Optimising Electric Vehicle Charging Station Placement Using Advanced Discrete Choice Models

Steven Lamontagne (), Margarida Carvalho (), Emma Frejinger (), Bernard Gendron (), Miguel F. Anjos () and Ribal Atallah ()
Additional contact information
Steven Lamontagne: CIRRELT and Département d’Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Quebec H3T 1J4, Canada
Margarida Carvalho: CIRRELT and Département d’Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Quebec H3T 1J4, Canada
Emma Frejinger: CIRRELT and Département d’Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Quebec H3T 1J4, Canada
Bernard Gendron: CIRRELT and Département d’Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Quebec H3T 1J4, Canada
Miguel F. Anjos: School of Mathematics, University of Edinburgh, Edinburgh EH8 9YL, United Kingdom
Ribal Atallah: Institut de Recherche d’Hydro-Québec, Varennes, Quebec J3X 1S1, Canada

INFORMS Journal on Computing, 2023, vol. 35, issue 5, 1195-1213

Abstract: We present a new model for finding the optimal placement of electric vehicle charging stations across a multiperiod time frame so as to maximise electric vehicle adoption. Via the use of stochastic discrete choice models and user classes, this work allows for a granular modelling of user attributes and their preferences in regard to charging station characteristics. We adopt a simulation approach and precompute error terms for each option available to users for a given number of scenarios. This results in a bilevel optimisation model that is, however, intractable for all but the simplest instances. Our major contribution is a reformulation into a maximum covering model, which uses the precomputed error terms to calculate the users covered by each charging station. This allows solutions to be found more efficiently than for the bilevel formulation. The maximum covering formulation remains intractable in some instances, so we propose rolling horizon, greedy, and greedy randomised adaptive search procedure heuristics to obtain good-quality solutions more efficiently. Extensive computational results are provided, and they compare the maximum covering formulation with the current state of the art for both exact solutions and the heuristic methods.

Keywords: electric vehicle charging stations; facility location; integer programming; discrete choice models; maximum covering (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/ijoc.2022.0185 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:35:y:2023:i:5:p:1195-1213

Access Statistics for this article

More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().