Electrical Engineering and Automation College of Wuhan University
National Natural Science Foundation of China,MOE (Ministry of Education in China) Project of Humanities and Social Sciences,National Key R&D Plan of China
In this paper, a framework of EV (electric vehicle) charging load forecasting is proposed, which considers user psychology and information interaction of road network and power network. Firstly, the destination of electric vehicle is obtained by travel chain and OD matrix. Secondly, considering driving time, queuing time and charging price, a model of choosing charging station based on Regret Theory is proposed. Thirdly, based on the car following model, the micro traffic analysis of vehicle driving process in the road network is carried out. And the framework of charging load forecasting considering the interaction of road network and power network driven by charging price is established. Finally, the Monte Carlo method was used to simulate the travel and charging of EVs, so as to predict the time-space distribution of charging load of EVs. Through the simulation on Beijing Third Ring Road Network and the corresponding power grid, the effectiveness of the proposed EV charging load prediction framework is verified. The results also showed that the road network and power grid through charging price make the charging load distribution of electric private cars and taxis significantly different in time and space.
LONG Xuemei,YANG Jun,WU Fuzhang,et al.Prediction of Electric Vehicle Charging Load Considering Road Network-Power Grid Interaction and User’s Psychology[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20191011008.