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

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC.

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

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

Contract No. DE-AC36-08GO28308

Modelling Concentrating Solar 

Power with Thermal Energy 

Storage for Integration Studies

Preprint

M. Hummon, P. Denholm, J. Jorgenson, 

and M. Mehos

To be presented at the 3rd International Workshop on 

Integration of Solar Power in Power Systems

London, England

October 21—22, 2013

Conference Paper

NREL/CP-6A20-60365

October 2013
NOTICE

The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 

(Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US 

Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of 

this contribution, or allow others to do so, for US Government purposes.

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

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

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

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

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

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

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

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

This report is available at no cost from the National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

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

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

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

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

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

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

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

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

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
1

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Modelling Concentrating Solar Power with Thermal 

Energy Storage for Integration Studies

Marissa Hummon, Paul Denholm, Jennie Jorgenson, and Mark Mehos

National Renewable Energy Laboratory

Golden, CO, USA

Author contact: marissa.hummon@nrel.gov

Abstract—Concentrating solar power with thermal energy 

storage (CSP-TES) can provide multiple benefits to the grid,

including low marginal cost energy and the ability to levelize 

load, provide operating reserves, and provide firm capacity. It is 

challenging to properly value the integration of CSP because of 

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. To 

optimize the use of this limited energy, CSP-TES must be 

implemented in a production cost model with multiple decision 

variables for the operation of the CSP-TES plant. We develop 

and implement a CSP-TES plant in a production cost model that 

accurately characterizes the three main components of the plant: 

solar field, storage tank, and power block. We show the effect of 

various modelling simplifications on the value of CSP, including: 

scheduled versus optimized dispatch from the storage tank and 

energy-only operation versus co-optimization with ancillary 

services.

Keywords–concentrating solar power; thermal energy storage;

production cost model; optimization

I. INTRODUCTION

Concentrating solar power with thermal energy storage 

(CSP-TES) can provide multiple benefits to the electric grid,

including low marginal cost energy and the ability to levelize 

load, provide ancillary services, and provide firm capacity

[1]. Variable generation integration studies use a variety of 

traditional utility planning tools to evaluate the costs, 

benefits, and operation strategies of high penetration solar and 

wind energy [2]. Production cost models (PCMs), which 

simulate the operation of grid, are often used to estimate the 

operational value of different generation mixes. PCMs are 

also used to evaluate aspects of system reliability and 

operation and estimate fuel costs and emissions. PCMs have 

the primary objective function of committing and dispatching 

the generator fleet to minimize the total cost of energy 

production, while maintaining adequate operating reserves to 

meet contingency events and regulation requirements. 

CSP-TES has historically had limited analysis in 

commercial PCMs. CSP-TES is an energy-limited,

dispatchable source of renewable electricity generation. This 

makes it challenging to quantify the value of CSP-TES and 

provide comparisons to alternative generation sources. Some 

simplifications include optimizing CSP-TES outside of a 

production cost model using a price-taker model [3] or 

reducing the complexity (number of decision variables) of the 

commitment and dispatch [4]. Several studies have examined 

CSP in greater detail using PLEXOS [5,6]. This paper 

demonstrates the operational and production cost differences 

between pre-scheduled, optimized, and co-optimized dispatch 

of CSP-TES. We discuss the implementation of a 

concentrating solar power plant in a PCM in Section II and 

the performance and relative value of CSP-TES under 

different modelling conditions in Section III.

II. CONCENTRATING SOLAR POWER MODEL

A. Concentrating Solar Power With Thermal Energy Storage

A CSP plant with TES consists of three independent, but 

interrelated, components that can be sized differently: the 

solar field, which produces thermal energy from solar 

radiation; the thermal storage tank; and the power block,

which converts thermal energy into electricity. These 

components are shown in Fig. 1. The plant modelled in this 

simulation is a parabolic trough system, which collects the 

sun's energy using curved mirrors that focus sunlight on 

receiver tubes that run the length of the solar field. The 

reflected sunlight heats a fluid flowing through the receiver 

tubes. This heat transfer fluid is passed through a steam 

generator, producing steam for use in a conventional steamturbine generator.

