Deep Reinforcement Learning for Optimal Planning of Fast Electric Vehicle Charging Stations at a Large Scale

Electric vehicles (EVs) have been widely adopted with the expectation of reducing CO₂ emissions. However, the lack of public fast charging stations has hindered the growth of EVs. Despite extensive research on optimal installation of EV charging stations (EVCS), a decision model for a large scale and diverse spatial conditions has been still lacking. This research intends to explore a deep reinforcement learning model using deep Q-network (DQN) algorithms and test the model for optimal planning of fast EVCS at a large scale. The DQN model considers geographic (e.g., building footprints, street network), economic (e.g., capacity of charging station), and environmental (e.g., solar energy) perspectives. The learning model identifies the energy balance between electricity generation and consumption and investigates spatial patterns nearby potential charging stations. This study can aid in decision-making for suitable EVCS sites with advancing the microgrid approach-based infrastructure systems, ultimately enhancing urban sustainability considering vehicle-to-building integration.