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Cloud-edge collaborative optimal control strategy for virtually aggregated EV charging facilities in a market environment

Author

Listed:
  • Gao, Shuang
  • Huang, Shengyu
  • Jin, Xiaolong
  • Jia, Hongjie
  • Zhao, Yuming
  • Tang, Wenjun

Abstract

Vehicle-to-grid operation is an important application of the future smart grid with high penetration of renewable energy sources. IoT-based charging infrastructure and cloud computing enable effective EV management at grid edges for grid-support services and gain economic benefits. This paper proposes a cloud-edge collaborative control model to optimize the charging process and participate in electricity markets. The cloud minimizes the net cost by optimizing the bidding strategy in the day-ahead energy and frequency regulation market. Meanwhile, the edges adjust the charging power of individual EV in each EV aggregation to achieve the committed energy and regulation capacity in the market while satisfying the diverse charging needs of EVs. The iterative optimization problem between cloud and edges is solved by adopting an improved Benders decomposition method. The parallel computing of each edge at the EV aggregator reduces the computation time and protects the user's privacy. As each EV aggregator has online access to all EVs, the autonomous control mode at edge layer enables dynamic EV charging optimization. A case study of EVs plugged into the power network through various charging facilities demonstrates that aggregating massive EVs to participate in electric markets reduces net costs by 64 %. The proposed cloud-edge collaborative optimization model increases the algorithm solving efficiency by 61.7 % and reduces the amount of information transferred between cloud and edges by 98.6 %. The physical constraints of power grid and uncertain EV availability are considered to provide a more realistic solution to the cloud-edge collaborative EV charging optimization and multi-market participation.

Suggested Citation

  • Gao, Shuang & Huang, Shengyu & Jin, Xiaolong & Jia, Hongjie & Zhao, Yuming & Tang, Wenjun, 2025. "Cloud-edge collaborative optimal control strategy for virtually aggregated EV charging facilities in a market environment," Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:energy:v:337:y:2025:i:c:s0360544225044056
    DOI: 10.1016/j.energy.2025.138763
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    References listed on IDEAS

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