HetGPS: Scalable Graph Multi-Agent Reinforcement Learning with Physics-Anchored Adaptive Safety for EV Charging

HetGPS combines an action-conditioned graph residual risk model with a physics-based correction mechanism, separating how strongly to intervene from which corrective direction to take. Coupled with a parameter-shared heterogeneous-graph soft actor-critic policy, it targets scalable coordination for EV charging. Across five nested distribution networks containing 200–3,218 EVs and 100 evaluation days, the authors report voltage-violation rates falling from 3.93–7.74% without filtering to 0.52–3.44%, while departure success remains 99.06–100%. The deployed policy and risk model use 383,702 learned parameters at every scale, and an eight-transformer policy transfers zero-shot to larger systems.