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Decentralized frequency regulation service provision for virtual power plants: A best response potential game approach

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  • Sun, Xiaotian
  • Xie, Haipeng
  • Qiu, Dawei
  • Xiao, Yunpeng
  • Bie, Zhaohong
  • Strbac, Goran

Abstract

The high-renewable-penetrated power system frequently requires frequency regulation services. By aggregating heterogeneous demand-side flexible resources, the virtual power plants (VPP) are able to quickly respond to the frequency regulation signal, enabling them promising frequency regulation service providers. However, due to the difference in ownership, it is unlikely that the self-interested demand-side resources are willing to follow the VPP-oriented dispatch results. To bridge this gap, this paper proposes a decentralized game-based mechanism for the frequency regulation service provision inside the VPP. Firstly, the supply function-based bidding models of aggregated demand-side resources, namely, demand-side flexible resources and renewable distributed generations, are established. Then, a non-cooperative generalized Nash game for frequency regulation service provision is formulated. The existence, uniqueness, and competitive efficiency loss of the game are analyzed by designing an equivalent best response potential game of the original game. Additionally, a decentralized look-ahead real-time market clearing mechanism for frequency regulation service provision inside the VPP is proposed based on dual decomposition algorithm. Finally, the effectiveness and computation efficiency of the proposed method are validated in the case studies. Meanwhile, it is shown that the competition efficiency loss can be effectively reduced from around 80% to 6% when increasing the demand-side resource population from 20 to 2000 as well as balancing the demand-side resource composition.

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  • Sun, Xiaotian & Xie, Haipeng & Qiu, Dawei & Xiao, Yunpeng & Bie, Zhaohong & Strbac, Goran, 2023. "Decentralized frequency regulation service provision for virtual power plants: A best response potential game approach," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s030626192301351x
    DOI: 10.1016/j.apenergy.2023.121987
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    References listed on IDEAS

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    Cited by:

    1. Zhao, Zeyu & Yang, Xiyun & Chen, Han & Chen, He, 2025. "An improved heuristic dynamic programming framework for multi-source frequency regulation," Applied Energy, Elsevier, vol. 387(C).
    2. Muhammad Ikram & Daryoush Habibi & Asma Aziz, 2025. "Networked Multi-Agent Deep Reinforcement Learning Framework for the Provision of Ancillary Services in Hybrid Power Plants," Energies, MDPI, vol. 18(10), pages 1-34, May.
    3. Li, Zhihao & Xu, Yinliang, 2025. "Pricing balancing ancillary services for low-inertia power systems under uncertainty and nonconvexity," Applied Energy, Elsevier, vol. 377(PC).
    4. Zhang, Jiarui & Mu, Yunfei & Wu, Zhijun & Jia, Hongjie & Jin, Xiaolong & Qi, Yan, 2024. "Two-stage affine assessment method for flexible ramping capacity: An inverter heat pump virtual power plant case," Applied Energy, Elsevier, vol. 365(C).
    5. Yintao Lu & Bo Hu & Shengming Qiu & Shuchang Liu & Jiayan Wang & Jiashuai Zhao & Hong Yao, 2025. "Driving Mechanism and Energy Conservation Strategy for China’s Railway Passenger Stations Towards Carbon Neutrality," Energies, MDPI, vol. 18(11), pages 1-19, May.
    6. Qiu, Dawei & Baig, Aimon Mirza & Wang, Yi & Wang, Lingling & Jiang, Chuanwen & Strbac, Goran, 2024. "Market design for ancillary service provisions of inertia and frequency response via virtual power plants: A non-convex bi-level optimisation approach," Applied Energy, Elsevier, vol. 361(C).
    7. Esfahani, Moein & Alizadeh, Ali & Amjady, Nima & Kamwa, Innocent, 2024. "A distributed VPP-integrated co-optimization framework for energy scheduling, frequency regulation, and voltage support using data-driven distributionally robust optimization with Wasserstein metric," Applied Energy, Elsevier, vol. 361(C).

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