NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Simulating the Value of 

Concentrating Solar Power with 

Thermal Energy Storage in a 

Production Cost Model

Paul Denholm and Marissa Hummon

Technical Report

NREL/TP-6A20-56731

November 2012
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Simulating the Value of 

Concentrating Solar Power with 

Thermal Energy Storage in a 

Production Cost Model

Paul Denholm and Marissa Hummon 

Prepared under Task No. SM12.2013

Technical Report

NREL/TP-6A20-56731

November 2012
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iii

Acknowledgments

This project was funded by the U.S. Department of Energy Solar Energy Technology program. 

Model datasets in this analysis were derived from publicly available sources; individual utilities 

modeled in this work did not provide proprietary data or review our interpretation of the data, 

model, methods, or results. The authors would like to thank Victor Diakov for performing the 

price sensitivity runs and Craig Turchi for performing the SAM CSP runs. The following 

individuals provided valuable input and comments during the analysis and publication process: 

Nate Blair, Adam Green, Udi Helman, Trieu Mai, Mark Mehos, and Frank Wilkins. Any errors 

or omissions are solely the responsibility of the authors.
iv

Executive Summary

Concentrating solar power (CSP) deployed with thermal energy storage (TES) provides a 

dispatchable source of renewable energy. The value of CSP with TES, as with other potential 

generation resources, needs to be established using traditional utility planning tools. Production 

cost models, which simulate the operation of grid, are often used to estimate the operational 

value of different generation mixes. CSP with TES has historically had limited analysis in 

commercial production simulations. This document describes the implementation of CSP with 

TES in a commercial production cost model. It also describes the simulation of grid operations 

with CSP in a test system consisting of two balancing areas located primarily in Colorado.

CSP was implemented in the PLEXOS model, which is a security-constrained unit commitment 

and dispatch model. CSP both with and without storage was implemented and compared to solar 

photovoltaics (PV) and to a “flat block” resource equivalent to a baseload generator with zero 

fuel costs. The CSP plant with storage was modeled as a trough-type plant with a solar multiple 

of 2.0 and 6 hours of storage. The test system consisted of two balancing areas located in 

Colorado and Wyoming—Public Service of Colorado (PSCO) and Western Area Colorado 

Missouri (WACM), simulated using publicly available data. The generation characteristics and 

fuel prices were based on a 2020 scenario and included two renewable energy cases—a low RE 

scenario where wind and solar provide 13% of the annual generation and a high RE scenario 

where wind and solar provide 34% of generation, including 8% from PV.

In each scenario, a small amount of a new generator type (CSP, PV, or flat block resource) was 

added to determine its value to the system. The operational value of the different generation 

sources is based on which power plant types they displace as calculated by the least-cost dispatch 

within the model. In the low RE case, gas-fired generators are on the margin most of the time, so 

all generators displace mostly gas. However, the dispatchability of CSP allows it to time its 

output to periods when the most expensive gas units are on the margin.

Figure ES-1 shows how CSP with TES dispatches energy during periods of highest price, 

compared to a CSP plant without storage. During this 3-day period in January, CSP produces 

energy during periods of peak demand in the morning and evening, both periods without 

significant solar availability.
v

Figure ES-1. System marginal price and corresponding CSP generation on January 22–24 (low 

RE case)

CSP with TES displaces more fuel and higher-cost fuel than the flat block or solar generation 

without storage. Table ES-1 shows the avoided fuel per unit of generation (MMBTU/MWh). It 

also shows the impact of the high RE case, where much of the gas is displaced and coal is the 

marginal generator for many hours of the year. Generators without storage increasingly displace 

lower value coal generation, and curtailment of variable generation sources begins to occur due 

to system flexibility constraints. CSP with TES continues to displace mostly gas generation due 

to its dispatchability.

Table ES-1. Avoided Fuel

Avoided Fuel (MMBTU/MWh)

Low RE Scenario High RE Scenario

Flat 

Block 

PV CSP 

(no 

TES)

CSP (6-

hr TES)

Flat 

Block 

PV CSP (no 

TES)

CSP (6-

hr TES)

Coal 1.1 -0.7 -0.7 -0.9 5.8 5.2 5.4 1.9

Gas 7.4 8.9 8.9 9.7 3.5 3.6 2.9 7.1

Total 8.5 8.2 8.2 8.8 9.3 8.8 8.3 9.0

The avoided fuel, as well as plant start costs and operations and maintenance (O&M) were 

translated into an avoided operational value, as shown in Table ES-2. (This does not include 

value of system capacity or other potential benefits such as ancillary services.) Key drivers 

behind these results are the assumed cost of natural gas ($4.1/MMBTU) and coal 

($1.4/MMBTU) in 2020.
vi

Table ES-2. Operational Value of Simulated Generators

Marginal Value ($/MWh)

Low RE High RE

Flat 

Block 

PV CSP (no 

TES)

CSP (6-hr 

TES)

Flat 

Block 

PV CSP (no 

TES)

CSP (6-hr 

TES)

Fuel 31.7 35.2 33.9 37.7 22.6 21.2 18.7 31.1

Variable

O&M

1.2 1.0 1.0 0.8 2.1 2.0 1.9 1.4

Start 0.4 0.4 0.6 3.5 0.5 -0.9 -1.7 3.1

Total 33.3 36.6 35.5 42.1 25.2 22.3 18.9 35.6

In the low RE scenarios, the operational energy value of CSP with TES is about $8.8/MWh 

higher than the flat block (constant output) resource and $5.5–$6.6/MWh higher than the solar 

technologies without storage. The relatively small difference between solar technologies with 

and without storage is largely due to the natural coincidence of solar generation with periods of 

high demand and price. At higher RE penetrations the value of all generation decreases due to 

the reduced amount of gas on the system. In addition, the solar resource is no longer correlated 

with net load. As a result, the value of CSP with TES increases relative to the other generation 

technologies with a difference of $10.4/MWh relative to the flat block and $13.3–$16.7/MWh 

relative to the non-storage solar technologies.

The operational energy value can be combined with capacity value to get an estimate of the 

overall system value of the different generation sources. Combining operational benefits and 

capacity benefits requires summing an energy value (typically expressed in $/MWh) and a 

capacity value (often expressed as $/kW of installed capacity, or $/kW-year). To perform this 

calculation first requires an estimate of capacity credit of each plant, or the fraction of nameplate 

capacity that contributes to reliably meeting peak demand. Capacity credit was calculated by 

examining output during periods of high net demand (and highest risk of generation shortfall).

This capacity credit is then multiplied by an annualized value of an equivalent resource, such as 

a combustion turbine or combined cycle gas generator. This annualized value can then be 

divided by annual energy production to derive a capacity value per unit of generation. Table ES3 summarizes the results of this study, which uses two values for the cost of capacity: $77/kWyear and $147/kW-year. The results show that at low penetration, solar technologies have high 

capacity value, and non-storage solar technologies can actually have higher capacity value than 

CSP with TES on a per-unit of energy basis. At high penetration of solar, the highest net load 

hours shift to periods of low solar availability. This substantially reduces the incremental 

capacity value of solar technologies without storage. 
vii

Table ES-3. Capacity Value Estimates

Low RE Scenario High RE Scenario

Flat 

Block 

PV CSP (no 

TES)

CSP (6-hr 

TES)

Flat Block PV CSP 

(no 

TES)

CSP (6-hr 

TES)

Capacity 

Credit (%)

100 70 75 98 100 13 3 78

Capacity 

Value 

(Low/High) 

($/kW)

77/147 54/103 58/110 76/144 77/147 10/19 3/5 60/115

Capacity 

Value 

(Low/High)

($/MWh)

8.8/

16.8

29.7/

56.6

29.1/

55.3

21.2/

40.4 

8.8/

16.8

5.3/

10.1

1.3/

2.4

17.1/

32.6

The total value in the test system is summarized in Figure ES-2. This shows that the dominant 

source of value is avoided fuel and the potential of these technologies to avoid new capacity. The 

low-cost capacity case assumes replacement of combustion turbines, while the high-cost case is 

the replacement of combined cycle units, which may be a more appropriate comparison to the 

flat block case and the higher capacity factor CSP with TES generator. 

Figure ES-2. Total value of generation sources in the test system

-10

0

10

20

30

40

50

60

70

80

90

100

Value ($/MWh)

Capacity 

(High Cost)

Capacity 

(Low Cost)

Start Costs

Variable 

O&M

Operational 

Fuel

Low RE Scenario High RE Scenario
viii

Several sensitivities to these scenarios were performed. The strongest driver behind value is the 

price of natural gas, and adjusting the price of natural gas to historic levels results in operational 

values similar to previous studies of the value of CSP. Additional analysis is required to evaluate 

the other potential benefits of CSP, including provision of ancillary services and intra-hour 

dispatch flexibility.
ix

Table of Contents

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

2 Production Cost Simulations .............................................................................................................. 2

3 Implementation of CSP in a Production Cost Model ........................................................................ 4

3.1 Characteristics of CSP Plants........................................................................................................... 4

3.2 Implementation of CSP in PLEXOS................................................................................................ 6

4 Test System........................................................................................................................................... 9

5 Value of CSP ....................................................................................................................................... 16

5.1 Solar Scenarios Evaluated.............................................................................................................. 16

5.2 Operational Value .......................................................................................................................... 16

5.3 Capacity Value............................................................................................................................... 26

5.4 Total Value .................................................................................................................................... 31

5.5 Sensitivities and Comparison to Previous Work............................................................................ 31

5.5.1 Hours of Storage ................................................................................................................... 31

5.5.2 Natural Gas Price .................................................................................................................. 31

5.5.3 Comparison to Previous Estimates ....................................................................................... 32

5.6 Study Limitations and Future Work .............................................................................................. 34

6 Conclusions ........................................................................................................................................ 35

References................................................................................................................................................. 36
x 

List of Figures

Figure 1. Components of a trough-type CSP plant with TES..........................................................4

Figure 2. Impact of solar multiple on energy flow in a CSP plant ..................................................5

