Spatial-temporal distribution prediction of charging load for electric vehicles based on dynamic traffic information

Charging load prediction of electric vehicles is an important prerequisite for studying the interaction between electric vehicles and power grid. Aiming at the influence of traffic road network information on the driving rule of electric vehicles, the characteristics of both transportation and mobile load are taken into consideration and a spatial-temporal distribution prediction method of charging load for electric vehicles based on dynamic traffic information is presented. In this methodology, given the characteristic of multiple intersections in the urban road network, a dynamic road network model with the impedance of the road segment and the impedance of the node is firstly established. And also, the corresponding interactive model of transportation network-distribution network is determined according to the scale of road network. And then, the OD matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced to assign start-stop nodes and plan driving paths for electric vehicles and simulate their dynamic driving process and charging behavior. At last, the electric vehicle path planning experiment and the actual road network charging load prediction experiment in typical regions are designed. The results show that the charging load of electric vehicles varies in different functional regions and their temporal distribution is also uneven, verifying the effectiveness and feasibility of the proposed strategy. © 2020, Power System Protection and Control Press. All right reserved.