Topology-Aware Physics-Informed Reinforcement Learning for Resilient Operation and Adaptive Restoration of Renewable-Integrated Active Distribution Networks

The increasing penetration of photovoltaic generation in active distribution networks (ADNs) introduces significant operational variability and challenges for resilient network restoration. This paper proposes a Topology-Aware Physics-Informed Reinforcement Learning (PIRL) framework for adaptive operation and self-healing of renewable-integrated ADNs. The proposed approach integrates topology-aware network representation, physics-informed reward guidance, coordinated distributed generation (DG) and battery energy storage system (BESS) control, and feeder reconfiguration within a unified learning framework. The controller is evaluated on the IEEE 33-bus ADN under different combinations of PV generation, load demand, and feeder contingencies. Simulation results show that PIRL achieves a minimum voltage of 0.976 p.u. and reduces network power loss to 121.3 kW. Under a feeder fault, the proposed framework restores the interrupted load within 13 min with an energy not supplied of 72 kWh and achieves a resilience index of 0.96. In addition, PIRL reaches stable policy convergence at approximately 560 training episodes with a final normalized cumulative reward of 0.91. These results demonstrate that combining physical operating information with adaptive reinforcement learning and coordinated DG-BESS control can improve voltage security, network efficiency, restoration capability, and learning efficiency in renewable-integrated ADNs.

Physics-Informed Reinforcement Learning, Active Distribution Networks, Self-Healing, Battery Energy Storage Systems, Feeder Reconfiguration.

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