Figure 1. Components of a concentrating solar power with thermal energy 

storage power plant.
2

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 2. Implementation of CSP-TES in PLEXOS: convert direct normal 

irradiance to electrical equivalent energy from solar field, specify operational 

characteristics, and optimize CSP storage and generation in PLEXOS.

Fig. 2 shows how the solar irradiance is converted into 

electrical energy and implemented in PLEXOS. First, an 

hourly flow of solar-generated electric energy is produced 

using the System Advisor Model (SAM) version 2013-1-15 

[7]. This occurs outside the production cost model. The CSP 

simulations used the dry-cooled physical trough model with 

hourly inputs for direct normal irradiance, dry-bulb 

temperature, wet-bulb temperature, pressure, relative 

humidity, and dew point [8]. The model converts hourly 

irradiance and meteorological data into thermal energy and 

then models the flow of thermal energy through the various 

system components, finally converting the thermal energy 

into net electrical generation output. Irradiance and 

psychometric data were derived from the National Solar 

Radiation Database (NSRDB) [9,10].

The “electrical equivalent” thermal energy generated by 

SAM is an input to PLEXOS as the limited energy available 

for either generation or storage. The modelled dispatch of 

CSP energy in PLEXOS is based on the hydro generation 

module, modified to incorporate the thermal losses and 

startup costs specific to CSP. In each hour, PLEXOS can 

directly generate electricity from the solar field energy, store 

solar field energy, draw energy from storage, or a 

combination. The ability to store energy is limited by the 

capacity of the storage tank, measured here in terms of hours 

of rated plant output that can be stored. The simulations in 

this paper assume 6 hours of storage. In addition to the hours 

of storage, a key parameter in the CSP simulation is the solar 

multiple (SM), which is a measure of the relative size of the 

solar field and power block and is an important factor in 

determining a plant’s capacity factor and effective use of 

solar radiation. 1 The SM in PLEXOS was established by 

 1 The SM normalizes the size of the solar field in terms of the 

power-block size. A solar field with an SM of 1.0 is sized to provide 

sufficient energy to operate the power block at its rated capacity 

scaling the power block to some fraction of the maximum 

output of the solar field. This simulations in this paper use an 

SM of 2.2.

B. CSP-TES Operation Parameters: Losses, Flexibility, 

Operating Reserve Provision

The modelled power block includes the essential 

parameters of the CSP power block, including start-up 

energy, minimum generation level, limited ramp rate, 

minimum up/down times, and maximum starts per day.

Jorgenson et al. (2013) thoroughly explores the thermal losses 

and operating properties of CSP-TES that are summarized 

here [11]. TABLE I contains the power block properties for 

two operating paradigms: low and high operational flexibility. 

Operational flexibility is not well established, and thus a 

range enables an analysis of its impact. Analysis of gas-fired 

steam generators in the Western Electricity Coordinating 

Council (WECC) Transmission Expansion Policy Planning 

Committee (TEPPC) database guided the range of the 

property values (see [11] and [12]).

TABLE I. CSP-TES PROPERTIES WITH HIGH AND LOW 

OPERATION FLEXIBILITY FOR THE 300 MW (MAXIMUM 

CAPACITY OF THE POWER BLOCK) SIMULATED

Operation Property Low Flex High Flex

Minimum generation 

Point 75 MW 45 MW

Ramp rate 12 MW/min 30 MW/min

Minimum up/down time 6 hours 1 hour

Number of starts per day 1 Unconstrained

Start-up energy 180 MWh 60 MWh

Start-up cost $30,000 $3,000

Variable O&M $3/MWh $1.1/MWh

The model considers start-up losses in the dispatch 

decision by assuming that a certain amount of energy is lost 

in the start-up process. Start-up losses are calculated relative 

to the amount of energy the power block could produce in one 

hour. For instance, if the power block requires 20% start-up 

energy, that is equal to 20 MWh per 100 MW of plant 

capacity [3]. 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.

