Impact of Dynamic Storage 

Capacity Valuation in 

Capacity Expansion Models

Bethany Frew

June 19-21, 2018

2018 International Energy Workshop, Gothenburg, Sweden
NREL | 2

Acknowledgments

• Full NREL team: Wesley Cole, Nina Vincent, Andrew Reimers, 

and Robert Margolis

• Valuable input and data from Paul Denholm

• Funding provided by U.S. Department of Energy Solar Energy 

Technologies Office
NREL | 3

Who are we and what do we do?

Regional Energy Deployment System (ReEDS) is a 

capacity expansion model (CEM) that optimizes the 

buildout of the future U.S. power system

https://www.nrel.gov/analysis/reeds/
NREL | 4

What have we been studying?

• U.S. Department of Energy “SunShot 2030” 

PV (3¢/kWh) and low-cost battery storage 

scenarios

(Cole et al. 2017b)

At high storage penetration levels, storage capacity valuation becomes important...
NREL | 5

What is capacity value (CV)?

• Fraction of the installed capacity that reliably contributes to meeting load 

during times of highest system risk

– Ideally calculated with probabilistic approach, but CEMs typically use 

approximation methods

– Implemented in planning reserve margin constraints in CEMs

– Widely applied to wind and solar, but less understood for storage (active 

area of research in CEMs, see Cole et al. 2017c)

• Storage CV=1 means device can discharge at rated capacity for full duration 

of “high risk” periods

– Longer duration storage tends to have larger CVs because they are more 

likely to provide full discharge for full duration of high risk periods
NREL | 6

CEM approaches to storage CV

• Static CV

– Regardless of amount of storage 

deployed

– May vary by duration (Sioshansi et al. 2014)

– May vary by technology

• Dynamic CV

– Simple dispatch simulation between 

sequential optimization periods (Hale 

et al. 2016)

– Based on pre-determined functional 

fit (current ReEDS approach for 

batteries)

(Denholm and Margolis 2018)

Sioshansi et al. (204) curve represents an analysis of two 

regions in California using multiple years of historic price 

and load data with storage dispatch based on electricity 

prices; “CPUC 4-hour rule” curve is based on resource 

adequacy rules for California’s investor-owned utilities 
NREL | 7

Our approach: peak net load reduction 

as a proxy for storage CV

Change in California net load shape due to PV

(Denholm and Margolis 2018)

Impact of 4-hour storage dispatch on net 

load on the peak demand day in 2011 in 

California (Denholm and Margolis 2018)

The potential for storage to 

reduce peak net load depends on 

storage penetration and duration 

(left) and PV penetration (right)
NREL | 8

ReEDS dynamic storage CV method

• 3 key parameters for battery storage CV:

– Storage penetration (capacity-basis)

– PV penetration (energy-basis)

– Storage duration

• Multiple regression fit of peak net load 

reduction potential as a function of 

these 3 parameters

– Based on results from NREL’s REFlex

model using California data for 2007-

2015 (Denholm and Margolis 2018)

Storage CV Fit

“Drop-off” point
NREL | 9

ReEDS “Functional Form” fit

MMMMMMMMMMMMMMMMMMMM( SSSSnnsssssssssssss, )

= � 1, SSSSnnsssssssssssss ≤ dd dd_ffff

SS SSSSSS nnsssssssssssss+sssssssss( PP )

, SSSSnnsssssssssssss > dd dd_ffff

Where:

dd dd_ffff = �

ll cc ∗ − mm + 1 + mm , < mm

ll bb dd ∗ − mm + 1 + mm , ≥ mm

sss ( ) = ∗ 5 + jj ∗ 4 + kk ∗ 3 + ll ∗ 2 + mm ∗ + nn

“Drop-off” point

Shifted to normalize 

across years

Piecewise logarithmic 

drop-off point fit, then a 

polynomial fit for the 

declining storage CV curve
NREL | 10

36 Scenarios

(all use low cost storage = LCS)

Scenario Durations 

(hours)

Battery Storage CV 

Methods

Notes

Mid Case LCS 2, 4, 8 Static, Functional, 

Functional*Static, 

CV=1

ATB Mid Scenario 

with low cost storage

RPS 80 LCS 2, 4, 8 Static, Functional, 

Functional*Static, 

CV=1

RPS 80% target with 

low cost storage

High PV LCS 2, 4, 8 Static, Functional, 

Functional*Static, 

CV=1

Low cost PV and low 

cost storage

All scenarios are derived from the 2017 NREL Standard Scenarios (Cole et al. 2017a), which 

use input cost values from the 2017 NREL Annual Technology Baseline (ATB) (NREL 2017) 

Static:

Battery CV = 

• 0.555 for 2-hour 

• 0.76 for 4-hour 

• 0.937 for 8-hour 

(Sioshansi et al. 2014)

For all:

Pumped hydro and CAES 

CV = 1
NREL | 11

Storage CV method differences

RPS 80 LCS scenario

MISO-West RTO
NREL | 12

Storage deployment

(less favorable storage conditions)

Cumulative storage deployment in ReEDS by year for each storage CV method 

for Mid Case LCS scenario with 4- and 8-hour durations

ReEDS builds more 

storage with 

methods that give 

storage a CV of 1 

for some portion 

of capacity
NREL | 13

Storage deployment

(more favorable storage conditions)

