A Coordinated Charging Strategy for Electric Vehicles Based on Valley Load Tracking

He, Lei; Yan, Jiong; Sang, Zixia; Wu, Lei; Du, Zhi; Yang, Danting; Fang, Rengcun; Zhang, Hui; Wang, Yingxiang; Xu, Jiawei; Xu, Xingyuan; Wang, Sicong

doi:10.1088/1742-6596/1314/1/012075

Cited by 1 publication

(2 citation statements)

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“…To begin, a probability distribution model describing EV charging behavior is established. Then, the daily charging power of EVs was predicted using the Monte Carlo method [22]. Second, the price-based demand response strategy is primarily composed of two components: load period division and charging electricity price formulation, both of which are modeled and analyzed in this paper.…”

Section: Ev Charging Model Based On Demand Responsementioning

confidence: 99%

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

et al. 2022

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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.

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Section: Ev Charging Model Based On Demand Responsementioning

confidence: 99%

“…The value of λ directly affects the result of classification. The F test method from statistics is used in this paper to assist in determining the optimal λ. and its calculation method is shown in Equations ( 17)- (22).…”

Section: Load Time Division Based On Fuzzy Clusteringmentioning

confidence: 99%