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Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming

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  • Algafri, Mohammed
  • Alghazi, Anas
  • Almoghathawi, Yasser
  • Saleh, Haitham
  • Al-Shareef, Khaled

Abstract

In view of recent developments in urban transport systems, there has been a significant increase in the presence of electric vehicles on the market. One of the major challenges faced is the effective allocation of these electric vehicles to appropriate charging stations. The aim of this study is to tackle this challenge by leveraging various input parameter models within a smart city context. The primary objective is to assign electric vehicles to the most suitable charging stations, taking into account four objectives: maximizing energy requested by electric vehicles, minimizing total response time, reducing charging costs, and minimizing battery degradation. Several factors, such as travel distance, state of charge, grid-to-vehicle energy trading, time, road traffic, driver priorities, and charging station capacity, are taken into account to address this problem. In addition, we propose a novel methodology that combines the analytic hierarchy process with a multi-objective goal programming model to aid decision-making regarding the allocation of electric vehicles–charging stations. The aim of this methodology is to assist electric vehicle owners in selecting effective charging stations within a complex decision-making environment. In addition, practical examples of how these methodologies can be used to solve a real-life problem have been highlighted. We validated the effectiveness of the proposed method in terms of enhancing the satisfaction factor of electric vehicles by updating their energy levels and reducing range anxiety through simulation results using SUMO and GAMS with the BARON solver. These results confirm the efficacy of the proposed method.

Suggested Citation

  • Algafri, Mohammed & Alghazi, Anas & Almoghathawi, Yasser & Saleh, Haitham & Al-Shareef, Khaled, 2024. "Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming," Applied Energy, Elsevier, vol. 372(C).
    • Handle: RePEc:eee:appene:v:372:y:2024:i:c:s0306261924011589
    DOI: 10.1016/j.apenergy.2024.123775
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    References listed on IDEAS

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    Cited by:

    1. Piotr Soczówka & Michał Lasota & Piotr Franke & Renata Żochowska, 2024. "Method of Determining New Locations for Electric Vehicle Charging Stations Using GIS Tools," Energies, MDPI, vol. 17(18), pages 1-27, September.
    2. Jingzhe Hu & Xu Wang & Shengmin Tan, 2024. "Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review," Energies, MDPI, vol. 17(19), pages 1-20, September.
    3. Jie Sheng & Zhenhai Xiang & Pengfei Ban & Chuang Bao, 2024. "How Does the Urban Built Environment Affect the Accessibility of Public Electric-Vehicle Charging Stations? A Perspective on Spatial Heterogeneity and a Non-Linear Relationship," Sustainability, MDPI, vol. 17(1), pages 1-24, December.
    4. Houzhi Li & Qingwen Han & Xueyuan Bai & Li Zhang & Wen Wang & Wenjia Chen & Lin Xiang, 2024. "Electric Vehicle Charging Station Recommendations Considering User Charging Preferences Based on Comment Data," Energies, MDPI, vol. 17(21), pages 1-17, November.

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