C. Power System and CSP-TES Scenarios

To evaluate the impact of different modelling approaches 

to CSP-TES on an electric power system, we developed a test 

case composed of two balancing areas largely in the State of 

Colorado, USA. The test system is described extensively in 

[5,13,14]. The Colorado test system consists of two balancing 

 

under reference conditions (in this case, 950 W/m2 of direct solar 

irradiance at solar noon on the summer solstice). The collector area 

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

solar field with a multiple of one (i.e., a field with an SM of 2.0 will 

cover roughly twice the collector area of a field with an SM of 1.0).
3

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

areas (Public Service of Colorado (PSCO) and Western Area 

Colorado Missouri [WACM]) using data derived from the 

TEPPC model and other publicly available datasets. 

Transmission is modelled zonally, without transmission limits 

within each balancing authority area. Projected generation 

and loads were derived from the TEPPC 2020 scenario [12]. 

Hourly load, wind, and solar profiles were based on 2006 data 

and scaled to match the projected TEPPC 2020 annual load. 

The system peaks in the summer with a 2020 coincident peak 

demand of 13.7 GW and annual demand of 79.0 TWh. A total 

of 201 thermal and hydro generators are included in the test 

system, with total capacities listed in Table 2. We adjusted 

the conventional generator mix to ensure the available 

capacity (after outages) was always at least 9% greater than 

demand by adding a total of 1,450 MW (690 MW of 

combustion turbines and 760 MW of combined cycle (CC) 

units). This adjustment was necessary, in part because the 

simulated system does not include contracted capacity from 

surrounding regions, nor any capacity contribution from solar 

and wind resources. The base case of the test system assumes 

a wind and PV penetration of 16% on an energy basis. For 

comparison, Colorado received about 11% of its electricity 

from wind in 2012 [15]. Discrete wind and solar plants were 

added from the WWSIS datasets until the installed capacity 

produced the targeted energy penetration.2

TABLE II.TEST SYSTEM GENERATOR CAPACITY IN 2020

Generator Class System Capacity 

[MW]

 Coal 6,178

 Combined cycle (CC) 3,724

 Gas turbine/gas steam 4,045

 Hydro 773

 Pumped storage 560

 Wind 3,347 (10.7 TWh)

 Solar PV 878 (1.8 TWh)

 Solar CSPa 300

 Otherb 513

Total 15,793

a

CSP with thermal energy storage (6 hours) is not present in the base case. b Includes oil- and gas-fired internal combustion generators and demand 

response.

Fuel prices were derived from the TEPPC 2020 database. 

Coal prices were $1.42/MMBtu for all plants. Natural gas 

prices varied by month and range from $3.90/MMBtu to 

$4.20/MMBtu, with an average of $4.10/MMBtu. No 

constraints or costs were applied to carbon or other emissions.

We generated hourly requirements for contingency, 

regulation, and flexibility reserves. Contingency reserves are 

based on the single largest unit (a 810-MW coal plant), and 

allocated with 451 MW to PSCO and 359 MW to WACM, 

with 50% met by spinning units. Regulation and flexibility 

 2 The sites were chosen based on capacity factor and do not 

necessarily reflect existing or planned locations for wind and solar 

plants.

reserve3 requirements vary over time based on the statistical 

variability of load, wind, and PV, with the methodology 

described in detail by Ibanez et al. [16]. The technical report 

by Hummon et al. describes the application of the 

methodology to the test system [14].

An additional cost was assigned to plants providing 

regulation, associated with additional wear and tear and heat 

rate degradation associated with non-steady-state operation. 

This is functionally equivalent to a generator regulation “bid 

cost” in restructured markets, discussed in PJM Manual 15: 

Cost Development Guidelines [17]. The assumed regulation 

costs, by unit type, are provided in TABLE III.

TABLE III. REGULATION BID COSTS BY GENERATOR 

TYPE FOR THE COLORADO TEST SYSTEM.

