A multi-stage stochastic integer programming approach for locating electric vehicle charging stations

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 E...

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Bibliographic Details
Published in:Computers & operations research Vol. 117; pp. 104888 - 19
Main Authors: Kadri, Ahmed Abdelmoumene, Perrouault, Romain, Boujelben, Mouna Kchaou, Gicquel, Céline
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.05.2020
Pergamon Press Inc
Elsevier
Subjects:
Benders decomposition
Computer Science
Computer simulation
Decision making
Demand
Electric vehicle charging
Electric vehicle charging stations
Electric vehicles
Exact solutions
Facility location problem
Genetic algorithm
Genetic algorithms
Heuristic methods
Integer programming
Integers
Mathematical programming
Multi-stage stochastic optimization
Operations Research
Optimization
Recharging
Scenario tree
Uncertainty
Electric vehicles
Scenario tree
Facility location problem
Benders decomposition
Genetic algorithm
Multi-stage stochastic optimization
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
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Summary: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 paper, 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 focus on incorporating two important features into the optimization problem modeling: a multi-period decision making horizon and uncertainties on the recharging demand in terms of both the number of EVs to recharge and the set of long-distance trips to cover. Our objective is to determine the charging stations to be opened at each time period so as to maximize the expected value of the satisfied recharging demand over the entire planning horizon. To model the problem, we propose a multi-stage stochastic integer programming approach based on the use of a scenario tree to represent the uncertainties on the recharging demand. To solve the resulting large-size integer linear program, we develop two solution algorithms: an exact solution method based on a Benders decomposition and a heuristic approach based on a genetic algorithm. Our numerical results show that both methods perform well as compared to a stand-alone mathematical programming solver. Moreover, we provide the results of additional simulation experiments showing the practical benefit of the proposed multi-stage stochastic programming model as compared to a simpler multi-period deterministic model.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2020.104888