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<p>The rapid decline in fossil fuel reserves due to tremendous advances in technological and fiscal needs is inevitable, and hence emissions. Implementing demand side management (DSM) strategy in smart electric grids ensures the potent exploitation of sustainable energy resources and optimizes consumers’ consumption patterns while enhancing the grids’ stability. This paper proposes an optimal day-ahead load shifting DSM approach for a smart distribution grid comprising three load sectors, viz. residential, commercial, and industries. The objective function is mathematically formulated as an elementary quadratic minimization problem to achieve lower peak load and cost of operation. To optimize the minimization function, this paper proposes a hybrid optimization approach that combines class topper optimization (CTO) and modified Grey Wolf optimization (mGWO) algorithm to address the challenges of energy dispatch and load balancing in microgrids. The CTO algorithm provides robust global search capabilities to explore complex solution spaces. At the same time, mGWO ensures precise local convergence to optimize specific parameters such as energy cost, emission levels, and renewable resource utilization. The hybrid method leverages the strengths of both algorithms to overcome the limitations of standalone approaches. The proposed hybrid algorithm has significantly reduced peak load by 27.27%, 22.99%, and 28.96% in residential, commercial, and industrial areas while reducing total consumer tariff by 15.42%, 22.55%, and 23.19%, respectively. Envisioning accurate justification, the results obtained by the proposed algorithm have been compared with similar research findings.</p>

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A novel hybrid class topper and modified grey wolf algorithm for optimal day-ahead scheduling of microgrid resources

  • 期刊论文

  • 发表日期：

    2026年7月3日

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  • Nanibabu Sampatirao,
  • Shakila Baskaran,
  • Prakash Marimuthu,
  • Krishna Mohan Reddy Pothireddy

摘要

The rapid decline in fossil fuel reserves due to tremendous advances in technological and fiscal needs is inevitable, and hence emissions. Implementing demand side management (DSM) strategy in smart electric grids ensures the potent exploitation of sustainable energy resources and optimizes consumers’ consumption patterns while enhancing the grids’ stability. This paper proposes an optimal day-ahead load shifting DSM approach for a smart distribution grid comprising three load sectors, viz. residential, commercial, and industries. The objective function is mathematically formulated as an elementary quadratic minimization problem to achieve lower peak load and cost of operation. To optimize the minimization function, this paper proposes a hybrid optimization approach that combines class topper optimization (CTO) and modified Grey Wolf optimization (mGWO) algorithm to address the challenges of energy dispatch and load balancing in microgrids. The CTO algorithm provides robust global search capabilities to explore complex solution spaces. At the same time, mGWO ensures precise local convergence to optimize specific parameters such as energy cost, emission levels, and renewable resource utilization. The hybrid method leverages the strengths of both algorithms to overcome the limitations of standalone approaches. The proposed hybrid algorithm has significantly reduced peak load by 27.27%, 22.99%, and 28.96% in residential, commercial, and industrial areas while reducing total consumer tariff by 15.42%, 22.55%, and 23.19%, respectively. Envisioning accurate justification, the results obtained by the proposed algorithm have been compared with similar research findings.