacity during periods of peak demand. A greater understanding of these 

issues could be important to CSP technology developers and system planners to maximize the 

value of CSP in an evolving grid with increased levels of variable generation resources, such as 

wind and solar photovoltaics (PV). 

The purpose of this document is to provide greater detail in the operation of the simulated CSP 

plants in WWSIS-2 in all scenarios. (In this document we use the term CSP to indicate CSP with 

TES, as only CSP plants with TES were simulated in the study.) It makes the following 

observations based on results from the WWSIS-2 simulations: 

• Use of storage by the CSP plants increases in the higher solar scenarios, meaning a 

greater fraction of solar energy is stored for use later in the day. CSP operation becomes

more variable, including more frequent starts.

• In all scenarios, CSP plants generate at nearly full output during periods of peak net 

demand, providing high capacity value.

• CSP plants are often ramped during periods of high variability of wind and solar, thereby 

reducing the ramping requirements of conventional thermal and hydroelectric generators. 

Combined with the high capacity value, this implies these plants provide a potentially 

significant source of grid flexibility.

• CSP output is often very low during the day in the High Solar Scenario. This helps 

decrease overall renewable curtailment (overgeneration). However, the configuration 

studied may not be optimal for the High Solar Scenario, implying further analysis of CSP 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
2 

plant configuration is needed to understand its role in enabling high renewable scenarios 

in the western United States.1

 

Section 2 of this document provides an overview of WWSIS-2, including the various scenarios 

evaluated and the implementation of CSP. Section 3 summarizes the CSP-specific results, 

including each of the four findings stated above. Section 4 concludes and suggests additional 

work to analyze the potential value and role of CSP in high renewable scenarios in the western 

United States. 

 

1 More recent analysis has demonstrated a higher economic value for plants with a lower solar multiple (Jorgenson 

et al. 2013; Jorgenson et al. 2014), but further analysis is needed to understand how different plant configurations 

would fit into an optimized portfolio.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
3 

2 Overview of WWSIS-2 

WWSIS-1, released in May 2010, examined the viability, benefits, and challenges of integrating 

high penetrations of wind and solar power into the western grid. WWSIS-1 found it to be 

technically feasible if certain operational changes could be made, but it raised questions 

regarding the impact of cycling on wear-and-tear costs and emissions. 

WWSIS-2 was initiated to investigate the detailed impact of wind and solar power on the fossilfueled fleet in the West, including wear-and-tear costs and emissions impacts of power plant 

cycling. The Western Interconnection was simulated using a production simulation model 

(PLEXOS) including unit commitment, 5-minute economic dispatch, and power flow for a 

variety of scenarios in the 2020 study year. The basis for the model was the Transmission 

Expansion Planning Policy Committee’s (TEPPC) 2020 Portfolio Case 1. Transmission was 

modeled zonally, using 20 zones based on the Western Electricity Coordinating Council Loads 

and Resources Subcommittee. Metrics such as production cost, generation displacement, 

emissions, starts, hours run, ramps, and reserve violations were used to assess performance of 

the scenarios.

WWSIS-2 modeled four renewable scenarios in the U.S. portion of the Western Interconnection, 

including the TEPPC 2020 “base” scenario and three 33% renewable scenarios:

• TEPPC Scenario (9.4% wind, 3.6% solar) 

• High Wind Scenario (25% wind, 8% solar) 

• High Solar Scenario (8% wind, 25% solar) 

• High Mix Scenario (16.5% wind, 16.5% solar). 

A capacity expansion model (the Regional Energy Deployment System) was used to select 

which regions were optimal locations for siting the wind and solar based on resources, load, and 

transmission (Short et al. 2011). Transmission was expanded to bring the new wind and solar 

resources to load. Existing fossil generators were not retired. 

In each of the scenarios, the ratio of solar PV to CSP was about 60%/40% on an energy basis. 

Because this ratio of PV to CSP was fixed, the study did not consider the impact of different 

solar mixes on grid operation or system costs. PV included both distributed (rooftop) and utilityscale PV. CSP was deployed using performance characteristics based on a wet-cooled troughtype plant.2 Additional details regarding implementation of CSP in the PLEXOS model is 

described by Denholm and Hummon (2012). The plant is configured with a solar multiple of 2.0, 

meaning the solar field provides the power block with twice the energy needed to operate at its 

rated capacity under reference solar conditions. Excess energy from the solar field is stored for 

later use. The plant is modeled as a “direct” thermal storage plant, where the heat transfer 

medium is the same as the storage medium, and as a result assumes no losses associated with 

 

2 This and other assumptions regarding plant type and configuration were based largely on data available at the time 

of the study. Additional plant types and configurations have since been modeled and have been incorporated into 

ongoing studies (Jorgenson et al. 2013; Jorgenson et al. 2014).

