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Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market

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  • Zhang, Qian
  • Wu, Xiaohan
  • Deng, Xiaosong
  • Huang, Yaoyu
  • Li, Chunyan
  • Wu, Jiaqi

Abstract

Aiming at the problem of insufficient research on the interactions of various participants in energy and frequency regulation (FR) market that takes into account the participation of wind power (WP) and large-scale electric vehicles (EV), a bidding strategy for WP and large-scale EVs in day-ahead energy-FR market is proposed in this paper. Firstly, based on the analysis of the influence factors of the whole process EV behavior boundaries, a classification and aggregation method of EV cluster is proposed. Then, considering EV battery loss and wind power deviation penalty, a two-layer model is established. The upper layer is the bidding model of maximum revenue of wind power producer (WPP) and electric vehicle aggregator (EVA), and the lower layer is the clearing model with the lowest system operation cost for the power trading center. The competitive relationship between EVA and WPP is described based on Nash game. Finally, the simulated results show that the suggested classification and aggregation method can achieve fast and accurate solution of large-scale electric vehicles optimization model, and the independent bidding mode is more consistent with the optimal market operation compared with the price taker bidding mode and the collaborative bidding mode, which can protect the benefit for each participant.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:341:y:2023:i:c:s0306261923004270
    DOI: 10.1016/j.apenergy.2023.121063
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    References listed on IDEAS

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    1. Dai, Xuemei & Li, Yaping & Zhang, Kaifeng & Feng, Wei, 2020. "A robust offering strategy for wind producers considering uncertainties of demand response and wind power," Applied Energy, Elsevier, vol. 279(C).
    2. Hongze Li & Xuejie Wang & Fengyun Li & Yuwei Wang & Xinhua Yu, 2018. "A Robust Day-Ahead Electricity Market Clearing Model Considering Wind Power Penetration," Energies, MDPI, vol. 11(7), pages 1-18, July.
    3. Khaloie, Hooman & Abdollahi, Amir & Shafie-khah, Miadreza & Anvari-Moghaddam, Amjad & Nojavan, Sayyad & Siano, Pierluigi & Catalão, João P.S., 2020. "Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model," Applied Energy, Elsevier, vol. 259(C).
    4. 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.
    5. Faddel, Samy & Aldeek, A. & Al-Awami, Ali T. & Sortomme, Eric & Al-Hamouz, Zakariya, 2018. "Ancillary Services Bidding for Uncertain Bidirectional V2G Using Fuzzy Linear Programming," Energy, Elsevier, vol. 160(C), pages 986-995.
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    Cited by:

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