Akademik Veri Yönetim Sistemi
4th IEEE Global Power, Energy and Communication Conference, GPECOM 2022, Cappadocia, Türkiye, 14 - 17 Haziran 2022, ss.473-477
© 2022 IEEE.The huge increase in the number of electric vehicles in recent years has been the focus of researchers with different types of demand response programs and the charge management of electric vehicles. However, a large number of electric vehicles connected to the distribution system may cause the problems including increase in peak demand and voltage fluctuation in the power system. In this study, the optimum energy management model of electric vehicle parking lots has been implemented by heuristic methods which are Genetic Algorithm and Particle Swarm Optimization. The proposed methods include a demand response program that considers the peak load limitation and aims to maximize the load factor, and also consider the uncertainties such as the arrival time of the electric vehicles at the electric vehicle parking lots and their state of energy on arrival. Various cases were created by using both algorithms to test the accuracy of the electric vehicle parking lots and significant improvements in the load factor were obtained. In this study, better results were obtained with Genetic Algorithm compared to Particle Swarm Optimization for the proposed approach.