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

Hui Hwang Goh, Lian Zong, Dongdong Zhang, Wei Dai, Chee Shen Lim, Tonni Agustiono Kurniawan and Kai Chen Goh
Additional contact information
Hui Hwang Goh: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Lian Zong: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Dongdong Zhang: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Wei Dai: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Chee Shen Lim: Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 111 Ren’ai Road Suzhou Industrial Park, Suzhou 215123, China
Tonni Agustiono Kurniawan: College of Environment and Ecology, Xiamen University, Xiamen 361102, China
Kai Chen Goh: Department of Technology Management, Faculty of Construction Management and Business, University Tun Hussein Onn Malaysia, Johor Bahru 86400, Johor, Malaysia

Energies, 2022, vol. 15, issue 5, 1-25

Abstract: 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.

Keywords: electric vehicle; demand response; fuzzy clustering; demand price elasticity; adaptive genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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