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Data-driven multi-objective optimization for electric vehicle charging infrastructure

Lookup NU author(s): Farzaneh FarhadiORCiD, Professor Roberto Palacin, Professor Phil BlytheORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 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%.

Publication metadata

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

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