Matching analysis of new energy vehicle charging demand and charging infrastructure power supply capacity: A case study of China's capital Beijing

Abstract

The random charging behavior of new energy vehicles (NEVs) will bring new challenges to the matching between electric vehicle charging facilities (EVCF) and NEVs. In order to explore whether the power supply capacity of urban EVCF can meet the charging requirements of NEVs, using the progression of NEVs in Beijing as a basis, initially, the Monte Carlo simulation (MCS) approach simulates the power demand trajectory for NEVs in the region. Subsequently, to forecast the ownership trends of NEVs across three distinct scenarios from 2021 to 2030, the study employs Grey correlation analysis combined with the bidirectional long short-term memory model (GRA-BiLSTM), facilitating the determination of NEVs’ charging needs. Second, the charging supply of EVCF for the next 10 years is derived by analyzing different development scenarios with three growth rates of EVCF and three combinations of fast and slow pile ratios. The findings indicate a discrepancy between the rate of increase in ownership of NEVs and the rate of increase in charging infrastructure in Beijing between 2021 and 2030. Even under a scenario of high growth in NEV ownership, the balance between supply and demand for charging capacity is not achieved, resulting in suboptimal utilization of charging infrastructure.