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Risk-averse frequency regulation strategy of electric vehicle aggregator considering multiple uncertainties

Author

Listed:
  • Wang, Qi
  • Huang, Chunyi
  • Wang, Chengmin
  • Li, Kangping
  • Shafie-khah, Miadreza

Abstract

Electric vehicles (EVs) are regarded as potential frequency regulation resources owing to their quick response characteristics and low standby costs. The electric vehicle aggregator (EVA) can profit from the regulation market by orderly aggregating the regulation potential of EVs while meeting charging demand. However, existing EVA bidding strategies mostly ignore the non-performance risk due to the uncertainty of EV behavior and frequency regulation signals, which not only results in the EVA failing to deliver the expected profit, but may even face penalties. To this end, a novel risk-averse bidding framework for an EVA coordinating the regulation potential of EVs and energy storage (ES) to participate in the regulation market is proposed. Firstly, the capacity allocation of ES is considered in the bidding process. A portion of ES capacity is used to mitigate the uncertainty of EVs' available regulation capacity upon regulation deployment, and the remaining spare capacity of ES will be leveraged by EVA to participate in the regulation market to obtain extra benefit. Secondly, capacity reservation determined by the maximum energy deviation caused by the awarded regulation capacity per megawatt is also embedded in the bidding process, which avoids EVs and ES from failing to continue following regulation signals due to state-of-charging (SOC) limit violations. Finally, the proposed formulation can be converted into a convex mixed-integer linear program problem, which can be easily solved by the commercial solvers. Numerical results verify the effectiveness of the proposed method in improving EVA profit and preventing not being able to fully follow the regulation signals.

Suggested Citation

  • Wang, Qi & Huang, Chunyi & Wang, Chengmin & Li, Kangping & Shafie-khah, Miadreza, 2025. "Risk-averse frequency regulation strategy of electric vehicle aggregator considering multiple uncertainties," Applied Energy, Elsevier, vol. 382(C).
    • Handle: RePEc:eee:appene:v:382:y:2025:i:c:s0306261924026436
    DOI: 10.1016/j.apenergy.2024.125259
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    References listed on IDEAS

    as
    1. Zheng, Yanchong & Wang, Yubin & Yang, Qiang, 2023. "Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: A risk-averse approach," Energy, Elsevier, vol. 283(C).
    2. Wang, Qi & Huang, Chunyi & Wang, Chengmin & Li, Kangping & Xie, Ning, 2024. "Joint optimization of bidding and pricing strategy for electric vehicle aggregator considering multi-agent interactions," Applied Energy, Elsevier, vol. 360(C).
    3. Chang, Hsiu-Chuan & Ghaddar, Bissan & Nathwani, Jatin, 2022. "Shared community energy storage allocation and optimization," Applied Energy, Elsevier, vol. 318(C).
    4. Li, Kangping & Li, Zhenghui & Huang, Chunyi & Ai, Qian, 2024. "Online transfer learning-based residential demand response potential forecasting for load aggregator," Applied Energy, Elsevier, vol. 358(C).
    5. de la Torre, S. & Aguado, J.A. & Sauma, E., 2023. "Optimal scheduling of ancillary services provided by an electric vehicle aggregator," Energy, Elsevier, vol. 265(C).
    6. Zheng, Yanchong & Yu, Hang & Shao, Ziyun & Jian, Linni, 2020. "Day-ahead bidding strategy for electric vehicle aggregator enabling multiple agent modes in uncertain electricity markets," Applied Energy, Elsevier, vol. 280(C).
    7. Sevdari, Kristian & Calearo, Lisa & Andersen, Peter Bach & Marinelli, Mattia, 2022. "Ancillary services and electric vehicles: An overview from charging clusters and chargers technology perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    8. Baringo, L. & Conejo, A.J., 2013. "Correlated wind-power production and electric load scenarios for investment decisions," Applied Energy, Elsevier, vol. 101(C), pages 475-482.
    9. Zhang, Xuehan & Son, Yongju & Cheong, Taesu & Choi, Sungyun, 2022. "Affine-arithmetic-based microgrid interval optimization considering uncertainty and battery energy storage system degradation," Energy, Elsevier, vol. 242(C).
    Full references (including those not matched with items on IDEAS)

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