Location Optimization of Charging Stations for Electric Vehicles Based on Heterogeneous Factors Analysis and Improved Genetic Algorithm

It is critical to properly plan charging station locations for wider acceptance and penetration of electric vehicles (EVs). Siting and optimizing charging stations in large cities are a massive combinatorial optimization problem involving various factors, and finding a satisfactory solution remains challenging. To solve this problem, this study analyzes the multisource heterogeneous data related to charging station planning and predicts potential charging demands, thus paving the road for siting location optimization. A multiobjective siting optimization model of charging stations is established with the consideration of minimal investment, maintenance, and user access costs. An improved genetic algorithm (GA) is developed to seek the optimization solution by merging the simulated annealing algorithm, adaptive intersection crossover operator, and elite retention strategy. A case study validation in Chongqing, China shows that the latent charging demand and other factors can provide data to support siting optimization, making charging stations with different grades more evenly distributed and more convenient for users to access. More broadly, the proposed GA enables the avoidance of prematurity and achieves a better solution than the state-of-the-art bionic intelligent algorithms, with a cost decrease of at least 3.22%.