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
4 

transferring thermal energy into or out of storage.3 The plant assumes 6 hours of storage capacity 

at rated output, a minimum generation level equal to 14% of rated capacity, and the ability to 

ramp over its entire range in 1 hour. The plant also includes start-up losses but does not include 

additional operational parameters, such as part-load heat rate and other system losses, that have 

since been modeled (Jorgenson et al. 2013; Jorgenson et al. 2014). 

CSP deployment scenarios are described in Table 1, with a map of deployment by scenario in 

Figure 1. 

Table 1. Installed Capacity of CSP Plants in the Four Renewable Scenarios4

State Installed CSP Capacity (MW)

TEPPC High Wind High Mix High Solar

Arizona 472 3,303 9,374 9,644

California 3,221 2,469 3,594 9,197

Colorado 169 169 169 1,440

Nevada 334 439 562 672

New Mexico 156 156 298 574

Total 4,352 6,536 13,997 21,526

 

3 More recent NREL analysis of CSP assumes a 2% loss rate for direct storage tower plants and a 7% loss rate for 

indirect trough plants (Jorgenson et al. 2013). Additional analysis of the operation and value of different CSP 

technologies and configuration is provided by Jorgenson et al. (2013) and Jorgenson et al. (2014). 

4 The peak output of these plants can be about 10% higher than the rated capacity The version of the System Advisor 

Model (SAM) used to generate the solar input profiles allows the turbine to run at 10% “over design” conditions 

during summer months. Reduced solar field parasitic loads will also increase he net plant output when dispatching 

solely from storage. These limitations have been addressed in more recent PLEXOS CSP dispatch simulations 

(Jorgenson et al. 2013). 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
5 

Figure 1. Regional deployment of CSP in the four WWSIS-2 renewable scenarios. (a) TEPPC; (b) 

High Wind; (c) High Mix; (d) High Solar

The PLEXOS model optimally dispatches all generators in the system (including storage) from 

the perspective of a system operator with the goal of minimizing overall system production 

cost. This means that all generators (including CSP) will operate to minimize the sum of systemwide fuel costs (both start and operating fuel) and variable O&M costs. Two unit-commitment 

cycles were simulated: a day-ahead (DA) “market” and 4-hour ahead (4HA) “market.” The DA 

market is used to commit units with long start times or high start costs (coal, nuclear, and 

biomass generators), using a 48-hour optimization horizon. The extra 24 hours in the unit 

commitment horizon (for a full 48-hour window) also helps properly schedule storage (including 

CSP with thermal storage).5 The unit commitment from the first 24 hours of each step was saved 

and input to the 4HA market. The 4HA market uses updated wind and solar forecasts and allows 

 

5 Without a longer look ahead, storage plants tend to fully discharge each day, not considering possible value to 

carry over storage to the following day. This is important for CSP when it is used to help address early morning 

ramps using energy carried over from the previous day, as observed in Section 3.3.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
6 

the combined-cycle, oil, and gas steam units to change commitment status in response.6 The 

4HA market was modeled in 24-hour windows. Results from only the first 4 hours from each run 

were saved for the real-time model, and the extra 20 hours in the unit commitment window 

helped optimally dispatch the CSP thermal storage. It was sometimes optimal for CSP units to 

store their energy and deliver it when prices were highest, which occasionally occurred during 

the load rise the following morning before PV generation began. This demonstrates the value of 

a 24-hour optimization window. Unit-commitment status from the 4HA market is then passed to 

the “real-time” economic dispatch simulations, which occur in 5-minute intervals. CSP plants 

were allowed to change output in the real-time simulations in response to local price variations 

resulting from forecast errors. 

 

6 Day-ahead and 4HA forecasts of direct normal irradiance were generated for CSP using a numerical weather 

prediction model. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
7 

3 CSP Operation

3.1 Overview of CSP Operation

A number of studies have demonstrated several of the key drivers behind how a CSP plant might 

be dispatched in different locations, under different market conditions (Madaeni et al. 2012; 

Denholm et al. 2013). The PLEXOS simulations in WWSIS-2 allow the CSP plants to be 

dispatched to maximize their value to the grid as a whole, minimizing the system-wide 

production cost. During each interval of the simulation, the plant can store some or all of the 

energy produced by the solar field or draw energy from storage. As a result, maximizing the 

value of a CSP plant often falls into achieving one or more of the following characteristics: 

• CSP plants are dispatched to avoid the use of the highest-cost resources. These are often 

peaking plants that are needed during periods of highest demand. 

• CSP plants are dispatched to manage the net variability on the system. This acts to reduce 

ramping requirements from thermal generators, decreasing the number of starts and partload operation. 

• CSP plants are dispatched to avoid generation during periods of very low energy value.

