Improvement of Distribution Network Performance by Optimally Allocating EV Charging Station

Abstract

With zero emissions to the environment, electric vehicles (EVs) are the most environmentally friendly mode of transportation. The placement of EV charging stations (EVCS) in the Radial Distribution Network (RDN) is necessary to satisfy the demand of charging in various places while minimizing power loss on the power system networks. In the distribution system, distributed generation (DG) not only reduces power loss but also enhances power quality. To fully utilize the benefits of DG, it is necessary to find the optimal location and size in the distribution system. In this work, the ideal installation of EVCS has been consistently demonstrated in the IEEE 33 bus distribution network. In order to provide widespread charging facilities, the RDN has been split into three regions, and it has been established that each area has one charging station placed. The main purpose is to minimize the Active Power Loss and Voltage Deviation Index (VDI) to maintain a healthy power system network. Adding DG to the appropriate EV Station is obtained through optimization. This problem has been formulated as a problem of optimization for finding the best location to install EVCS in the IEEE 33 bus RDN by using the Symbiotic Organisms Search (SOS) algorithm. The obtained results have been validated and compared using the Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA).

A hybrid optimization approach for strategically placing electric vehicle charging stations in a radial distribution IEEE-33 bus system

“…A strategic plan for the placement of electric vehicle charging stations that supports the growing adoption of EVs while maintaining the reliability and efficiency of the power distribution system is likely to be proposed using this approach, which likely takes into account a number of factors including electrical load, network stability, and accessibility. Furthermore, the goal function is compared to other methods in the literature [31,38,39,43,44,45,46,47,48]. This shows that the hybrid optimizer presented in this research shows more better results than the other state of the art techniques.…”