arXiv cs.AI· Chenghao Huang, Yue Yang, Hao Wang·· 12 小时前AI 评分22
基于物理信息图表示与约束专用Critic的安全强化学习，用于配电网社区电池协调
Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network
AI 导读
研究提出物理信息安全深度强化学习框架，通过双层物理信息图表示与Lagrangian Dedicated-Critic PPO（LDCPPO）协调社区电池储能，在高屋顶光伏与EV充电渗透的配电网案例中，将关键电压越限程度较基线最多降低80.5%，同时取得最低电压越限次数与线路过载程度，运行成本具竞争力。
正文
Abstract:High penetrations of distributed energy resources (DERs) and fast-varying electric vehicle (EV) charging intensify voltage and line-limit violations in distribution networks (DNs), making safe, low-cost operation increasingly challenging. This paper proposes a physics-informed safe deep reinforcement learning (DRL) framework for coordinating community battery energy storage systems to mitigate constraint violations while minimizing operating cost. To represent nonlocal spatiotemporal interactions, we develop a dual-level physics-informed graph representation in which line impedance and capacity shape spatial information propagation, while power-balance, voltage-profile, and flow-reconstruction losses regularize the latent state toward control-relevant physical consistency. Building on this representation, we develop Lagrangian Dedicated-Critic PPO (LDCPPO), which implements constraint-specific value decomposition: independent reward and cost critics preserve heterogeneous voltage- and line-loading-related learning signals and combine their advantages only during the Lagrangian policy update. Case studies on DNs with high rooftop photovoltaic penetration and EV charging demonstrate that the proposed method reduces the key voltage-violation degree by up to 80.5% compared with the tested baselines, while also achieving the lowest voltage-violation count and line-overload degree with competitive operating cost.
| Subjects: | Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Optimization and Control (math.OC) |
| Cite as: | arXiv:2610.33654 [cs.AI] |
| (or arXiv:2610.33654v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33654 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | IEEE Transactions on Smart Grid, 2026 |
| Related DOI: | https://doi.org/10.1109/TSG.2026.3736659
DOI(s) linking to related resources |
Submission history
From: Hao Wang [view email]
[v1]
Sat, 24 Oct 2026 13:11:29 UTC (6,330 KB)
来源：arXiv cs.AI · arxiv.org