We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Improved Multiobjective Genetic Algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity.
- Authors
Yansen Chen; Kai Cheng; Zhuohuan Li; Shixian Pan; Xudong Hu
- Abstract
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.
- Subjects
PHOTOVOLTAIC power systems; PARALLEL algorithms; DISTRIBUTED algorithms; ELECTRICITY
- Publication
Journal of Computing & Information Technology, 2024, Vol 32, Issue 4, p251
- ISSN
1330-1136
- Publication type
Academic Journal
- DOI
10.20532/cit.2024.1005870