In the extreme case, this means avoiding generation during periods where the supply of 

renewables exceeds the ability of the system to absorb that energy resulting in renewable 

curtailment (overgeneration).7

Each of the following sections examines in detail how CSP plants are operated to minimize 

overall production cost, achieving the characteristics described above. All results presented in 

this include only the U.S. portion of the Western Interconnection.8 They also show how CSP 

operation changes in the various scenarios of increasing solar penetration, generally relying more 

on the use of energy storage and shifting energy to different times, often with more frequent 

starts and more variable operation. 

3.2 Operation of CSP Plants to Provide Peak Capacity

One of the most significant benefits of CSP with TES is to provide firm system capacity by 

shifting energy to periods of peak demand. The ability of a CSP plant to provide firm capacity 

can be observed by its performance during periods of net peak demand—or the residual demand 

not met by variable generation resources, such as wind and solar PV. In the lowest renewable 

case (the TEPPC Scenario), there is a relatively small contribution of wind and PV. Figure 2

shows the aggregated load for the entire U.S. portion of the Western Interconnection during the 

week of highest demand in the TEPPC Scenario (July 24–30). It also shows the contribution 

from wind and PV and the corresponding net load. 

 

7 Overgeneration does not necessarily mean the supply of renewables exceeds the demand. It means the generation 

from renewables plus the minimum generation required from conventional generators exceeds demand. The 

minimum generation from conventional generators is a function of many factors, including generator flexibility 

(ability to vary output over various time scales), system reserve requirements, predictability of renewable supply, 

and local transmission constraints. 8 There were no CSP plants deployed in Mexico or Canada in the WWSIS-2 simulations.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
8 

Figure 2. Load, PV, wind, and net load for the week of highest demand in the TEPPC Scenario

Figure 3 demonstrates the combined output of the CSP plants in the TEPPC Scenario during the 

same week. It also shows the thermal energy coming from the solar field (defined as electrical 

energy equivalent inflow, incorporating the efficiency of thermal to electrical conversion), or 

approximately the output of the CSP plants if they did not employ energy storage. These curves 

correspond to the right y-axis, with the net load shown on the left y-axis for comparison. It 

shows that the natural inflow of solar energy is not entirely coincident with demand, with an 

offset of about 4 hours. However, the use of storage enables the CSP plants to shift output to 

periods of highest net demand. The output of the plants is roughly equal to the “block” dispatch 

characteristic of previous studies of CSP operation in the summer (Sioshansi and Denholm 2010; 

Denholm et al. 2013.) The irregularities are due to the fact that each individual plant is 

responding to local needs, which are obscured by aggregating the plants together. 

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

0 24 48 72 96 120 144 168

Generation/Demand (MW)

Hour

Electricity Demand Net Load with Wind and PV PV Wind

July 24 July 25 July 26 July 27 July 28 July 29 July 30 July 31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
9 

Figure 3. CSP dispatch for the week of highest demand in the TEPPC Scenario

While the previous figures provide the dispatch for all CSP plants aggregated, the dispatch of 

individual plants is fairly similar, with regional differences based on solar resource and local 

load patterns. Figure 4 illustrates the average dispatch profile for CSP aggregated to the state 

level over the entire summer period. The difference in timing is partially due to location, as the 

sun effectively rises earlier in Colorado than California because the output profiles are all 

synchronized to the same time (Pacific standard time). 

Figure 4. Average CSP dispatch profile in the summer aggregated to the state level in the 

TEPPC Scenario

0

2,000

4,000

6,000

8,000

10,000

12,000

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

0 24 48 72 96 120 144 168

CSP Inflow/Generation (MW)

Net Load (MW)

Hour

Net Load with Wind and PV CSP Inflow CSP Generation

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 2 4 6 8 10 12 14 16 18 20 22

Average Output (% of Rated Capacity)

Hour of Day

AZ

CA

CO

NM

NV

July 24 July 25 July 26 July 27 July 28 July 29 July 30 July 31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
10 

As greater amounts of wind and solar are added to the system, the timing of peak demand can 

shift, potentially increasing the importance of energy storage in CSP plants. Figure 5

demonstrates this for the High Wind and High Solar Scenarios. Figure 5a illustrates the High 

Wind Scenario, which produces a relatively small shift in peak demand. Figure 5b, however, 

shows a much greater shift, with the peak net demand moving from about 2 pm to about 6 pm 

but also with a narrower peak window. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
11 

(a)

(b)

Figure 5. Net load profiles for the (a) High Wind and (b) High Solar Scenarios for one week in July

Figure 6 shows the corresponding CSP output for each of these two scenarios for the same 

period. It shows the significant shift in CSP generation to later in the day, particularly in the 

High Solar Scenario. 

