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Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory

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  • Morro-Mello, Igoor
  • Padilha-Feltrin, Antonio
  • Melo, Joel D.
  • Heymann, Fabian

Abstract

Fast charging stations for electric vehicles require a high-power demand, meaning that electricity distribution companies must define the connection locations within the distribution network to guarantee adequate power supply levels. Due to electric vehicle users' driving patterns and equipment's high costs, these stations must be concentrated in certain regions. This paper presents a methodology for assisting electricity distribution companies in identifying candidate connection points for fast charging stations to reduce new installations and network reinforcement investments. First, possible connection points are analyzed with graph theory to find the least costly connection; this strategy prioritizes the current network elements' unused capacity. As a second step, the electric distribution network is analyzed after fast-charging stations have been connected, evaluating the networks' operational limits. The methodology is applied in a Brazilian city combining spatial information with a realistic representation of the network and network total supply capability to connect new loads. Model outcomes are spatial maps that help identify suitable connection locations, determine new capacity values, and calculate the necessary investment. We compare the proposed methodology with other conventional approaches, demonstrating how the developed methodology can assist distribution companies in reducing overall investment and operational costs of fast charging stations for electric vehicles.

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  • Morro-Mello, Igoor & Padilha-Feltrin, Antonio & Melo, Joel D. & Heymann, Fabian, 2021. "Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory," Energy, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:energy:v:235:y:2021:i:c:s0360544221016285
    DOI: 10.1016/j.energy.2021.121380
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    References listed on IDEAS

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    1. Sadeghi-Barzani, Payam & Rajabi-Ghahnavieh, Abbas & Kazemi-Karegar, Hosein, 2014. "Optimal fast charging station placing and sizing," Applied Energy, Elsevier, vol. 125(C), pages 289-299.
    2. González, L.G. & Siavichay, E. & Espinoza, J.L., 2019. "Impact of EV fast charging stations on the power distribution network of a Latin American intermediate city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 309-318.
    3. Elma, Onur, 2020. "A dynamic charging strategy with hybrid fast charging station for electric vehicles," Energy, Elsevier, vol. 202(C).
    4. Yue Zhang & Qi Zhang & Arash Farnoosh & Siyuan Chen & Yan Li, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Post-Print hal-02009151, HAL.
    5. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2016. "Electricity costs for an electric vehicle fueling station with Level 3 charging," Applied Energy, Elsevier, vol. 169(C), pages 813-830.
    6. De Gennaro, Michele & Paffumi, Elena & Martini, Giorgio, 2015. "Customer-driven design of the recharge infrastructure and Vehicle-to-Grid in urban areas: A large-scale application for electric vehicles deployment," Energy, Elsevier, vol. 82(C), pages 294-311.
    7. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    8. Awasthi, Abhishek & Venkitusamy, Karthikeyan & Padmanaban, Sanjeevikumar & Selvamuthukumaran, Rajasekar & Blaabjerg, Frede & Singh, Asheesh K., 2017. "Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm," Energy, Elsevier, vol. 133(C), pages 70-78.
    9. Erbaş, Mehmet & Kabak, Mehmet & Özceylan, Eren & Çetinkaya, Cihan, 2018. "Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis," Energy, Elsevier, vol. 163(C), pages 1017-1031.
    10. Morro-Mello, Igoor & Padilha-Feltrin, Antonio & Melo, Joel D. & Calviño, Aida, 2019. "Fast charging stations placement methodology for electric taxis in urban zones," Energy, Elsevier, vol. 188(C).
    11. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
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    Cited by:

    1. Hemmatpour, Mohammad Hasan & Rezaeian Koochi, Mohammad Hossein & Dehghanian, Pooria & Dehghanian, Payman, 2022. "Voltage and energy control in distribution systems in the presence of flexible loads considering coordinated charging of electric vehicles," Energy, Elsevier, vol. 239(PA).
    2. 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).

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