Generator Type Regulation Bid Cost 

($/MW-h)

Supercritical Coal 15

Subcritical Coal 10

Combined Cycle (CC) 6

Gas/Oil Steam 4

CSP 4

Hydro 2

Pumped Storage 2

There is no CSP in the base case of the Colorado test 

system. Six CSP-TES scenarios are compared to the base 

case. There are three CSP plant optimization scenarios (prescheduled dispatch, system-optimized dispatch, and cooptimized dispatch and operating reserve provision) and two 

sets of operating properties (high and low flexibility, see 

TABLE I), for six total scenarios. In each scenario, the 

maximum capacity of the CSP power block is 300 MW, the 

solar field is sized to have an SM of 2.2, and the storage 

block is 6 hours (1.8 GWh). They differ slightly in total 

energy delivered to the grid due to losses and operation 

strategy of the CSP-TES plant, 0.4% and 5.0% amongst the 

high and low flexible operation groups, respectively.

The pre-scheduled dispatch profiles are generated outside 

of PLEXOS using a simple dispatch routine written in 

Matlab. The pre-scheduled dispatch honors the high and low 

flexibility properties, including minimum generation point, 

start-up energy, minimum up/down times, and maximum 

number of starts per day. It is not pre-scheduled against load 

but assumes that the CSP-TES operates at full output until 

there is insufficient energy from the solar field or storage 

tank. This may decrease the pre-scheduled value during 

winter months when the Colorado test system has a double 

demand peak in the morning and evening.

 3 For these services, only the “upward” reserve requirements were 

evaluated. The need for downward reserves becomes of greater 

importance at high renewable penetration when conventional 

thermal generators are operated at or near their minimum generation 

points for more hours of the year. Future work will evaluate the cost 

and price of separate up and down reserve products in these 

scenarios.
4

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

System-optimized dispatch scenarios allow PLEXOS to 

optimize the storage and release of the “electrical equivalent” 

thermal energy for dispatch (see Section IIA and IIB). The 

third optimization scenario uses the same system-optimized 

CSP-TES model and includes co-optimization of the CSPTES capacity for energy and reserves. Reserve provision 

requires that the CSP-TES power block be operating at or 

above the minimum generation point. The total reserves 

provisioned and energy dispatched from CSP-TES is limited 

by the total capacity of the plant as well as the mutual 

exclusivity of the ramp rate (e.g., if the generator is ramping 

at full ramp rate for energy, then there is no ramp available 

for reserve provision).

The PLEXOS simulations performed in this analysis 

used day-ahead scheduling with a 48-hour optimization 

window, rolling forward in 24-hour increments. The extra 24 

hours in the unit commitment horizon (for a full 48-hour 

window) were necessary to properly commit the generators 

with high start-up costs and the dispatch of energy storage, 

including CSP-TES. All scenarios were run for one

chronological year using PLEXOS version 6.207 R08, using 

the Xpress-MP 23.01.05 solver, with the model performance 

relative gap set to 0.5%.

III. RESULTS

A. Performance of CSP-TES

The simulation of the three CSP optimization scenarios 

results in differing daily dispatch of CSP-TES for a common 

set of flexible operation properties. Fig. 3a and Fig. 3b

demonstrate the hourly CSP-TES energy from the solar field 

(high flex operation) and three optimization scenarios for 

three days in May and October, respectively. The solar field 

energy is greater than the maximum capacity of the CSP-TES 

power block (300 MW) during peak solar hours. The “extra” 

energy from the solar field is stored, to be dispatched later.

The pre-scheduled dispatch keeps the power block at full 

capacity until the thermal storage is depleted. Optimally 

dispatched CSP differs from the pre-scheduled dispatch. For 

example, on October 20 and 21 during the midday, generation 

from the CSP power block is reduced and excess solar field 

energy stored; then, in early evening, the power block 

resumes full output at 300 MW.

Fig. 3c shows the annual daily average performance of 

CSP-TES. The simulation of optimally dispatched CSP-TES 

peaks between 6:00 pm and 9:00 pm, while the pre-scheduled 

dispatch declines steadily during those hours. Overnight, cooptimized CSP-TES operates near minimum generation 

instead of shutting down in order to provide reserves. Fig. 3c 

also demonstrates that the optimally scheduled CSP 

generation closely follows the marginal price of energy.

