A Demand-Based Placement of Optimal EV Fast Charging Stations in Urban Transportation Network

Improper placement and inadequate sizing of electric vehicle (EV) charging stations can have detrimental impacts on the progress of EVs, the configuration of the transportation network, and the convenience of EV drivers, ultimately resulting in increased construction costs for charging stations. In order to deal with this problem, this research proposes a two-stage approach to determine the optimal locations for fast charging stations (FCS) in an urban transport network (UTN) to meet the charging requirements of EVs. During the first stage, a zonal division model (ZDM) is used to determine charging station candidate locations (CSCL). In this stage, the UTN is divided into separate zones based on certain distance and charger number constraints. This ensures that charging stations are not allocated at every node. In the second stage, the charging station optimal locations (CSOL) are determined from the CSCLs with the goal of minimizing objective functions, which include station construction cost (SCC) and electric vehicle customer expenditure (EVCE). To minimize the objective functions, a point reduction technique (PRT) is proposed in this paper to determine the optimal FCS locations. The simulation results show that the proposed algorithm is effective in minimizing the SCC and EVCEs compared to the genetic algorithm (GA) and conventional mixed integer linear program (MILP).