Demand Forecast of Electric Vehicle Charging Stations Based on User Classification

Yan Qing Li; Zi Hang Jia; Fei Long Wang; Ying Zhao

doi:10.4028/www.scientific.net/AMM.291-294.855

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 291-294Demand Forecast of Electric Vehicle Charging...

Demand Forecast of Electric Vehicle Charging Stations Based on User Classification

Abstract:

The need of electric vehicle charging stations is described in this paper, and in the paper, the electric vehicle users are classified into three categories, among which Type A users are of strong regular, type B users weak while type C users are randomness. On the basis of historical data, the tenure at a future time is easily to be predicted with the method of multiple linear regressions. Then according to the relevant national policies, the investigation and the Bass diffusion model, the proportion and charging power demand of all types of users can be predicted. Finally, based on the number of all types of users, the request of charging duration, the reserve capacity of routine maintenance, special events and other factors, a reasonable prediction of the conventional charging stations in the particular region is reasonably made. The result of the example shows that the method has strong practicality.

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Periodical:

Applied Mechanics and Materials (Volumes 291-294)

Pages:

855-860

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.291-294.855

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Online since:

February 2013

Authors:

Yan Qing Li, Zi Hang Jia, Fei Long Wang, Ying Zhao

Keywords:

Charging Power Demand, Charging Station, Electric Vehicle (EV), Forecast, User Classification

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References

[1] Ling LI, Yanqing LI, Yuhai YAO, et al. East China Electric Power, 2011, 39(6): 1004-1006.

Google Scholar

[2] Sili ZHOU. Design and Simulation Research on EV Charging Stations with Photovoltaic Power[D]. Anhui University, (2008).

Google Scholar

[3] Fan XU, Guoqing YU, Linfeng GU, et al. East China Electric Power, 2009, 37(10): 1677-1680.

Google Scholar

[4] Jianxin YAO, Mei WANG, Weiming LUO. East China Electric Power, 2008, 36(8): 107-110.

Google Scholar

[5] Zhuowei LUO, Zechun HU, Yonghua SONG, et al. Automation of Electric Power System, 2011, 35(14): 36-42.

Google Scholar

[6] Juan HAO, Qiang LI, Jianhua YUE. Inner Mongolia Electric Power, 2010, 28(S2): 7-9.

Google Scholar

[7] Lingfeng KOU, Zifa LIU, Huan ZHOU. Modern Electric Power, 2010, 27(4): 44-48.

Google Scholar