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<p>The increasing integration of wind energy into distribution networks introduces challenges like heightened power losses and voltage instability. Addressing these issues, this study is motivated by the need for robust optimization methods to ensure efficient and reliable wind generator (WG) placement, this research proposes a novel hybrid optimization approach combining Binary Greylag Goose Optimization (BGGO) and Golden Jackal Optimization (GJO) for efficient wind Distributed Generation (DG) unit placement in distribution networks. The BGGJO algorithm enhances convergence speed and solution accuracy by leveraging the coordinated movement of greylag goose and the hunting strategy of golden jackals, resulting in improved exploration and exploitation capabilities. This proposed method is applied in IEEE 85-bus radial distribution network, minimizes power loss, reduces voltage deviation, and enhances stability through an adaptive binary search. Numerical results demonstrate that the proposed BGGJO algorithm significantly reduces active power loss to 150.03&#xa0;kW and improves minimum bus voltage to 0.965 p.u. compared to conventional techniques across various load conditions. In conclusion, BGGJO proves to be a superior and promising solution for efficient wind energy integration, ensuring enhanced system reliability and stability.</p>

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Wind Distributed Generation Sizing and Placement in Distribution Networks Using Binary Greylag Goose with Golden Jackal Optimization Under Load Variability

  • 期刊论文

  • 发表日期：

    2025年10月2日

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  • Swathi Sankepally,
  • Sravana Kumar Bali

摘要

The increasing integration of wind energy into distribution networks introduces challenges like heightened power losses and voltage instability. Addressing these issues, this study is motivated by the need for robust optimization methods to ensure efficient and reliable wind generator (WG) placement, this research proposes a novel hybrid optimization approach combining Binary Greylag Goose Optimization (BGGO) and Golden Jackal Optimization (GJO) for efficient wind Distributed Generation (DG) unit placement in distribution networks. The BGGJO algorithm enhances convergence speed and solution accuracy by leveraging the coordinated movement of greylag goose and the hunting strategy of golden jackals, resulting in improved exploration and exploitation capabilities. This proposed method is applied in IEEE 85-bus radial distribution network, minimizes power loss, reduces voltage deviation, and enhances stability through an adaptive binary search. Numerical results demonstrate that the proposed BGGJO algorithm significantly reduces active power loss to 150.03 kW and improves minimum bus voltage to 0.965 p.u. compared to conventional techniques across various load conditions. In conclusion, BGGJO proves to be a superior and promising solution for efficient wind energy integration, ensuring enhanced system reliability and stability.