Cumulative storage deployment in ReEDS by storage CV method for each 

storage CV method for RPS 80 LCS scenario with 4- and 8-hour durations

ReEDS builds more 

storage capacity with 

declining storage CVs 

because storage is the 

lowest-cost capacity 

resource even as the 

CV declines; cost of 

additional storage 

capacity is outweighed 

by the energy value 

(and other values) 

provided by that 

additional capacity
NREL | 14

Key takeaways

• Storage deployment is very nuanced, driven by the value to cost 

tradeoff

– Assumed cost inputs and value streams, including storage CV 

method

– Relative economics of next-cheapest option (usually gas CTs)

• Larger storage deployment is seen in ReEDS (low cost storage) with:

– Storage CV methods that assign larger CVs under less favorable 

storage conditions

– Storage CV methods that assign smaller CVs under more 

favorable storage conditions
NREL | 15

Next steps

• Continue to improve upon this simple dynamic storage CV 

method (Functional*Static)

– Fit storage CV curves to region-specific data

– Incorporate wind

– Capture full set of storage value streams

– Mixed portfolio of storage durations

– Incorporate chronology- and forecast-error-related 

impacts

 End goal: fully endogenize
NREL | 16

References and other resources

Cole, Wesley, Trieu Mai, Paul Donohoo-Vallett, James Richards, and Paritosh Das. 2017a. “2017 Standard Scenarios Report: A U.S. Electricity 

Sector Outlook.” Technical Report NREL/TP-6A20-68548. Golden, CO: National Renewable Energy Laboratory. 

https://www.nrel.gov/docs/fy18osti/68548.pdf.

Cole, Wesley, Bethany Frew, Pieter Gagnon, James Richards, Yinong Sun, Jarett Zuboy, Michael Woodhouse, and Robert Margolis. 2017b. “SunShot 2030 for Photoboltaics(PV): Envisioning a Low-cost PV Future.” Golden, CO: National Renewable Energy Laboratory. 

NREL/TP-6A20-68105. https://www.nrel.gov/docs/fy17osti/68105.pdf

Cole, Wesley, Bethany Frew, Trieu Mai, Yinong Sun, John Bistline, and Geoffrey Blanford, Cara Marcy, et al. 2017c. “Variable Renewable 

Energy in Long-Term Planning Models: A Multi-Model Perspective.” Golden, CO: National Renewable Energy Laboratory. NREL/TP- 6A20-70528. https://www.nrel.gov/docs/fy18osti/70528.pdf.

Denholm, Paul, and Robert Margolis. 2018. “The Potential for Energy Storage to Provide Peaking Capacity in California Under Increased 

Penetration of Solar Photovoltatics.” Technical Report NREL/TP-6A20-70905. Golden, CO: National Renewable Energy Laboratory. 

https://www.nrel.gov/docs/fy18osti/70905.pdf. 

EPRI. 2017. “Energy Storage Integration Council (ESIC) Energy Storage Modeling Bibliography: References for Energy Storage in Electric 

Power Planning, Operations, and Markets.” 3002009356. Palo Alto, CA.

Hale, Elaine, Brady Stoll, and Trieu Mai. 2016. “Capturing the Impact of Storage and Other Flexible Technologies on Electric System 

Planning.” NREL/TP-6A20-65726. Golden, CO: National Renewable Energy Laboratory. 

http://www.nrel.gov/docs/fy16osti/65726.pdf.

NREL. 2017. “2017 Annual Technology Baseline.” Golden, CO: National Renewable Energy Laboratory. 

http://www.nrel.gov/analysis/data_tech_baseline.html.

Sioshansi, Ramteen, Seyed Hossein Madaeni, and Paul Denholm. 2014. “A Dynamic Programming Approach to Estimate the Capacity Value 

of Energy Storage.” IEEE Transactions on Power Systems 29 (1): 395–403. https://doi.org/10.1109/TPWRS.2013.2279839. 
www.nrel.gov

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.

Thank you

bethany.frew@nrel.gov

NREL/PR-6A20-71858

Full report available: https://www.nrel.gov/docs/fy18osti/71462.pdf
NREL | 18

Appendix
NREL | 19

Functional Form fit coefficients

Functional Form fit coefficients by storage duration

Nonlinear squares estimate Calculated from data

durationHr i j k l m n r a b c d minPVPen minStorPen

2 7.29E-2 2.11 3.12E-1 -2.67E+2 -2.14E+3 1.43E+3 1.15E-1 2.09E+23 8.43E+35 1.20E+2 2.00E+1 0.09 1.961E-3

3 -2.36E+1 1.98 1.43E-1 9.78E+1 2.18E+2 -2.20E+2 5.55E-2 4.88E+7 7.29E+32 2.75E+1 5.44E+1 0.07 9.215E-3

4 -3.22E+1 1.55 6.83E-2 2.72E+2 1.29E+3 -1.01E+3 2.87E-2 1.03E+4 5.49E+20 2.01E+1 5.21E+1 0.07 1.779E-2

5 -1.47E+1 4.15E-1 2.17E-2 2.09E+2 1.22E+3 -9.12E+2 1.60E-2 2.93E+2 2.42E+16 1.49E+1 4.81E+1 0.07 3.789E-2

6 1.43E+1 -4.51E-1 -3.94E-2 -3.17E+1 6.21E+2 -2.23E+2 4.37E-3 8.47E+2 1.98E+22 2.16E+1 1.64E+2 0.07 6.010E-2

7 5.76E+1 -1.44 -8.11E-2 -5.35E+2 -2.32E+3 1.89E+3 6.17E-4 3.87E+3 1.65E+11 2.50E+1 5.06E+1 0.07 9.431E-2

8 5.83E+1 -1.97 2.30E-5 -4.95E+2 -1.95E+3 1.65E+3 2.34E-3 3.43E+2 2.57E+9 9.56 3.64E+1 0.07 1.357E-1