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A Fast-Converging Virtual Power Plant Game Trading Model Based on Reference Ancillary Service Pricing

Author

Listed:
  • Jiangfan Yuan

    (School of Electrical and Power Engineering, Hohai University, Nanjing 210024, China)

  • Min Zhang

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 102206, China)

  • Hongxun Tian

    (State Grid Shanxi Electric Power Company, Taiyuan 030021, China)

  • Xiangyu Guo

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 102206, China)

  • Xiao Chang

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 102206, China)

  • Tengxin Wang

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 102206, China)

  • Yingjun Wu

    (School of Electrical and Power Engineering, Hohai University, Nanjing 210024, China)

Abstract

In order to improve the trading efficiency of virtual power plants (VPPs) participating in the market of multi-type auxiliary services under the gaming environment, an initial trading price setting method based on the information of VPPs’ response characteristics and real-time supply and demand changes is proposed to accelerate the convergence speed of the game. Firstly, a master–slave game trading model is established based on the reference auxiliary service pricing, which consists of a tariff coefficient and a basic tariff. Secondly, the tariff coefficient model is constructed based on response information, including response rate, quality, and reliability. Again, the basic tariff model is constructed based on the real-time supply and demand situation and the real-time grid tariff. Finally, the effectiveness of the proposed method in accelerating the convergence speed of the game is verified by analyzing 12 VPPs under the three auxiliary service scenarios of peaking, frequency regulation, and reserve.

Suggested Citation

  • Jiangfan Yuan & Min Zhang & Hongxun Tian & Xiangyu Guo & Xiao Chang & Tengxin Wang & Yingjun Wu, 2025. "A Fast-Converging Virtual Power Plant Game Trading Model Based on Reference Ancillary Service Pricing," Energies, MDPI, vol. 18(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2567-:d:1656533
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    References listed on IDEAS

    as
    1. Shang, Yitong & Li, Duo & Li, Yang & Li, Sen, 2025. "Explainable spatiotemporal multi-task learning for electric vehicle charging demand prediction," Applied Energy, Elsevier, vol. 384(C).
    2. Esfahani, Moein & Alizadeh, Ali & Cao, Bo & Kamwa, Innocent & Xu, Minghui, 2025. "Virtual power plant formation strategy based on Stackelberg game: A three-step data-driven voltage regulation coordination scheme," Applied Energy, Elsevier, vol. 377(PA).
    3. Matheus Sabino Viana & Dorel Soares Ramos & Giovanni Manassero Junior & Miguel Edgar Morales Udaeta, 2023. "Analysis of the Implementation of Virtual Power Plants and Their Impacts on Electrical Systems," Energies, MDPI, vol. 16(22), pages 1-14, November.
    4. Weishi Zhang & Chuan He & Haichao Wang & Hanhan Qian & Zhemin Lin & Hui Qi, 2024. "Optimal Operation of Virtual Power Plants Based on Stackelberg Game Theory," Energies, MDPI, vol. 17(15), pages 1-15, July.
    5. Linru Jiang & Chenjie Yan & Chaorui Zhang & Weiqi Wang & Biyu Wang & Taoyong Li, 2024. "A Master–Slave Game Model of Electric Vehicle Participation in Electricity Markets under Multiple Incentives," Energies, MDPI, vol. 17(17), pages 1-17, August.
    6. Tiankai Yang & Jixiang Wang & Yongliang Liang & Chuan Xiang & Chao Wang, 2023. "Economic Dispatch between Distribution Grids and Virtual Power Plants under Voltage Security Constraints," Energies, MDPI, vol. 17(1), pages 1-16, December.
    Full references (including those not matched with items on IDEAS)

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