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<p>Integrating solar and wind energy with battery storage systems into microgrids is gaining prominence in both remote areas and high-rise urban buildings. Optimally designing all distributed energy resources (DERs) within a microgrid enhances self-sufficiency, reliability, and economic feasibility. However, due to the inherent unpredictability of DERs, a robust stochastic-based optimization approach is crucial. This article proposes a Grey Wolf-based multi-objective optimization technique for wind-solar-battery-assisted residential microgrids. The method aims to minimize renewable energy costs by determining the optimal sizing of components based on a given microgrid load profile. To address the global energy trilemma, the microgrid is modeled with economic, reliability, and energy indices, ensuring a balanced three-dimensional objective. The proposed algorithm is evaluated across three different configurations, with a numerical analysis of the capacity degradation factor to assess battery lifetime.</p>

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Optimizing wind-PV-battery microgrids for sustainable and resilient residential communities

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月8日

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  • Jyotismita Mishra,
  • Ajay Shankar

摘要

Integrating solar and wind energy with battery storage systems into microgrids is gaining prominence in both remote areas and high-rise urban buildings. Optimally designing all distributed energy resources (DERs) within a microgrid enhances self-sufficiency, reliability, and economic feasibility. However, due to the inherent unpredictability of DERs, a robust stochastic-based optimization approach is crucial. This article proposes a Grey Wolf-based multi-objective optimization technique for wind-solar-battery-assisted residential microgrids. The method aims to minimize renewable energy costs by determining the optimal sizing of components based on a given microgrid load profile. To address the global energy trilemma, the microgrid is modeled with economic, reliability, and energy indices, ensuring a balanced three-dimensional objective. The proposed algorithm is evaluated across three different configurations, with a numerical analysis of the capacity degradation factor to assess battery lifetime.