Optimal Planning of Electric Vehicle Charging Station Based on Spatio-Temporal Distribution Prediction of Charging Demand

ObjectivesIn order to solve the mismatch between charging demand and supply caused by the rapid growth of electric vehicles, and the problems of high user dissatisfaction and great impact on the power grid caused by unreasonable planning of electric vehicle charging station (EVCS), a multi-objective EVCS optimal planning method based on the prediction of the space-time distribution of electric vehicle charging demand is proposed.MethodsFirst, the road network data is obtained through the OpenStreetMap open-source website, and it is visualized using QGIS software to establish a road network topology model. On this basis, a dynamic road network model including road grade, traffic flow, speed, and other parameters is established. Second, the road network nodes are divided into functional areas using point of interest data, and the spatiotemporal distribution of charging demand is predicted based on the analysis of relevant factors affecting the prediction of charging demand, the establishment of an energy consumption model and the judgment of charging demand. Third, the multi-objective EVCS planning model is established with the minimum comprehensive cost of EVCS, user dissatisfaction, and grid voltage fluctuation as the objective functions. Finally, the planning model is applied to the actual region, and the fast convergence multi‑objective equilibrium optimizer with archive evolution path (FC-MOEO/AEP) is used to optimize the model.ResultsThe proposed EVCS optimal planning model is feasible in the actual region, and the charging demand prediction model can accurately predict the charging demand distribution in the actual region, which provides the necessary data support for the reasonable planning of EVCS to a certain extent.ConclusionsThe proposed planning method provides a reference for the layout of EVCS and the participation of electric vehicles in power grid dispatching, and achieves a tripartite win-win situation among investors, electric vehicle users, and the power grid.