NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Modelling Concentrating Solar Power 

with Thermal Energy Storage for 

Integration Studies

Marissa Hummon

3rd International Solar Power 

Integration Workshop

October 20-22, 2013

London, UK

NREL/PR-6A20-60629
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Executive Summary

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. Corresponding paper:

Hummon, M., Jorgenson, J., Denholm, P., Mehos, M., “Modelling

Concentrating Solar Power with Thermal Energy Storage for Integration 

Studies”, 3rd International Solar Power Integration Workshop, London, UK, 

October 20-22, 2013. (NREL CP-6A20-60365).
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Motivation

SEGS Solar Power Plant Photo via Shutterstock

PV currently has lower installation costs.

CSP with thermal energy storage offers services to 

the grid that increase its value. Modeling CSP in grid 

operations helps us estimate the value of CSP.
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Outline

• Production Cost Modeling – An Integration 

Study Tool

• Concentrating Solar Power (CSP)

o Components: solar field, thermal energy storage (TES), 

and power block

o Operation of CSP-TES

• CSP-TES modelling framework

o Fixed dispatch

o Optimize storage and dispatch

o Allow CSP-TES to provide ancillary services

• Results
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Production Cost Modeling

Objective: Balance generation and load, every hour, at least cost
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Production Cost Modeling

Objective: Balance generation and load, every hour, at least cost

• PV and Wind Generation are variable and uncertain (similar to Load)

• Generation from Hydro is often constrained by other competing uses, for 

example recreational use of reservoirs or fish habitat
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Production Cost Modeling

Objective: Balance generation and load, every hour, at least cost

• Storage and CSP-TES are low marginal cost generation and are dispatched 

during peak prices
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Production Cost Modeling

Objective: Balance generation and load, every hour, at least cost

• Coal generation is the next least cost source of generation. Coal generation 

is committed for multiple days at a time.
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Production Cost Modeling

Objective: Balance generation and load, every hour, at least cost

• Natural-gas fired power plants have the least constraint on on/off decisions; 

higher marginal operating cost that can recover startup costs within 2-8 

hours 
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Wide variety of electricity generation systems

The costs and benefits of integrating a new technology will change between systems; 

being able to model new technologies reduces the barriers to integration.
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Concentrating Solar Power Plant
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Concentrating Solar Power

Solar Energy

Solar Field

Steam Turbine

Generator
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Concentrating Solar Power with Thermal 

Solar Energy Energy Storage

Solar Field

Storage Tank

Steam Turbine

Generator
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Another Optimization Problem:

Relative sizes of the CSP-TES components.
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Solar Energy (Electrical Equivalent)

Credit: Jeffrey R. S. Brownson
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Dispatch of CSP-TES

Credit: Jeffrey R. S. Brownson
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Dispatch of CSP-TES

Credit: Jeffrey R. S. Brownson
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Concentrating Solar Power

Direct Normal Irradiance

Solar Field

Storage Tank

Steam Turbine

Generator

System Advisor 

Model to 

convert DNI to 

electrical 

equivalent of 

solar field 

thermal output

Empirical CSP studies 

provide quantities for 

thermal losses: starting up 

the steam turbine, thermal 

decay in storage, heat 

exchanger
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SAM: Electrical equivalent for solar field
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Colorado Test System

System peak: 14 GW

Installed Capacity: ~ 18 GW

Annual simulation, hourly resolution, 48-hour optimization 

horizon, 24-hour rolling optimization
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Scenarios

CSP-TES

Max Cap: 300 MW

Solar multiple (SM) = 2.2

Storage = 6 hours

High Flex Low Flex

Operation Property High Flex

Minimum Generation Point 45 MW

Ramp Rate 30 MW/min

Minimum up/down time 1 hour

Number of starts per day Unconstrained

Start-up energy 60 MWh

Start-up cost $3,000 

Variable O&M $1.1/MWh

Operation Property Low Flex

Minimum Generation Point 75 MW

Ramp Rate 12 MW/min

Minimum up/down time 6 hours

Number of starts per day 1

Start-up energy 180 MWh

Start-up cost $30,000 

Variable O&M $3/MWh

Pre-scheduled Optimal Co-optimized

Solar Field energy is 

scheduled for 

storage/dispatch 

(outside of PLEXOS)

