DRL-Based Backup Power Scheduling for Resilient Smart Grid Recovery

Efficient recovery in smart grids is critical to minimize outages and maintain stability. This paper addresses the backup power scheduling problem using Deep Reinforcement Learning (DRL). By modeling the recovery process as a Markov Decision Process, we propose a Deep Q-Learning (DQN) solution that dynamically activates backup power units and selects power nodes for repair. Simulation results on a standard test system demonstrate that our method reduces recovery time, improves resource utilization, and enhances grid stability compared to heuristic approaches.