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<p>The growing integration of renewable and distributed energy resources has increased the complexity of microgrid (MG) operation due to inherent uncertainties and the trade-off between economic and environmental objectives. Developing an optimization framework that simultaneously addresses renewable intermittency, electric vehicle (EV) participation, and stochastic variations in weather-dependent generation and demand remains a significant challenge. This study presents a multi-objective and stochastic optimization framework for MG scheduling, including photovoltaic (PV), microturbines (MT), fuel cells (FC), battery energy storage systems (BESS), and EVs under demand response (DR) based on real data. The MG is optimized to meet daily demand under three weather conditions—sunny, cloudy, and rainy—while minimizing operating cost and emissions. An Improved Sine Cosine Optimizer (ISCHO), enhanced with chaos theory, is developed to prevent premature convergence. Furthermore, an Adaptive Weighted Unscented Transformation (AWUT-MG) is introduced to efficiently capture uncertainties in PV generation and load demand while maintaining computational efficiency. Three operational cases are examined: (i) MG without DR, (ii) MG with DR and EV integration, and (iii) stochastic MG optimization under uncertainty. The results demonstrate that EV integration substantially improves flexibility, reducing both operating cost and emissions across different weather conditions. ISCHO consistently outperforms SCHO, PSO, and SCA in terms of convergence speed and solution quality. Incorporating stochastic modeling through AWUT-MG provides increases of 13.81%, 14.84%, and 16.01% in operating cost and 6.17%, 6.41%, and 6.63% in emissions across sunny, cloudy, and rainy conditions, respectively—highlighting the effectiveness of the proposed AWUT-MG framework for realistic, uncertainty-aware MG scheduling.</p>

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An improved sinh cosh optimizer for optimal scheduling of a microgrid with multi-energy resources and storage considering different weather conditions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月8日

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  • Junjie Ma,
  • Huao Shen,
  • Saber Arabi Nowdeh

摘要

The growing integration of renewable and distributed energy resources has increased the complexity of microgrid (MG) operation due to inherent uncertainties and the trade-off between economic and environmental objectives. Developing an optimization framework that simultaneously addresses renewable intermittency, electric vehicle (EV) participation, and stochastic variations in weather-dependent generation and demand remains a significant challenge. This study presents a multi-objective and stochastic optimization framework for MG scheduling, including photovoltaic (PV), microturbines (MT), fuel cells (FC), battery energy storage systems (BESS), and EVs under demand response (DR) based on real data. The MG is optimized to meet daily demand under three weather conditions—sunny, cloudy, and rainy—while minimizing operating cost and emissions. An Improved Sine Cosine Optimizer (ISCHO), enhanced with chaos theory, is developed to prevent premature convergence. Furthermore, an Adaptive Weighted Unscented Transformation (AWUT-MG) is introduced to efficiently capture uncertainties in PV generation and load demand while maintaining computational efficiency. Three operational cases are examined: (i) MG without DR, (ii) MG with DR and EV integration, and (iii) stochastic MG optimization under uncertainty. The results demonstrate that EV integration substantially improves flexibility, reducing both operating cost and emissions across different weather conditions. ISCHO consistently outperforms SCHO, PSO, and SCA in terms of convergence speed and solution quality. Incorporating stochastic modeling through AWUT-MG provides increases of 13.81%, 14.84%, and 16.01% in operating cost and 6.17%, 6.41%, and 6.63% in emissions across sunny, cloudy, and rainy conditions, respectively—highlighting the effectiveness of the proposed AWUT-MG framework for realistic, uncertainty-aware MG scheduling.