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Smart green charging scheme of centralized electric vehicle stations

descriptionPublicationkeyboard_double_arrow_right Article , Journal 27 Jul 2021 Egypt Publisher:Informa UK LimitedJournal:International Journal of Green Energy, volume 19, pages 490-498 (issn: 1543-5075, eissn: 1543-5083,

Authors: M. A. Elkasrawy; Hani A. Ghali; Sameh O. Abdullatif; Saim Memon; Peter Makeen; Peter Makeen;

doi: 10.1080/15435075.2021.1947822

Smart green charging scheme of centralized electric vehicle stations

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Abstract

This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process. The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively.

Country

Egypt

Related Organizations

London South Bank University
United Kingdom
London South Bank University
United Kingdom
British University in Egypt
Egypt

Keywords

Electronic Devices and Semiconductor Manufacturing, Electric vehicles station (EVs)Pv-grid integrated supplywater cycle optimization technique (wcot)electric vehicles (Ev) charging timelevelized cost of energy (LCOE), 330, 006

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).	26
	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.	Top 10%