Figure 3. General process of implementing CSP.............................................................................7

Figure 4. The flow of energy through a trough CSP plant with TES in PLEXOS ..........................8

Figure 5. Load duration curve in 2020 for the PSCO/WACM test system ...................................10

Figure 6. Dispatch stack during the period of July 25–28 in the low RE Case .............................13

Figure 7. Dispatch stack during the period of February 8–11 in the high RE Case ......................14

Figure 8. System marginal price duration curve in the PSCO balancing area for the two cases...15

Figure 9. System net load and marginal price for January 22–24 (low RE case)..........................18

Figure 10. System marginal price and corresponding CSP generation on January 22–24 (low RE 

case) .........................................................................................................................................19

Figure 11. System net load and marginal price for July 14–16 (low RE case) .............................19

Figure 12. System marginal price and corresponding CSP generation on July 14–16 (low RE 

case) .........................................................................................................................................20

Figure 13. System net load and marginal price for January 22–24 (high RE case).......................21

Figure 14. System marginal price and corresponding CSP generation on January 22–24 (high RE 

case) .........................................................................................................................................22

Figure 15. Net load and price for a 3-day period starting February 8 ...........................................23

Figure 16. System marginal price and corresponding CSP generation on February 8–10 (high RE 

case) .........................................................................................................................................24

Figure 17. Correlation of demand and solar generation on a 3-day period starting July 26 (low 

RE case) ...................................................................................................................................27

Figure 18. Correlation of demand and solar generation on a 3-day period starting July 17 (high 

RE case) ...................................................................................................................................28

Figure 19. Net demand with different solar generation types on July 17 (high RE case) .............28

Figure 20. Comparison of solar output on a high-demand day (July 17) ......................................30

Figure 21. Total value of generation sources in the test system....................................................31

Figure 22. Estimated operational value of CSP as a function of gas prices ..................................32

List of Tables

Table 1. Characteristics of the Test System Conventional Generators in 2020 .............................10

Table 2. Renewable Scenarios in the Test System in 2020 ...........................................................11

Table 3. Base Case Results............................................................................................................12

Table 4. Operational Value of Simulated Generators....................................................................17

Table 5. Avoided Thermal Generation ..........................................................................................25

Table 6. Avoided Fuel....................................................................................................................25

Table 7. Avoided Starts..................................................................................................................26

Table 8. Capacity Value.................................................................................................................30

Table 9. Previous Estimates of the Operational Value of CSP With and Without TES................32
1

1 Introduction

Concentrating solar power (CSP) can becomes a dispatchable source of renewable energy by 

adding thermal energy storage (TES). There have been a limited number of analyses that 

examine the value of this energy source and how this value varies as a function of grid 

configuration and fuel prices. 

Challenges of properly valuing CSP include the complicated nature of this technology. Unlike 

completely dispatchable fossil sources, CSP is a limited energy resource, depending on the 

hourly and daily supply of solar energy. This supply of energy is both variable and not entirely 

predictable. This requires the limited energy available to be optimally dispatched to provide 

maximum value to the grid. The actual dispatch of a CSP plant, including its ability to provide 

ancillary services, will vary as a function of generator mix, the penetration of variable generation 

(VG) sources, such as wind and solar photovoltaics (PV), and the amount of storage deployed 

with CSP.

The ability to evaluate CSP under multiple scenarios requires the use of detailed grid simulation 

tools, such as a production cost model. A number of commercial production cost models exist, 

and these are routinely used by utilities, system operators, and researchers to evaluate the 

impacts of various generation sources. However, there have been limited studies of CSP with 

TES in the United States using commercial production cost models. Several studies that included 

CSP assumed that the dispatch of CSP is fixed and did not evaluate the complete benefits of this 

dispatchable resource (CAISO 2011; GE 2010). Other studies that have included dispatchable 

CSP made no attempt to isolate the value proposition for CSP or how that value proposition 

changes with increased levels of wind and solar generation (US DOE 2012; Mai et al. 2012;

Denholm et al. 2012). 

To completely identify the benefits of CSP and perform analysis in a framework accepted by 

utilities and system operators, CSP with TES needs to be incorporated into commercially 

available software. This document describes the methodology of implementing CSP with and 

without TES into the PLEXOS production cost model. It also provides a preliminary analysis of 

CSP with TES in a test system, based on two balancing areas located largely in the State of 

Colorado. It compares the dispatch of systems with CSP and TES to systems with only variable 

solar generation and examines several performance metrics, including avoided fuel and total 

system production cost. 
2

2 Production Cost Simulations

A number of commercial production cost models are available to utilities, system operators, and 

planners to evaluate the operation of the grid. These models are used to help plan system 

expansion, evaluate aspects of system reliability, and estimate fuel costs, emissions, and other 

factors related to system operation.1 The models have the primary objective function of 

committing and dispatching the generator fleet to minimize the total cost of production while 

maintaining adequate operating reserves to meet contingency events and regulation 

requirements. Modern production cost models often include transmission power flow simulations 

to ensure basic transmission adequacy for the generator dispatch. These models are increasingly 

used to evaluate the impact of incorporating VG sources, such as wind and solar. Integration 

studies evaluate the impact of VG on power plant ramping and reserve requirements and explore 

changes to grid operations needed to incorporate increasing amounts of VG (GE 2010). 

As the penetration of VG increases, studies have found an important role for grid flexibility 

techniques and technologies, including new market structures, flexible generators, demand 

response, and energy storage. One option for flexible renewable generation is CSP with TES.

This dispatchable energy source can provide grid flexibility by shifting energy over time, 

providing ancillary services, and ramping rapidly on demand, enabling a greater penetration of 

VG sources, such as wind and solar PV (Denholm and Mehos 2011). Several previous studies 

have included CSP to various degrees. The Western Wind and Solar Integration Study (WWSIS) 

(GE 2010) included CSP with TES but assumed CSP was dispatched in fixed schedules.

Integration studies by the California Independent System Operator have included CSP but 

assumed very little storage (CAISO 2011).

2 Two more recent studies, the SunShot Vision Study 

(U.S. DOE 2012) and the Renewable Electricity Futures Study (Mai et al. 2012) incorporated 

CSP with TES into a commercial production cost model (Brinkman et al. 2012) and allowed the 

model to dispatch the TES resource. These studies demonstrate qualitatively the value of 

dispatchable solar but did not attempt to isolate the value of CSP with TES or compare how the 

value of CSP changes as a function of storage or other grid components. Alternatively, there 

have been studies that focused on the value of CSP with TES but were limited in modeling 

resolution. An example is a study that used a “price-taker” approach to dispatch a CSP plant 

against historic prices, assuming these prices (and solar availability) are known with varying 

degrees of certainty (Sioshansi and Denholm 2010). This type of study can identify some of the 

additional value that TES adds in terms of energy shifting and ancillary services; however, the 

value of this analysis is limited because it cannot examine the impact of different fuel prices, grid 

mixes, or the ability of CSP to interact with variable renewable sources, such as wind and PV. 

A more comprehensive 2012 study evaluated CSP using a reduced form commitment and 

dispatch model and quantitatively identified a number of benefits of TES (Mills and Wiser 

2012). This study evaluated changes in the long-run benefits of CSP with and without TES in 

California using an investment model that included a “fleet-based” commitment and dispatch 

component for conventional generators. The study also isolates the value proposition for CSP 

with TES, including energy, day-ahead forecast error, ancillary service requirements, and 

 1 These models are also used by wholesale market participants. In regions with wholesale power markets, production 

cost models can also be used to predict market clearing prices on a short-term or long-term basis. 2 Some scenarios included a small amount of CSP with storage but assumed flat block dispatch.
3

capacity value, although the simplified commitment and dispatch component of their model did 

not have the fidelity to represent detailed individual unit commitment and dispatch decisions.

Several studies initiated in 2011, including the second phase of WWSIS (Lew et al. 2012),

examine CSP in greater detail. These studies use the PLEXOS production cost model and

simulate the operation of the Western Interconnection in the United States. Simulation of the grid 

over large areas is important because of its interconnected nature and the corresponding ability of 

utilities to share resources over large areas. The Western Interconnection consists of thousands of 

generators, each of which must be simulated in detail. 

Given the complexity of a large grid, it can be difficult to validate proper operation of a new 

generator type and isolate the cost impacts of a relatively small change in the system. As a result, 

to evaluate the performance of CSP, we began with a test system within a subsection of the 

Western Interconnection. This test system was used to evaluate the performance of CSP and the 

incremental value of TES under various grid conditions, including penetration of renewable 

generators, and compare CSP with storage to other generation sources.
4 

3 Implementation of CSP in a Production Cost Model

3.1 Characteristics of CSP Plants

Two common designs of CSP plants—parabolic troughs and power towers—concentrate sunlight 

onto a heat transfer fluid (HTF), which is used to drive a steam turbine.3 An advantage of CSP 

over non-dispatchable renewables is that it can be built with TES, which can be used to provide 

multiple grid services, including shifting generation to periods with reduced solar resource. 

A CSP plant with TES consists of three independent but interrelated components that can be 

sized differently: the power block, the solar field, and the thermal storage tank. Figure 1 

illustrates the main components of a trough-type CSP plant incorporating a two-tank TES

system. 

Figure 1. Components of a trough-type CSP plant with TES

Source: EPRI (2010) 

The size of the solar field, in conjunction with solar irradiance, determines the amount of thermal 

energy that will be available to the power block. The sizing of the solar field is important 

because the relative size of the solar field and power block will determine the capacity factor of 

the CSP plant and the extent to which thermal energy will be utilized. Undersizing the solar field 

will result in an underused power block and a low capacity factor for the CSP plant because of 

the lack of thermal energy during all hours except those with the highest solar resource.4

However, an oversized solar field, when deployed without storage, can result in wasted energy 

because the production from the solar field may exceed the power block capacity during many 

 

3 This is an oversimplification. For a more complete description of CSP technologies, see DOE (2012).

4 A CSP plant can be designed with a fossil-fueled backup system. 
5

hours. The size of the solar field can either be measured in the actual area of the field or by using 

the concept of a solar multiple, which normalizes the size of the solar field in terms of the power 

block size. A solar field with a solar multiple of 1.0 is sized to provide sufficient energy to 

operate the power block at its rated capacity under reference conditions (in this case 950 W/m2 of 

direct solar irradiance at solar noon on the summer solstice).

