Towards efficient coordination of power distribution network and electric vehicles: deep reinforcement learning with robust reward function

The rapid increase in electric vehicle (EV) usage has led to an urgent need for coordinating EV charging activities with power distribution networks (PDNs) to accommodate the resulting redistributed electrical demand and implement greater operational flexibility for PDNs. However, current efforts relying on the implementation of EV charging price strategies to influence the decision-making activities of EV users at charging stations face challenges arising from the uncertainty of EV charging behavior and data privacy concerns. The present work addresses these issues by developing a robust reinforcement learning approach that requires no personal EV user information to determine charging schedules and pricing strategies at charging stations, while the uncertainty in EV behavior is addressed by applying a robust reward function. The loss function is relaxed using Holder's and Cauchy-Schwarz inequalities, which yields an upper bound for the loss caused by the worst-case scenario and thereby enhances the computational efficiency of the solution process. Numerical results demonstrate that the proposed method contributes to balancing the PDN load and improving the utilization of fast charging stations (FCSs). Compared to deterministic and traditional robust methods, the proposed method reduces computation time in uncertain environments while ensuring moderate revenue of FCSs.