Effect of uncertainties on siting and sizing of charging stations and renewable energy resources: A modified capacitated flow-refueling location model

Abstract

Fast-charging stations (FCSs) are critical charging infrastructures for electric vehicles (EVs) that can impose a significant load demand on the distribution network. Therefore, one of the original challenges in the development of EVs is the optimal allocation of FCSs considering the goals and limitations of both transportation and distribution networks. This paper proposes a model for locating FCSs and renewable energy resources (RESs) taking into account the EVs behavior as well as a wide range of affecting factors, including the uncertainty associated with the RESs, EVs arrival rate, and state of charge (SoC), different driving ranges, and all-shortest and deviation paths. The proposed model minimizes the costs of installing FCSs and RESs and substation expansion and maximizes the traffic flow captured by the FCSs. For this purpose, first, the modeling of the uncertainties of RESs and the capacitated diversion flow-refueling location model based on expanded networks (CDFRLM-EN) is introduced to cover the charging demand of EVs on the transportation network. Then the problem of locating FCSs and RESs is formulated based on the mixed-integer linear programming (MILP) model. The proposed framework is implemented on a 14-bus distribution test system coupled with a 25-node transportation network. The results show that the presence of RESs, diversion paths, and the uncertainty associated with the arrival state of charge and the driving range differences influence the location and size of FCSs, the investment cost, and the EVs’ satisfaction.