5 The collector area of a solar field 

with a higher or lower solar multiple will be scaled based on the solar field with a multiple of 

one (i.e., a field with a solar multiple of 2.0 will cover roughly twice the collector area of a field 

with a solar multiple of 1.0).

6

The size of storage is measured by both the thermal power capacity of the heat exchangers 

between the storage tank and the HTF (measured in MW-t) and the total energy capacity of the 

storage tank. The power capacity of the thermal storage will equal some fraction of the 

maximum solar field output. The energy capacity of the storage tank is commonly measured in 

terms of hours of plant output that can be stored. Thermal storage allows an oversized solar field 

and a higher plant capacity factor than a plant without storage.

The concept of solar multiple, and its relation to the role of energy storage, is shown in greater 

detail in Figure 2, depicting the hourly flow of energy from a plant with a solar multiple of 2.0.

The maximum thermal output from the solar field during any hour is 100 MWe, but the power 

block rating is 50 MW, meaning that energy that exceeds the power block rating must be stored 

for use at a later time regardless of the instantaneous demand for electricity or other grid 

conditions. 

Figure 2. Impact of solar multiple on energy flow in a CSP plant

 5 This is not a universal definition. Different CSP technology developers use different reference conditions. 6 This is an oversimplification because there will be some variance in the relationship between collector area and 

solar multiple based on CSP technology type.

0

10

20

30

40

50

60

70

80

90

100

0 6 12 18 24 30 36 42 48 54 60 66 72

Power from the Solar Field (MWe)

Hour

Energy that must be stored Energy stored or sent to power block

Power block rating
6 

3.2 Implementation of CSP in PLEXOS 

The primary function of a production cost model is to determine which generators must be 

committed and dispatched during each interval of the simulation and the associated cost of 

operation. Simulation intervals are typically 1 hour (as performed in this analysis), but there is 

increased interest in sub-hourly simulations, especially in scenarios of increased VG penetration 

where sub-hourly net load variability can require increased dispatch flexibility (CAISO 2011). A 

simple dispatch is determined by “stacking” generators in order of production cost (from lowest 

to highest) until the sum of the individual generator output is equal to load in each time interval. 

The actual dispatch is complicated by the many additional constraints imposed by individual 

generators, such as minimum up and down times and ramp rates. The actual dispatch also 

depends on the need for system security, including spinning reserves, which consist of partially 

loaded generators with the ability to rapidly ramp in response to a generator outage or 

unexpected increase in demand. To determine the optimal dispatch requires detailed information 

for each generator. Primary characteristics include maximum capacity, minimum stable output 

level, plant heat rate (ideally as a function of load), fuel cost, ramp rates, start time, and 

minimum up and down time. The software then co-optimizes the need for energy and reserves 

subject to the various constraints and finds the least-cost mix of generators in each time interval. 

VG plants with little or no variable cost are typically placed into production cost models as a 

fixed hourly generation profile. Because they have no variable cost, and may also have 

production incentives, they are typically dispatched first but may be curtailed when operational 

constraints do not allow the system to accept their output.7

 These constraints might occur when 

the VG exceeds the local capacity of the transmission network or during periods when 

conventional generators have reduced output to their minimum generation levels. This second 

phenomenon can be referred to as a “minimum generation” problem (Rogers et al. 2010) or an 

“over generation” problem (CAISO 2010). CSP plants without storage can be placed into the 

model in the same manner as a wind or PV plant, using the hourly output from a CSP simulation 

model. 

For this study, trough CSP plants (both with and without storage) and PV were simulated using 

the System Advisor Model (SAM) (Gilman et al. 2008; Gilman and Dobos 2012) version 2012-

5-11. The CSP simulations used the wet-cooled empirical trough model (Wagner and Gilman 

2011). The model converts hourly irradiance and meteorological data into thermal energy and 

then models the flow of thermal energy through the various system components, such as losses in 

the HTF, finally converting the thermal energy into net electrical generation output. The CSP 

plant without storage assumes a solar multiple of 1.3, the SEGS VIII default power block with 

turbine over-design operation allowed at 105% and used default settings for all parameters, such 

as parasitics. Meteorological data was derived from the National Solar Radiation Database from 

2006 (NREL 2007). 

CSP with TES was implemented in this study using a two-step process. First, hourly electrical 

energy from the CSP plant was simulated using SAM, and then the electrical energy was 

dispatched in PLEXOS using a combination of algorithms that largely existed within that model. 

This process is illustrated in Figure 3 and described in more detail in the following sections. 

 

7

 Wind and solar may also be modeled as “must-take” resources due to contractual obligations to produce. 
7

Figure 3. General process of implementing CSP

The first step (hourly electrical energy) was created using SAM in a manner similar to the case

without storage with several important differences. Essentially all parameters that are affected by 

plant dispatch were moved out of SAM and into the PLEXOS framework. First, the solar 

multiple was set to 1; a larger solar multiple and storage was implemented in the PLEXOS model 

as described later in this section. Second, the minimum generation levels and start-up energy 

requirements were set to 0 and also accounted for in PLEXOS. Parasitics were removed from the 

gross CSP generation to derive a net hourly generation. Operational parasitics calculated by 

SAM were subtracted from the electrical profile in a manner similar to other thermal power 

stations. We also considered the constant parasitic loads (e.g., associated with fluid pumps) that 

occur even when the plant is not operating. This means that the CSP plant will draw a small 

amount of energy from the grid and incur a small associated cost. This constant load was 

calculated separately based on SAM CSP simulations.

The product of the SAM simulation is “raw” electrical energy output, which is then processed in 

PLEXOS using a modified form of the PLEXOS hydro algorithm to simulate storage, generator 

operation, and the effect of an oversized solar field. In each hour, the model can send the 

electrical energy from the SAM simulations directly to the grid via a simulated power block, to 

storage, or a combination of both. The model can also choose to draw energy from storage. The 

simulated power block includes the essential parameters of the CSP power block, including startup energy, minimum generation level, and ramp-rate constraints. The model considers start-up 

losses in the dispatch decision by assuming a certain amount of energy (equivalent to 20 MWhe 

for a 100-MW power block) is lost in the start-up process.

In CSP plants that use indirect storage, the additional efficiency losses in the storage process are 

also simulated. The storage losses are set to 7%, which capture both the efficiency losses in the 

heat exchangers and the longer-term decay losses.

8 Constant parasitics were added by placing a 

constant load on the same bus as the CSP plant. The general implementation is illustrated in 

Figure 4, representing a 200-MW CSP plant with a solar multiple of 2.0 and 6 hours of storage.

 8 Longer-term decay losses could theoretically be modeled separately; however, they are relatively small and each 

new parameter in the model introduces complexity and increases run time. To consider decay losses, we reduce the 

total round-trip efficiency slightly to represent the “average” losses associated with both the heat transfer process 

and losses over time. 

Solar Data SAM CSP 

Model

Hourly 

CSP 

Electricity 

PLEXOS

CSP 

Operational 

Characteristics System Advisor Model Simulations 

(Outside PLEXOS)
8 

Figure 4. The flow of energy through a trough CSP plant with TES in PLEXOS 

Figure 4 also shows the effect of solar multiple, which is captured in the sizing of the power 

block and storage components. For example, a solar multiple of 2.0 can be simulated by setting 

the maximum size of the power block to 50% of the maximum output from the CSP simulations 

from SAM. Likewise, the storage system can be sized to accommodate some fraction of the 

maximum CSP output. The storage energy capacity (hours of storage) can be set independently. 

 

400 MWe max from 

SAM Generator 

rating: 

40–200 

MW 

200 MW 

Storage release 

loss: 

7% of released 

energy

Storage: 6 hrs 

1.2 GWh

Start-up loss 

40 MWh 
9

4 Test System

CSP was implemented in a test system to better verify the basic performance of this dispatchable 

energy source and to more easily isolate the relative value of TES under various scenarios.

The best locations for CSP in the United States are in the desert southwest within the Western 

Interconnection. Simulating the entire interconnection makes it difficult to isolate the 

performance of CSP, so a smaller test system was created to develop and validate the modeling 

approach. Most of the existing and proposed CSP is in California; however, simply running the 

California system in isolation ignores the substantial interconnections between the California and 

bordering states. As an alternative, we developed a system composed of two balancing areas

largely in the State of Colorado: Public Service of Colorado (PSCO) and Western Area Colorado 

Missouri (WACM). These balancing areas consist of multiple individual utilities and this 

combined area is relatively isolated from the rest of the Western Interconnection. In addition,

Colorado has sufficient solar resource for CSP deployment in the San Luis Valley in the southcentral part of the state, and there have been proposals for large-scale solar development in the 

area (Xcel 2011). The test system also has sufficient wind resources for large-scale deployment, 

which makes evaluation of high renewable scenarios more realistic.

