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Valley-Period Dispatched Strategy of Electric Vehicles in Charging Station

descriptionPublicationkeyboard_double_arrow_right Conference object , Article 01 Sep 2019Publisher:IEEEJournal:2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG)

Authors: Yinghao Ma; Yeyu Zhang; Hao Zhenyu; Hejun Yang;

doi: 10.1109/igbsg.2019.8886268

Valley-Period Dispatched Strategy of Electric Vehicles in Charging Station

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Abstract

Electric vehicles (EVs) have been gradually known to the public because of their advantages of energy saving and environmental protection. However, its charging behavior is random in space and time, which has an impact on the quality of the power grid and its safe and economic operation. A valley-period dispatched charging model and strategy based on the particle swarm optimization (PSO) algorithm is proposed in this paper. It can make full use of each period of the valley-period to complete the charging of EVs, and reduce the peak-valley ratio of load profiles in the valley-period significantly. The Monte Carlo simulation is used to simulate the charging load profiles of EVs under different charging strategies. The results show that the proposed strategy can effectively optimize the charging load profiles of EVs in the valley-period.

Related Organizations

Hefei University of Technology
China (People's Republic of)
Hefei University of Technology
China (People's Republic of)

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	3
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average