Optimal Fast Charging Station Development Model Using Real Time Traffic Information

Carbon emissions from conventional vehicles have significantly damaged the ecosystem balance, and have pushed the transportation sector to switch towards cleaner fuel sources that can power vehicles more efficiently. Electric vehicles (EV) offer an excellent substitute for fossil fuel-fired vehicles; however, the availability of publicly accessible charging stations is essential for wider adoption and acceptance of electric vehicles by customers. This paper presents an optimal fast charging station planning method to determine station location and station capacity. The method aims to minimize the overall station cost that includes investment costs, operation costs, and EV charging costs without compromising the EV user benefits and distribution network performance constraints. In addition, queuing theory based station capacity estimation is employed to ensure adequate station efficiency. Furthermore, this planning methodology uses Google Map Services to estimate the average time a user is required to access the charging station from the demand point, taking into account real road traffic flow information. The developed model is solved and optimized using a Binary Particle Swarm Optimization algorithm. The simulation result validates the novelty and feasibility of the developed charging station model.