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Frequency Regulation of Electric Vehicle Aggregator Considering User Requirements with Limited Data Collection

Author

Listed:
  • Fei Zeng

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Zhinong Wei

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Guoqiang Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Mingshen Wang

    (Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

  • Haiteng Han

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users’ charging requirements, the EV aggregator’s power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users’ requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users’ requirements, and improving the system frequency stability.

Suggested Citation

  • Fei Zeng & Zhinong Wei & Guoqiang Sun & Mingshen Wang & Haiteng Han, 2023. "Frequency Regulation of Electric Vehicle Aggregator Considering User Requirements with Limited Data Collection," Energies, MDPI, vol. 16(2), pages 1-21, January.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:848-:d:1032530
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    References listed on IDEAS

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    1. Hasan Huseyin Coban & Wojciech Lewicki & Ewelina Sendek-Matysiak & Zbigniew Łosiewicz & Wojciech Drożdż & Radosław Miśkiewicz, 2022. "Electric Vehicles and Vehicle–Grid Interaction in the Turkish Electricity System," Energies, MDPI, vol. 15(21), pages 1-19, November.
    2. Paolo Scarabaggio & Raffaele Carli & Graziana Cavone & Mariagrazia Dotoli, 2020. "Smart Control Strategies for Primary Frequency Regulation through Electric Vehicles: A Battery Degradation Perspective," Energies, MDPI, vol. 13(17), pages 1-19, September.
    3. 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.
    4. Han, Sekyung & Han, Soohee & Aki, Hirohisa, 2014. "A practical battery wear model for electric vehicle charging applications," Applied Energy, Elsevier, vol. 113(C), pages 1100-1108.
    5. Yunpeng Guo & Weijia Liu & Fushuan Wen & Abdus Salam & Jianwei Mao & Liang Li, 2017. "Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets," Energies, MDPI, vol. 10(1), pages 1-20, January.
    6. Liu, Hui & Huang, Kai & Wang, Ni & Qi, Junjian & Wu, Qiuwei & Ma, Shicong & Li, Canbing, 2019. "Optimal dispatch for participation of electric vehicles in frequency regulation based on area control error and area regulation requirement," Applied Energy, Elsevier, vol. 240(C), pages 46-55.
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