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Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets

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Listed:
  • Yunpeng Guo

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Weijia Liu

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Fushuan Wen

    (Department of Electrical and Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei)

  • Abdus Salam

    (Department of Electrical and Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei)

  • Jianwei Mao

    (Division of Electric Vehicle Service, State Grid Zhejiang Electric Power Company, Hangzhou 310007, China)

  • Liang Li

    (Division of Electric Vehicle Service, State Grid Zhejiang Electric Power Company, Hangzhou 310007, China)

Abstract

To make full use of the flexible charging and discharging capabilities of the growing number of electric vehicles (EVs), a bidding strategy for EV aggregators to participate in a day-ahead electricity energy market is proposed in this work. The proposed bidding strategy is able to reduce the operating cost of the EV aggregators and to handle the uncertainties of day-ahead market prices properly at the same time. Agreements between the EV owners and the aggregators are discussed, and a hierarchical market structure is proposed. While assuming the aggregators as economic rational entities, the bidding strategy is established based on the market prices, extra battery charging/discharging costs and the expected profits. The bidding clearing system will display the current/temporal market clearance results of the day-ahead market before the final clearance, and hence the market participants can revise their bids and mitigate the risks, to some extent, of forecasted market price forecast errors. Numerical results with a modified IEEE 30-bus system have demonstrated the feasibility and effectiveness of the proposed strategy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:144-:d:88583
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    References listed on IDEAS

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    Cited by:

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    4. Bomiao Liang & Weijia Liu & Fushuan Wen & Md. Abdus Salam, 2017. "Well-Being Analysis of Power Systems Considering Increasing Deployment of Gas Turbines," Energies, MDPI, vol. 10(7), pages 1-18, July.
    5. Yusuf A. Sha’aban & Augustine Ikpehai & Bamidele Adebisi & Khaled M. Rabie, 2017. "Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control," Energies, MDPI, vol. 10(10), pages 1-20, September.
    6. Feng Qi & Fushuan Wen & Xunyuan Liu & Md. Abdus Salam, 2017. "A Residential Energy Hub Model with a Concentrating Solar Power Plant and Electric Vehicles," Energies, MDPI, vol. 10(8), pages 1-17, August.
    7. Qing Deng & Changsen Feng & Fushuan Wen & Chung-Li Tseng & Lei Wang & Bo Zou & Xizhu Zhang, 2019. "Evaluation of Accommodation Capability for Electric Vehicles of a Distribution System Considering Coordinated Charging Strategies," Energies, MDPI, vol. 12(16), pages 1-20, August.
    8. Stergios Statharas & Yannis Moysoglou & Pelopidas Siskos & Pantelis Capros, 2021. "Simulating the Evolution of Business Models for Electricity Recharging Infrastructure Development by 2030: A Case Study for Greece," Energies, MDPI, vol. 14(9), pages 1-24, April.
    9. Riccardo Iacobucci & Benjamin McLellan & Tetsuo Tezuka, 2018. "The Synergies of Shared Autonomous Electric Vehicles with Renewable Energy in a Virtual Power Plant and Microgrid," Energies, MDPI, vol. 11(8), pages 1-20, August.
    10. Wu, Hongbin & Wang, Jingjie & Lu, Junhua & Ding, Ming & Wang, Lei & Hu, Bin & Sun, Ming & Qi, Xianjun, 2022. "Bilevel load-agent-based distributed coordination decision strategy for aggregators," Energy, Elsevier, vol. 240(C).
    11. Tepe, Benedikt & Figgener, Jan & Englberger, Stefan & Sauer, Dirk Uwe & Jossen, Andreas & Hesse, Holger, 2022. "Optimal pool composition of commercial electric vehicles in V2G fleet operation of various electricity markets," Applied Energy, Elsevier, vol. 308(C).

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