An Efficient Gated-Attention Spatiotemporal Convolutional Network for Economical Operation of Electric Vehicle Charging Stations

The rapid development of electric vehicles raises a higher requirement for charging station operation and management. Therefore, this work proposes a data-driven method aimed at enhancing the economical operation of charging stations. Considering the privacy of charging data and the influence of traffic flow, this data-driven method simulates the spatiotemporal charging demand based on the predicted traffic flow. To obtain precise prediction, this work develops an efficient gated-attention spatiotemporal convolutional network (GSTCN) to explore the long-term spatial and temporal dependence of traffic flow. GSTCN is constructed by two main components: a spatial gated attention (SGA) unit and a temporal gated (T-Gated) attention layer. The spatial pattern of traffic flow is unearthed by the well-designed SGA unit, while the temporal correlation between different time steps is captured through the proposed T-Gated attention layer. Then, an energy storage system (ESS) is employed to improve the effective management of charging stations. Numerical results demonstrate the efficiency of GSTCN in charging station management. GSTCN can produce more accurate prediction data, which leads to a more economical operation of the energy storage system compared with the benchmarks.