A Graph-Based Genetic Algorithm for Distributed Photovoltaic Cluster Partitioning

Abstract

To easily control distributed photovoltaic power stations and provide fast responses for their regulation, this paper proposes an optimal cluster partitioning method based on a graph-based genetic algorithm (GA). In this approach, a novel structure utilizing a graph model is designed for chromosomes, and enhancements are made to the selection, crossover, and mutation models of the evolutionary to generate a search population for dividing distributed photovoltaic (PV) power grids into clusters. Moreover, the modularity and active power balance degree of the classic Girvan–Newman algorithm are employed as optimal objectives to establish a basis and evaluation system for cluster partitioning. Additionally, a Simulink simulation platform is established for the IEEE 33-bus time-varying scenario to validate its performance. A comparative analysis with some classic PV cluster partitioning algorithms demonstrates that the proposed method can achieve a more accurate and stable division of distributed PV units.