考虑出行需求和引导策略的电动汽车充电负荷预测OA北大核心CSTPCD

With electric vehicles (EVs) gradually replacing fueled vehicles,the impact of their charging load on the power grid is increasing. Therefore,this study proposes a spatial-temporal distribution prediction method for the charging load of EVs that considers travel demand and a guidance strategy. First,a semi-dynamic traffic network model that divides functional areas was developed based on a road travel time model. Furthermore,an energy-consumption model of EVs was established,and the charging demand,semi-dynamic transportation network model,energy consumption model,and traditional travel chain were revised according to the influence of the electricity price,climate,and season on the travel demand of vehicle owners. Considering the limited rationality of vehicle owners based on the influence of external factors,a charging load prediction method for private cars and taxis based on a guidance strategy is proposed. Finally,the modified trip chain and OD matrix were used to simulate the travel behavior of private cars and taxis,respectively,in the semi-dynamic traffic network model during the study period,and the validity of the proposed prediction method was verified through a simulation experiment of the semi-dynamic traffic network in the divided regions. The results show that the spatial-temporal distribution of the charging load for EVs is consistent with the analysis of external influencing factors,and the proposed guidance strategy can improve the satisfaction of vehicle owners.