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

0 24 48 72 96 120 144 168

Generation/Demand (MW)

Hour

Electricity Demand Net Load with Wind and PV PV Wind

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

0 24 48 72 96 120 144 168

Generation/Demand (MW)

Hour

Electricity Demand Net Load with Wind and PV PV Wind

New mid-day 

off-peak period

July 24 July 25 July 26 July 27 July 28 July 29 July 30 July 31

July 24 July 25 July 26 July 27 July 28 July 29 July 30 July 31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
12 

(a) 

(b) 

Figure 6. CSP generation profiles for the (a) High Wind and (b) High Solar Scenarios for one week 

in July

This shift in generation profile can be observed more directly by comparing seasonal average 

operation of the CSP plants. Figure 7 illustrates the average CSP generation profile for CSP 

plants in California during the summer. The output has been normalized for comparison, as the 

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

0 24 48 72 96 120 144 168

CSP Inflow/Generation (MW)

Net Load (MW)

Hour

Net Load with Wind and PV CSP Inflow CSP Generation

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

0 24 48 72 96 120 144 168

CSP Inflow/Generation (MW)

Net Load (MW)

Hour

Net Load with Wind and PV CSP Inflow CSP Generation

July 24 July 25 July 26 July 27 July 28 July 29 July 30 July 31

July 24 July 25 July 26 July 27 July 28 July 29 July 30 July 31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
13 

High Solar Scenario has about three times the installed CSP capacity compared to the TEPPC 

Scenario. In the TEPPC Scenario, CSP generation typically peaks between 2 pm and 3 pm, 

compared to 5 pm and 6 pm in the High Solar Scenario. This is due to the large amount of PV 

generation that occurs during the earlier part of the day. 

Figure 7. Average CSP dispatch profile in California during the summer

The effect of the CSP energy shift is to reduce the net load observed by the system in all 

scenarios, with CSP plants generating at nearly full output during periods of local and systemwide peak demand. Figure 8 illustrates the WECC-wide net load during the same period as 

Figure 7 with and without the CSP dispatch. It shows the significant reduction in demand that 

would otherwise need to be met with conventional generators. This supports previous (more 

detailed) analysis of the capacity credit of CSP plants, which finds that plants with several hours 

of storage have an effective load carrying capacity similar to conventional generation resources 

(Madaeni et al. 2012). The High Solar Scenario also demonstrates two other characteristics of 

CSP dispatch discussed in more detail in the following sections. The first is the reduction in 

system ramping requirements, as CSP plants reduce the overall ramp range and, in many cases, 

the ramp rate. The second is the general ability of CSP to avoid generation during periods of low 

demand. The net load curve with PV and wind (but before the addition of CSP) in the High Solar 

Scenario shows a new off-peak period that occurs in the late morning period after the sun rises 

(and significant PV production begins) but before load greatly increases. (This is easiest to 

observe in Figure 5b). Ideally, a CSP plant would avoid generation in this period in favor of later 

in the day. However, the configuration chosen for this study (solar multiple of 2 with 6 hours of 

storage) forces some of the CSP energy to be dispatched during this period, further reducing the 

net load during this new off-peak period in the late morning. This does not lead to significant 

operational challenges (or curtailment) in this scenario but points to the importance of evaluating 

various CSP configurations, as discussed in Section 3.4. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 2 4 6 8 10 12 14 16 18 20 22

Average Output (% of Rated Capacity)

Hour of Day

TEPPC

High Solar

High Wind

High Mix

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
14 

(a)

(b)

Figure 8. Net load before and after the dispatch of CSP in the (a) High Wind and (b) High Solar 

Scenarios from July 24–30

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

0 24 48 72 96 120 144 168

CSP Inflow/Generation (MW)

Net Load (MW)

Hour

Net Load with Wind and PV Net Load with CSP CSP Generation

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

0 24 48 72 96 120 144 168

CSP Inflow/Generation (MW)

Net Load (MW)

Hour

Net Load with Wind and PV Net Load with CSP CSP Generation

July 24 July 25 July 26 July 27 July 28 July 29 July 30 July 31

July 24 July 25 July 26 July 27 July 28 July 29 July 30 July 31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
15 

3.3 CSP Operation to Reduce Ramping Requirements

In addition to providing firm capacity, CSP can also replace the need for conventional generators 

to vary output during periods of high net load variability. This benefit occurs during all seasons, 

including periods with some of the highest instantaneous net load ramp rates (MW/minute) that 

occur near sunset on winter days. These ramp requirements are often associated with short 

duration peak periods. These winter peaks are much lower in magnitude than summer peaks, so 

typically do not drive peak capacity requirements. However, they often require the use of lower 

efficiency combustion turbines because the duration of the demand is not long enough to warrant 

starting a more efficient combined-cycle unit (exacerbated by the need for high ramp rates). This 

ramp requirement is increased in the High Solar Scenario. 

