Ultra-Short-Term Load Forecast of Charging Stations Considering the Travelling and Charging Behaviours of Electric Vehicles

Abstract

The traditional electric vehicle (EV) load forecasting methods are mostly used to predict large areas such as cities, which makes it difficult to serve practical applications. The existing charging station load forecasting methods ignore the traveling and charging behaviors of EVs. To this end, a charging station ultra-short-term load prediction method considering the traveling and charging behaviors of EVs is proposed. Firstly, EV types are classified according to charging behaviors. On this basis, a rolling forecast model of the number of arrivals per unit time of each vehicle type is established, and then the impact of charging randomness on load forecast is reduced by repeated Monte Carlo sampling. The total load of charging stations during the prediction period can be obtained by adding up the load of individual vehicles. The results show that the method enables accurate prediction of ultra-short-term load at charging stations, which has higher accuracy and reference value than the traditional load forecasting methods.