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

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.