Figure 9 illustrates the net load for a week period starting on January 4 in the High Wind and 

High Solar Scenarios. They show the morning load increase and a sharp narrow evening peak, 

often met by lower efficiency generators. Examining the normal electricity demand, of the 

50 hours with the highest ramp rates, 47 occur from December to early February, typically at 

about 5 pm. The PV generation in the High Solar Scenario produces particularly sharp evening 

peaks but also introduces short morning peaks. The High Solar Scenario also creates a new 

paradigm for the definition of “on-peak” and “off-peak” as the mid-day period has a lower net 

load than during overnight hours. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
16 

(a)

(b)

Figure 9. Net load profiles for the (a) High Wind and (b) High Solar Scenarios for one week 

in January

The impact of wind and PV on system ramping requirements is show in Figure 10. These values 

represent the average upward ramp rates of the normal load and net load (before curtailment) 

during each 1-hour interval equal to the rate (MW/minute) at which conventional dispatchable 

0

20,000

40,000

60,000

80,000

100,000

120,000

0 24 48 72 96 120 144 168

Generation/Demand (MW)

Hour

Electricity Demand Net Load with Wind and PV PV Wind

0

20,000

40,000

60,000

80,000

100,000

120,000

0 24 48 72 96 120 144 168

Generation/Demand (MW)

Hour

Electricity Demand Net Load with Wind and PV PV Wind

New mid-day offpeak period

Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 Jan 9 Jan 10 Jan 11

Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 Jan 9 Jan 10 Jan 11

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
17 

generation must ramp to meet the variability in the net load. Only 4,000 of the 8,760 1-hour 

periods are shown, as the remainders have very low ramp rates or are periods of down ramp 

requirements which are typically not as challenging as up ramp requirements. Of note is the 

limited impact in the High Wind Scenario. In this case, the PV penetration is insufficient to cause 

the dramatic drop in net load in the spring and winter compared to the High Solar Scenario, as 

shown in Figure 9. The High Solar Scenario creates much higher net load ramp rates during the 

period of normally high up ramp requirement (at about 5 pm), but shifted slightly on a seasonal 

basis to period of greater solar output (instead of peaking in December and January evenings the 

net peaks occur more often during November and February evenings). 

Figure 10. Upward ramp duration curve for the normal load and net load in the High Wind and 

High Solar Scenarios

The increased ramp rates demonstrated in Figure 9 and Figure 10 must be met by dispatchable 

resources. In both the High Wind and High Solar Scenarios, CSP plants are often dispatched to 

meet demand during the period of highest net load, avoiding the use of other thermal generators, 

including lower efficiency combustion turbines. Figure 11 shows the CSP generation during the 

two-week periods that correspond to Figure 9 . It shows a very different mode of operation in 

response to system demand compared to the summer operation observed in Section 3.2. The 

overall availability of solar energy is lower, and the plants tend to operate in a fairly narrow 

window, primarily generating at nearly full output during the peak period. However, the plants 

also often carry over energy to the following day to meet the morning load peak. During the 

overnight hours the CSP plants either operate at minimum generation levels or shut down 

completely. Overall, unlike operation during the summer, CSP plants in the winter generate in a 

pattern anti-correlated with solar availability.

0

50

100

150

200

250

300

350

0 500 1000 1500 2000 2500 3000 3500 4000

System-Wide Conventional Plant Upwared Ramp Requirements 

(MW/Minute)

Hours of Upward Ramp Requirement

High Solar Scenario Net Load with Wind and PV

Normal Load

High Wind Scenario Net Load with Wind and PV

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
18 

(a)

(b)

Figure 11. CSP generation profiles for the (a) High Wind and (b) High Solar Scenarios for one week 

in January

The net load shapes after CSP generation are shown in Figure 12. They show how CSP plants 

reduce the peak demand in both the morning and evening peaks, allowing a greater fraction of 

load to be met by more efficient units.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

0 24 48 72 96 120 144 168

CSP Inflow/Generation (MW)

Net Load (MW)

Hour

Net Load with Wind and PV CSP Inflow CSP Generation

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

0 24 48 72 96 120 144 168

CSP Inflow/Generation (MW)

Net Load (MW)

Hour

Net Load with Wind and PV CSP Inflow CSP Generation

Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 Jan 9 Jan 10 Jan 11

Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 Jan 9 Jan 10 Jan 11

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
19 

(a) 

(b)

Figure 12. Net load before and after the dispatch of CSP in the (a) High Wind and (b) High Solar 

Scenarios from January 4–11

The impact on the net load duration curve is shown in Figure 13, demonstrating the reduction in 

overall upward ramping requirement that would need to be met by conventional resources. Only 

the High Solar Scenario is shown. The High Wind Scenario shows a similar but smaller benefit. 