The Colorado test system was isolated by physically “turning off” the generation and load and 

aggregating the transmission outside of the PSCO and WACM balancing areas.9 Transmission 

was modeled zonally, without transmission limits within each balancing authority area. It is very 

difficult to simulate any individual or group of balancing authority areas as actually operated 

because the modeled system is comprised of vertically integrated utilities that balance their 

system with their own generation and bilateral transactions with their neighbors that are 

confidential. Not having access to that information, we modeled the test system assuming leastcost economic dispatch. We based our inputs and assumptions as much as possible on the 

Western Electricity Coordinating Council (WECC) Transmission Expansion Policy Planning 

Committee (TEPPC) model and other publicly available datasets. Projected generation and loads 

were derived from the TEPPC 2020 scenario (TEPPC 2011). Hourly load profiles were based on 

2006 data and scaled to match the projected TEPPC 2020 annual load. The system is a strongly 

summer peaking system with a 2020 coincident peak demand of 13.7 GW and annual demand of 

79.0 TWh. The system load duration curve for 2020 is shown in Figure 5.

 9 This simplification effectively allows the two simulated balancing areas to use the transmission system in the rest 

of the Western Interconnection to share energy. As discussed later, transmission constraints between the two areas 

were not binding, and the analysis assumes sufficient additional transmission is constructed to access new renewable 

resources including both wind and new CSP. The costs and challenges of constructing the required new transmission 

are not considered in this analysis.
10 

Figure 5. Load duration curve in 2020 for the PSCO/WACM test system

The generation dataset was derived from the TEPPC 2020 database and included plant 

capacities, heat rates, outage rates (planned and forced), and several operational parameters, such 

as ramp rates. A total of 201 thermal and hydro generators were included in the test system, with 

total capacities listed in Table 2. The generator database was modified to include part-load heat 

rates based on Brinkman et al. (2012). Start-up costs were added using the start-up fuel 

requirements in the generator database plus the operations and maintenance (O&M) related costs 

based on estimates prepared for the WWSIS II study (Intertek/APTECH 2012). We adjusted the 

generator mix to achieve a generator planning reserve margin of 15% by adding a total of 

1,450 MW (690 MW of combustion turbines and 760 MW of combined cycle units). 

Table 1. Characteristics of the Test System Conventional Generators in 2020

System Capacity (MW)

 Coal 6,178

 Combined Cycle (CC) 3,724

 Gas Turbine/Gas Steam 4,045

 Hydro 773

 Pumped Storage 560

 Othera 513

Total 15,793

a Includes oil and gas-fired internal combustion generators and demand response. 

Two renewable energy scenarios were created by adding wind and solar generation. PV profiles 

were generated using the SAM model with 2006 meteorology. Wind data was derived from the 

WWSIS dataset.10 A low renewable energy (RE) case was created by adding wind and solar to 

 

10 All generation profiles were adjusted to be time synchronized with 2020, which is a leap year.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0 1000 2000 3000 4000 5000 6000 7000 8000

System Load (MW)

Hours at Load
11

achieve a penetration of about 13% on an energy basis. This is a relatively small increase over 

the renewable penetration in 2011; Colorado received about 12% of its electricity from wind in 

the year ending June 2012 (EIA 2012).

11 We also considered a high RE case where wind and 

solar provide about 35% of the region’s energy. In each case, discrete wind and solar plants were 

added from the WWSIS data sets until the installed capacity produced the targeted energy 

penetration. The sites were chosen largely based on capacity factor, and do not necessarily reflect 

existing or planned locations for wind and solar plants. Table 2 lists characteristics of the system

in the two cases, while Table 3 provides additional details of the renewable and conventional 

generation mix.

Table 2. Renewable Scenarios in the Test System in 2020

System Capacity (MW)

Low RE 

Scenario

High RE 

Scenarioa

Wind Capacity (MW) 3,054 6,489

Wind Energy (GWh) 9,791 20,210

Solar Capacity (MW) 395 3,630

Solar Energy (GWh) 625 6,493

a This is the potential generation and does not include curtailment that results in actual dispatch. About 31 GWh of 

wind and 23 GWh of solar were curtailed in the base high RE scenario.

Three classes of ancillary service requirements were included. The contingency reserve is 

810 MW based on the single largest unit (Comanche 3). This reserve is allocated with 451 MW

to PSCO and 359 MW to WACM, with 50% met by spinning units.12 Regulation and flexibility 

reserve requirements were calculated based on the statistical variability of net load described by 

Ibanez et al. (2012). Reserves were modeled as “soft constraints,” meaning the system was 

allowed to not meet reserves if the cost exceeded $4,000/MWh. This high cost could result 

during periods where a power plant would need to start up for a very short period of time just to 

provide reserves. Load was also modeled as a soft constraint, with a loss-of-load cost of 

$6,000/MWh (though the reserve margin was adequate to avoid lost load).

Fuel prices were derived from the TEPPC 2020 database. Coal prices were $1.42/MMBTU for 

all plants. Natural gas prices varied by plant, and for most plants were in the range of 

$3.9/MMBTU to $4.2/MMBTU, with a generation weighted average of $4.1/MMBTU. This is 

slightly lower than the EIA’s 2012 Annual Energy Outlook projection for the delivered price of 

natural gas to the electric power sector in the Rocky Mountain region of $4.46/MMBTU in 2020

(EIA 2012). Sensitivity to natural gas price was also analyzed.

Both cases were run for 1 full year (2020, with 2006 meteorology and load pattern). The model 

run begins with two scheduling models to determine outage scheduling and allocate certain 

 11 Colorado generated 3,070 GWh from wind in the year ending June 2012 compared to total generation of 25,614 

GWh. EIA “Electric Power Monthly with Data for June 2012” August 2012. 12 The PSCO and WACM balancing areas are part of the Rocky Mountain Reserve group, which shares contingency 

reserves based on these values. 
12

limited energy resources.13 The model then performs a chronological hourly security-constrained 

unit commitment and economic dispatch to minimize the overall production cost under 

operational and system constraints. The model performs a 24-hour ahead commitment with an 

additional 24-hour look-ahead period, allowing the model to effectively optimize storage 

utilization over a 48-hour period.14 The analysis in this report was performed using PLEXOS 

version 6.207 R01, using the Xpress-MP 23.01.05 solver, with the model performance relative 

gap set to 0.5%.

Table 3 provides a summary of the operational results for the two base simulations. This 

represents on the variable cost of system operation, dominated by the cost fuel for thermal power 

plants. There was no loss of load and a small number of reserve violations (less than 40 hours per 

year in both cases).

15 

Table 3. Base Case Results

Low RE High RE

Total Production Cost (M$) 1,491.37 1,024.38

Average Production Cost

($/MWh)

18.9 13.0

Total Generation (GWH)a 78,957 79,098

Generation Mix

 Coal 58.8% 52.0%

 Gas Combined Cycle (CC) 20.7% 7.2%

 Gas Combustion Turbine

 (CT)/Gas Steam

1.4% 1.1%

 Hydro 4.8% 4.8%

 Wind 12.4% 25.5%

 Solar PV 0.8% 8.2%

 Other 1.1% 1.2%

Fuel Use (1,000 MMBTU)

Coal 490,923 434,426

Gas 140,447 53,928

a

While the load is the same, the total generation is slightly different (by about 0.2%), due primarily to different 

operation of the pumped hydro units.

 13 Within PLEXOS, maintenance outages are scheduled in the “Projected Assessment of System Adequacy” model, 

which generally assigns planned outages to periods of low net demand. This is followed by the “mid-term” 

scheduling model, which uses monthly load duration curves to assign limited energy resources, such as certain 

hydro units. The resulting allocation of resources from these two models is then passed to the chronological 

commitment and dispatch model. The model also includes random forced outages based on plant-level outage rates. 

The random number seed used to generate forced outages was kept the same throughout the various simulations for 

consistent treatment of these outages and associated cost impacts.

14 Without a look-ahead period, production cost models see no value in carrying energy in storage across 

commitment intervals. 

15 In the low RE scenario there were a total of 39 hours of violations between the two systems. Of these, 31 were of 

contingency reserve violations. The violations were partial violations meaning that most of the contingency reserve 

requirements were met but there was some shortfall. The overall shortage represents about 0.14 GWh of the 

5,839.4 GWh reserve requirement, or about 0.002%. There were a total of 17 hours of reserve violations in the high 

RE case. 
13

System dispatch stacks can provide additional insight into system operation. The generation mix 

and dispatch was as expected, with coal units operating as baseload units, and CC and CT units 

operating as mid-merit and peaking units as needed. Figure 6 shows the dispatch stack for the 

low RE case during the week of peak demand in the summer. This figure demonstrates the 

opportunity for mid-day solar generation to reduce the use of the highest cost generators. 

Figure 6. Dispatch stack during the period of July 25–28 in the low RE Case

The high RE case removes much of the gas generation from the system and leaves coal on the 

margin for a large number of hours. Figure 7 shows a four-day period in February, which 

includes the day of the lowest net demand on the system in the high RE scenario. In the first two 

days, coal generators reduce their output to minimum levels and renewable generation is 

curtailed. Any additional renewable generation from 1 p.m. to 3 p.m. on February 8 and from 11 

a.m. to 2 p.m. on February 9 will be unusable in this scenario, and likely curtailed. Generation 

during many other periods will offset mainly lower-cost coal generation. Also of note is the rapid 

increase in net demand that occurs after 3 p.m. when the decrease in solar output and increase in 

electricity demand require large ramps of the coal units, use of higher-cost combustion turbines, 

and dispatch of the pumped storage plants.16 Previous integration studies such as WWSIS have 

found significant increases in ramping requirements of coal units, and a major focus of the 

second phase of WWSIS is to examine the potential cost implications of increased unit cycling.

 16 The large amount of solar energy creates a new “off-peak” period in the middle of the day. Among the interesting 

system changes resulting from large mid-day solar generation is the extensive use of the pumped hydro units to 

absorb otherwise curtailed solar energy during this period.

0

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3,000

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5,000

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7,000

8,000

9,000

10,000

11,000

12,000

13,000

14,000

7/25/2020 

0:00:00

7/25/2020 

12:00:00

7/26/2020 

0:00:00

7/26/2020 

12:00:00

7/27/2020 

0:00:00

7/27/2020 

12:00:00

7/28/2020 

0:00:00

7/28/2020 

12:00:00

Generation (MW)

PV

Wind 

Other

Pumped 

Storage

Hydro

Gas Turbine

Combined 

Cycle

Coal
14

Figure 7. Dispatch stack during the period of February 8–11 in the high RE Case

In any hour of the year, the value of solar or other incremental generation in this system is 

determined by the marginal generators and associated price. Figure 8 is a price duration curve for 

the test system showing system marginal cost for the PSCO balancing area. The marginal prices 

for the WACM balancing area were almost identical because transmission constraints between 

the PSCO and WACM system were not binding; only very small price differences occurred in a 

few hours due to different reserve requirements. The price duration curve shows three main 

“zones” of prices, based on the marginal generators: coal at about $17–$20/MWh, combined 

cycle units at about $25–$35/MWh, and combustion turbines at about $38–$45/MWh. In the low 

RE case, coal is on the margin about for about 1,600 hours, while in the high RE case, coal is on 

the margin for over 5,000 hours. In addition, renewable energy is effectively on the margin for 

about 100 hours of the year in the high RE case; additional renewable generation during these 

hours would likely be curtailed and provides no incremental benefit to the system.17 There are 

also a small number (less than 40 hours per year) of extremely high prices, set by the reserve 

violation conditions. 

