Demand allocation policies for the charging station location and sizing problem

We investigate the problem of optimizing the expansion of an existing public charging network for electric vehicles, over multiple years and multiple daily demand periods. Considering node-based demand, we optimize decisions related to setting up new charging stations (CSs), and the number of charging points to install at each CS. The objective of the problem is minimizing the installation costs subject to a minimum coverage requirement. One important assumption of the problem pertains to how demand is allocated to CSs. To this end, we propose seven compact models, considering four different demand allocation policies, two of which are new, considering fractional and binary assignment for three such policies. We investigate the implications of different allocation policies on the solutions to the problem, which we quantify using an adaptation of the Wasserstein distance. We conducted a set of computational experiments considering instances adapted from the literature and instances based on data from Bologna and Genova, Italy. Through our experiments, we demonstrate the impact of the different allocation policies.