Real-Time Resilient Power System Operation with Defender-Attacker Soft Actor-Critic Reinforcement Learning

Threatened by weather disasters and operational uncertainties, power systems require resilient and cost-effective decision making to ensure security. This article proposes a novel deep reinforcement learning algorithm, namely defender-attacker soft actor-critic (DA-SAC), designed for contingency-constrained optimal power flow under N-k security criteria. A two-agent Markov decision process is formulated, where the defender learns robust control actions and the attacker identifies worst-case contingencies. The core soft actor-critic algorithm is enhanced by integrating constraint violation levels into the reward function and employing a two-timescale learning scheme to improve feasibility and stability. The proposed method is validated on the IEEE 30-bus and 118-bus systems. Simulation results show that DA-SAC significantly reduces unserved energy, load shedding, and constraint violations, outperforming conventional and deep-reinforcement-learning-based benchmarks under N-1, N-2, and N-3 scenarios. These results demonstrate that DA-SAC offers a fast, resilient, and practical solution for real-time power system operation under severe contingencies.