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<p>This paper presents the Multi-Objective Reptile Search algorithm for identifying ideal rating and location of distributed generation in unbalanced grids, focusing on minimizing power losses, total costs, and carbon emissions. The proposed methodology integrates DIgSILENT PowerFactory and Python platforms to evaluate unbalanced IEEE distribution feeders under varying power factor conditions. The results demonstrate that optimal DG configurations involve strategically positioning multiple units to enable both active and reactive power injection, significantly improving overall system performance across multiple objectives and voltage deviation index. The analysis identifies an optimal PF range between 0.75 and 0.89, with unity power factor operations yielding suboptimal outcomes. Moreover, increasing the number of DG units generally enhances grid efficiency and reduces power losses, although a trade-off emerges between cost savings and carbon emission reductions, particularly when fewer DG units are deployed. The findings highlight the importance of balancing these objectives based on system operator priorities for sustainable and efficient power system management. A comparative study validates that proposed algorithm outperforms the Multi-Objective Particle Swarm Optimization method, demonstrating efficient capabilities in addressing the complexities of multi-objective DG optimization.</p>

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Optimizing the Size and Siting of Distributed Generation in Unbalanced Distribution Systems with Multi-objective Reptile Search Algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Pema Dorji,
  • Chimi Pelden Dorji,
  • Stefan Lachowicz,
  • Octavian Bass

摘要

This paper presents the Multi-Objective Reptile Search algorithm for identifying ideal rating and location of distributed generation in unbalanced grids, focusing on minimizing power losses, total costs, and carbon emissions. The proposed methodology integrates DIgSILENT PowerFactory and Python platforms to evaluate unbalanced IEEE distribution feeders under varying power factor conditions. The results demonstrate that optimal DG configurations involve strategically positioning multiple units to enable both active and reactive power injection, significantly improving overall system performance across multiple objectives and voltage deviation index. The analysis identifies an optimal PF range between 0.75 and 0.89, with unity power factor operations yielding suboptimal outcomes. Moreover, increasing the number of DG units generally enhances grid efficiency and reduces power losses, although a trade-off emerges between cost savings and carbon emission reductions, particularly when fewer DG units are deployed. The findings highlight the importance of balancing these objectives based on system operator priorities for sustainable and efficient power system management. A comparative study validates that proposed algorithm outperforms the Multi-Objective Particle Swarm Optimization method, demonstrating efficient capabilities in addressing the complexities of multi-objective DG optimization.