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Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm

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  • Md Mainul Islam
  • Hussain Shareef
  • Azah Mohamed

Abstract

The electric vehicle (EV) is considered a premium solution to global warming and various types of pollution. Nonetheless, a key concern is the recharging of EV batteries. Therefore, this study proposes a novel approach that considers the costs of transportation loss, buildup, and substation energy loss and that incorporates harmonic power loss into optimal rapid charging station (RCS) planning. A novel optimization technique, called binary lightning search algorithm (BLSA), is proposed to solve the optimization problem. BLSA is also applied to a conventional RCS planning method. A comprehensive analysis is conducted to assess the performance of the two RCS planning methods by using the IEEE 34-bus test system as the power grid. The comparative studies show that the proposed BLSA is better than other optimization techniques. The daily total cost in RCS planning of the proposed method, including harmonic power loss, decreases by 10% compared with that of the conventional method.

Suggested Citation

  • Md Mainul Islam & Hussain Shareef & Azah Mohamed, 2017. "Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-20, December.
    • Handle: RePEc:plo:pone00:0189170
    DOI: 10.1371/journal.pone.0189170
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    Cited by:

    1. Nur Ayeesha Qisteena Muzir & Md. Rayid Hasan Mojumder & Md. Hasanuzzaman & Jeyraj Selvaraj, 2022. "Challenges of Electric Vehicles and Their Prospects in Malaysia: A Comprehensive Review," Sustainability, MDPI, vol. 14(14), pages 1-40, July.
    2. 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).
    3. Amaro García-Suárez & José-Luis Guisado-Lizar & Fernando Diaz-del-Rio & Francisco Jiménez-Morales, 2021. "A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 13(10), pages 1-14, May.
    4. Nandini K. Krishnamurthy & Jayalakshmi N. Sabhahit & Vinay Kumar Jadoun & Dattatraya Narayan Gaonkar & Ashish Shrivastava & Vidya S. Rao & Ganesh Kudva, 2023. "Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method," Energies, MDPI, vol. 16(4), pages 1-27, February.

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