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International Journal of Green Energy Volume 19, 2022 - Issue 5
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Research Article

Smart green charging scheme of centralized electric vehicle stations

Peter Makeena London Centre for Energy Engineering, Division of Electrical and Electronic Engineering, School of Engineering, London South Bank University, UK;b FabLab in the Center for Emerging Learning Technologies (CELT) and Electrical Engineering Department, Faculty of Engineering, The British University in Egypt (BUE), Cairo, EgyptCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-2907-9800
ORCID Icon,
Saim Memona London Centre for Energy Engineering, Division of Electrical and Electronic Engineering, School of Engineering, London South Bank University, UKORCID Iconhttps://orcid.org/0000-0003-2598-8935
ORCID Icon,
M. A. Elkasrawyc FabLab in the Center for Emerging Learning Technologies (CELT) and Renewable Energy Postgraduate Programme, Faculty of Engineering, The British University in Egypt (BUE), Cairo, Egypt
,
Sameh O. Abdullatifb FabLab in the Center for Emerging Learning Technologies (CELT) and Electrical Engineering Department, Faculty of Engineering, The British University in Egypt (BUE), Cairo, Egypt
&
Hani A. Ghalib FabLab in the Center for Emerging Learning Technologies (CELT) and Electrical Engineering Department, Faculty of Engineering, The British University in Egypt (BUE), Cairo, Egypt
Pages 490-498 | Received 09 Oct 2020, Accepted 20 Jun 2021, Published online: 27 Jul 2021
 
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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.

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