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The symbiotic relationship of solar power and energy storage in providing capacity value

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  • Sodano, Daniel
  • DeCarolis, Joseph F.
  • Rodrigo de Queiroz, Anderson
  • Johnson, Jeremiah X.

Abstract

Ensuring power system reliability under high penetrations of variable renewable energy is a critical task for system operators. In this study, we use a loss of load probability model to estimate the capacity credit of solar photovoltaics and energy storage under increasing penetrations of both technologies, in isolation and in tandem, to offer new understanding on their potential synergistic effects. Increasing penetrations of solar PV alter the net load profile on the grid, shifting the peak net load to hours with little or no solar generation and leading to diminishing capacity credits for each additional increment of solar. However, the presence of solar PV decreases the duration of daily peak demands, thereby allowing energy-limited storage capacity to dispatch electricity during peak demand hours. Thus, solar PV and storage exhibit a symbiotic relationship when used in tandem. We find that solar PV and storage used together make a more significant contribution to system reliability: as much as 40% more of the combined capacity can be counted on during peak demand hours compared to scenarios where the two technologies are deployed separately. Our test case demonstrates the important distinction between winter and summer peaking systems, leading to significantly different seasonal capacity values for solar PV. These findings are timely as utilities replace their aging peaking plants and are taking energy storage into consideration as part of a low carbon pathway.

Suggested Citation

  • Sodano, Daniel & DeCarolis, Joseph F. & Rodrigo de Queiroz, Anderson & Johnson, Jeremiah X., 2021. "The symbiotic relationship of solar power and energy storage in providing capacity value," Renewable Energy, Elsevier, vol. 177(C), pages 823-832.
  • Handle: RePEc:eee:renene:v:177:y:2021:i:c:p:823-832
    DOI: 10.1016/j.renene.2021.05.122
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    References listed on IDEAS

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    Cited by:

    1. Cheng, Hsu-Yung & Yu, Chih-Chang & Lin, Chih-Lung, 2021. "Day-ahead to week-ahead solar irradiance prediction using convolutional long short-term memory networks," Renewable Energy, Elsevier, vol. 179(C), pages 2300-2308.
    2. Li, Xiaozhu & Chen, Laijun & Sun, Fan & Hao, Yibo & Du, Xili & Mei, Shenwei, 2023. "Share or not share, the analysis of energy storage interaction of multiple renewable energy stations based on the evolution game," Renewable Energy, Elsevier, vol. 208(C), pages 679-692.
    3. de Faria, Victor A.D. & de Queiroz, Anderson R. & DeCarolis, Joseph F., 2022. "Optimizing offshore renewable portfolios under resource variability," Applied Energy, Elsevier, vol. 326(C).
    4. Keskar, Aditya & Galik, Christopher & Johnson, Jeremiah X., 2023. "Planning for winter peaking power systems in the United States," Energy Policy, Elsevier, vol. 173(C).
    5. Ángel A. Bayod-Rújula & Juan A. Tejero-Gómez, 2022. "Analysis of the Hybridization of PV Plants with a BESS for Annual Constant Power Operation," Energies, MDPI, vol. 15(23), pages 1-18, November.
    6. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    7. Mirosława Szewczyk & Anna Szeliga-Duchnowska, 2022. "Make Hay While the Sun Shines: Beneficiaries of Renewable Energy Promotion," Energies, MDPI, vol. 15(9), pages 1-15, May.

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