Physics-Informed Reinforcement Learning for electric vehicle charging scheduling

Large-scale electric vehicle charging behavior impacts power balance, load fluctuations, and operational safety in power distribution networks. To mitigate these risks while maintaining grid stability, the constraint satisfaction problem faced by scheduling strategies urgently needs to be addressed. Traditional RL methods indirectly characterize constraints by processing them through reward and penalty functions, hindering the explicit representation of key physical constraints, thereby compromising policy feasibility and stability. To address these issues, this paper proposes Physics-Informed Reinforcement Learning (PIRL) for EV charging scheduling. By formulating the scheduling problem as a Constrained Markov Decision Process (CMDP), PIRL embeds physical information into the policy optimization process via differentiable equations, thereby ensuring the rigorous satisfaction of complex constraints. Simulation results show that PIRL average return increased by 137% while keeping physical loss increased by only 0.6%, demonstrating good generalization and applicability in different datasets.