30,000

40,000

50,000

60,000

70,000

80,000

90,000

0 24 48 72 96 120 144 168

Net Load (MW)

Hour

Net Load with Wind and PV Net Load with CSP

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

0 24 48 72 96 120 144 168

Net Load (MW)

Hour

Net Load with Wind and PV Net Load with CSP

Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 Jan 9 Jan 10 Jan 11

Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 Jan 9 Jan 10 Jan 11

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
20 

Figure 13. Upward ramp duration curve for the net load with and without CSP dispatch in the High 

Solar Scenarios

By shifting generation, CSP also avoids reducing net demand during periods of the new mid-day 

off-peak period, especially in the High Solar Scenario. This ability becomes particularly

important in the spring as discussed in Section 3.4. 

3.4 CSP Operation to Reduce Renewable Curtailment and 

Overgeneration

The WWSIS-2 scenarios demonstrate that spring presents the most difficult challenges in terms 

of potential curtailment. Curtailment is driven by a number of factors, including the coincidence 

of renewable supply with demand patterns as well as grid flexibility. Grid flexibility is driven by 

factors such as transmission capacity and generation mix, including the ability of conventional 

generators to ramp over a large range and at a high rate (NERC 2010). During the spring both 

wind and solar output can be relatively high, but mild weather produces some of the lowest load 

periods of the year. Figure 12 shows the net load profiles for the week with the lowest net load of 

the year, which occurs at about 2 am on March 18 in the High Wind Scenario and at about noon 

on March 29 in the High Solar Scenario. 

0

50

100

150

200

250

300

350

0 500 1000 1500 2000 2500 3000 3500 4000

System-Wide Conventional Plant 

Upwared Ramp Requirements 

(MW/Minute)

Hours of Upward Ramp Requirement

Load with Wind and PV

Net Load with Wind, PV and CSP

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
21 

(a)

(b)

Figure 14. Net load profiles for the (a) High Wind and (b) High Solar Scenarios during the week 

with the lowest net load

0

20,000

40,000

60,000

80,000

100,000

120,000

0 24 48 72 96 120 144 168

Generation/Demand (MW)

Hour

Electricity Demand Net Load with Wind and PV PV Wind

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

0 24 48 72 96 120 144 168

Generation/Demand (MW)

Hour

Electricity Demand Net Load with Wind and PV PV Wind

Mar 17 Mar 18 Mar 19 Mar 20 Mar 21 Mar 22 Mar 23 Mar 24 

Mar 25 Mar 26 Mar 27 Mar 28 Mar 29 Mar 30 Mar 31 Apr 1 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
22 

The High Solar Scenario produced more consistent low net loads and had the highest curtailment 

rates of the three WWSIS-2 high renewable scenarios at about 5% of total renewable potential. 

As a result, we focus on the operation of CSP in the High Solar Scenario. 

As observed in Figure 12, the net load drops rapidly and to low levels in the middle of the day, 

followed by a significant up-ramp as solar production drops. In these cases, the net load drops 

below what the grid can reliably meet with the installed generation mix. Wind or solar energy 

must be curtailed so that the conventional generation fleet can maintain generation at some 

minimal level. The actual generation from PV and wind allowed by the grid in the simulations is 

shown in Figure 13, which shows significant curtailment.

Figure 15. Net load for one week in March after curtailment in the High Solar Scenario

The flexibility limits that create this curtailment can be observed in Figure 14, which is a 

complete system-wide dispatch for the week of lowest net load in the High Solar Scenario. 

During periods of lowest net load, nearly all online thermal generation is generating at minimum 

stable levels around noon each day when PV output is the greatest but before load has peaked. 

This is most obvious on March 29 when the coal generation is flat. Significant solar energy is 

curtailed during the day as shown by the dotted line. This energy is curtailed partly because the 

start costs of coal generators do not justify turning them off in the morning and back on for the 

evening load peak. 

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

0 24 48 72 96 120 144 168

Generation/Demand (MW)

Hour

Net Load with Curtailment PV Potential PV Generation after Curtailment

Mar 25 Mar 26 Mar 27 Mar 28 Mar 29 Mar 30 Mar 31 Apr 1 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
23 

Figure 16. Dispatch stack for the week with lowest net load in the High Solar Scenario

CSP with TES can reduce curtailment in at least two ways. The first and most obvious is the 

ability of CSP to shift energy during times of low net demand. This operation can be observed in 

the CSP dispatch in Figure 14 and more directly in Figure 15, which shows CSP inflow 

and generation. 

