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, Kai Chen Goh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

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.

Original languageEnglish
Article number1869
JournalEnergies
Volume15
Issue number5
DOIs
Publication statusPublished - 1 Mar 2022

Keywords

  • Adaptive genetic algorithm
  • Demand price elasticity
  • Demand response
  • Electric vehicle
  • Fuzzy clustering

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