Placement of Electric Vehicle Fast Charging Stations using Grey Wolf Optimization in Electrical Distribution Network

Abstract

Automotive industries and government organizations are paying close attention to electric vehicles (EVs) because of their lower CO2 emissions, cheap maintenance, and operating costs. As the number of EVs on the road grows, the charging station's load has an impact on distribution network parameters such as power loss, voltage profile, and harmonic distortion. As a result, the distribution network's stability depends on the right location of electric vehicle fast-charging stations (EVFCSs). In this paper, two approaches are proposed for the placement of EVFCS as distribution network operator (DNO) approach and charging station investor (CSI) approach. In the DNO approach power loss of distribution network whereas in the CSI approach the installation cost of EVFCS is proposed as the objective function for the problem formulation. The proposed formulated problem has binary decisions variables therefore the optimization problem is solved by the binary version of grey wolf optimization (GWO). The optimal location of EVFCS is proposed for IEEE 34 bus distribution system with the vehicle to grid (V2G) strategies. Optimal location of EVFCSs with V2G strategies results are also compared without V2G strategies. The direct load flow approach is used in this paper for a load flow analysis of the IEEE 34 distribution system. The optimal location of solar power distributed generation (SPDG) is also considered in this paper for minimizing the grid stress due to EV load. 2022 IEEE