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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Veröffentlicht in:Journal of computing and information technology Jg. 32; H. 4; S. 251 - 264
Hauptverfasser: Chen, Yansen, Cheng, Kai, Li, Zhuohuan, Pan, Shixian, Hu, Xudong
Format: Journal Article Paper
Sprache:Englisch
Veröffentlicht: Sveuciliste U Zagrebu 01.12.2024
Sveučilište u Zagrebu Fakultet elektrotehnike i računarstva
University of Zagreb Faculty of Electrical Engineering and Computing
Schlagworte:
Algorithms
Analysis
Artificial intelligence
cluster partitioning
Distributed generation (Electric power)
distributed photovoltaic
Genetic algorithms
multi-objective genetic algorithm
Photovoltaic power generation
Solar energy
Solar power plants
China
ISSN:1330-1136, 1846-3908
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:326540
ISSN:1330-1136
1846-3908
DOI:10.20532/cit.2024.1005870