 17 Because this system is isolated from the rest of the Western Interconnection, it does not consider the opportunity 

for this energy to be exported to neighboring systems.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

11,000

2/8/2020 

0:00:00

2/8/2020 

12:00:00

2/9/2020 

0:00:00

2/9/2020 

12:00:00

2/10/2020 

0:00:00

2/10/2020 

12:00:00

2/11/2020 

0:00:00

2/11/2020 

12:00:00

Generation (MW)

Curtailment

PV

Wind 

Other

Pumped Storage

Hydro

Gas Turbine

Combined Cycle

Coal
15

Figure 8. System marginal price duration curve in the PSCO balancing area for the two cases

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000

System Marginal Price ($/MWh)

Hour

Low RE Case

High RE Case
16

5 Value of CSP

The value of CSP was determined by adding a relatively small amount of generation to the base 

system and evaluating the change in production costs and the value of system capacity. We also 

examined the operation of CSP with TES, including how energy was shifted over time. Several 

technologies were evaluated; for uniform comparison, each technology provided an 

approximately equivalent amount of energy, based on the annual production of a 300-MW 

CSP/TES plant with a solar multiple of 2.0. This is about 1,070 GWh, or enough to provide 

about 1.4% of the test system demand.18

5.1 Solar Scenarios Evaluated

Four main cases were added to the test system, each providing an approximately equal amount of 

energy.

19

1. A flat block of zero-cost energy. This provides a point of comparison to examine how the 

temporal characteristics of solar energy compare to a constant or “baseload” resource. To 

provide an equal amount of energy, the capacity of this block was set to 123 MW.

2. Solar PV. Discrete-sized 1-axis tracking PV plants generated for the WWSIS project 

were added in each case until the generation equaled about 1,070 GWh, with a total 

installed capacity of about 580 MW.

20 The total operating reserve requirement was also 

increased due to the additional short-term variability.21

3. CSP no storage. A single 568-MW plant with a solar multiple of 1.3 was added to the San 

Luis Valley in southern Colorado. This plant produced 1,130 GWh.

4. CSP with 6 hours of storage. This case adds a 300-MW CSP plant with a solar multiple 

of 2.0, as discussed previously. The actual amount of energy delivered to the system 

varied slightly in each scenario based on the amount of energy stored (due to storage 

losses).

Each of the cases was simulated in both the low and high RE scenarios. Several sensitivity 

scenarios were considered, as discussed in Section 5.4.

5.2 Operational Value

The operational value of each technology represents its ability to avoid the variable cost of 

operation. These costs were tracked in three cost categories—operating fuel, variable O&M, and 

start-up costs. Operating fuel includes all fuel used to operate the power plant fleet while 

 18 The potential generation was about 1,100 GWh, but the actual delivered energy was determined by the plant 

dispatch, considering a net storage loss of 7%. 19 While each technology produced a slightly different total amount of energy, value of each was calculated on a per 

megawatt-hour basis as opposed to total system value. This normalization eliminates any effect caused by the small 

differences in annual generation. 

20 The total amount of capacity added was actually 591 MW in the low RE case and 578 MW in the high RE case. 

The difference is due to the selection of discrete plants. The slightly different energy production is accounted for by 

dividing changes in production cost by total solar generation in each scenario. 21 The additional reserve requirement was about 74 GW-h of additional reserves (51 GW-h of flexibility reserves 

and 23 GW-h of regulation reserves). This additional reserve requirement should decrease the overall benefits of 

PV slightly; however the inherent flexibility of generators in the TEPPC database resulted in very little reserve 

costs. This issue is discussed in more detail in section 5.6.
17 

generating and includes the impact of variable heat rates and operating plants at part load to 

provide ancillary services. Start-up costs include both the start fuel, as well as additional O&M 

required during the plant start process. In each case the operational value was calculated by 

dividing the total avoided generation cost in each cost category by the total potential solar 

generation.

Table 4 summarizes the results from the production simulations.

Table 4. Operational Value of Simulated Generators

Marginal Value ($/MWh)

Low RE High RE

Flat 

Block 

PV CSP (no 

TES)

CSP (6-

hr TES)

Flat 

Block 

PV CSP (no 

TES)

CSP (6-

hr TES)

Fuel 31.7 35.2 33.9 37.7 22.6 21.2 18.7 31.1

Variable

O&M

1.2 1.0 1.0 0.8 2.1 2.0 1.9 1.4

Start 0.4 0.4 0.6 3.5 0.5 -0.9 -1.7 3.1

Total 33.3 36.6 35.5 42.1 25.2 22.3 18.9 35.6

Table 4 demonstrates three significant findings: (1) at low penetration, the value of solar 

generation technologies is greater than the constant (flat block) resource; (2) the value of all 

generation decreases as a function of renewable penetration, but the value of non-dispatchable

solar resources decreases at a greater rate than the flat block or dispatchable CSP; (3) the value of 

CSP with storage is higher than solar technologies without storage. The range of values for 

different generation technologies largely can be explained by understanding the avoided fuel mix 

in the two different scenarios. 

In the test system, the added generators (flat block or solar) reduce the output from a mix of 

generator types and with different efficiencies, depending on the time of day and season. Figures 

9–16 illustrate how the relative value of a renewable generator is affected by the varying 

marginal generators and the dispatchability of the resource.

Figure 9 illustrates the relationship between price and net load for a 3-day period starting on 

January 22. The net load is the normal load minus wind and solar PV generation and reflects the 

load that must be met by other (mostly fossil fueled) generators with non-zero generation cost. 

The figure illustrates three zones of prices, which are seen earlier in the price duration curves in 

Figure 8. The lowest price occurs in the overnight periods at the beginning of days 2 and 3 when 

coal is the marginal generator with total incremental cost of about $20/MWh. During much of 

the middle of the day, combined cycle units are the marginal generators, with variable costs of 

about $30–$35/MWh. In several periods in the morning and evening, there is an increase in net 

demand, where the high ramp rate or the relatively short period of increased demand requires the 

use of combustion turbines, resulting in a price spike to about $45/MWh. Any renewable 

generator added to this mix will offset energy within these three price zones but with a value 

depending on the temporal pattern of its output. 
18

Figure 9. System net load and marginal price for January 22–24 (low RE case)

Figure 10 keeps the marginal price curve but adds the generation profile for CSP with and 

without storage. The CSP dispatch is isolated from cases where CSP is added.22 The total 

generation by these two plants is very similar, but CSP with storage is dispatched during the 

highest cost periods. In much of the winter, the price of electricity peaks in periods where solar 

output is low or zero (the morning and evening). This corresponds to when higher-cost gas-fired 

units are started and ramped to meet peak demand. PV and CSP without storage are unable to 

generate during this period and typically offset more efficient gas-fired units. Alternatively, CSP 

with TES is able to shift generation to the evening and carry over energy to start and pick up the 

morning load ramp that occurs before significant solar energy is available. As a result, CSP 

avoids the use of higher-cost and lower-efficiency gas-fired units, producing overall higher value 

to the system.

 22 Figure 10 combines the system marginal prices from the base case dispatch with the CSP results isolated from the 

two runs with CSP. Because CSP itself affects the entire system dispatch, the actual system marginal prices from the 

CSP cases are slightly different. Also it should be noted that the simulated dispatch represents a least cost dispatch 

based on the various generator characteristics and constraints, and does not necessarily represent the dispatch that 

would occur under wholesale market conditions, which would have additional operating parameters and constraints 

such as conditions of power purchase agreements.

0

5

10

15

20

25

30

35

40

45

50

0

2,000

4,000

6,000

8,000

10,000

12,000

0 12 24 36 48 60 72

System Marginal Price ($/MWh)

Net Load (MW)

Hour

Load

System Marginal Price
19

Figure 10. System marginal price and corresponding CSP generation on January 22–24 (low RE 

case)

During the summer, operation of CSP with storage is more continuous due to higher solar output 

and a different load and price profile. Figure 11 shows the relationship between net load and 

system marginal price for a 3-day period starting on July 14.

Figure 11. System net load and marginal price for July 14–16 (low RE case)

0

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0

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CSP Output (MW)

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CSP with TES

CSP No Tes

System 

Marginal 

Price

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System Marginal Price ($/MWh)

Net Load (MW)

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Load

System Marginal Price
20

The corresponding CSP operation is shown in Figure 12. There are several operational issues 

that affect the overall and relative value of CSP with TES. First, CSP with TES is able to operate 

more continuously and avoid the impact of cloud cover that reduces output and increases the 

variability of the plant without TES.

23 Second, CSP is able to start earlier in the day and help 

pick up the early morning load ramp. Finally, CSP is able to continue operation longer into the 

late afternoon and early evening. This is particularly important for the plant capacity value 

discussed in Section 5.3.

