A Load Forecast Method for Fast Charging Stations of Electric Vehicles on the freeway considering the information interaction. (December 2017)

• Record Type: Journal Article
• Authors: Dong, Xiaohong; Yuan, Kai; Song, Yi; Mu, Yunfei; Jia, Hongjie
• Is Part Of: Energy procedia. Volume 142(2017)
• Page Start: 2171
• Page End: 2176
• Publication Date: 2017-12
• Subjects: Electric vehicle (EV) -- load forecast of fast charging stations -- freeway -- information interaction; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.12.584
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: A load forecast method for fast charging stations (FCSs) of electric vehicles (EVs) on the freeway considering the information interaction is proposed. Firstly, an EV travelling simulation model is proposed to obtain the optional FCSs and the time arriving at the corresponding FCS. A queuing model is proposed to determine the estimated average waiting time of each FCS. Then, in order to minimize the time cost of the EV user, an active charging choice model for EV users is proposed to determine the selected FCS and the time arriving at the corresponding FCS based on the information interaction. Finally, the charging load of each EV FCS is determined considering the impacts from the information interactions between the EV users and the FCSs. Simulation results show that the information interaction has a significant impact on the charging load allocation among the different FCSs.