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Performance of Gradient-Based Optimizer on Charging Station Placement Problem

Author

Listed:
  • Essam H. Houssein

    (Faculty of Computers and Information, Minia University, Minia 61519, Egypt)

  • Sanchari Deb

    (School of Engineering, University of Warwick, Coventry CV4 7AL, UK)

  • Diego Oliva

    (División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara 44430, Mexico
    School of Computer Science & Robotics, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Hegazy Rezk

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 11911, Saudi Arabia)

  • Hesham Alhumade

    (Chemical and Materials Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Center of Excellence in Desalination Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Mokhtar Said

    (Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 43518, Egypt)

Abstract

The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer.

Suggested Citation

  • Essam H. Houssein & Sanchari Deb & Diego Oliva & Hegazy Rezk & Hesham Alhumade & Mokhtar Said, 2021. "Performance of Gradient-Based Optimizer on Charging Station Placement Problem," Mathematics, MDPI, vol. 9(21), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2821-:d:673512
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    References listed on IDEAS

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    1. Csiszár, Csaba & Csonka, Bálint & Földes, Dávid & Wirth, Ervin & Lovas, Tamás, 2020. "Location optimisation method for fast-charging stations along national roads," Journal of Transport Geography, Elsevier, vol. 88(C).
    2. Juuso Lindgren & Peter D. Lund, 2015. "Identifying bottlenecks in charging infrastructure of plug-in hybrid electric vehicles through agent-based traffic simulation," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 10(2), pages 110-118.
    3. Hu, Junjie & Morais, Hugo & Sousa, Tiago & Lind, Morten, 2016. "Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1207-1226.
    4. Deb, Sanchari & Gao, Xiao-Zhi & Tammi, Kari & Kalita, Karuna & Mahanta, Pinakeswar, 2021. "A novel chicken swarm and teaching learning based algorithm for electric vehicle charging station placement problem," Energy, Elsevier, vol. 220(C).
    5. Andrenacci, N. & Ragona, R. & Valenti, G., 2016. "A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas," Applied Energy, Elsevier, vol. 182(C), pages 39-46.
    6. 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.
    7. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    8. 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. Alaa A. K. Ismaeel & Essam H. Houssein & Doaa Sami Khafaga & Eman Abdullah Aldakheel & Ahmed S. AbdElrazek & Mokhtar Said, 2023. "Performance of Osprey Optimization Algorithm for Solving Economic Load Dispatch Problem," Mathematics, MDPI, vol. 11(19), pages 1-19, September.
    2. Alma Y. Alanis, 2022. "Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications," Mathematics, MDPI, vol. 10(13), pages 1-2, July.

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