Intelligent Energy Management Method of EV Charging Station with Photovoltaic and Energy Storage System Considering EV Charging Scheduling

To capture the charging station dynamics caused by uncertain user behavior and photovoltaic power generation(PV), we propose a deep reinforcement learning(DRL) approach for optimizing the energy flow paths of electric vehicle charging station(CS). By aggregating the charging state of individual electric vehicles(EVs) into characteristic parameters of CS, we aim to reduce the computational burden of time-varying dimensions of state space caused by the uncertain users charging behaviors. The proposed approach facilitates charging scheduling by allocating charging power to single EVs. Through numerical simulations using real-world data, we demonstrate that the proposed DRL approach achieves on average 13% higher profit for CS compared to the rule-based approach, while also reducing the impact of power fluctuation on grid.