Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
More distributed energy resources are being integrated into microgrid systems, making scheduling more complex and challenging. In order to achieve the utilization of renewable energy and peak load shifting on a microgrid system, an optimal scheduling model is established. Firstly, a microgrid operat...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/975 |
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| author | Yuntao Yue Haoran Ren Dong Liu Lenian Zhang |
| author_facet | Yuntao Yue Haoran Ren Dong Liu Lenian Zhang |
| author_sort | Yuntao Yue |
| collection | DOAJ |
| description | More distributed energy resources are being integrated into microgrid systems, making scheduling more complex and challenging. In order to achieve the utilization of renewable energy and peak load shifting on a microgrid system, an optimal scheduling model is established. Firstly, a microgrid operation model including a photovoltaic array, wind turbine, micro gas turbine, diesel generator, energy storage, and grid connection is constructed, considering the demand response and the uncertainty of wind and solar power. The modeling demand response is determined via a price–demand elasticity matrix, whereas the uncertainty of wind and solar power is established using Monte Carlo sampling and a K-means clustering algorithm. Secondly, a multi-objective function that includes operational and environmental treatment costs is constructed. To optimize the objective function, an Improved Dung Beetle Optimization algorithm (IDBO) is proposed. A tent mapping, non-dominated sorting, and reverse elite learning strategy is proposed to improve the Dung Beetle Optimization algorithm (DBO); therefore, the IDBO is developed. Finally, the proposed model and algorithm are validated through some simulation experiments. A benchmark function test proves that IDBO has a fast convergence speed and high accuracy. The microgrid system scheduled by IDBO has the lowest total cost, and its ability to achieve peak load shifting and improve the utilization of renewable energy is proved through tests involving different scenarios. The results show that compared with traditional optimal scheduling models and algorithms, this approach is more reliable and cost-effective. |
| format | Article |
| id | doaj-art-a24a45a17d5741c7b400109c8ec84175 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a24a45a17d5741c7b400109c8ec841752025-01-24T13:21:32ZengMDPI AGApplied Sciences2076-34172025-01-0115297510.3390/app15020975Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization AlgorithmYuntao Yue0Haoran Ren1Dong Liu2Lenian Zhang3School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaMore distributed energy resources are being integrated into microgrid systems, making scheduling more complex and challenging. In order to achieve the utilization of renewable energy and peak load shifting on a microgrid system, an optimal scheduling model is established. Firstly, a microgrid operation model including a photovoltaic array, wind turbine, micro gas turbine, diesel generator, energy storage, and grid connection is constructed, considering the demand response and the uncertainty of wind and solar power. The modeling demand response is determined via a price–demand elasticity matrix, whereas the uncertainty of wind and solar power is established using Monte Carlo sampling and a K-means clustering algorithm. Secondly, a multi-objective function that includes operational and environmental treatment costs is constructed. To optimize the objective function, an Improved Dung Beetle Optimization algorithm (IDBO) is proposed. A tent mapping, non-dominated sorting, and reverse elite learning strategy is proposed to improve the Dung Beetle Optimization algorithm (DBO); therefore, the IDBO is developed. Finally, the proposed model and algorithm are validated through some simulation experiments. A benchmark function test proves that IDBO has a fast convergence speed and high accuracy. The microgrid system scheduled by IDBO has the lowest total cost, and its ability to achieve peak load shifting and improve the utilization of renewable energy is proved through tests involving different scenarios. The results show that compared with traditional optimal scheduling models and algorithms, this approach is more reliable and cost-effective.https://www.mdpi.com/2076-3417/15/2/975microgrid optimal schedulingdistributed energy resourcesrenewable energy sourcesImproved Dung Beetle Optimization algorithm |
| spellingShingle | Yuntao Yue Haoran Ren Dong Liu Lenian Zhang Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm Applied Sciences microgrid optimal scheduling distributed energy resources renewable energy sources Improved Dung Beetle Optimization algorithm |
| title | Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm |
| title_full | Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm |
| title_fullStr | Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm |
| title_full_unstemmed | Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm |
| title_short | Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm |
| title_sort | optimal scheduling of microgrids based on an improved dung beetle optimization algorithm |
| topic | microgrid optimal scheduling distributed energy resources renewable energy sources Improved Dung Beetle Optimization algorithm |
| url | https://www.mdpi.com/2076-3417/15/2/975 |
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