B. Production Cost Savings

To understand the impact of CSP-TES optimization and 

flexible operation, we compare the total production cost of 

each scenario with the base case. The total production cost 

savings from adding co-optimized CSP-TES ranges from 2%

to 3% of the base case total production cost ($1,210 million), 

depending on the flexibility of the operation of the CSP 

power block. Fig. 4 shows the distribution of production cost 

savings by the services CSP-TES provides to the grid, for the 

low and high flexibility operation properties. Low marginal 

cost energy accounts for ~75% of the total production cost 

savings of the co-optimized CSP-TES unit. Enabling the 

model to optimize the dispatch of the solar field energy 

increases the total production cost savings by 20% and 

accounts for ~15% of the total production cost savings of the 

co-optimized CSP-TES unit. This is calculated by finding the 

incremental total production savings from the pre-scheduled 

scenario to the optimal dispatch scenario. Reserve provision 

from CSP accounts for ~10% of the total production cost 

savings for the co-optimized CSP-TES unit. Again, this is 

calculated by finding the incremental increase in production 

savings between the optimal and co-optimized CSP-TES 

dispatch scenarios.

Figure 3. CSP-TES energy for three days in (a) May, (b) October, and (c) 

annual daily average. Panel (c) also displays the annual daily average 

marginal price of energy (right axis). Solar field energy is the electrical 

equivalent of the solar energy collected by the solar field. The dispatch 

profiles are the generation out of the CSP power block with high flex 

operation properties.
5

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 4. Total production cost savings calculated by comparing the total 

production cost of each incremental change in CSP-TES modelling: fixed 

dispatch of CSP-TES, dispatchable CSP-TES, and dispatchable CSP-TES

with operating reserve provision.

TABLE IV shows the components of total production 

cost: fuel, variable operation and maintenance (VO&M), start 

and shutdown, and regulation bid. Most of the production 

cost savings from adding CSP-TES energy to the system is 

from displaced fuel costs. The addition of optimally

dispatched CSP-TES results in fewer starts and an increase in 

the fuel cost savings over the pre-scheduled CSP. Cooptimized CSP-TES avoids additional fuel costs as well but 

also avoids regulation bid costs by displacing the slightly 

higher bid cost of CC units, $6/MW-h, with the CSP-TES bid 

cost of $4/MW-h.

TABLE V shows the annual generation for the base case 

and the change in annual generation for the high flexibility 

optimization scenarios. The addition of low marginal cost 

generation preferentially displaces high marginal cost 

generation: gas-fired turbines (CT) and CC units. Optimal 

CSP-TES dispatch increases the displacement of generation 

from combustion turbines. The displaced fuel in the prescheduled scenario, measured in thousands of MMBTU, is 

90% natural gas and 10% coal and increases to 95% natural 

gas in the optimal dispatch scenario. 

Fig. 5 shows the effect of pre-scheduled and optimal 

dispatch of CSP generation for three seasons: summer, fall, 

and winter. The change in seasonal average daily generation 

for CSP, coal, CC, and CT generators is calculated by 

subtracting the scenario generation profiles from the base 

case. Positive change in generation means that the 

optimization scenario has higher generation than the base 

case. Average daily summer pre-scheduled (Fig. 5a) and 

optimized (Fig. 5b) dispatch of CSP-TES closely resemble 

one another, and thus the operation of all generators is similar 

between the two scenarios. Summer CSP generation displaces 

high-cost gas-fired CCs and CTs during peak demand hours. 

Optimal dispatch in the fall and winter peaks in the early 

evening, displacing CTs, while pre-scheduled dispatch 

primarily displaces CCs during the daytime. Winter dispatch 

(Fig. 5e-f), under either optimization, shows a distinct 

increase in coal generation during all hours, while generation 

from CCs decreases during all hours. CSP generation 

displaces some CCs during the daytime, thus reducing the 

magnitude of CCs necessary to run at minimum generation 

overnight. The dispatch from the co-optimized scenario is 

nearly the same as the optimized scenario. In other words, the 

provision of reserves does not significantly change the 

dispatch of CSP-TES.

