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A Cloud-Edge-End Collaboration Framework for Fixed-Time Distributed Optimization of Virtual Power Plants

Author

Listed:
  • Kai Kang

    (PowerChina Hubei Engineering Co., Ltd., Wuhan 430040, China)

  • Nian Shi

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Keqi Zhang

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Si Cai

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Liang Zhang

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Xinan Shao

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Lei Shu

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Renjie Hu

    (School of Automation, China University of Geosciences, Wuhan 430074, China)

  • Leimin Wang

    (School of Automation, China University of Geosciences, Wuhan 430074, China)

Abstract

As the power grid expands, concerns about system computation speed and information privacy are becoming more critical. While distributed optimization methods protect individual privacy effectively, they struggle with computational efficiency in complex topologies. To address these issues, this paper proposes a cloud–edge–end collaboration framework consisting of a cloud server and multiple edge servers. This framework enables parallel computation of multiple distributed optimization algorithms. Additionally, a distributed fixed-time optimization consensus algorithm is designed for virtual power plants, allowing the convergence time to be predetermined offline. The fixed-time convergence of the algorithm is proven and its effectiveness and superiority are demonstrated through simulation cases.

Suggested Citation

  • Kai Kang & Nian Shi & Keqi Zhang & Si Cai & Liang Zhang & Xinan Shao & Lei Shu & Renjie Hu & Leimin Wang, 2025. "A Cloud-Edge-End Collaboration Framework for Fixed-Time Distributed Optimization of Virtual Power Plants," Mathematics, MDPI, vol. 13(11), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1883-:d:1671859
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    References listed on IDEAS

    as
    1. Yang, Qing & Wang, Hao & Wang, Taotao & Zhang, Shengli & Wu, Xiaoxiao & Wang, Hui, 2021. "Blockchain-based decentralized energy management platform for residential distributed energy resources in a virtual power plant," Applied Energy, Elsevier, vol. 294(C).
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    3. Moradi-Sarvestani, Sajjad & Jooshaki, Mohammad & Fotuhi-Firuzabad, Mahmud & Lehtonen, Matti, 2023. "Incorporating direct load control demand response into active distribution system planning," Applied Energy, Elsevier, vol. 339(C).
    4. Ju, Liwei & Lv, ShuoShuo & Zhang, Zheyu & Li, Gen & Gan, Wei & Fang, Jiangpeng, 2024. "Data-driven two-stage robust optimization dispatching model and benefit allocation strategy for a novel virtual power plant considering carbon-green certificate equivalence conversion mechanism," Applied Energy, Elsevier, vol. 362(C).
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