Cluster Partitioning Method for High-PV-Penetration Distribution Network Based on mGA-PSO Algorithm

Zhu Liu, Guowei Guo, Dehuang Gong, Lingfeng Xuan, Feiwu He, Xinglin Wan and Dongguo Zhou ()
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Zhu Liu: China Southern Power Grid Research Technology Co., Ltd., Guangzhou 510663, China
Guowei Guo: Guangdong Electric Power Co., Ltd. Foshan Power Supply Bureau, Foshan 528000, China
Dehuang Gong: Guangdong Electric Power Co., Ltd. Qingyuan Yingde Power Supply Bureau, Yingde 513000, China
Lingfeng Xuan: Guangdong Electric Power Co., Ltd. Qingyuan Yingde Power Supply Bureau, Yingde 513000, China
Feiwu He: Guangdong Electric Power Co., Ltd. Qingyuan Yingde Power Supply Bureau, Yingde 513000, China
Xinglin Wan: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Dongguo Zhou: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2025, vol. 18, issue 5, 1-15

Abstract: To tackle the issues of scattered distributed photovoltaic access points and unbalanced cluster partitioning scales, an iterative clustering partitioning method is proposed, a which integrates micro-evolution genetic algorithm and particle swarm optimization (mGA-PSO). In this method, the complementary aspects of active and reactive power are quantified as key indicators, and node membership is incorporated to construct a comprehensive metric for the partitioning of a distributed PV cluster. Additionally, to improve the optimal search performance of high-penetration photovoltaic cluster partitioning, an enhanced learning-based modification factor is introduced in the genetic algorithm population selection, and a search and transfer mechanism based on historical population information is incorporated into the particle swarm algorithm. This enhances the particle swarm optimization capability with individual intelligent feedback. Experimental tests on the IEEE 34-node and IEEE 110-node systems demonstrate that the proposed method outperforms GA and PSO approaches in cluster partitioning, improving the convergence speed of the algorithm while avoiding local optima.

Keywords: genetic algorithm; particle swarm optimization; distributed photovoltaic; cluster partitioning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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