A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithm
Abstract Microgrids possess robust self—operation and management capabilities, enabling effective utilization of renewable energy sources and enhancing the reliability and security of power supply within power systems. However, challenges such as low economic benefits and inefficient operation have...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02446-5 |
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| author | Yutong Chen Wu Ning Wei Du |
| author_facet | Yutong Chen Wu Ning Wei Du |
| author_sort | Yutong Chen |
| collection | DOAJ |
| description | Abstract Microgrids possess robust self—operation and management capabilities, enabling effective utilization of renewable energy sources and enhancing the reliability and security of power supply within power systems. However, challenges such as low economic benefits and inefficient operation have long plagued microgrids. To address these issues, this paper presents a microgrid scheduling strategy based on the Non—Dominated Sorting Dung Beetle Optimization Algorithm (NSDBO). By incorporating the non—dominated sorting mechanism into the dung beetle optimization algorithm, the solution set is divided into different tiers. Solutions within each tier demonstrate superior performance compared to those in other tiers. Moreover, when tackling multi—objective optimization problems, this approach effectively avoids falling into local optima and significantly enhances the algorithm’s global search capability. Experimental results reveal that, in comparison to other algorithms, the proposed NSDBO algorithm outperforms them in terms of hypervolume (HV) and generational distance (GD), indicating its advantages in both convergence and diversity. Specifically, compared with the Grey Wolf Optimizer (GWO) and the standard Dung Beetle Optimization (DBO) algorithm, the overall operation cost of NSDBO is reduced by 52% and 8.1%, respectively. These findings fully demonstrate the effectiveness of the proposed algorithm in improving users’ economic benefits and reducing environmental pollution. |
| format | Article |
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| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-fe8f6be3cece458080a141ba031bb74c2025-08-20T03:08:25ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-02446-5A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithmYutong Chen0Wu Ning1Wei Du2Department of Electronic and Information Engineering, Liaoning University of TechnologyDepartment of Electronic and Information Engineering, Liaoning University of TechnologyDepartment of Electronic and Information Engineering, Liaoning University of TechnologyAbstract Microgrids possess robust self—operation and management capabilities, enabling effective utilization of renewable energy sources and enhancing the reliability and security of power supply within power systems. However, challenges such as low economic benefits and inefficient operation have long plagued microgrids. To address these issues, this paper presents a microgrid scheduling strategy based on the Non—Dominated Sorting Dung Beetle Optimization Algorithm (NSDBO). By incorporating the non—dominated sorting mechanism into the dung beetle optimization algorithm, the solution set is divided into different tiers. Solutions within each tier demonstrate superior performance compared to those in other tiers. Moreover, when tackling multi—objective optimization problems, this approach effectively avoids falling into local optima and significantly enhances the algorithm’s global search capability. Experimental results reveal that, in comparison to other algorithms, the proposed NSDBO algorithm outperforms them in terms of hypervolume (HV) and generational distance (GD), indicating its advantages in both convergence and diversity. Specifically, compared with the Grey Wolf Optimizer (GWO) and the standard Dung Beetle Optimization (DBO) algorithm, the overall operation cost of NSDBO is reduced by 52% and 8.1%, respectively. These findings fully demonstrate the effectiveness of the proposed algorithm in improving users’ economic benefits and reducing environmental pollution.https://doi.org/10.1038/s41598-025-02446-5Microgrid scheduling strategyNon-dominated sortingDung beetle algorithmOverall operating cost |
| spellingShingle | Yutong Chen Wu Ning Wei Du A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithm Scientific Reports Microgrid scheduling strategy Non-dominated sorting Dung beetle algorithm Overall operating cost |
| title | A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithm |
| title_full | A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithm |
| title_fullStr | A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithm |
| title_full_unstemmed | A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithm |
| title_short | A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithm |
| title_sort | study of scheduling strategies for microgrids based on the non dominated sorting dung beetle optimization algorithm |
| topic | Microgrid scheduling strategy Non-dominated sorting Dung beetle algorithm Overall operating cost |
| url | https://doi.org/10.1038/s41598-025-02446-5 |
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