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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Vydáno v:	Journal of computing and information technology Ročník 32; číslo 4; s. 251 - 264
Hlavní autoři:	Chen, Yansen, Cheng, Kai, Li, Zhuohuan, Pan, Shixian, Hu, Xudong
Médium:	Journal Article Paper
Jazyk:	angličtina
Vydáno:	Sveuciliste U Zagrebu 01.12.2024 Sveučilište u Zagrebu Fakultet elektrotehnike i računarstva University of Zagreb Faculty of Electrical Engineering and Computing
Témata:	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
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Shrnutí:	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.
Bibliografie:	326540
ISSN:	1330-1136 1846-3908
DOI:	10.20532/cit.2024.1005870