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A simple and fast algorithm for estimating the capacity credit of solar and storage

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  • Mills, Andrew D.
  • Rodriguez, Pía

Abstract

Energy storage is a leading option to enhance the resource adequacy contribution of solar energy. Detailed analysis of the capacity credit of solar energy and energy storage is limited in part due to the data intensive and computationally complex nature of probabilistic resource adequacy assessments. This paper presents a simple algorithm for calculating the capacity credit of energy-limited resources that, due to the low computational and data needs, is well suited to exploratory analysis. Validation against benchmarks based on probabilistic techniques shows that it can yield similar insights. The method is used to evaluate the impact of different solar and storage configurations, particularly with respect to the strategy for coupling storage and solar photovoltaic systems. Application of the method to a case study of utilities in Florida, where solar is rapidly growing and demand peaks in the winter and summer, demonstrates that it can improve on rules of thumb used in practice by some utilities. If storage is required to charge only from solar, periods of high demand driven by cold weather events accompanied by lower solar production can result in a capacity credit of solar and storage that is less than the capacity credit of storage alone.

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  • Mills, Andrew D. & Rodriguez, Pía, 2020. "A simple and fast algorithm for estimating the capacity credit of solar and storage," Energy, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:energy:v:210:y:2020:i:c:s0360544220316959
    DOI: 10.1016/j.energy.2020.118587
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    4. Kim, James Hyungkwan & Mills, Andrew D. & Wiser, Ryan & Bolinger, Mark & Gorman, Will & Crespo Montañes, Cristina & O'Shaughnessy, Eric, 2021. "Project developer options to enhance the value of solar electricity as solar and storage penetrations increase," Applied Energy, Elsevier, vol. 304(C).
    5. Chen, Jiahao & Sun, Bing & Li, Yunfei & Jing, Ruipeng & Zeng, Yuan & Li, Minghao, 2022. "Credible capacity calculation method of distributed generation based on equal power supply reliability criterion," Renewable Energy, Elsevier, vol. 201(P1), pages 534-547.
    6. Nolting, Lars & Praktiknjo, Aaron, 2022. "The complexity dilemma – Insights from security of electricity supply assessments," Energy, Elsevier, vol. 241(C).
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    8. Wang, Renshun & Wang, Shilong & Geng, Guangchao & Jiang, Quanyuan, 2024. "Multi-time-scale capacity credit assessment of renewable and energy storage considering complex operational time series," Applied Energy, Elsevier, vol. 355(C).
    9. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    10. AL-Rasheedi, Majed & Al-Khayat, Mohammad, 2024. "Variable renewable energy modeling system to study challenges that impact electrical load at different penetration levels: A case study on Kuwait's load profile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    11. Gorman, Will & Montañés, Cristina Crespo & Mills, Andrew & Kim, James Hyungkwan & Millstein, Dev & Wiser, Ryan, 2022. "Are coupled renewable-battery power plants more valuable than independently sited installations?," Energy Economics, Elsevier, vol. 107(C).
    12. Song, Qianqian & Wang, Bo & Wang, Zhaohua & Wen, Lei, 2024. "Multi-objective capacity configuration optimization of the combined wind - Storage system considering ELCC and LCOE," Energy, Elsevier, vol. 301(C).
    13. Huclin, Sébastien & Ramos, Andrés & Chaves, José Pablo & Matanza, Javier & González-Eguino, Mikel, 2023. "A methodological approach for assessing flexibility and capacity value in renewable-dominated power systems: A Spanish case study in 2030," Energy, Elsevier, vol. 285(C).
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