Quantitative Evaluation Method on Electric Vehicle Accommodation Capability of Urban Distribution Network

Large scale of electric vehicles (EVs) integration will have a significant impact on the operation and planning of urban distribution network (UDN). The assessment of the adaptability of the UDN to the integration of large-scale EVs is an important factor to be considered in the future operation and planning of the UDN. Under this background, this paper proposes a quantitative evaluation method for the electric vehicle accommodation capability (EVAC) of UDN. Firstly, kernel density estimation (KDE) method is used to approximate the probability distribution function (PDF) over two characteristic variables of the charging behaviors of EVs in electric vehicle charging stations (EVCSs), namely, the charging start time and the charging duration. Secondly, Monte Carlo (MC) method is used to model EVs charging demand. On this basis, with the goal of maximizing the penetration rate of EVs in UDN, a quantitative evaluation model for EVAC of UDN is established. The EVAC of UDN under different scenarios is compared, and the model is verified to be effective. This method is a powerful tool for UDN operation and planning.