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<p>The increasing integration of renewable energy sources in microgrids (MGs) necessitates the use of advanced optimization techniques to ensure cost-effective and reliable power management. In this study, a modified moth-flame optimization (mMFO) algorithm has been proposed, integrating roulette wheel selection and opposition-based learning to enhance both exploration and exploitation capabilities. This novel hybrid approach effectively mitigates premature convergence issues commonly observed in traditional methods. Performance evaluations conducted on two benchmark systems—the IEEE 37-node and IEEE 141-node test systems—demonstrate that mMFO reduces daily generation costs from 1181.38 to 1167.29&#xa0;USD in the 37-node system and from 3100.59 to 3087.68&#xa0;USD in the 141-node system. Comparative analyses with state-of-the-art algorithms, including the original MFO, slime mold algorithm, sine cosine algorithm, salp swarm algorithm, and ant lion optimizer, further validate the robustness and efficiency of mMFO. These findings underscore the potential of the proposed method as a powerful tool for achieving sustainable and economically optimized MG operations.</p>

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Efficient power generation in microgrids: an advanced optimization framework for improved operations

  • 期刊论文

  • 发表日期：

    2025年5月9日

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  • Vu Hong Son Pham,
  • Nghiep Trinh Nguyen Dang,
  • Khoi Van Tien Nguyen,
  • Tuan Le Duc Anh

摘要

The increasing integration of renewable energy sources in microgrids (MGs) necessitates the use of advanced optimization techniques to ensure cost-effective and reliable power management. In this study, a modified moth-flame optimization (mMFO) algorithm has been proposed, integrating roulette wheel selection and opposition-based learning to enhance both exploration and exploitation capabilities. This novel hybrid approach effectively mitigates premature convergence issues commonly observed in traditional methods. Performance evaluations conducted on two benchmark systems—the IEEE 37-node and IEEE 141-node test systems—demonstrate that mMFO reduces daily generation costs from 1181.38 to 1167.29 USD in the 37-node system and from 3100.59 to 3087.68 USD in the 141-node system. Comparative analyses with state-of-the-art algorithms, including the original MFO, slime mold algorithm, sine cosine algorithm, salp swarm algorithm, and ant lion optimizer, further validate the robustness and efficiency of mMFO. These findings underscore the potential of the proposed method as a powerful tool for achieving sustainable and economically optimized MG operations.