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Multi-Objective Optimization of Distributed Generation Placement in Electric Bus Transit Systems Integrated with Flash Charging Station Using Enhanced Multi-Objective Grey Wolf Optimization Technique and Consensus-Based Decision Support

Author

Listed:
  • Yuttana Kongjeen

    (Intelligent Power System and Energy Research (IPER), Department of Electrical Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand)

  • Pongsuk Pilalum

    (Intelligent Power System and Energy Research (IPER), Department of Electrical Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand)

  • Saksit Deeum

    (Department of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Thanyaburi (RMUTT), Pathum Thani 12110, Thailand)

  • Kittiwong Suthamno

    (Intelligent Power System and Energy Research (IPER), Department of Electrical Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand)

  • Thongchai Klayklueng

    (Intelligent Power System and Energy Research (IPER), Department of Electrical Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand)

  • Supapradit Marsong

    (Department of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Thanyaburi (RMUTT), Pathum Thani 12110, Thailand)

  • Ritthichai Ratchapan

    (Department of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Thanyaburi (RMUTT), Pathum Thani 12110, Thailand)

  • Krittidet Buayai

    (Intelligent Power System and Energy Research (IPER), Department of Electrical Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand)

  • Kaan Kerdchuen

    (Intelligent Power System and Energy Research (IPER), Department of Electrical Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand)

  • Wutthichai Sa-nga-ngam

    (Intelligent Power System and Energy Research (IPER), Department of Electrical Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand)

  • Krischonme Bhumkittipich

    (Department of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Thanyaburi (RMUTT), Pathum Thani 12110, Thailand)

Abstract

This study presents a comprehensive multi-objective optimization framework for optimal placement and sizing of distributed generation (DG) units in electric bus (E-bus) transit systems integrated with a high-power flash charging infrastructure. An enhanced Multi-Objective Grey Wolf Optimizer (MOGWO), utilizing Euclidean distance-based Pareto ranking, is developed to minimize power loss, voltage deviation, and voltage violations. The framework incorporates realistic E-bus operation characteristics, including a 31-stop, 62 km route, 600 kW pantograph flash chargers, and dynamic load profiles over a 90 min simulation period. Statistical evaluation on IEEE 33-bus and 69-bus distribution networks demonstrates that MOGWO consistently outperforms MOPSO and NSGA-II across all DG deployment scenarios. In the three-DG configuration, MOGWO achieved minimum power losses of 0.0279 MW and 0.0179 MW, and voltage deviations of 0.1313 and 0.1362 in the 33-bus and 69-bus systems, respectively, while eliminating voltage violations. The proposed method also demonstrated superior solution quality with low variance and faster convergence, requiring under 7 h of computation on average. A five-method compromise solution strategy, including TOPSIS and Lp-metric, enabled transparent and robust decision-making. The findings confirm the proposed framework’s effectiveness and scalability for enhancing distribution system performance under the demands of electric transit electrification and smart grid integration.

Suggested Citation

  • Yuttana Kongjeen & Pongsuk Pilalum & Saksit Deeum & Kittiwong Suthamno & Thongchai Klayklueng & Supapradit Marsong & Ritthichai Ratchapan & Krittidet Buayai & Kaan Kerdchuen & Wutthichai Sa-nga-ngam &, 2025. "Multi-Objective Optimization of Distributed Generation Placement in Electric Bus Transit Systems Integrated with Flash Charging Station Using Enhanced Multi-Objective Grey Wolf Optimization Technique ," Energies, MDPI, vol. 18(14), pages 1-46, July.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3638-:d:1698325
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    References listed on IDEAS

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    1. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
    2. Sankar, Matta Mani & Chatterjee, Kalyan, 2023. "A posteriori multiobjective approach for techno-economic allocation of PV and BES units in a distribution system hosting PHEVs," Applied Energy, Elsevier, vol. 351(C).
    3. Soliman, Ismail A. & Tulsky, Vladimir & Abd el-Ghany, Hossam A. & ElGebaly, Ahmed E., 2025. "Efficient allocation of capacitors and vehicle-to-grid integration with electric vehicle charging stations in radial distribution networks," Applied Energy, Elsevier, vol. 377(PD).
    4. Matthias Rogge & Sebastian Wollny & Dirk Uwe Sauer, 2015. "Fast Charging Battery Buses for the Electrification of Urban Public Transport—A Feasibility Study Focusing on Charging Infrastructure and Energy Storage Requirements," Energies, MDPI, vol. 8(5), pages 1-20, May.
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