A Spatial-temporal Electric Vehicle Charging Load Forecasting Method Considering the Coordination among the Multiple Charging Behaviors

Electric vehicle (EV) charging load forecasting is important for the planning and operation of the transportation and distribution networks. The current forecasting methods are mainly aimed at the single charging behavior at destinations or en-route fast charging stations (FCSs). But EV users have multiple charging behaviors in a day, which are continuous and interactive. Therefore, a spatial-temporal EV charging load forecasting method considering the coordination among the multiple charging behaviors is proposed. Firstly, the vehicle mobility model is proposed to obtain the spatial-temporal distribution (STD) of EVs based on the trip chain and Dijkstra algorithm. Next, the energy consumption model is proposed to calculate the State of Charge (SOC) of EVs considering the traffic condition and ambient temperature. Then, considering the multiple charging behaviors at different activity destinations and en-route FCSs, the charging load determination model is proposed to determine the EV charging load. Finally, the actual urban traffic network in Berlin, German is selected as an example to verify the method's effectiveness. The result shows that the method proposed in this paper can analyze the impact of multiple charging behaviors' interaction on the STD of EV charging load. The proposed method can provide the basis for the overall operation management among the multiple types of charging facilities and the collaborative guidance among the multiple charging behaviors.