Physics-Informed Neuro-Symbolic AI for Resilient Multi-Energy Microgrid Control Under Extreme Climate Events

Abstract

Modern multi-energy microgrids (MEMGs) integrating electricity, district heating, and green hydrogen storage face severe operational vulnerabilities during extreme climate shocks such as the 2021 Texas freeze event. Traditional Model Predictive Control (MPC) suffers from computational intractability under nonlinear multi-carrier coupling, while standard data-driven Deep Reinforcement Learning (DRL) lacks safety guarantees. This paper presents a Physics-Informed Neuro-Symbolic DRL (PINS-DRL) framework that embeds alternating-current (AC) power flow equality constraints, electrolyzer thermodynamic kinetics, and symbolic safety logic directly into the policy optimization loop. Numerical experiments on an islanded hybrid microgrid subjected to synthetic 72-hour freezing and heatwave profiles demonstrate zero tracked voltage and thermal constraint violations, an 80.2% reduction in unserved energy compared to standard Proximal Policy Optimization (PPO) with no physics regularization, a 41.2% reduction in unserved energy relative to a nonlinear model predictive control (NMPC) benchmark, and a sub-15-millisecond execution time, roughly 1,350 times faster than the NMPC benchmark, suitable for real-time edge deployment.