Figure 12. System marginal price and corresponding CSP generation on July 14–16 (low RE case)

Figure 12 shows the impact of the solar multiple, which can provide some disadvantages at low 

solar penetration. At low penetration of solar, PV and CSP without storage are largely coincident 

with demand (and relatively high prices) during the summer. As illustrated in Figure 2, 

whenever the thermal output of the solar field exceeds the power block capacity, energy must be 

stored, regardless of the system demand for energy or price. As a result, the plant is forced to 

store this energy and generate at a later time, even if this later time has a lower demand or lower 

cost of energy. This is shown in Figure 12 on the first and second day, when during some hours,

CSP without storage sells more energy at periods of high prices than CSP with TES. CSP with 

storage is forced to shift some energy to the evening when prices are slightly lower.24

The value of solar and dispatchable CSP is strongly dependent on the mix of generator types and 

amount of renewable energy. As the penetration of renewables increases, the patterns of net 

demand for electricity change, and different mixes of generation are needed to address the 

increasing variability and uncertainty of the wind and solar supply. Figure 13 is a duplication of 

Figure 9, showing price and load during 3 days in January, except for the high RE case. The 

large amounts of wind and solar PV have suppressed the marginal price, and coal is on the 

 23 Note that this only considers hourly operation. 24 This result is due in large part to the combination of solar multiple and amount of storage. A lower solar multiple 

and few hours of storage would reduce this effect but would be need to be analyzed on a case-by-case basis.

0

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0 12 24 36 48 60 72

System Marginal Price ($/MWh)

CSP Output (MW)

Hour

CSP with TES

CSP No Tes

System 

Marginal 

Price
21

margin for more hours. The load shape (and price) is also much more volatile, with operation of 

combustion turbines to address the shorter peaks.

Figure 13. System net load and marginal price for January 22–24 (high RE case)

In the high RE scenario the absolute value of all energy sources drops due to lower system 

marginal prices. However, the value of variable energy sources drops at a much faster rate than

dispatchable sources, as a plant with TES is able to change output to capture the remaining 

periods of high prices. Figure 14 shows how CSP with TES is able to generate during the hours 

of highest price during this period in January. 

0

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Load

System Marginal Price
22

Figure 14. System marginal price and corresponding CSP generation on January 22–24 (high 

RE case)

Dispatchability becomes increasingly important during periods of very high renewable output to 

avoid generating during periods of zero value and associated renewable curtailment.

25 Figure 15

shows a period of low net demand due to high solar (and wind) output during the middle of the 

day. During the first 2 days shown shortly after noon, the net load drops to the point where all 

thermal generators have reduced output to their minimum. This is the same period as the first 3 

days in Figure 8, where during the middle of the day all coal plants in the system cannot reduce 

output further without incurring a costly shut down. Any additional zero-cost renewable energy 

generated during these hours cannot be used by the system so have zero value, and the system 

marginal price is $0/MWh. 

 25 In locations with wholesale energy markets, periods of curtailment are often associated with negative prices, 

driven by wind units bidding negative values to capture the production tax credit and large thermal plants bidding 

negative to avoid shut down.

0

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0 12 24 36 48 60 72

System Marginal Price ($/MWh)

CSP Output (MW)

Hour

CSP with TES

CSP No Tes

System 

Marginal Price
23

Figure 15. Net load and price for a 3-day period starting February 8

Figure 16 shows the operation of CSP plants in these three days. CSP without storage generates 

in the middle of the day, producing some output that provides zero incremental value (when the 

system marginal price is zero). During these periods, CSP with thermal storage generates at low 

output, or shuts down, avoiding curtailed energy and maximizing value by shifting energy to 

periods of higher net demand and providing potentially valuable ramping services.

26

 26 As discussed later, the value of ramping is not quantified in this analysis. While most production cost models can 

consider the impact of ramping constraints, they do not calculate a cost of plant ramping. WWSIS II will evaluate 

the cost impact of ramping in high RE scenarios.

0

5

10

15

20

25

30

35

40

45

50

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0 12 24 36 48 60 72

System Marginal Price ($/MWh)

Net Load (MW)

Hour

Load

System Marginal Price
24 

Figure 16. System marginal price and corresponding CSP generation on February 8–10 (high 

RE case)

The ability to avoid renewable generation during periods of low or zero value will be an 

increasingly important source of value as renewable penetration increases. In the high RE 

scenario, about 5% of the additional PV and about 6% of the CSP without storage has zero value 

and is effectively curtailed.27 The number of hours of zero value generation (resulting in 

renewable curtailment) is highly non-linear as a function of renewable penetration and would be 

expected to increase without additional measures to increase system flexibility (Denholm and

Margolis 2007). 

The sum of these factors, including the mix of generation, fuel cost, and curtailment can be 

translated into the source of avoided fuel costs in Table 4. Tables 5 and 6 further explain the 

source of avoided costs for the different generator types. Table 5 indicates the type of generation 

avoided by each unit of generation. In the low RE case, each kilowatt-hour of CSP without 

storage avoids 0.9 kWh of combined cycle generation and 0.1 kWh of combustion turbine 

generation. In some cases, the smoothing of the load can actually increase the use of some lowercost generator types; in the low RE case, CSP with storage and PV can improve the system 

dispatch and increase low-cost coal generation slightly. The flat block results show a greater 

displacement of coal because it generates at constant output, including at night when coal is often 

on the margin.28 Table 5 demonstrates how, in the high RE scenario, much of the gas generation 

 

27 In reality, PLEXOS assigned most of the incremental curtailment associated with the addition of PV or CSP in the 

base case to the existing mix of renewable generators, including wind, solar, and hydro (see Table 5). This is 

because these sources have no variable cost, so there is no difference in production cost regardless of which 

renewable generator is curtailed. If we assign all the incremental renewable curtailment that occurs with the addition 

of PV or CSP to the incremental solar generator, we arrive at the 5% PV and 6% CSP (no storage) numbers cited 

above. This analysis makes no recommendations regarding actual allocation of curtailment. For additional 

discussion, see Fink et al. (2010).

28 The fraction of displaced generation from the flat block resource is not exactly equal to the marginal fuel mix 

shown in Figure 8. This is partially due to the operation of pumped hydro plants. 

0

5

10

15

20

25

30

35

40

45

50

0

50

100

150

200

250

300

350

400

450

500

0 12 24 36 48 60 72

System Marginal Price ($/MWh)

CSP Output (MW)

Hour

CSP with TES

CSP No Tes

System 

Marginal Price
25

has been removed by the system, and coal is on the margin for more hours. Both PV and CSP 

without storage remove similar amounts of combined cycle and coal; however, CSP with storage

continues to avoid mostly gas generation due to the dispatchability of the resource.

In some cases, each unit of generation removes more or less than 1 unit of thermal generation.

This is due to two factors: pumped storage operation and curtailment. The flat block in the low 

RE case frees up coal generation to displace more costly gas plant operation via the use of 

pumped storage. However, because storage incurs losses, this results in a small increase in 

thermal generation. The opposite occurs in the low RE PV case and the CSP with storage cases.

The displacement of higher-cost generation in these cases reduces the economic operation of 

pumped storage, decreasing storage losses and resulting in more than 1 unit of avoided 

generation per unit of solar generation. At higher RE penetration, solar without storage displaces 

less than 1 unit of thermal generation due to curtailment of renewable generators.

Table 5. Avoided Thermal Generation

Avoided Thermal Generation (kWh/kWh)

Low RE Scenario High RE Scenario

Flat 

Block 

PV CSP (no 

TES)

CSP (6-

hr TES)

Flat 

Block 

PV CSP (no 

TES)

CSP (6-

hr TES)

Coal 0.09 -0.06 -0.03 -0.08 0.55 0.50 0.52 0.17

Gas 

Combined 

Cycle

0.78 0.99 0.91 0.79 0.39 0.50 0.39 0.72

Gas 

Turbine/Steam

0.10 0.09 0.11 0.27 0.05 -0.02 -0.01 0.11

Total 0.98 1.03 1.00 1.02 0.99 0.97 0.90 1.04

While Table 5 is a useful illustration of the type of generation avoided, the ultimate cost driver is 

the type and amount of fuel actually displaced. Table 6 provides the actual avoided operational 

fuel in each scenario (in MMBTU per MWh of solar generation). Of note is the fact that the 

avoided fuel rate increases in the high RE scenario. This is due to the displacement of lower cost, 

higher heat rate coal units compared to more efficient, higher-cost gas generators. The product of 

the avoided fuel in Table 6 and fuel costs produce the fuel value ($/MWh) in Table 4.

Table 6. Avoided Fuel

Avoided Fuel (MMBTU/MWh)

Low RE Scenario High RE Scenario

Flat 

Block 

PV CSP 

(no 

TES)

CSP (6-

hr TES)

Flat 

Block 

PV CSP (no 

TES)

CSP (6-

hr TES)

Coal 1.1 -0.7 -0.7 -0.9 5.8 5.2 5.4 1.9

Gas 7.4 8.9 8.9 9.7 3.5 3.6 2.9 7.1

Total 8.5 8.2 8.2 8.8 9.3 8.8 8.3 9.0

An additional important secondary source of value for CSP with TES is the ability to avoid

thermal plant starts and associated fuel use and maintenance. Even at low penetration, PV and 

CSP without storage tends to increase the variability of the net load, increasing the number of 

plant starts but decreasing the total amount of energy produced by the generation fleet. Table 7

provides the estimated number of avoided starts and percentage reduction. Consistent with the 
26 

previous tables, a positive number represents actual avoided starts (a net benefit), while a 

negative number means an increase in starts. This table demonstrates a significant reduction in 

starts due to the flexible operation of CSP with TES. 

Table 7. Avoided Starts

Avoided Starts (Total/%)

Low RE Scenario High RE Scenario

Flat 

Block 

PV CSP (no 

TES)

CSP (6-hr 

TES)

Flat Block PV CSP 

(no 

TES)

CSP (6-hr 

TES)

Coal 3/

0.4%

-1/

-1.1%

-5/

-0.7%

-8/

-1.1%

-16/

-2.2%

4/

0.6%

-3/

-0.4%

-18/

-2.5%

Combined 

Cycle

-77/

-6.3%

18/

1.5%

53/

4.3%

56/

4.6%

9/

1.2%

17/

2.2%

-56/

-7.2%

129/

16.5%

Gas

Turbine/

Steam

362/

4.6%

-412/

-5.2%

-271/

-3.4%

1,099/

13.8%

432/

4.1%

-640/

-6.1%

-361/

-3.4%

871/

8.3%

5.3 Capacity Value

The value calculated in Section 5.2 only addresses the variable operational value. Both CSP and 

PV have the ability to provide system capacity and replace new generation. However, the actual 

capacity value of solar technologies depends on their coincidence with demand patterns and how 

this coincidence changes as a function of penetration.