In the High Solar Scenario, CSP plants in the spring tend to start up in the morning, using as 

much solar energy as is possible before the large amount of PV generation exceeds what the grid 

can accommodate due to system flexibility limits. At this point significant curtailment of solar 

energy begins to occur. CSP plants reduce output or even shut down during the middle of the 

afternoon and the CSP plant stores as much as possible. It should be noted that this operation is 

based on a plant utilizing direct storage, capable of sending all energy from the solar field to 

storage, even during times of high solar field output. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
24 

Figure 17. CSP generation profiles for the High Solar Scenario for the week with lowest load

The shift in generation from CSP plants by reducing mid-day output is one of the more 

significant changes in CSP operation observed between the WWSIS scenarios. Figure 16

provides the average dispatch profile during the spring season in Arizona for the High Wind and 

High Solar Scenarios. It shows the CSP plant shifting as much energy as possible to the evening 

hours in an attempt to avoid curtailment. However, the ability of CSP to avoid curtailment is 

limited by the configuration of the CSP plant modeled in the study. In all scenarios, the CSP 

plant configurations are the same—a solar multiple of 2.0 with 6 hours of TES capacity. In this 

configuration only 3 hours of incident solar energy (at reference conditions) can be stored by the 

plant. While reference conditions typically do not occur for several hours, this limited storage 

capacity has a clear impact on the ability of CSP to shift energy during periods of low net 

demand. Because the modeled CSP plants cannot store a greater fraction of the incident solar 

energy, this leads to some production during periods of low demand (further reducing the net 

load) but also resulting in curtailment of CSP generation. (This explains why the area under the 

High Solar curve is lower than the High Wind curve.) This also introduces more frequent starts, 

with the average plant (of all plants in the study) increasing starts from about 1.4 times per day to 

about twice per day during this period. 

0

10,000

20,000

30,000

40,000

50,000

60,000

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

0 24 48 72 96 120 144 168

CSP Inflow/Generation (MW)

Net Load (MW)

Hour

Net Load with Wind and PV CSP Inflow CSP Generation

Mar 25 Mar 26 Mar 27 Mar 28 Mar 29 Mar 30 Mar 31 Apr 1 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
25 

Figure 18. Average CSP dispatch profile in Arizona during the spring for the High Wind and High 

Solar Scenarios

Figure 17 shows the resulting net load and another indication of the impact of the CSP 

configuration used in WWSIS-2. Unlike in the winter scenario, where all energy is shifted to 

peak periods, a CSP plant with a solar multiple of 2.0 and 6 hours of storage is required to 

generate some energy during the new mid-day off-peak period, further reducing the net load and 

increasing potential renewable curtailment. 

Figure 19. Net load before and after the dispatch of CSP in the High Solar Scenario for the week 

with lowest load

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0 2 4 6 8 10 12 14 16 18 20 22

Average Output (% of Rated Capacity)

AZ High Wind

AZ High Solar

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

0 24 48 72 96 120 144 168

Net Load (MW)

Hour

Net Load with Wind and PV Net Load with CSP

Mar 25 Mar 26 Mar 27 Mar 28 Mar 29 Mar 30 Mar 31 Apr 1 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
26 

While the WWSIS-2 results clearly show the importance of energy shifting to avoid curtailment, 

there is another potential impact of CSP on grid flexibility. The dispatch in Figure 14 (and many 

of the other figures of net load in this analysis) shows the need for rapid ramping capability at 

sundown when PV output drops. Much of this ramping capability is based on operating units at 

part-load or quickly starting units. The rapid ramping of the CSP plants observed in the WWSIS2 simulations replaces ramping capacity that would otherwise be provided by conventional units. 

If these units were required to operate at part load, the system might not have been able to reduce 

generation as much during the middle of the day, leading to greater curtailment. This issue was 

not examined explicitly in the WWSIS-2 scenarios, as it would require running scenarios with 

and without CSP. However, other analysis has indicated this could potentially reduce curtailment 

(Denholm and Mehos 2011) and further examination of this “portfolio” benefit of CSP is needed. 

Overall, these results indicate that CSP is a potentially important tool to avoid “overgeneration” 

events where renewable energy supply exceeds demand, considering grid flexibility limits. 

However, this will require further examination of different CSP plant configuration, as well as 

their associated costs and benefits. In the High Solar Scenario, a large fraction of CSP generation 

is curtailed in this spring period due to the limited thermal storage capacity and high solar 

multiple. However, increased storage capacity needs to be compared to its cost, particularly 

when this capacity might only be needed for a few weeks or months when the most significant 

mismatch between solar energy supply and demand patterns occur. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
27 

4 Conclusions

WWSIS-2 evaluated four different penetrations and mixes of variable generation, and as a result, 

it shows how CSP operation may change as the grid mix evolves. Examining the operation of 

CSP plants as modeled in this study in detail, we observe the following:

• Variation in CSP plant operation is driven mostly by the increases in solar penetration. In 

the lower penetration of solar, optimal CSP operation is observed to be similar to 

previous analysis. This includes a “block” dispatch in the summer and a diurnal peaking 

dispatch in the winter. 

