Optimal Allocation Of Distributed Generation And Fast Electric Vehicle Charging Stations On The Ashanti Region Network.

Abstract

The focus of the study was the optimal placement and sizing of distributed generation(DG) units and fast electric charging stations on the Ashanti region network. DG units would have to be injected while electricity demand from EV charging infrastructure increases. Two metaheuristics were adopted to solve the allocation problem. The IEEE 69 bus and 33KVA Ashanti region distribution networks were used in the study. ETAP and MATLAB environments were used to simulate the design. The particle swarm optimization (PSO) and artificial bee colony(ABC) algorithms were adopted for the solution. The proposed PSO outperformed ABC, TLBO, GA, HHO, WOA, and SA. The proposed PSO and ABC reduced power losses by 68% while maintaining the system voltage profile within 4% of the IEC standard. The two methods can be employed for simultaneous placement even at high penetration levels to reduce network active power loss and improve voltage profile. The Ashanti region analysis predicted a DG-powered fast EVCS on bus 14 to reduce system power loss. The model used can be adopted for examining network loss and voltage profile enhancement when DG units and fast EVCS are to be allocated on other complicated power system networks. An AI-based sizing and placement of distributed generation and fast electric vehicle charging stations will enable power systems to meet consumer needs (e.g., electric vehicle charging) and enhance the deployment of DG units on any network.