At low penetration, the capacity credit (equal to the fraction of capacity that is available during 

periods of high net demand) of PV and CSP without TES is relatively high. Figure 17 shows the 

simulated solar output during three peak demand days in the low RE system, including the 

system annual peak on July 27 showing high correlation. As a result, each megawatt of PV or 

CSP without TES reduces the net demand by a significant amount and eliminates the need for 

conventional generation.
27

Figure 17. Correlation of demand and solar generation on a 3-day period starting July 26 (low 

RE case)

As the penetration of PV or CSP without storage increases, the capacity credit drops 

significantly. Solar energy shifts the peak to later in the day, to periods where solar output is low 

or zero. In Figure 17, the annual peak demand occurred in the hour ending at 2 p.m. on July 27.

(Other peak demand hours are typically an hour or two later.) However, in the high RE case, 

where solar provides 8% of total demand, the net demand has been shifted to later in the day 

where solar is no longer highly correlated with load. Figure 18 shows an example of a new 

period of high peak demand in the high RE case where the net load peaks in the hour ending at 

7 p.m. on the first and third day. On these 3 days beginning on July 17, there is still strong solar 

output, but PV and CSP without storage no longer provide significant amounts of net demand 

reduction. CSP with storage shifts generation to later in the day and provides a net demand 

reduction equal to the plant’s rated capacity, resulting in a capacity credit of close to 100%. This 

is shown in detail in Figure 19, which enlarges the net load on July 17 and shows the net demand 

after removing the generation from the three different solar technologies. 

0

100

200

300

400

500

600

700

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0 12 24 36 48 60 72

Solar Generation (MW)

Net Load (MW)

Hour

Base Case 

Net Load

Net Load 

with PV

Net Load 

with CSP no 

TES

CSP no TES

PV
28

Figure 18. Correlation of demand and solar generation on a 3-day period starting July 17 (high 

RE case)

Figure 19. Net demand with different solar generation types on July 17 (high RE case)

Estimation of the monetary value of system capacity begins with an estimate of each plant’s 

capacity credit. There are a number of methods used to estimate the capacity credit of VG 

sources. We used the simple capacity factor approximation technique, which has been shown to 

0

100

200

300

400

500

600

700

0

2,000

4,000

6,000

8,000

10,000

12,000

0 12 24 36 48 60 72

Solar Output (MW)

Base Net Load

Hour

Base Case Net 

Load

CSP with TES

CSP No Tes

PV

7,000

7,500

8,000

8,500

9,000

9,500

10,000

10,500

11,000

11,500

0 6 12 18 24

Net Load (MW)

Hour

Base Case Net Load

Net Load with CSP no TES

Net Load with PV

Net Load with CSP with TES
29

be a reasonable approximation for more computationally complex methods (Madaeni et 

al. 2012).

Based on 2006 load and solar patterns, in the low RE case, where PV provides about 1% of total 

demand, each 100 MW (AC rating) of PV or CSP without storage provides about 70–75 MW of 

system capacity value before taking into account forced outages.

29 This is comparable to a 

previous estimate of PV in Colorado (Xcel 2009).

30 Adding TES to CSP increases the capacity 

value substantially.

Table 8 summarizes the capacity value estimates from this analysis. The first row in Table 8 is 

the capacity credit in terms of fraction of rated capacity. This value assumes an equal outage rate 

for maintenance across technologies. The second row translates this into an annualized value per 

installed kilowatt of the corresponding technology by multiplying the capacity credit by the low 

and high estimated annual value of a reference generator with 100% availability. The low value 

of the reference generator is $77/kW, based on the estimated annualized cost of a combustion 

turbine, while the high value is $147/kW, based on the annualized cost of combined cycle 

generator.

31

Row 3 of Table 8 translates this value per installed kilowatt into a value per unit of generation. 

This is calculated by multiplying the value per unit of capacity by the total capacity (to get the 

total annual value of the installed generator), then dividing this value by the total energy 

production. This introduces some unusual and somewhat counterintuitive outcomes, resulting 

largely from the impact of solar multiple and the use of TES, as demonstrated previously by 

Mills and Wiser (2012). A CSP plant with storage and a PV plant providing equal amounts of 

energy on an annual basis will have a different installed capacity. In the test system, 300 MW of 

CSP with a solar multiple of 2.0 and 6 hours of storage provides the same amount of energy as 

577 MW of PV capacity. In the low penetration case, CSP provides 294 MW of system capacity 

at a capacity credit of 98%, while PV at a 70% capacity value provides 404 MW. This means the 

 29 Because outages are not considered, the capacity values reported here correspond closer to an “equivalent 

conventional power” metric where PV and CSP have the same forced outage rate as a conventional generator. If the 

outage rates are not the same, this could increase or decrease the capacity value metric. For comparison, a forced 

outage rate in the range of 5%–10% is commonly used for combustion turbines (a typical proxy resource for 

capacity planning purposes). If the forced outage rate of a CSP plant is the same as a combustion turbine, this means 

there is no net impact on the relative capacity value for comparison purposes. Alternatively, the outage rate of a PV 

system is likely lower than that of a conventional thermal generator or CSP plant, therefore adding slightly to its 

capacity value relative to these technologies.

30 Our estimate included the turbine overdesign, meaning the peak output of 568 MW exceeds the nameplate rating 

of 530 MW. This means that during many hours, the capacity value of the plant exceeds 100%. The estimated 

capacity value based on the nameplate rating is about 70%.

31 There are significant ranges of the estimated cost of new capacity. The values in this report are derived from 

PSCO (2011) based on a monthly cost of $6.44 for a combustions turbine and $12.25 for a 1 x 1 combined cycle 

unit. The high value is less than that used in Mills and Wiser (2012) who used a value of $200/kW-year for the cost 

of a new combined cycle unit. The range is largely driven by the type of generator that would be effectively replaced 

by a new solar generator. CSP with TES has a higher overall capacity factor (closer to a mid-merit unit such as 

combined cycle) and provides relatively high levels of flexibility. CSP without storage or PV replaces units with a 

much lower capacity factor. A capacity expansion model evaluating different technologies would need to 

appropriately consider the change in chronological dispatch associated with each generator type for appropriate 

comparison, similar to the approach taken by Mills and Wiser (2012), who found solar primarily replaces combined 

cycle units in California. 
30 

aggregated PV plant has a higher overall capacity value than the CSP plant, and because both 

plants produce the same amount of energy, PV produces a higher value of capacity on a per unit 

of energy basis. This effect disappears in the high RE case where the capacity value of PV and 

CSP without storage is very low. This issue is illustrated conceptually in Figure 20, where the 

output of PV and CSP is shown for a single day (July 17). It shows that at the peak hour, a CSP 

plant without storage has a higher capacity value than the CSP plant with storage. It also shows 

that this benefit on this particular day is at the very edge of production and again demonstrates 

the dramatic drop in capacity value of PV and CSP without storage at fairly low penetration. 

Table 8. Capacity Value

Low RE Scenario High RE Scenario

Flat 

Block 

PV CSP (no 

TES)

CSP (6-

hr TES)

Flat Block PV CSP 

(no 

TES)

CSP (6-

hr TES)

Capacity 

Credit (%)

100 70 75 98 100 13 3 78

Capacity 

Value 

(Low/High) 

($/kW)

77/147 54/103 58/110 76/144 77/147 10/19 3/5 60/115

Capacity 

Value 

(Low/High)

($/MWh)

8.8/

16.8

29.7/

56.6

29.1/

55.3

21.2/

40.4 

8.8/

16.8

5.3/

10.1

1.3/

2.4

17.1/

32.6

Figure 20. Comparison of solar output on a high-demand day (July 17)

In the high RE scenario, CSP with storage is able to generate at nearly full output during 

remaining high demand periods in the summer. However, it experiences a reduction in overall

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0

100

200

300

400

500

600

700

0 6 12 18 24

Base Case Net Load

CSP Output (MW)

Hour

CSP with TES

CSP No Tes

PV

LOAD

Hour of peak 

demand
31

capacity value due primarily to limited energy availability during a few hours of relatively high 

demand in the winter. 

5.4 Total Value

The total value of the different generation sources is the sum of the operational value and 

capacity value. Figure 21 summarizes the values for the different cases by combining the 

operational value from Table 4 and the capacity value from Table 8. 

Figure 21. Total value of generation sources in the test system

5.5 Sensitivities and Comparison to Previous Work

5.5.1 Hours of Storage

Additional cases were run with CSP with 8 hours of storage and no changes to the solar multiple.

The value of CSP increased slightly, by about $0.9/MWh in the low RE case and about 

$1.0/MWh in the high RE case. This decrease in marginal value of additional storage for a 

constant solar multiple or lower has been observed previously (Sioshansi and Denholm 2010.)

However, additional analysis is needed to quantify the impact of various amounts of storage and 

different technology types, particularly under various RE penetration scenarios.

5.5.2 Natural Gas Price

The natural gas price used in the test system ($4.1/MMBTU) is much lower than historical 

prices. Given the uncertainty over fuel prices and the high level of sensitivity of the results to 

natural gas prices, we estimated the impact of varying the cost of natural gas. We ran scenarios 

with generation-weighted natural gas prices equal to $5.9/MMBTU and $7.8/MMBTU. We 

-10

0

10

20

30

40

50

60

70

80

90

100

Value ($/MWh)

Capacity 

(High Cost)

Capacity 

(Low Cost)

Start Costs

Variable 

O&M

Operational 

Fuel

Low RE Scenario High RE Scenario
32 

found that total fuel use is similar in the various cases and the value of avoided fuel is 

proportional to fuel price. (This relationship would not necessarily hold for lower gas prices, 

where at some point the dispatch stack of gas and coal generation would be inverted, and the 

variable cost of coal generation would put a floor on the value of avoided generation.)32

Additional analysis and discussion of this is provided by Diakov et al. (2012). Figure 22 provides 

an estimate of the change in the operational value of CSP as a function of natural gas prices for 

the two RE penetration cases, using a linear fit to the results from the three fuel price scenarios. 

