A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile

This paper investigates the electric vehicle (EV) charging scheduling problem for public EV charging stations (EVCSs) that can accommodate heterogeneous charging demands, aiming to flatten the aggregate load on the power grid and reduce the peak demand. In contrast to existing works that mainly focus on a single scheduling strategy, a two-level hierarchical charging scheduling method is proposed, which includes an online booking system (OBS) and a pricing-based charging control system (PCCS). Specifically, the implementation of OBS can reduce the service capacity of bookable chargers, whereas PCCS can facilitate the redistribution of charging demand from peak to off-peak time periods. Moreover, the nonlinear charging profile of the battery is incorporated into the model to better reflect the real charging process. A deep reinforcement learning (DRL)-based approach with a discrete Markov decision process (MDP) formulation is designed to find the optimal scheduling solution, where deep Q-network (DQN) and deep deterministic policy gradient (DDPG) are combined to handle the hybrid action space with both discrete and continuous actions. A comprehensive set of experiments is carried out to examine the effectiveness of the developed two-level scheduling scheme. The results indicate that the proposed method improves the scheduling effectiveness compared to single-strategy methods by 6.7% to 49.44%.