EV Charging Load Forecasting Considering Urban Traffic Road Network and User Psychology under Multi-time Scenarios

Abstract

Aiming at the problem of space-time transfer and charging decision-making of household electric vehicles (EVs) and taxis under complex road networks in different scenarios, a space-time forecasting model of EV charging load considering urban traffic road network and user psychology is proposed. First, the space-time transfer model of household EV trip is established based on the national family trip survey data and the trip chain theory. The taxi trip rules are simulated according to the trip order data and origin-destination (OD) analysis method, and the Markov dynamic decision model based on optimal policy is used to choose the path. Second, the influence of user psychology on charging decisions is analyzed through the anchoring effect, and the charging decision model is established. Third, according to the measured data of temperature and real-time traffic conditions, the EV trip energy consumption model is built. Simulation results show that complex traffic conditions and extreme temperature will lead to a continuous increase of charging load demand, the random charging behavior influenced by users’ psychology will lead to significant differences in charging loads in the spatial and temporal distribution.