Research on Optimization of Distribution Network Connection Mode Based on Graph Neural Network and Genetic Algorithm - Chen, Guo; Hui, Wang; Huan, Yan; Bingchen, Li; Xingxing, Zhou (Distributed generation and alternative energy journal, 2025)
With the deep integration of electric power and information technology systems, the distribution system shows the trend of increasingly complex structures and increasing external risk factors. This leads to more diversified types of faults in the distribution network, so it is crucial to optimize its topology. In this paper, we first compare the main connection modes of high-voltage and medium-voltage distribution networks in China, and combine them with the specific needs of Shaanxi Power Grid to propose a differentiated flexible network model and its scope of application. Using Graph Neural Network and Genetic Algorithm, an innovative optimization method of distribution network connection is proposed to support the typical network structure of the new distribution network. Analysis of examples shows that the proposed algorithm can improve the original network’s network loss and voltage deviation by 32.8% and 37.3%, respectively, and the improvement effect is better than that of the traditional genetic algorithm. At the same time, considering the different stages of distribution network development and the uncertainties that may be faced, this paper also explores the flexible transition scheme of each typical network structure to ensure a smooth transition to a more efficient, green and intelligent distribution network model without affecting the reliability of the existing power supply.