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<p>Smart grids (SGs) enhance energy management (EM) by integrating distributed energy resources and optimizing energy use across the grid. However, challenges include managing the high costs of integrating diverse technologies and ensuring efficiency in optimizing energy distribution and usage across a distributed network. To prevail over these challenges, this paper proposes a hybrid approach for enhancing SM resilience with distributed energy management. The proposed hybrid method is the joint execution of both the golden eagle optimization (GEO) and dilated residual convolutional neural network (DRCNN), and it is commonly named as GEO-DRCNN technique. The primary goal of the proposed method is to minimize operating costs while simultaneously enhancing the overall efficiency of the system. The proposed GEO is used to optimize energy distribution and load management. DRCNN is utilized to predict the grid performance. By then, the proposed method is executed in the MATLAB platform and contrasted with existing methods. The proposed technique provides optimum outcomes in every existing system such as Grasshopper Optimization Algorithm (GOA), Multi-Objective Improved Slime Mould Algorithm (MOISMA), and Pelican Optimization Algorithm (POA). The existing method shows the total efficiency and total operating cost is 97% and 8365.2$,97.89% and 8265.2$, 95.48% and 7723.8$. The proposed method shows the total efficiency and total operating cost is 98.97%, 7655.1$. From the result, it concluded that the proposed approach based on total efficiency is high and the total operating cost is low compared to existing methods.</p>

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Enhance smart grid resilience for distributed energy management: a hybrid approach

  • 期刊论文

  • 发表日期：

    2025年6月13日

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  • B. Rajani,
  • T Mahendiran Vellingiri,
  • P. Rajesh

摘要

Smart grids (SGs) enhance energy management (EM) by integrating distributed energy resources and optimizing energy use across the grid. However, challenges include managing the high costs of integrating diverse technologies and ensuring efficiency in optimizing energy distribution and usage across a distributed network. To prevail over these challenges, this paper proposes a hybrid approach for enhancing SM resilience with distributed energy management. The proposed hybrid method is the joint execution of both the golden eagle optimization (GEO) and dilated residual convolutional neural network (DRCNN), and it is commonly named as GEO-DRCNN technique. The primary goal of the proposed method is to minimize operating costs while simultaneously enhancing the overall efficiency of the system. The proposed GEO is used to optimize energy distribution and load management. DRCNN is utilized to predict the grid performance. By then, the proposed method is executed in the MATLAB platform and contrasted with existing methods. The proposed technique provides optimum outcomes in every existing system such as Grasshopper Optimization Algorithm (GOA), Multi-Objective Improved Slime Mould Algorithm (MOISMA), and Pelican Optimization Algorithm (POA). The existing method shows the total efficiency and total operating cost is 97% and 8365.2$,97.89% and 8265.2$, 95.48% and 7723.8$. The proposed method shows the total efficiency and total operating cost is 98.97%, 7655.1$. From the result, it concluded that the proposed approach based on total efficiency is high and the total operating cost is low compared to existing methods.