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Optimal siting and sizing of electric taxi charging stations considering transportation and power system requirements

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  • Clairand, Jean-Michel
  • González-Rodríguez, Mario
  • Kumar, Rajesh
  • Vyas, Shashank
  • Escrivá-Escrivá, Guillermo

Abstract

Electric vehicles (EVs) have become more popular to address transportation-related environmental concerns. However, to integrate a massive fleet of EVs, it is crucial to properly build charging stations by considering the optimal geographical placement and number of charging spots. This task is particularly challenging for users with rigid schedules such as taxi drivers. Hence, an optimal siting and sizing approach for an electric taxi (ET) charging station is proposed in this study, considering both transportation and power system needs. In addition, particular attention to taxi drivers' needs is considered. Fixed installation costs, land costs, and trip costs are the factors evaluated in this proposed approach. A network modeling approach based on a winner-takes-all edge trimming was used to identify interest points of the city in terms of traffic flows. Ecuador's capital, Quito, was considered a case study. A sensitivity analysis was also carried out to address traffic flow uncertainties such as trip expenses and restrictions.

Suggested Citation

  • Clairand, Jean-Michel & González-Rodríguez, Mario & Kumar, Rajesh & Vyas, Shashank & Escrivá-Escrivá, Guillermo, 2022. "Optimal siting and sizing of electric taxi charging stations considering transportation and power system requirements," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s036054422201475x
    DOI: 10.1016/j.energy.2022.124572
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    References listed on IDEAS

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    Cited by:

    1. Nadia V. Panossian & Haitam Laarabi & Keith Moffat & Heather Chang & Bryan Palmintier & Andrew Meintz & Timothy E. Lipman & Rashid A. Waraich, 2023. "Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area," Energies, MDPI, vol. 16(5), pages 1-18, February.
    2. Li, Mingyang & Tang, Jinjun, 2023. "Simulation-based optimization considering energy consumption for assisted station locations to enhance flex-route transit," Energy, Elsevier, vol. 277(C).
    3. Zhao, Zhonghao & Lee, Carman K.M. & Huo, Jiage, 2023. "EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning," Energy, Elsevier, vol. 267(C).
    4. Ullah, Zia & Wang, Shaorong & Wu, Guan & Hasanien, Hany M. & Rehman, Anis Ur & Turky, Rania A. & Elkadeem, Mohamed R., 2023. "Optimal scheduling and techno-economic analysis of electric vehicles by implementing solar-based grid-tied charging station," Energy, Elsevier, vol. 267(C).
    5. Hao Qiang & Yanchun Hu & Wenqi Tang & Xiaohua Zhang, 2023. "Research on Optimization Strategy of Battery Swapping for Electric Taxis," Energies, MDPI, vol. 16(5), pages 1-15, February.

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