• In the higher penetration of solar cases, operation of CSP begins to shift to later in the 

day with greater use of energy storage, more frequent starts, and lower generation in the 

middle of the day. 

• In all scenarios evaluated, CSP plants are able to reduce the net peak demand, 

demonstrating high capacity credit and the potential ability to replace 

conventional capacity.

• CSP plants with rapid ramping capability reduce the need for operation of peaking units 

during all seasons, including winter when short-term peaks are often observed. 

• CSP plants with TES can avoid curtailment of mid-day solar, which becomes more 

important with increased PV penetration. 

• The optimal configuration of a CSP plant can vary depending on the mix of renewable 

generators and grid flexibility requirements. In particular, as solar penetration increases 

and the net load becomes “peakier,” lower solar multiples might be needed to maximize 

the flexibility of CSP to effectively respond to system variability. This optimal 

configuration must be balanced against the increased cost of delivered energy due to 

lower utilization of the plant. This “net-benefit” will be addressed in future studies. 

Overall, this study observed a number of quantifiable benefits of CSP with TES. However, 

several aspects of CSP’s ability to help integrate renewables (including both PV and wind) need 

further analysis to understand the potential contribution of CSP to overall system flexibility. In 

particular, the role of CSP in lowering minimum generation constraints and provision of fast 

ramping capability and other ancillary services will need further analysis in scenarios comparing 

CSP to other grid flexibility options. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
28 

References

Denholm, P.; Wan, Y.; Hummon, M.; Mehos, M. (2013). Analysis of Concentrating Solar Power 

with Thermal Energy Storage in a California 33% Renewable Scenario. P-6A20-58186. Golden, 

CO: National Renewable Energy Laboratory.

Denholm, P.; Hummon, M. (2012). Simulating the Value of Concentrating Solar Power with 

Thermal Energy Storage in a Production Cost Model. TP-6A20-56731. Golden, CO: National 

Renewable Energy Laboratory.

Denholm, P.; Mehos, M. (2011) Enabling Greater Penetration of Solar Power via the Use of 

Thermal Energy Storage. TP-6A20-52978. Golden, CO: National Renewable Energy 

Laboratory.

Lew, D.; Brinkman, G.; Ibanez, E.; Hodge, B.-M.; Hummon, M.; Florita, A.; Heaney, M.; Stark, 

G.; King, J.; Kumar, N.; Lefton, S.; Agan, D.; Jordan, G.; Venkataraman, S. (2013). The Western 

Wind and Solar Integration Study Phase 2. NREL/TP-5500-55588. Golden, CO: National 

Renewable Energy Laboratory. 

GE Energy. (2010). Western Wind and Solar Integration Study. NREL/SR-5500-47434. Work 

performed by GE Energy, Schenectady, NY. Golden, CO: National Renewable Energy 

Laboratory. Accessed September 2013: www.nrel.gov/docs/fy10osti/47434.pdf. 

Jorgenson, J.; Denholm, P.; Mehos, M.; Turchi, C. (2013). Estimating the Performance and 

Economic Value of Multiple Concentrating Solar Power Technologies in a Production Cost 

Model. TP-6A20-58645. Golden, CO: National Renewable Energy Laboratory. 

Jorgenson, J.; Denholm, P.; Mehos, M.; (2014). Estimating the Value of Utility-Scale Solar 

Technologies in California Under a 40% Renewable Portfolio Standard. TP-6A20-61685. 

Golden, CO: National Renewable Energy Laboratory. 

Madaeni, S.; Sioshansi, R.; Denholm, P. (2012). "How Thermal Energy Storage Enhances the 

Economic Viability of Concentrating Solar Power." Proceedings of the IEEE (100:2); 

pp. 335–347. 

Madaeni, S. H.; Sioshansi, R.; Denholm, P. (2012). “Estimating the Capacity Value of 

Concentrating Solar Power Plants: A Case Study of the Southwestern United States.” IEEE 

Transactions on Power Systems (27:2); pp. 1116–1124. 

NERC (North American Electric Reliability Corporation). (2010). “Flexibility Requirements and 

Metrics for Variable Generation: Implications for System Planning Studies.” Princeton, NJ. 

Sioshansi, R.; Denholm, P. (2010). “The Value of Concentrating Solar Power and Thermal 

Energy Storage.” IEEE Transactions on Sustainable Energy (1:3); pp. 173–183. 

Short, W.; Sullivan, P.; Mai, T.; Mowers, M.; Uriarte, C.; Blair, N.; Heimiller, D.; Martinez, A. 

(2011). Regional Energy Deployment Systems (ReEDS). NREL/TP-6A20-46534. Golden, CO: 

National Renewable Energy Laboratory. 

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