Solar Field energy is 

optimally scheduled 

by PLEXOS

Solar Field energy 

and power block 

capacity is cooptimized for energy 

and reserves
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Performance of CSP-TES

Pre-scheduled dispatch is not a terrible 

estimate

Optimal dispatch and co-optimized 

dispatch improve the use of CSP-TES 

from the system perspective: 

overnight operation and evening peak.
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CSP-TES Schedule Effects Displaced Generation
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Displaced Generation and Fuel

• CSP-TES displaces gas-fired generation (higher marginal cost 

than coal without emission penalties)

• Optimal CSP dispatch increases displacement of gas-fired CTs

• Co-optimized CSP-TES has a complex affect on system 

operation

CSP-TES with High Flexibility Operation

Base Case Pre-scheduled 

Dispatch Optimal Dispatch

Co-optimized 

Dispatch and Reserve 

Provision

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

Coal 46089 -65 / -0.1 -31 / -0.1 125 / 0.3

Combined Cycle (CC) 14791 -802 / -5.4 -760 / -5.1 -960 / -6.5

Gas Turbine/Gas Steam 1035 -146 / -14 -232 / -22.2 -225 / -21.6

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

Hydro 3792 0 / 0 0 / 0 0 / 0

Pumped Hydro Storage 1040 11 / 1.1 -2 / -0.2 -103 / -9.9

Wind 10705 0 / 0 0 / 0 0 / 0

PV 1834 0 / 0 0 / 0 0 / 0

CSP 0 1017 / - 1021 / - 1018 / -

Fuel Class [MMBTU] Increase from Base Case [MMBTU/ %]

Coal Offtake 487589 -772 / -0.2 -390 / -0.1 1310 / 0.3

Gas Offtake 126771 -7871 / -6.2 -8749 / -6.9 -10659 / -8.4
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Change in Production Costs

• Most of the production cost savings is displaced fuel. 

• Optimal dispatch of CSP-TES results in fewer starts. 

• Co-optimized CSP-TES avoids regulation bid costs by displacing 

slightly higher bid cost of combined cycle units, $6/MWh, with 

the CSP-TES bid cost of $4/MWh.

CSP-TES with High Flexibility Operation

Base Case Pre-scheduled 

Dispatch Optimal Dispatch

Co-optimized 

Dispatch and 

Reserve Provision

[M$] change from base case [M$ / %]

Fuel Cost 1210 -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 1426 -34 / -2.4 -41 / -2.9 -45 / -3.2
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Co-optimized CSP-TES provides Reserves

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

annual reserve requirement in the 

high (low) flexibility scenario, split 

equally between regulation and 

contingency reserves.

Regulation is energy neutral over 

25 minutes; Contingencies are 

estimated to be drawn once every 

2-3 days for 10-20 minutes.

CSP-TES is ramp rate constrained is 

responding to ancillary service 

requests.
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Co-optimized CSP-TES

Optimal CSP-TES Dispatch Co-optimized CSP-TES Dispatch

And Reserve Provision 
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Reserve Prices

CSP-TES reduces the marginal 

price of regulation and 

contingency reserves.
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Production Cost Savings

Production cost savings 

ranges from 2-3% of the 

total production cost. It is 

attributed to:

~75% due to energy 

from CSP-TES

~15% due to 

optimally dispatching 

the CSP-TES energy

~10% due to 

provisioning reserves 

from CSP-TES 

spinning capacity
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Conclusions

• Most of the value of CSP is in displacing highcost fuels; which is captured in fixed-dispatch 

modeling.

• Further 25% increase in system value when CSP 

is modeled with separate storage & generation 

components and co-optimized for energy and 

operating reserves.

• Co-optimization yields complicated results; the 

effect and value of CSP-TES on new systems can 

be be captured more accurately with more 

detailed modeling.
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Thank You

Questions?

Team: 

Marissa Hummon

Jennie Jorgenson

Paul Denholm

Mark Mehos

Contact: marissa.hummon@nrel.gov