Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network

• Published in: Energies (Basel) Vol. 15; no. 5; p. 1869
• Main Authors: Goh, Hui Hwang, Zong, Lian, Zhang, Dongdong, Dai, Wei, Lim, Chee Shen, Kurniawan, Tonni Agustiono, Goh, Kai Chen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2022
• Subjects: Adaptive algorithms; adaptive genetic algorithm; Automobile industry; Clustering; demand price elasticity; demand response; Demand side management; Electric power demand; electric vehicle; Electric vehicles; Electricity; Electricity distribution; Energy consumption; Energy management; fuzzy clustering; Genetic algorithms; Linear programming; Load; Monte Carlo simulation; Optimization; Peak load; Permeability; Price elasticity; Probability distribution; Renewable resources; Time of use; China
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15051869

In order to manage electric vehicles (EVs) connected to charging grids, this paper presents an orderly charging approach based on the EVs’ optimal time-of-use pricing (OTOUP) demand response. Firstly, the Monte Carlo approach is employed to anticipate charging power by developing a probability distribution model of the charging behavior of EVs. Secondly, a scientific classification of the load period is performed using the fuzzy clustering approach. Then, a matrix of demand price elasticity is developed to measure the link between EV charging demand and charging price. Finally, the charging scheme is optimized by an adaptive genetic algorithm from the distribution network and EV user viewpoints. This paper describes how to implement the method presented in this paper in an IEEE-33-bus distribution network. The simulation results reveal that, when compared to fixed price and common time-of-use pricing (CTOUP), the OTOUP charging strategy bears a stronger impact on reducing peak–valley disparities, boosting operating voltage, and decreasing charging cost. Additionally, this paper studies the effect of varied degrees of responsiveness on charging strategies for EVs. The data imply that increased responsiveness enhances the likelihood of new load peak, and that additional countermeasures are required.