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Lookup NU author(s): Farzaneh FarhadiORCiD, Professor Roberto Palacin, Professor Phil BlytheORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 The Author(s). This paper presents a data-driven methodology combining simulation and multi-objective optimization to efficiently implement transportation policy commitments, using as a case study the electric vehicle (EV) charging infrastructure in Newcastle upon Tyne, United Kingdom. The methodology leverages a baseline simulation model developed by our industry partner, Arup Group Limited, to estimate EV demand and quantities from 2020 to 2050. Four future energy scenarios are considered, and a multi-objective optimization approach is employed to determine the optimal types, locations, and quantities of charging points, along with the corresponding total capital and operational expenditures and charging point operating hours. Quantitatively, the variations of the portions of different types of charging points for the four scenarios are relatively small and within 3% range of the total number of charging points. The optimal solutions put priority on the slower charging points, with faster charging points having smaller portions each around 10%–13%.
Author(s): Farhadi F, Wang S, Palacin R, Blythe P
Publication type: Article
Publication status: Published
Journal: iScience
Year: 2023
Volume: 26
Issue: 10
Online publication date: 31/08/2023
Acceptance date: 02/04/2018
Date deposited: 28/09/2023
ISSN (electronic): 2589-0042
Publisher: Elsevier Inc.
URL: https://doi.org/10.1016/j.isci.2023.107737
DOI: 10.1016/j.isci.2023.107737
Data Access Statement: Original datasets for electric vehicle charging optimization are publicly available with links listed in the key resources table. (https://www.sciencedirect.com/science/article/pii/S258900422301814X?via%3Dihub#sec4.1) All original code has been deposited at Github and is publicly available as of the date of publication. DOI is listed in the key resources table (https://www.sciencedirect.com/science/article/pii/S258900422301814X?via%3Dihub#sec4.1) Any additional information required to reanalyse the data reported in this paper is available from the lead contact (Farzaneh Farhadi, F.Farhadi2@newcastle.ac.uk) upon request.
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