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A game theory-based price bidding strategy for electric vehicle aggregators in the presence of wind power producers

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  • Shojaabadi, Saeed
  • Talavat, Vahid
  • Galvani, Sadjad

Abstract

A game theory-based approach was proposed for energy exchange between the electric vehicles (EV) load and wind power producers (WPP) active in the regulation, balancing, and day-ahead markets. An optimal Bidding strategy was developed to reduce risks arising from the wind energy–EV imbalance in the energy markets where EV aggregators (EVAs) Bid price packages to WPPs for charging or not charging EVs to compensate for energy deviations. Generally, the WPP collects price Bids by aggregators to determine the share of each aggregator in energy exchange contracts by maximizing its profit function. On the other hand, there is competition between EV aggregators to sell their services to WPPs for compensating losses of EV owners. A non-cooperative game was to serve as a model for the competition among EV aggregators due to insufficient information. The Nash equilibrium was employed to solve this non-cooperative game.

Suggested Citation

  • Shojaabadi, Saeed & Talavat, Vahid & Galvani, Sadjad, 2022. "A game theory-based price bidding strategy for electric vehicle aggregators in the presence of wind power producers," Renewable Energy, Elsevier, vol. 193(C), pages 407-417.
    • Handle: RePEc:eee:renene:v:193:y:2022:i:c:p:407-417
    DOI: 10.1016/j.renene.2022.04.163
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    References listed on IDEAS

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    Cited by:

    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. Cesar Diaz-Londono & José Vuelvas & Giambattista Gruosso & Carlos Adrian Correa-Florez, 2022. "Remuneration Sensitivity Analysis in Prosumer and Aggregator Strategies by Controlling Electric Vehicle Chargers," Energies, MDPI, vol. 15(19), pages 1-24, September.
    3. Xiang Liao & Beibei Qian & Zhiqiang Jiang & Bo Fu & Hui He, 2023. "Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies," Energies, MDPI, vol. 16(13), pages 1-26, July.
    4. Zhang, Qian & Wu, Xiaohan & Deng, Xiaosong & Huang, Yaoyu & Li, Chunyan & Wu, Jiaqi, 2023. "Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market," Applied Energy, Elsevier, vol. 341(C).
    5. Morteza Nazari-Heris & Mehdi Abapour & Behnam Mohammadi-Ivatloo, 2022. "An Updated Review and Outlook on Electric Vehicle Aggregators in Electric Energy Networks," Sustainability, MDPI, vol. 14(23), pages 1-24, November.

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