Federated reinforcement learning-based dynamic hosting domain for electric vehicles charging management in distribution networks with renewable energy resources

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The increasing penetration of renewable energy resources and electric vehicles (EVs) introduces considerable uncertainty into distribution networks, where renewable intermittency and uncoordinated charging can cause voltage violations, congestion, and reduced hosting capability. Conventional static assessment methods, based on fixed operating assumptions, fail to capture the spatiotemporal interactions induced by renewable variability and aggregated charging demand under network constraints. To address these challenges, this study proposes a dynamic hosting domain (DHD) framework based on reinforcement learning. The DHD defines the time-varying feasible region of system states, providing a quantitative representation of admissible operating boundaries with explicit physical safety guarantees. It adapts to spatiotemporal charging variations and incorporates a margin-based index for hosting capacity evaluation. A station-level delay perception model is embedded into the reward design to account for aggregated service-quality constraints as macroscopic economic feedback, discouraging excessive charging postponement while preserving operational feasibility at the system level. Furthermore, a federated reinforcement learning architecture based on proximal policy optimization (PPO) enables collaborative policy learning among multiple charging stations without raw data sharing, thereby enhancing scalability, privacy preservation, and spatiotemporal coordination under non-IID conditions. Case studies on renewable-integrated distribution networks demonstrate faster convergence, enhanced voltage stability, and lower operational costs compared with benchmarks. The proposed DHD framework offers a unified pathway for secure, efficient, and behavior-aware EV charging management in renewable-energy-integrated systems.