Turkish Journal of Electrical Engineering and Computer Sciences




Recently, electric vehicles (EVs) have been seen as a felicitous option towards a less carbon-intensive road transport. The key issue in this system is recharging the EV batteries before they are exhausted. Thus, charging stations (CSs) should be carefully located to make sure EV users can access a CS within their driving range. Considering geographic information and traffic density, this paper proposes an optimization overture for optimal siting and sizing of a rapid CS (RCS). It aims to minimize the daily total cost (which includes the cost of substation energy loss, traveling cost of EVs to the CS, and investment, variable, and operational costs of the stations simultaneously) while maintaining system constraints. The binary gravitational search algorithm, genetic algorithm, and binary particle swarm optimization algorithm were employed to optimize the daily total cost by finding the best location and sizing of the RCS in a given metropolitan area in Malaysia. The results show that the proposed methods can find optimal locations and sizing of a RCS that can benefit EV users, CS developers, and the power grid.


Electric vehicles, rapid charging station, optimal planning, gravitational search algorithm, genetic algorithm, particle swarm optimization

First Page


Last Page