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Dynamic Optimization and Placement of Renewable Generators and Compensators to Mitigate Electric Vehicle Charging Station Impacts Using the Spotted Hyena Optimization Algorithm

Author

Listed:
  • Thangaraj Yuvaraj

    (Centre for Smart Energy Systems, Chennai Institute of Technology, Chennai 600069, India)

  • Natarajan Prabaharan

    (School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, India)

  • Chinnappan John De Britto

    (Department of Electrical and Electronics Engineering, Saveetha Engineering College, Chennai 602105, India)

  • Muthusamy Thirumalai

    (Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai 602105, India)

  • Mohamed Salem

    (School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal 14300, Malaysia
    Libyan Authority for Scientific Research, Tripoli 80045, Libya)

  • Mohammad Alhuyi Nazari

    (Faculty of New Sciences and Technologies, University of Tehran, Tehran 1417935840, Iran
    School of Engineering & Technology, Duy Tan University, Da Nang 50217, Vietnam
    Research and Development Cell, Lovely Professional University, Phagwara 144411, India)

Abstract

The growing adoption of electric vehicles (EVs) offers notable benefits, including reduced maintenance costs, improved performance, and environmental sustainability. However, integrating EVs into radial distribution systems (RDSs) poses challenges related to power losses and voltage stability. The model accounts for hourly variations in demand, making it crucial to determine the optimal placement of electric vehicle charging stations (EVCSs) throughout the day. This study proposes a new approach that combines EVCSs, distribution static compensators (DSTATCOMs), and renewable distributed generation (RDG) from solar and wind sources, with a focus on dynamic analysis over 24 h. The spotted hyena optimization algorithm (SHOA) is employed to determine near-global optimum locations and sizes for RDG, DSTATCOMs, and EVCSs, aiming to minimize real power loss while meeting system constraints. The SHOA outperforms traditional methods due to its unique search mechanism, which effectively balances exploration and exploitation, allowing it to find superior solutions in complex environments. Simulations on an IEEE 34-bus RDS under dynamic load conditions validate the approach, demonstrating a reduction in average power loss from 180.43 kW to 72.04 kW, a 72.6% decrease. Compared to traditional methods under constant load conditions, the SHOA achieves a 77.0% reduction in power loss, while the BESA and PSO achieve reductions of 61.1% and 44.7%, respectively. These results underscore the effectiveness of the SHOA in enhancing system performance and significantly reducing real power loss.

Suggested Citation

  • Thangaraj Yuvaraj & Natarajan Prabaharan & Chinnappan John De Britto & Muthusamy Thirumalai & Mohamed Salem & Mohammad Alhuyi Nazari, 2024. "Dynamic Optimization and Placement of Renewable Generators and Compensators to Mitigate Electric Vehicle Charging Station Impacts Using the Spotted Hyena Optimization Algorithm," Sustainability, MDPI, vol. 16(19), pages 1-34, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8458-:d:1488346
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    References listed on IDEAS

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    1. Rahman, Syed & Khan, Irfan Ahmed & Khan, Ashraf Ali & Mallik, Ayan & Nadeem, Muhammad Faisal, 2022. "Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    2. Verónica Anadón Martínez & Andreas Sumper, 2023. "Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review," Energies, MDPI, vol. 16(14), pages 1-41, July.
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