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Categorization of Attributes and Features for the Location of Electric Vehicle Charging Stations

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  • Andrea Mazza

    (Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Angela Russo

    (Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Gianfranco Chicco

    (Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Andrea Di Martino

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Cristian Giovanni Colombo

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Michela Longo

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Paolo Ciliento

    (Atlante Srl, Piazzale Lodi 3, 20137 Milan, Italy)

  • Marco De Donno

    (Atlante Srl, Piazzale Lodi 3, 20137 Milan, Italy)

  • Francesca Mapelli

    (Atlante Srl, Piazzale Lodi 3, 20137 Milan, Italy)

  • Francesco Lamberti

    (Atlante Srl, Piazzale Lodi 3, 20137 Milan, Italy)

Abstract

The location of Electric Vehicle Charging Stations (EVCSs) is gaining significant importance as part of the conversion to a full-electric vehicle fleet. Positive or negative impacts can be generated mainly based on the quality of service offered to customers and operational efficiency, also potentially involving the electrical grid to which the EVCSs are connected. The EVCS location problem requires an in-depth and comprehensive analysis of geographical, market, urban planning, and operational aspects that can lead to several potential alternatives to be evaluated with respect to a defined number of features. This paper discusses the possible use of a multi-criteria decision-making approach, considering the differences between multi-objective decision making (MODM) and multi-attribute decision-making (MADM), to address the EVCS location problem. The conceptual evaluation leads to the conclusion that the MADM approach is more suitable than MODM for the specific problem. The identification of suitable attributes and related features is then carried out based on a systematic literature review. For each attribute, the relative importance of the features is obtained by considering the occurrence and the dedicated weights. The results provide the identification of the most used attributes and the categorization of the selected features to shape the proposed MADM framework for the location of the electric vehicle charging infrastructure.

Suggested Citation

  • Andrea Mazza & Angela Russo & Gianfranco Chicco & Andrea Di Martino & Cristian Giovanni Colombo & Michela Longo & Paolo Ciliento & Marco De Donno & Francesca Mapelli & Francesco Lamberti, 2024. "Categorization of Attributes and Features for the Location of Electric Vehicle Charging Stations," Energies, MDPI, vol. 17(16), pages 1-32, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3920-:d:1452263
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    References listed on IDEAS

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    4. Huiru Zhao & Nana Li, 2016. "Optimal Siting of Charging Stations for Electric Vehicles Based on Fuzzy Delphi and Hybrid Multi-Criteria Decision Making Approaches from an Extended Sustainability Perspective," Energies, MDPI, vol. 9(4), pages 1-22, April.
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    Cited by:

    1. Cesar Diaz-Londono & Yang Li, 2024. "Smart Electric Vehicle Charging Approaches for Demand Response," Energies, MDPI, vol. 17(24), pages 1-3, December.

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