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Maximum income resulting from energy arbitrage by battery systems subject to cycle aging and price uncertainty from a dynamic programming perspective

Author

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  • Díaz, Guzmán
  • Gómez-Aleixandre, Javier
  • Coto, José
  • Conejero, Olga

Abstract

This paper describes an approach to compute the maximum value of energy storage systems (ESS) in grid applications under uncertain energy prices. The value obtained is based on an optimal operation (consisting of charge/discharge sequences) of the ESS. In other words, it is the maximum value that may be obtained when the ESS charge/discharge sequence is adapted to the expected operational conditions.

Suggested Citation

  • Díaz, Guzmán & Gómez-Aleixandre, Javier & Coto, José & Conejero, Olga, 2018. "Maximum income resulting from energy arbitrage by battery systems subject to cycle aging and price uncertainty from a dynamic programming perspective," Energy, Elsevier, vol. 156(C), pages 647-660.
    • Handle: RePEc:eee:energy:v:156:y:2018:i:c:p:647-660
    DOI: 10.1016/j.energy.2018.05.122
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    Citations

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    Cited by:

    1. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Optimal operation value of combined wind power and energy storage in multi-stage electricity markets," Applied Energy, Elsevier, vol. 235(C), pages 1153-1168.
    2. Coppitters, Diederik & De Paepe, Ward & Contino, Francesco, 2021. "Robust design optimization of a photovoltaic-battery-heat pump system with thermal storage under aleatory and epistemic uncertainty," Energy, Elsevier, vol. 229(C).
    3. Zhang, Yuanjian & Chu, Liang & Fu, Zicheng & Xu, Nan & Guo, Chong & Zhao, Di & Ou, Yang & Xu, Lei, 2020. "Energy management strategy for plug-in hybrid electric vehicle integrated with vehicle-environment cooperation control," Energy, Elsevier, vol. 197(C).
    4. Mukhopadhyay, Bineeta & Das, Debapriya, 2020. "Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    5. Zhang, Lizhi & Kuang, Jiyuan & Sun, Bo & Li, Fan & Zhang, Chenghui, 2020. "A two-stage operation optimization method of integrated energy systems with demand response and energy storage," Energy, Elsevier, vol. 208(C).
    6. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    7. Wu, Yaling & Liu, Zhongbing & Li, Benjia & Liu, Jiangyang & Zhang, Ling, 2022. "Energy management strategy and optimal battery capacity for flexible PV-battery system under time-of-use tariff," Renewable Energy, Elsevier, vol. 200(C), pages 558-570.
    8. Coppitters, Diederik & De Paepe, Ward & Contino, Francesco, 2020. "Robust design optimization and stochastic performance analysis of a grid-connected photovoltaic system with battery storage and hydrogen storage," Energy, Elsevier, vol. 213(C).
    9. Sepúlveda-Mora, Sergio B. & Hegedus, Steven, 2021. "Making the case for time-of-use electric rates to boost the value of battery storage in commercial buildings with grid connected PV systems," Energy, Elsevier, vol. 218(C).
    10. Mukhopadhyay, Bineeta & Das, Debapriya, 2021. "Optimal multi-objective expansion planning of a droop-regulated islanded microgrid," Energy, Elsevier, vol. 218(C).

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