Electric Vehicle Charging Infrastructure Location Optimization with Mixed and Forecasted Charging Requirements

Abstract

Electric vehicles are not widely adopted without proper charging infrastructure, despite their environmental benefits and growing popularity in transportation. This paper focuses on the location problem of charging infrastructure to achieve a more optimized charging facility layout. The charging demands of electric vehicles can be divided into two categories. The first category is generated at network points such as shopping malls, office buildings, parking lots, and residential areas. The second category is generated along the flow of network paths, such as on the highway and on the way to and from work. The goal of this problem is to maximize both categories of charging demands using a nonlinear integer programming model. We introduce the spatial intersection model to obtain the data on path demand. The spatial intersection model is introduced to obtain data on path demand. In addition, future demand is taken into account in the optimization through data forecasting. Then, the greedy algorithm is designed to solve the optimization model. The effectiveness is proved by a lot of random experiments. Finally, the effects of parameters are analyzed by a case study. The location decision of charging stations for both demands is more reasonable than only one type of demand consideration. The proposed model ensures the coverage and appropriate extension of the charging network.