PEGAS: Security Assessment of Power Grid Agents through Physics-informed Evolutionary Graph Adversarial Search

Reinforcement Learning (RL) agents and neural solvers are becoming the standard for safe & reliable power grid control. Standardized benchmarks and challenges (e.g., L2RPN) show that agents achieve reliable scores, and are resilient to adversaries; driving interest in real-world deployment. This paper argues that current evaluation settings understate real-world adversaries and introduces PEGAS, a query-based, gray-box attack framework that outperforms L2PRN adversarial evaluations. PEGAS scores candidate attacks using a Physics Informed Graph Attention Network surrogate for Optimal Power Flow solvers. PEGAS searches the mixed discrete–continuous, non-convex space with an elitist genetic algorithm, and projects candidates back onto feasible, connectivity-preserving actions before deploying them against static solvers and live RL-based grid-control agents. Our convergence analysis shows that PEGAS' best-found fitness converges almost surely to the global optimum. Empirically, on 118-bus grids and standard L2RPN benchmarks (IDF 2023, WCCI 2022), PEGAS substantially reduces defender survival rates compared to existing opponents and beats the winning approaches of these challenges. Specifically, PEGAS achieves a 93.2% success rate against static solvers and decreases the survival of the last L2RPN winner agent from 72% to 5.3%.