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Locating and Sizing Electric Vehicle Chargers Considering Multiple Technologies

Author

Listed:
  • 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)

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.

Suggested Citation

  • Tommaso Schettini & Mauro dell’Amico & Francesca Fumero & Ola Jabali & Federico Malucelli, 2023. "Locating and Sizing Electric Vehicle Chargers Considering Multiple Technologies," Energies, MDPI, vol. 16(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4186-:d:1150528
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    References listed on IDEAS

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    1. Bryden, Thomas S. & Hilton, George & Cruden, Andrew & Holton, Tim, 2018. "Electric vehicle fast charging station usage and power requirements," Energy, Elsevier, vol. 152(C), pages 322-332.
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    3. Zhu, Zhi-Hong & Gao, Zi-You & Zheng, Jian-Feng & Du, Hao-Ming, 2016. "Charging station location problem of plug-in electric vehicles," Journal of Transport Geography, Elsevier, vol. 52(C), pages 11-22.
    4. Cavadas, Joana & Homem de Almeida Correia, Gonçalo & Gouveia, João, 2015. "A MIP model for locating slow-charging stations for electric vehicles in urban areas accounting for driver tours," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 188-201.
    5. Vazifeh, Mohammad M. & Zhang, Hongmou & Santi, Paolo & Ratti, Carlo, 2019. "Optimizing the deployment of electric vehicle charging stations using pervasive mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 75-91.
    6. Asamer, Johannes & Reinthaler, Martin & Ruthmair, Mario & Straub, Markus & Puchinger, Jakob, 2016. "Optimizing charging station locations for urban taxi providers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 233-246.
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    Cited by:

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    2. Ayesha Abbasi & Kiran Sultan & Sufyan Afsar & Muhammad Adnan Aziz & Hassan Abdullah Khalid, 2023. "Optimal Demand Response Using Battery Storage Systems and Electric Vehicles in Community Home Energy Management System-Based Microgrids," Energies, MDPI, vol. 16(13), pages 1-22, June.

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