Co-optimization of CSP-TES increases coal generation 

relative to the optimal dispatch scenario, while generation 

from CCs and CTs decreases (see Table 5). To investigate the 

co-optimization of CSP-TES, we calculate the change in 

annual generation and reserves provision, by generation type, 

from the optimized dispatch scenario to the co-optimized 

scenario (see Fig. 6). The regulation reserves provision from 

CSP-TES displaces higher cost reserve provision, primarily 

CC generators. CC units are sometimes started only to 

provide regulation reserves [14]. When this occurs, other 

online generators back down in order for the new CC unit to 

operate at minimum generation. Thus, displacing CC 

regulation reserves may also displace energy from the 

minimum operating level of the CC units. Indeed, 34% of the 

displaced CC regulation provision corresponds to hours when 

the number of CC units online decreases.4 Other generators 

must make up the displaced CC energy. We calculate the ratio 

of the increase in generation from each type of unit during 

two sets of hours: first, when CC units reduced both their 

reserve provision and energy generation; and second, all other 

hours. We find that the ratio for increased coal generation is 

14.6, compared to -2, 0, and -1.6 for CT, PHS, and CPS, 

respectively. This suggests that coal generators are increasing 

energy production when some CC units are no longer 

providing regulation reserves and generation.

TABLE IV. BREAKDOWN OF PRODUCTION COST SAVINGS WITH 

THE ADDITION OF CSP-TES (HIGH FLEXIBILITY OPERATION)

Base 

Case

PreSchedule

d CSPTES 

Dispatch

Optimal 

CSP-TES 

Dispatch

Co-Optimized 

CSP-TES 

Energy and 

Reserve 

Provision

Increase from Base Case

[M$] [M$ / %]

Fuel cost 1,210 -34 / -2.8 -37 / -3.1 -43 / -3.5

VO&M 

cost 152 0 / 0 -1 / -0.7 -1 / -0.6

Start & 

shutdown 

cost

59 0 / 0.3 -2 / -4.2 -1 / -1.3

Regulation 

bid cost 5 0 / -0.1 0 / 1.2 -1 / -15.4

Total 

generation 

cost

1,426 -34 / -2.4 -41 / -2.9 -45 / -3.2

 4 The number of CC units committed (online) decreased during 

2,371 hours between the optimal and co-optimized scenarios. During 

those hours, the regulation provision from CC decreased 105 GW-h. 

The total regulation provision from CC decreased 305 GW-h.
6

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

TABLE V. CHANGE IN ANNUAL GENERATION AND FUEL 

OFFTAKE FROM BASE CASE WITH THE ADDITION OF CSP-TES 

(HIGH FLEXIBILITY OPERATION)

Generator 

Class

Base 

Case

PreScheduled 

Dispatch

Optimal 

Dispatch

Co-Optimized 

Dispatch and 

Reserve 

Provision

[GWh] Increase from Base Case [GWh / %]

Coal 46,089 -65 / -0.1 -31 / -0.1 125 / 0.3

Combined 

cycle (CC) 14,791 -802 / -5.4 -760 / -5.1 -960 / -6.5

Gas turbine/ 

gas steam 1,035 -146 / -14 -232 / -22.2 -225 / -21.6

Other 95 -1 / -0.9 -1 / -0.9 -6 / -6.2

Hydro 3,792 0 / 0 0 / 0 0 / 0

PHS 1,040 11 / 1.1 -2 / -0.2 -103 / -9.9

Wind 10,705 0 / 0 0 / 0 0 / 0

PV 1,834 0 / 0 0 / 0 0 / 0

CSP 0 1,017 / - 1,021 / - 1,018 / -

Fuel Class

[1,000 MMBTU]

Increase from Base Case 

[1,000 MMBTU / %]

Coal offtake 487,589 -772 / -0.2 -390 / -0.1 1,310 / 0.3

Gas offtake 126,771 -7,871 / 

-6.2 -8,749 / -6.9 -10,659 / -8.4

Figure 5. Change in seasonal (first column: summer, second column: fall, third column: winter) daily average generation for pre-scheduled (top row) and 

optimal dispatch (bottom row) scenarios, compared to the base case.

