Multi-Objective Optimization Scheduling of a Wind–Solar Energy Storage Microgrid Based on an Improved OGGWO Algorithm
To achieve the optimal solution between construction costs and carbon emissions in the multi-target optimization scheduling, this paper proposes a multi-objective optimization scheduling design for wind–solar energy storage microgrids based on an improved oppositional gradient grey wolf optimization...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/1/13 |
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| author | Dong Mo Qiuwen Li Yan Sun Yixin Zhuo Fangming Deng |
| author_facet | Dong Mo Qiuwen Li Yan Sun Yixin Zhuo Fangming Deng |
| author_sort | Dong Mo |
| collection | DOAJ |
| description | To achieve the optimal solution between construction costs and carbon emissions in the multi-target optimization scheduling, this paper proposes a multi-objective optimization scheduling design for wind–solar energy storage microgrids based on an improved oppositional gradient grey wolf optimization (OGGWO) algorithm. First, two new features were added to the traditional grey wolf optimization (GWO) algorithm to solve the multi-target optimization scheduling of grid-connected microgrids, aiming to improve solution quality and convergence speed. Furthermore, Gaussian walk and Lévy flight are introduced to enhance the search capability of the proposed OGGWO algorithm. This method expands the search range while sacrificing only a small amount of search speed, contributing to obtaining the global optimal solution. Finally, the gradient direction is considered in the feature search process, allowing for a comprehensive understanding of the search space, which facilitates achieving the global optimum. Experimental results indicate that, compared to traditional methods, the proposed improved OGGWO algorithm can achieve standard deviations of 4.88 and 4.46 in two different scenarios, demonstrating significant effectiveness in reducing costs and pollution. |
| format | Article |
| id | doaj-art-605876f6601d42f890244639fc638236 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-605876f6601d42f890244639fc6382362025-01-24T13:17:28ZengMDPI AGAlgorithms1999-48932025-01-011811310.3390/a18010013Multi-Objective Optimization Scheduling of a Wind–Solar Energy Storage Microgrid Based on an Improved OGGWO AlgorithmDong Mo0Qiuwen Li1Yan Sun2Yixin Zhuo3Fangming Deng4Power Dispatch and Control Center, Guangxi Power Grid, Nanning 530023, ChinaPower Dispatch and Control Center, Guangxi Power Grid, Nanning 530023, ChinaPower Dispatch and Control Center, Guangxi Power Grid, Nanning 530023, ChinaPower Dispatch and Control Center, Guangxi Power Grid, Nanning 530023, ChinaSchool of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaTo achieve the optimal solution between construction costs and carbon emissions in the multi-target optimization scheduling, this paper proposes a multi-objective optimization scheduling design for wind–solar energy storage microgrids based on an improved oppositional gradient grey wolf optimization (OGGWO) algorithm. First, two new features were added to the traditional grey wolf optimization (GWO) algorithm to solve the multi-target optimization scheduling of grid-connected microgrids, aiming to improve solution quality and convergence speed. Furthermore, Gaussian walk and Lévy flight are introduced to enhance the search capability of the proposed OGGWO algorithm. This method expands the search range while sacrificing only a small amount of search speed, contributing to obtaining the global optimal solution. Finally, the gradient direction is considered in the feature search process, allowing for a comprehensive understanding of the search space, which facilitates achieving the global optimum. Experimental results indicate that, compared to traditional methods, the proposed improved OGGWO algorithm can achieve standard deviations of 4.88 and 4.46 in two different scenarios, demonstrating significant effectiveness in reducing costs and pollution.https://www.mdpi.com/1999-4893/18/1/13wind–solar energy storage microgridmulti-objective optimization schedulingoppositional gradient grey wolf optimization (OGGWO) algorithmGaussian walk |
| spellingShingle | Dong Mo Qiuwen Li Yan Sun Yixin Zhuo Fangming Deng Multi-Objective Optimization Scheduling of a Wind–Solar Energy Storage Microgrid Based on an Improved OGGWO Algorithm Algorithms wind–solar energy storage microgrid multi-objective optimization scheduling oppositional gradient grey wolf optimization (OGGWO) algorithm Gaussian walk |
| title | Multi-Objective Optimization Scheduling of a Wind–Solar Energy Storage Microgrid Based on an Improved OGGWO Algorithm |
| title_full | Multi-Objective Optimization Scheduling of a Wind–Solar Energy Storage Microgrid Based on an Improved OGGWO Algorithm |
| title_fullStr | Multi-Objective Optimization Scheduling of a Wind–Solar Energy Storage Microgrid Based on an Improved OGGWO Algorithm |
| title_full_unstemmed | Multi-Objective Optimization Scheduling of a Wind–Solar Energy Storage Microgrid Based on an Improved OGGWO Algorithm |
| title_short | Multi-Objective Optimization Scheduling of a Wind–Solar Energy Storage Microgrid Based on an Improved OGGWO Algorithm |
| title_sort | multi objective optimization scheduling of a wind solar energy storage microgrid based on an improved oggwo algorithm |
| topic | wind–solar energy storage microgrid multi-objective optimization scheduling oppositional gradient grey wolf optimization (OGGWO) algorithm Gaussian walk |
| url | https://www.mdpi.com/1999-4893/18/1/13 |
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