Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity
The prediction of power output from photovoltaic generation clusters is crucial for optimizing the dispatch of regional photovoltaic generation. Enhancing the accuracy of power prediction for photovoltaic power plant clusters requires the segmentation of distributed photovoltaic systems into cluster...
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| Format: | Article |
| Language: | English |
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University of Zagreb Faculty of Electrical Engineering and Computing
2024-01-01
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| Series: | Journal of Computing and Information Technology |
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| Online Access: | https://hrcak.srce.hr/file/471982 |
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| author | Yansen Chen Kai Cheng Zhuohuan Li Shixian Pan Xudong Hu |
| author_facet | Yansen Chen Kai Cheng Zhuohuan Li Shixian Pan Xudong Hu |
| author_sort | Yansen Chen |
| collection | DOAJ |
| description | The prediction of power output from photovoltaic generation clusters is crucial for optimizing the dispatch of regional photovoltaic generation. Enhancing the accuracy of power prediction for photovoltaic power plant clusters requires the segmentation of distributed photovoltaic systems into clusters. This paper proposes a method for partitioning distributed photovoltaic clusters using a multiobjective genetic algorithm NSGA2, with spatial distance modularity and electricity similarity as optimization objectives to determine the optimal cluster partitioning scheme. The numerical examples and experimental results of the case analysis demonstrate a significant improvement in the convergence speed of the prediction system when employing the clustering partitioning method. This cluster segmentation algorithm significantly reduces the complexity and investment cost of the prediction system. |
| format | Article |
| id | doaj-art-71142bcf91634ba48c9913418170472d |
| institution | Kabale University |
| issn | 1846-3908 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Zagreb Faculty of Electrical Engineering and Computing |
| record_format | Article |
| series | Journal of Computing and Information Technology |
| spelling | doaj-art-71142bcf91634ba48c9913418170472d2025-01-09T14:17:41ZengUniversity of Zagreb Faculty of Electrical Engineering and ComputingJournal of Computing and Information Technology1846-39082024-01-0132425126410.20532/cit.2024.1005870Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power SimilarityYansen Chen0Kai Cheng1Zhuohuan Li2Shixian Pan3Xudong Hu4China Southern Grid Digital Grid Research Institute Co. Ltd, Guangzhou, ChinaChina Southern Grid Digital Grid Research Institute Co. Ltd, Guangzhou, ChinaChina Southern Grid Digital Grid Research Institute Co. Ltd, Guangzhou, ChinaChina Southern Grid Digital Grid Research Institute Co. Ltd, Guangzhou, ChinaChina Southern Grid Digital Grid Research Institute Co. Ltd, Guangzhou, ChinaThe prediction of power output from photovoltaic generation clusters is crucial for optimizing the dispatch of regional photovoltaic generation. Enhancing the accuracy of power prediction for photovoltaic power plant clusters requires the segmentation of distributed photovoltaic systems into clusters. This paper proposes a method for partitioning distributed photovoltaic clusters using a multiobjective genetic algorithm NSGA2, with spatial distance modularity and electricity similarity as optimization objectives to determine the optimal cluster partitioning scheme. The numerical examples and experimental results of the case analysis demonstrate a significant improvement in the convergence speed of the prediction system when employing the clustering partitioning method. This cluster segmentation algorithm significantly reduces the complexity and investment cost of the prediction system.https://hrcak.srce.hr/file/471982multi-objective genetic algorithmdistributed photovoltaiccluster partitioning |
| spellingShingle | Yansen Chen Kai Cheng Zhuohuan Li Shixian Pan Xudong Hu Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity Journal of Computing and Information Technology multi-objective genetic algorithm distributed photovoltaic cluster partitioning |
| title | Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity |
| title_full | Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity |
| title_fullStr | Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity |
| title_full_unstemmed | Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity |
| title_short | Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity |
| title_sort | improved multiobjective genetic algorithm for partitioning distributed photovoltaic clusters balancing spatial distance and power similarity |
| topic | multi-objective genetic algorithm distributed photovoltaic cluster partitioning |
| url | https://hrcak.srce.hr/file/471982 |
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