A Physics-Informed Reinforcement Learning Approach to Vehicle-to-Grid Control With Real-Time Battery Degradation

Abstract

The Vehicle-to-Grid (V2G) system has been a significant solution for enhancing the power grid's stability and supporting renewable energy. However, the primary barrier to the practical application of V2G technology has been a fundamental economic conflict: the accelerated battery degradation from aggressive battery cycling required to provide grid services creates a direct trade-off between generating revenue from grid services and preserving the battery asset's life. Therefore, the objective of this research is to develop an intelligent control framework to optimize both the profit generated from grid services provided and the longevity of the batteries. The proposed Physics-Informed Deep Reinforcement Learning (PI-DRL) framework utilizes a Digital Twin of the electrochemical behavior of the batteries to generate a real-time physics-based cost signal of degradation, which guides the learning of the policy by a deep reinforcement learning agent. Comprehensive VPP simulation results demonstrate that the proposed PI-DRL framework outperforms all benchmark approaches, achieving significant increases in net profitability and drastic reductions in fleet-wide capacity fade. The agent learned sophisticated control strategies, including making proactive deviations from the optimal control trajectory to avoid acute mechanical stress on the battery and optimizing operations across a heterogeneous fleet by using robust chemistries for high-intensity grid service tasks. A key implication of this research is that there is now a viable blueprint for the economically sustainable and equitable provision of V2G services, with an asset-preserving strategy being the most profitable method.