Figure 22. Estimated operational value of CSP as a function of gas prices

5.5.3 Comparison to Previous Estimates

The analysis of CSP in the test system shows much lower operational value for CSP compared to 

two previous analyses. Table 9 lists the value of CSP with and without storage from two 

previous studies. 

Table 9. Previous Estimates of the Operational Value of CSP With and Without TES

Study Location CSP Value ($/MWh) Value Considered Sourcea

CSP (no TES) CSP (with 6-

hour TES)

Arizona 47.0 50.5 Energy Only 1

New Mexico 61.2 66.2 Energy Only 1

California 58.5 67.9 Energy Only 1

Texas 89.4 98.4 Energy (with scarcity) 1

California 53.8 (energy) 56.3 (energy) Energy + Capacity 

(Separate)

2

a

Sources: 1= Sioshansi and Denholm (2010); 2 = Mills and Wiser (2012) 

The differences between the test system value and previous analysis result from several factors. 

For appropriate comparison, the type of market or value must be considered. The first three 

 

32 This does not include any potential impacts of constraints on carbon or other emissions.

0

10

20

30

40

50

60

70

80

90

100

4 5 6 7 8 9 10

Operational Value ($/MWh)

Natural Gas Cost ($/MMBTU)

CSP (w/TES) 

Low RE

CSP (no TES) 

Low RE

CSP (w/TES) 

High RE

CSP (no TES) 

High RE
33

locations in Table 9 are used historic energy-only market prices. These were derived from two 

regulated markets (Arizona and New Mexico) and a wholesale energy-only market (the 

California Independent System Operator - CAISO).

33 In these studies, CSP was dispatched 

against hourly system marginal prices published by the utility or system operator. For the 

regulated markets, these system marginal prices are calculated in a manner similar to the 

dispatch model used in this study. For the CAISO market, the data is the system market clearing 

price calculated by the system operator. The most appropriate comparison in this study is the low 

RE operational value in Table 4 (equal to $35.5/MWh and $42.1/MWh for plants without and 

with TES, respectively). These values are lower than results from the previous studies, and the 

differences can be largely explained by both fuel mix and fuel price. The previous study results 

used electricity price data from 2005. In 2005, the average delivered price of natural gas to 

utilities in Colorado was $7.41/MMBTU compared to $4.1/MMBTU in the 2020 test system.

Adjusting the value of the test system to this higher-priced gas value using the estimates in 

Figure 23 produces an approximate CSP operational value of $63/MWh for CSP without storage 

and $76/MWh for CSP with storage. These values are closer to the previous estimates in Table 9, 

with the difference likely explained by the fuel mix. The largest difference is between the test 

system and Arizona, where coal was on the margin for more hours than in the Colorado system 

where gas is on the margin for almost all hours of CSP generation.

34

The value for Texas is derived from an energy-only market (the Electric Reliability Council of 

Texas – ERCOT). However, the CSP value results from the ERCOT case are still higher than can 

be explained by higher natural gas prices (even after adjusting for the average Texas natural gas 

price of $8.1/MMBTU in 2005). This difference appears to be driven by scarcity pricing in the 

ERCOT market. In locations such as ERCOT that do not have capacity markets, high energy 

prices may be needed to recover capacity costs. These high prices will occur during peak periods 

where capacity reserves are short. In the ERCOT case, for example, there are over 400 hours 

where the price of energy exceeded $150/MWh. This means that the values from the ERCOT 

system represent more than the pure variable cost of CSP benefits. A more appropriate 

comparison would be to combine the operational value of CSP in the test system (adjusted for 

the difference in natural gas prices) and the capacity value. However, it is not clear that the 

scarcity pricing that occurred in ERCOT in 2005 would be sufficient to support new capacity, 

and ERCOT has changed its rules since 2005 in part to address this issue.35 As a result, it is 

difficult to directly compare the Texas results with those from the test system.

The final value in Table 9 is from the Mills and Wiser (2012) analysis in California that used a 

reduced form dispatch model to estimate both operational and capacity value independently. The 

analysis used a natural gas price of $6.4/MMBTU. Applying this value to the test system 

produces an estimated operational value of $55/MWh and $66/MWh for CSP plants without and 

with TES, respectively, which are closer to the values in the California study. The California 

study also estimated capacity value—it found somewhat lower capacity credit for CSP plants 

(particularly with storage), perhaps due to the different load profiles between the two states.

 33 Incentives for new capacity in CAISO are available through the state’s resource adequacy program 

http://www.cpuc.ca.gov/PUC/energy/Procurement/RA/. 34 For example, in the Arizona load lambda data, coal appears to be on the margin for over 3,000 hours, indicated by 

a price under $20/MWh. 

35 As an example, the Public Utility Commission of Texas approved an increase in the system-wide offer cap to 

$4500/MWh in June 2012. http://www.puc.texas.gov/industry/projects/rules/37897/37897adt.pdf
34 

More analysis is needed to understand how the dispatch decisions will affect the operation and 

associated planning capacity applied to CSP plants with TES. 

5.6 Study Limitations and Future Work

This analysis did not perform a complete assessment of the value of CSP with TES. In addition 

to its limited geographical scope, there are several limitations to the analysis that will be 

addressed in future studies, including WWSIS II. A primary limitation is related to sub-hourly 

operation and ramp rates due to both variability and uncertainty. The simulations were performed 

at an hourly level and did not consider additional ramping that would result at higher time 

resolution due to solar variability. Furthermore, while flexibility reserves were held to account 

for solar forecast error, these reserves were not dispatched. This would further increase generator 

ramp requirements. Finally, the high ramp rates that are observed in these hourly simulations 

impose no additional cost penalty. WWSIS II will include 5-minute dispatch and the impact of 

ramping costs. These simulations should aid in identifying cost impacts on conventional 

generators and possible mitigation through the use of CSP with TES.

This study also did not perform a detailed assessment of the ability of CSP to provide ancillary 

services, including regulation, flexibility, or spinning contingency reserve. In conventional 

power systems, these services incur a cost due to two factors. The first is the additional O&M 

and other operational costs associated with additional cycling and other plant operations required 

to provide ancillary services. The second is the “opportunity cost” associated with holding plants 

at part load compared to operating at full output. We did not have sufficient data on the 

operational cost of CSP plants in providing ancillary services and how these costs compare to 

other generators to evaluate them in this study.36 We did perform simulations in an attempt to 

calculate any reduction in production cost when providing ancillary services from CSP, and 

found the reduction in total system cost to be extremely small. However, the modeled system has 

very low costs in general for provision of ancillary services due to the assumed flexibility of 

many generator types in the database.37 Additional analysis will be required to further isolate 

benefits of CSP providing ancillary services.38

Finally, this analysis considered a single CSP technology type: wet-cooled trough-type plants 

with a limited range of storage capacities. Further analysis is needed to evaluate the impact of 

multiple CSP technology types under a range of renewable penetration scenarios.

 

36 As a result of this lack of data, no plant-level costs were assigned to generators for provision of ancillary services. 

This assumption will slightly overestimate the value of non-dispatchable solar resources because they will create an 

additional cost in ancillary service procurement not captured in the model. T

37 The TEPPC database allows for most gas-fired generators to provide ancillary services over their entire operating 

range, and the pumped storage units provide operating reserves over virtually their entire capacity. This results in 

extremely low ancillary service prices and virtually eliminates the opportunity for CSP plants to provide ancillary 

services and reduce overall system production costs. 38 Previous analysis demonstrated significant revenue opportunities for CSP in reserve markets (Sioshansi and 

Denholm 2010). However, this previous analysis did not consider the cost of providing reserves from CSP, and as a 

“price-taker” simulation it did not perform a system-wide co-optimization of energy and ancillary services, 

potentially overstating the dispatch of CSP in providing ancillary services. Additional discussion of CSP providing 

ancillary services is provided by Usaola (2012).
35

6 Conclusions

Implementation of CSP with TES in commercial production simulation and planning tools is an 

important component of valuing this technology. This study evaluated the operation of CSP with 

TES in two scenarios of renewable penetration in a test system based on two balancing areas in 

Colorado and Wyoming. Overall, we found that the simulated CSP plants were dispatched to 

avoid the highest-cost generation, generally shifting energy production to the morning and 

evening in non-summer months and shifting energy towards the end of the day in summer 

months. This minimized the overall system production cost by reducing use of the least-efficient 

gas generators or preferentially displacing combined cycle generation over coal generation. The 

system also dispatches CSP during the periods of highest net load, resulting in a very high 

capacity value.

Overall, the addition of TES to CSP increases its value; however, the difference in value between 

plants with and without storage is highly dependent on both the cost of natural gas and the 

penetration of other renewable sources, such as PV. At low penetration of renewables, the 

inherent coincidence of solar and price patterns means that CSP without storage (and PV) has 

relatively high value. Combined with a relatively low gas price of $4.1/MMBTU used in this 

study results in an incremental operational value of TES of about $6.6/MWh over a plant without 

TES (at low RE penetration). At higher RE penetration, this difference increases as the value of 

mid-day generation is reduced—in the high RE test system this difference in operational value 

grew to $16.7/MWh. In addition, the capacity value of CSP systems with TES remains high, 

further increasing the difference in value associated with TES. 
36

References

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Modeling for the SunShot Vision Study. NREL/TP-6A20-53310. Golden, CO: National 

Renewable Energy Laboratory. 

Brinkman, G.; Lew, D.; Denholm, P. (2012). “Impacts of Renewable Generation on Fossil Fuel 

Unit Cycling: Costs and Emissions.” PR-6A20-55828. Golden, CO: National Renewable Energy 

Laboratory, 42 pp.

California Independent System Operator (CAISO). (31 August 2010). “Integration of Renewable 

Resources. Operational Requirements and Generation Fleet Capability at 20% RPS.” Folsom, 

CA: CAISO.

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