EURO 2025 Leeds
Abstract Submission

1619. Multi-period planning for electric vehicle charging station location and sizing with station-pile compatibility constraints

Invited abstract in session MB-15: Location problems, stream Combinatorial Optimization.

Monday, 10:30-12:00
Room: Esther Simpson 1.08

Authors (first author is the speaker)

1. Song Wu
School of management, Northwestern Polytechnical University
2. Yang Wang
School of Management, Northwestern Polytechnical University
3. Wei-Neng Chen
School of Computer Science and Engineering, South China University of Technology
4. Jin-Kao Hao
LERIA, Université d’Angers

Abstract

Electric vehicles (EVs) are essential for advancing clean transportation and achieving carbon neutrality, yet their widespread adoption is hindered by insufficient charging infrastructure. This study addresses this challenge by introducing a novel multi-period optimization problem for the strategic location and sizing of charging stations. Over the planning horizon, demand points emerge progressively, with both fast-charging and slow-charging demands increasing over time. The objective is to minimize the total costs of station construction and pile deployment. Specifically, we consider stations offering a combination of fast and slow charging services, utilizing multiple pile types with varying power levels to meet diverse demands. Charging stations are further classified into different scales based on the number of piles, with each scale supporting a subset of available pile types. Additionally, station service capacity is constrained by the surrounding power grid. To solve this complex problem, we develop a mixed-integer programming model and propose an iterative kernel search matheuristic algorithm. Extensive computational experiments on 40 instances reveal that the proposed algorithm outperforms the Gurobi solver in 90% of instances, delivering better solutions within shorter computation times. Furthermore, a real-world case study based on the location data of 1,000 parking lots in Guangzhou is conducted, generating an optimal five-year deployment plan.

Keywords

Status: accepted

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