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<p>The rapid proliferation of electric vehicles (EVs) and their spatially clustered charging behaviors have imposed unprecedented challenges on the stability, efficiency, and fairness of power distribution networks. Coordinating large-scale EV clusters across geographically distributed charging stations requires intelligent scheduling strategies that can simultaneously respect grid constraints, maximize user satisfaction, and enhance renewable energy utilization—all while safeguarding data privacy and computational scalability. This paper proposes a novel multi-agent cooperative dispatch framework based on <i>Federated Deep Reinforcement Learning</i> (FDRL) to optimize the real-time coordination between EVs, chargers, and the underlying power grid infrastructure. The model adopts a hierarchical structure where local agents independently train deep reinforcement learning policies tailored to site-specific dynamics, while a central aggregator synchronizes global model parameters using federated averaging enhanced by entropy-based reward normalization and fairness-aware weighting. The optimization problem is formulated as a multi-objective constrained Markov decision process (CMDP), featuring long-horizon coupling, grid-aware feasibility, and user-centric reward shaping. Our formulation explicitly integrates peak transformer loading limits, charging demand satisfaction, temporal renewable absorption, and inter-agent equity, thereby capturing the full complexity of EV–grid interactions. A realistic case study involving 1,200 EVs, 60 chargers, and a 33-bus feeder system over 24 hours shows that the proposed FDRL framework achieves a <b>13.6% reduction in grid operating cost, a 21.4% increase in renewable absorption, and fairness with Jain’s index consistently above 0.95, while reducing average state-of-charge (SoC) deviation to below 2.5%</b>. These quantitative results highlight the effectiveness of the framework and confirm its promise as a privacy-preserving, scalable, and equitable solution for next-generation energy–cyber–physical systems.</p>

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Federated reinforcement learning with constrained markov decision processes and graph neural networks for fair and grid-constrained coordination of large-scale electric vehicle charging networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月12日

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  • Lixia Zhou,
  • Dawei Huo,
  • Jian Chen,
  • Bo Bo,
  • Hao Li

摘要

The rapid proliferation of electric vehicles (EVs) and their spatially clustered charging behaviors have imposed unprecedented challenges on the stability, efficiency, and fairness of power distribution networks. Coordinating large-scale EV clusters across geographically distributed charging stations requires intelligent scheduling strategies that can simultaneously respect grid constraints, maximize user satisfaction, and enhance renewable energy utilization—all while safeguarding data privacy and computational scalability. This paper proposes a novel multi-agent cooperative dispatch framework based on Federated Deep Reinforcement Learning (FDRL) to optimize the real-time coordination between EVs, chargers, and the underlying power grid infrastructure. The model adopts a hierarchical structure where local agents independently train deep reinforcement learning policies tailored to site-specific dynamics, while a central aggregator synchronizes global model parameters using federated averaging enhanced by entropy-based reward normalization and fairness-aware weighting. The optimization problem is formulated as a multi-objective constrained Markov decision process (CMDP), featuring long-horizon coupling, grid-aware feasibility, and user-centric reward shaping. Our formulation explicitly integrates peak transformer loading limits, charging demand satisfaction, temporal renewable absorption, and inter-agent equity, thereby capturing the full complexity of EV–grid interactions. A realistic case study involving 1,200 EVs, 60 chargers, and a 33-bus feeder system over 24 hours shows that the proposed FDRL framework achieves a 13.6% reduction in grid operating cost, a 21.4% increase in renewable absorption, and fairness with Jain’s index consistently above 0.95, while reducing average state-of-charge (SoC) deviation to below 2.5%. These quantitative results highlight the effectiveness of the framework and confirm its promise as a privacy-preserving, scalable, and equitable solution for next-generation energy–cyber–physical systems.