Locating and Sizing Electric Vehicle Chargers Considering Multiple Technologies

Tommaso Schettini (), Mauro dell’Amico, Francesca Fumero (), Ola Jabali and Federico Malucelli
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Tommaso Schettini: GERAD, École de Technologie Supérieure, HEC Montréal, Montréal, QC H3T 2A7, Canada
Mauro dell’Amico: Department of Sciences and Methods for Engineering, Università di Modena e Reggio Emilia, 42122 Modena, Italy
Francesca Fumero: Dipartimento di Ingegneria Gestionale, Politecnico di Milano, 20133 Milano, Italy
Ola Jabali: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
Federico Malucelli: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy

Energies, 2023, vol. 16, issue 10, 1-16

Abstract: In order to foster electric vehicle (EV) adoption rates, the availability of a pervasive and efficient charging network is a crucial requirement. In this paper, we provide a decision support tool for helping policymakers to locate and size EV charging stations. We consider a multi-year planning horizon, taking into account different charging technologies and different time periods (day and night). Accounting for these features, we propose an optimization model that minimizes total investment costs while ensuring a predetermined adequate level of demand coverage. In particular, the setup of charging stations is optimized every year, allowing for an increase in the number of chargers installed at charging stations set up in previous years. We have developed a tailored heuristic algorithm for the resulting problem. We validated our algorithm using case study instances based on the village of Gardone Val Trompia (Italy), the city of Barcelona (Spain), and the country of Luxembourg. Despite the variability in the sizes of the considered instances, our algorithm consistently provided high-quality results in short computational times, when compared to a commercial MILP solver. Produced solutions achieved optimality gaps within 7.5% in less than 90 s, often achieving computational times of less than 5 s.

Keywords: charging station location; charging infrastructure planning; electric vehicles; facility location (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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