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A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows

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  • Danny García Sánchez

    (Department of Electrical Engineering, São Paulo State University (UNESP), Ilha Solteira, São Paulo 15385-000, Brazil)

  • Alejandra Tabares

    (Department of Industrial Engineering, Los Andes University, Bogotá 111711, Colombia)

  • Lucas Teles Faria

    (Department of Energy Engineering, São Paulo State University (UNESP), Rosana, São Paulo 19274-000, Brazil)

  • Juan Carlos Rivera

    (Department of Mathematical Sciences, EAFIT University, Medellín 050022, Colombia)

  • John Fredy Franco

    (Department of Electrical Engineering, São Paulo State University (UNESP), Ilha Solteira, São Paulo 15385-000, Brazil
    Department of Energy Engineering, São Paulo State University (UNESP), Rosana, São Paulo 19274-000, Brazil)

Abstract

Transportation has been incorporating electric vehicles (EVs) progressively. EVs do not produce air or noise pollution, and they have high energy efficiency and low maintenance costs. In this context, the development of efficient techniques to overcome the vehicle routing problem becomes crucial with the proliferation of EVs. The vehicle routing problem concerns the freight capacity and battery autonomy limitations in different delivery-service scenarios, and the challenge of best locating recharging stations. This work proposes a mixed-integer linear programming model to solve the electric location routing problem with time windows (E-LRPTW) considering the state of charge, freight and battery capacities, and customer time windows in the decision model. A clustering strategy based on the k-means algorithm is proposed to divide the set of vertices (EVs) into small areas and define potential sites for recharging stations, while reducing the number of binary variables. The proposed model for E-LRPTW was implemented in Python and solved using mathematical modeling language AMPL together with CPLEX. Performed tests on instances with 5 and 10 clients showed a large reduction in the time required to find the solution (by about 60 times in one instance). It is concluded that the strategy of dividing customers by sectors has the potential to be applied and generate solutions for larger geographical areas and numbers of recharging stations, and determine recharging station locations as part of planning decisions in more realistic scenarios.

Suggested Citation

  • Danny García Sánchez & Alejandra Tabares & Lucas Teles Faria & Juan Carlos Rivera & John Fredy Franco, 2022. "A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows," Energies, MDPI, vol. 15(7), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2372-:d:778533
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    References listed on IDEAS

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    Cited by:

    1. Maximiliano Cubillos & Mauro Dell’Amico & Ola Jabali & Federico Malucelli & Emanuele Tresoldi, 2023. "An Enhanced Path Planner for Electric Vehicles Considering User-Defined Time Windows and Preferences," Energies, MDPI, vol. 16(10), pages 1-19, May.
    2. Ding, Yan & Wang, Qiaochu & Tian, Zhe & Lyu, Yacong & Li, Feng & Yan, Zhe & Xia, Xi, 2023. "A graph-theory-based dynamic programming planning method for distributed energy system planning: Campus area as a case study," Applied Energy, Elsevier, vol. 329(C).
    3. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    4. Peng Xu & Qixing Liu & Yuhu Wu, 2023. "Energy Saving-Oriented Multi-Depot Vehicle Routing Problem with Time Windows in Disaster Relief," Energies, MDPI, vol. 16(4), pages 1-15, February.
    5. Oluwasola O. Ademulegun & Paul MacArtain & Bukola Oni & Neil J. Hewitt, 2022. "Multi-Stage Multi-Criteria Decision Analysis for Siting Electric Vehicle Charging Stations within and across Border Regions," Energies, MDPI, vol. 15(24), pages 1-28, December.
    6. Qing Li & Xue Li & Zuyu Liu & Yaping Qi, 2022. "Application of Clustering Algorithms in the Location of Electric Taxi Charging Stations," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    7. Wojciech Cieslik & Weronika Antczak, 2023. "Research of Load Impact on Energy Consumption in an Electric Delivery Vehicle Based on Real Driving Conditions: Guidance for Electrification of Light-Duty Vehicle Fleet," Energies, MDPI, vol. 16(2), pages 1-19, January.

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