CSP-TES contingency reserve provision displaces coal, 

CT, and pumped hydro storage (PHS) contingency reserves, 

roughly equally. Coal units are primarily dispatched at full 

output both to reduce the cost of energy and to increase coal 

plant efficiency. Contingency reserves from coal often come

at the opportunity cost of providing energy, the more valuable 

service, to the system. Displacing coal capacity for

contingency requirements with CSP-TES increases the 

availability of coal capacity to provide energy. CTs primarily 

provide peak energy, and thus are rarely committed in order 

to provide contingency reserves. CTs provide contingency 

reserves from partly loaded units, at no opportunity cost, and 

therefore displacing the CT contingency reserves does not 

change the total generation from CTs. 5 CSP-TES displaces 

 5 We confirm this by calculating the average increase in generation 

from CTs under two conditions: first, hours when contingency 

reserves from CTs are reduced (4,242 hours); and second, hours 

when contingency reserves from CTs are increased (1,057 hours). 

CTs increase generation on average 24 MWh during the later hours, 

while generation decreases an average of 4.5 MWh during the 

former hours.

both energy and contingency reserve provision from PHS. 

The PHS schedule is influenced by energy arbitrage 

opportunities, operation limits, and operating reserve 

provision. Further discussion on the provision of reserves 

and energy from CTs and PHS see [13,14].

Figure 6. Increase in generation and reserve provision between the optimal 

CSP-TES dispatch scenario and the co-optimization scenario under high 

flexibility operation properties.
7

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

CSP-TES provides 17% (10%) of the annual reserve 

requirement in the high (low) flexibility scenario, split 

roughly evenly between regulation and contingency reserves,

with virtually no change to CSP-TES energy dispatch. Fig. 7

shows the depression of marginal reserve prices for 

contingency and regulation reserves between the optimal CSP 

dispatch scenario and co-optimized CSP scenario. The flat 

regulation price regions (hours 5,500- 8,000) are periods 

when the regulation price is set by the bid price of the 

marginal unit ($6/MW-h for CC and $4/MW-h for CSP).

During more than one-third hour in the co-optimized 

scenario, CSP sets the marginal price for regulation, primarily 

displacing regulation reserve from CCs.

Figure 7. Marginal price duration curves for contingency reserves and 

regulation reserves.

CONCLUSIONS

CSP-TES can provide multiple benefits to the electric 

grid, including low marginal cost energy and the ability to 

levelize load and provide operating reserves. Implementation 

of CSP-TES in commercial production simulation and 

planning tools is an important component of valuing this 

technology. This study evaluated the operation of CSP-TES 

in three optimization scenarios, with low and high operation 

flexibility, in a test system based on two balancing areas in 

Colorado and Wyoming. The highest value scenario, cooptimization of CSP-TES for energy and reserves with high 

operation flexibility, reduced total production cost by 3.2%. 

The high flexibility properties enabled 50% more value than

the low flexibility operation.

Pre-scheduled CSP-TES dispatch captures about 75% of 

the total production cost savings, by offering low marginal 

cost energy to the system during times that are well correlated 

to peak demand hours. Overall, we found that the optimally 

dispatched CSP plants avoided 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 leastefficient gas generators or preferentially displacing combined 

cycle generation over coal generation. Optimally dispatched 

CSP-TES captures about 15% of the total production cost 

savings. Co-optimized CSP-TES further reduced total 

production cost by reducing both regulation bid costs and fuel 

costs and accounts for about 10% of the total cost savings.

This analysis did not perform a complete assessment of 

the value of CSP with TES. A primary limitation is related to 

sub-hourly operation and dispatch of reserves in real time 

operation. Future CSP-TES production cost modelling work 

will assess methods of optimal real time dispatch of CSP 

generation and deployment of flexibility reserves held in the 

day-ahead model.

ACKNOWLEDGMENT

This work was supported by the U. S. Department of 

Energy under Contract No. DE-AC36-08-GO28308 with the 

National Renewable Energy Laboratory. The authors 

gratefully acknowledge the support of Kevin Lynn, Venkat 

Banunarayanan, and the U.S. Department of Energy Solar 

Energy Technologies Office SunShot Intitative for funding 

this work.

