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Research paper | Open Access

A bi-level optimization framework for charging station design problem considering heterogeneous charging modes

Le Zhang1( )Ziling Zeng2Kun Gao2
School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China
Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden
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Abstract

Purpose

The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes.

Design/methodology/approach

The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model.

Findings

This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically.

Originality/value

The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses.

References

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Journal of Intelligent and Connected Vehicles
Pages 8-16

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Cite this article:
Zhang L, Zeng Z, Gao K. A bi-level optimization framework for charging station design problem considering heterogeneous charging modes. Journal of Intelligent and Connected Vehicles, 2022, 5(1): 8-16. https://doi.org/10.1108/JICV-07-2021-0009

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Received: 08 July 2021
Revised: 05 December 2021
Accepted: 16 December 2021
Published: 24 January 2022
© 2022 Le Zhang, Ziling Zeng and Kun Gao. Published in Journal of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited.

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode