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Locating electric vehicle fast-charging stations under uncertain driving range: a chance-constrained programming approach

EasyChair Preprint no. 2592

2 pagesDate: February 6, 2020


Electric vehicles (EVs) represent one of the promising solutions to face environmental and energy concerns in transportation. Due to the limited range of EVs, deploying a charging infrastructure enabling EV drivers to carry out long distance trips is a key step to foster the widespread adoption of EVs. In this work, we study the problem of locating EV fast charging stations so as to satisfy as much recharging demand as possible within the available investment budget. We seek to take into account uncertainties on the vehicle driving range in the problem modeling. We propose to relax several modeling assumptions previously used in the literature to handle this problem. First, we allow the power consumption on a road segment to depend on the crossing direction. Second, we take into account uncertainties related to the energy available in the battery after recharging at a station as well as uncertainties related to the power consumption on each portion of the road network. Finally, we consider statistical dependencies between the stochastic power consumption on different arcs of the network.

We focus on the chance-constrained flow refueling location model, which seeks to maximize the number of drivers for whom the probability of running out of fuel when carrying out their trip is below a certain threshold. To solve the resulting stochastic optimization problem, we propose a solution approach based on a partial sample approximation of the stochastic parameters and compare its performance with the one of a previously published approach based on Bonferroni's inequality. We carry out numerical experiments on a set of medium size randomly generated and real life instances. Our results show that the proposed partial sample approximation approach outperforms the Bonferroni approach in terms of solution quality and gives station locations which provide a significantly improved demand coverage in practice.

Keyphrases: chance-constrained programming, charging stations, electric vehicle, Partial sample approximation, Stochastic facility location

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Mouna Kchaou-Boujelben and Céline Gicquel},
  title = {Locating electric vehicle fast-charging stations under uncertain driving range: a chance-constrained programming approach},
  howpublished = {EasyChair Preprint no. 2592},

  year = {EasyChair, 2020}}
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