A region-based model for optimizing charging station location problem of electric vehicles considering disruption - A case study. (15th February 2022)

• Record Type: Journal Article
• Authors: Sadeghi Ahangar, Shahin; Abazari, Seyed Reza; Rabbani, Masoud
• Is Part Of: Journal of cleaner production. Volume 336(2022)
• Publication Date: 2022-02-15
• Subjects: Charging station -- Location problem -- Lagrangian relaxation -- Electric vehicles -- Recharging -- Disruption risk; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.130433
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: To reduce the negative impact of the conventional vehicles, governments and authorities try to provide facilities in order to encourage people to use electric vehicles. For this purpose, determining the location and the number of charging stations is of paramount importance. It becomes more challenging when some charging stations cannot provide service due to the disruption or breakdown. In this paper, a bi-objective mixed-integer linear mathematical model for a charging station location problem is developed and solved by Lagrangian relaxation method. Two different chargers that could be installed in the charging stations and budget constraint is considered in the model. To evaluate the applicability of the proposed model on real-world problems, a case study on Tehran is conducted. Based on computational experiments and numerical results, sensitivity analysis on key parameters of the problem is carried out and some helpful suggestions are presented for decision-makers. Highlights: A bi-objective region-based model for optimizing CSLP is presented. Disruption in charging stations is taken into account. Two different types of chargers and tolerable distance for the users are considered. The proposed model is implemented in a real-world case study to evaluate the applicability of the model. Lagrangian relaxation algorithm is used to solve the model and some useful managerial insights are also presented.