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<p>In the day-ahead energy market of smart grids, demand response aggregators, grid operators, and consumers pursue individual objectives, making coordinated profit maximization a challenging task. This paper presents a multi-objective optimization framework that simultaneously addresses the interests of all stakeholders. The proposed model integrates dynamic economic emission dispatch (DEED) with demand side management (DSM) to enhance system efficiency, economic performance, and environmental sustainability. A deep learning model predicts load and power from renewable sources, supported by a battery energy storage system that stabilizes RES variability. The proposed hybrid fractional order-based class topper optimization (FOBCTO) algorithm along with optimal energy management strategy, intend to solve the DSM-DEED problem efficiently. The model’s performance is evaluated on an updated IEEE 39-bus test system under three operational scenarios. Comparative analysis demonstrates the competitiveness of the proposed approach, achieving a 6.06% improvement in load factor, a 9.74% reduction in generation cost, and a significantly reduced computational time of 244.873 seconds compared to existing optimization algorithms. The results demonstrate that the model effectively balances economic efficiency, environmental goals, and grid reliability, making it a practical approach for future smart grid energy markets.</p>

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An optimal energy management strategy for cost optimization in renewable-integrated smart grid using hybrid optimization approach

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Chitrangada Roy,
  • Dushmanta Kumar Das

摘要

In the day-ahead energy market of smart grids, demand response aggregators, grid operators, and consumers pursue individual objectives, making coordinated profit maximization a challenging task. This paper presents a multi-objective optimization framework that simultaneously addresses the interests of all stakeholders. The proposed model integrates dynamic economic emission dispatch (DEED) with demand side management (DSM) to enhance system efficiency, economic performance, and environmental sustainability. A deep learning model predicts load and power from renewable sources, supported by a battery energy storage system that stabilizes RES variability. The proposed hybrid fractional order-based class topper optimization (FOBCTO) algorithm along with optimal energy management strategy, intend to solve the DSM-DEED problem efficiently. The model’s performance is evaluated on an updated IEEE 39-bus test system under three operational scenarios. Comparative analysis demonstrates the competitiveness of the proposed approach, achieving a 6.06% improvement in load factor, a 9.74% reduction in generation cost, and a significantly reduced computational time of 244.873 seconds compared to existing optimization algorithms. The results demonstrate that the model effectively balances economic efficiency, environmental goals, and grid reliability, making it a practical approach for future smart grid energy markets.