Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network

Abstract

With the widespread use of electric vehicles, their charging power demand has increased and become a significant burden on power grids. The uncoordinated deployment of electric vehicle charging stations and the uncertainty surrounding charging behaviors can cause harmful impacts on power grids. The charging power demand during the fast charging process especially is severely fluctuating, because its charging duration is short and the rated power of the fast chargers is high. This paper presents a methodology to analyze and forecast the aggregated charging power demand from multiple fast-charging stations. Then, pattern of fast-charging power demand is analyzed to identify its irregular trend with the distribution of peak time and values. The forecasting model, based on long short-term memory neural network, is proposed in this paper to address the fluctuating of fast-charging power demand. The forecasting performance of the proposed model is validated in comparison with other deep learning approaches, using real-world datasets measured from fast-charging stations in Jeju Island, South Korea. The results show that the proposed model outperforms forecasting fast-charging power demand aggregated by multiple charging stations.