REFERENCES

[1] Denholm and M. Mehos, Enabling Greater Penetration of 

Solar Power via the Use of CSP with Thermal Energy 

Storage. NREL/TP-6A20-52978. Golden, CO: National 

Renewable Energy Laboratory, 2011.

[2] D. Lew, G. Brinkman, E. Ibanez, A. Florita, M. Heaney, 

B.-M. Hodge, M. Hummon, G. Stark, J. King, S. Lefton, 

N. Kumar, D. Agan, G. Jordan, and S. Venkataraman, . 

The Western Wind and Solar Integration Study Phase 2. 

NREL/TP-5500-55588. Golden, CO: National 

Renewable Energy Laboratory, 2013.

[3] R. Sioshansi and P. Denholm, The Value of Concentrating 

Solar Power and Thermal Energy Storage. NREL-TP6A2-45833. Golden, CO: National Renewable Energy 

Laboratory, 2010.

[4] A. Mills and R. Wiser, Changes in the Economic Value of 

Variable Generation at High Penetration Levels: A Pilot 

Case Study of California. LBNL-5445E. Berkeley, CA: 

Lawrence Berkeley National Laboratory, June 2012.

[5] P. Denholm and M. Hummon, Simulating the Value of 

Concentrating Solar Power with Thermal Energy Storage 

in a Production Cost Model. Golden, CO: National 

Renewable Energy Laboratory, 2012.

[6] P. Denholm, Y.-H. Wan, M. Hummon and M. Mehos, An 

Analysis of Concentrating Solar Power with Thermal 

Energy Storage in a California 33% Renewable Scenario. 

NREL/TP-6A20-58186. Golden, CO: National 

Renewable Energy Laboratory, March 2013.

[7] P. Gilman and A. Dobos, System Advisor Model, SAM 

2011.12.2: General Description. TP-6A20-53437. 
8

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Golden, CO: National Renewable Energy Laboratory, 

2012.

[8] M. J. Wagner and P. Gilman, Technical Manual for the 

SAM Physical Trough Model. TP-5500-51825. Golden, 

CO: National Renewable Energy Laboratory, 2011.

[9] NREL, National Solar Ration Database 1991-2005 

Update: User's Manual. TP-581-41364. Golden, CO: 

National Renewable Energy Laboratory, 2011.

[10] NSRDB, National Solar Radiation Database. Retrieved 

from http://rredc.nrel.gov/solar/old_data/nsrdb/.

[11] J. Jorgenson, P. Denholm and M. Mehos, Estimating the 

Performance and Economic Value of Multiple CSP 

Technologies in a Production Cost Model. NREL/TP6A20-58645. Golden, CO: National Renewable Energy 

Laboratory, unpublished.

[12] TEPPC, TEPPC 2010 Study Program 10-Year Regional 

Transmission Plan. Salt Lake City, UT: Western 

Electricity Coordinating Council Transmission 

Expansion Planning Policy Committee, September 2011.

[13] P. Denholm, J. Jorgenson, M. Hummon, T. Jenkin, D.

Palchak, B. Kirby, O. Ma and M. O'Malley, Value of 

Energy Storage for Grid Applications. TP-6A20-58465. 

Golden, CO: National Renewable Energy Laboratory, 

2013.

[14] M. Hummon, P. Denholm, J. Jorgenson, B. Kirby, O. Ma 

and D. Palchak, Fundamental Drivers of Operating 

Reserve Cost in Electric Power Systems. TP-6A20-

58491. Golden, CO: National Renewable Energy 

Laboratory, 2013.

[15] EIA Electric Power Monthly with Data. Washington, 

DC: Energy Information Administration, June 2013.

[16] E. Ibanez, G. Brinkman, M. Hummon, and D. Lew, "A 

solar reserve methodology for renewable energy 

integration studies based on sub-hourly variability 

analysis. 2nd Annual International Workshop on 

Integration of Solar Power into Power Systems 

Conference, 12-13 November 2012, Lisbon, Portugal, 

2012.

[17] PJM, PJM Manual 15: Cost Development Guidelines 

Rev. 20. Technical report . PJM, Norristown, PA.