Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity.

Yansen Chen; Kai Cheng; Zhuohuan Li; Shixian Pan; Xudong Hu

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 multi-objective 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.

PHOTOVOLTAIC power systems; PARALLEL algorithms; DISTRIBUTED algorithms; ELECTRICITY; GENETIC algorithms

Journal of Computing & Information Technology, 2024, Vol 32, Issue 4, p251

1330-1136

Academic Journal

10.20532/cit.2024.1005870