Real-time fast charging station recommendation for electric vehicles in coupled power-transportation networks: A graph reinforcement learning method

Abstract

With the increasing penetration rate of electric vehicles, the fast charging demands of electric vehicles will have a significant influence on the operation of coupled power-transportation networks. To promote the interests of the coupled system, fast charging stations, and electric vehicle users, in this paper, a multi-objective system-level fast charging station recommendation method is proposed to dynamically allocate electric vehicles to suitable stations. The recommendation problem is formulated as a sequential decision-making problem and a deep reinforcement learning method is adopted. To deal with the network-structure coupled system states, graph attention networks are introduced. Considering the heterogeneity between entities, we propose a physical connection-based graph formulation method with feature projection to integrate multi-dimensional information from charging stations, traffic nodes, and power grid buses into a graph. The graph convolution of coupled system states can then be realized to promote environment perception. Besides, to address the long time-delay action execution in recommendation problem, a double-prioritized DQN(λ) training mechanism is developed to update the guidance strategy, where an attention-prioritized cache construction method is proposed to improve the training efficiency cooperated with prioritized experience replay. The proposed graph reinforcement learning method is trained and evaluated in a joint power-transportation simulation platform. Simulation results show that the proposed strategy can promote the interest of multiple facets in coupled power-transportation networks by handling the requests in a real-time manner. Its feasibility and robustness in the urban transportation systems are also demonstrated.