Izvorni znanstveni članak
https://doi.org/10.20532/cit.2024.1005870
Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity
Yansen Chen
; China Southern Grid Digital Grid Research Institute Co. Ltd, Guangzhou, China
*
Kai Cheng
; China Southern Grid Digital Grid Research Institute Co. Ltd, Guangzhou, China
Zhuohuan Li
; China Southern Grid Digital Grid Research Institute Co. Ltd, Guangzhou, China
Shixian Pan
; China Southern Grid Digital Grid Research Institute Co. Ltd, Guangzhou, China
Xudong Hu
; China Southern Grid Digital Grid Research Institute Co. Ltd, Guangzhou, China
* Dopisni autor.
Sažetak
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.
Ključne riječi
multi-objective genetic algorithm; distributed photovoltaic; cluster partitioning
Hrčak ID:
326540
URI
Datum izdavanja:
20.12.2024.
Posjeta: 0 *