Physics-Informed Reinforcement Learning Framework for Real-Time Coordination of EV Charging with Renewable Energy Sources

Abstract

We made a Physics-Informed Reinforcement Learning (PI-RL) framework to coordinate the charging stations for electric vehicles (EVs) in real time. These stations are powered by different renewable energy sources (RES), like wind and photovoltaic (PV). Our methodology explicitly integrates energy conservation laws, state-of-charge (SOC) dynamics, and inverter limitations into the training process, unlike previous reinforcement learning (RL)-based methodologies that are confined to single-source renewable energy systems (RES) and do not incorporate physical system constraints. We changed the Soft Actor-Critic (SAC) algorithm by adding domain-informed reward shaping and adaptive Lagrangian multipliers in order to make sure that constraints were met. We subsequently structured the EV-RES coordination issue as a physics-constrained Markov Decision Process (MDP). We evaluated the proposed PI-RL approach using actual datasets of solar irradiance, synthetic wind generation profiles, and electric vehicle arrival patterns. In terms of operational profit, safety (constraint violation rate), and use of renewable energy, our approach worked better than traditional SAC and model-based rolling optimization. Also, our model made it much less common for charge-discharge switching to happen, which led to control strategies that are easier to understand and that help the battery last longer. These results show that the PI-RL framework is a reliable and widely applicable way to manage energy in real time in EV charging infrastructures that are getting more and more complicated as they use renewable energy.