Two-stage stochastic programming model to locate capacitated EV-charging stations in urban areas under demand uncertainty

Due to the dangerous effects of fossil fuels, policymakers tend to substitute fossil-fuel-based vehicles with electric ones. Thus, the optimal design of a charging station network providing convenient access for the users is of great importance. This paper presents a two-stage stochastic programming model for the problem of locating charging stations in urban areas. Parking lots around the buildings which may be visited by people during the day are considered as potential locations for charger installation. The model determines the parking lots that should be equipped with chargers and the number as well as the type of chargers that must be placed in each parking lot considering the demand as an uncertain parameter. The proposed model is examined on the dataset of a midtown area, taken from the literature, and an efficient heuristic algorithm based on Benders decomposition is utilized to solve the model. The results indicate that the heuristic method can find a near-optimal solution (with the optimality gap of at most 0.05%) in a short time.