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<p>The global transition to sustainable energy demands efficient integration of renewable resources and resilient operation of microgrids (MGs). This study aims to develop a cost-effective and sustainable Energy Management System (EMS) for MGs operating in both grid-connected and islanded modes. The inherent variability of renewable generation and fluctuating grid prices pose significant challenges to maintaining supply-demand balance. To address this, the proposed EMS employs an Improved Whale Optimization Algorithm (IWOA), incorporating a nonlinear swimming parameter and Lévy flight mechanism to prevent premature convergence. Simulation results on a benchmark low-voltage MG reveal that IWOA achieves a 39.66% reduction in operational costs compared to standard algorithms, while maintaining competitive runtime of 4.2&#xa0;min. Furthermore, a dynamic energy trading strategy is integrated to optimize real-time interactions with the main grid. The findings validate the proposed framework as a robust solution for enhancing the economic and environmental performance of modern power systems.</p>

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Cost-effective and sustainable operation of microgrids using Improved Whale Optimization Algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月3日

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  • Sohayla M. El-Zaher,
  • Aya M. Ahmed,
  • Eman M. Ahmed,
  • Yasmin T. Sedki,
  • Hager K. Al-Muntaser,
  • Ahmed N. Sheta

摘要

The global transition to sustainable energy demands efficient integration of renewable resources and resilient operation of microgrids (MGs). This study aims to develop a cost-effective and sustainable Energy Management System (EMS) for MGs operating in both grid-connected and islanded modes. The inherent variability of renewable generation and fluctuating grid prices pose significant challenges to maintaining supply-demand balance. To address this, the proposed EMS employs an Improved Whale Optimization Algorithm (IWOA), incorporating a nonlinear swimming parameter and Lévy flight mechanism to prevent premature convergence. Simulation results on a benchmark low-voltage MG reveal that IWOA achieves a 39.66% reduction in operational costs compared to standard algorithms, while maintaining competitive runtime of 4.2 min. Furthermore, a dynamic energy trading strategy is integrated to optimize real-time interactions with the main grid. The findings validate the proposed framework as a robust solution for enhancing the economic and environmental performance of modern power systems.