Research on Optimal Zoning of Energy Internet Source Load Matching Power Grid Based on Improved K-Means Algorithm

This article studies a power grid optimization partitioning method based on the improved K-means algorithm, aiming to solve the source load matching problem in the energy internet. As a core component of the energy internet, the efficiency of source load matching in the power system directly affects the rational allocation of power resources and the optimization of system operation. Traditional power grid partitioning methods have certain limitations, therefore an adaptive partitioning method is needed to better adapt to changes in the power system. In this study, we first conducted an in-depth analysis of the source load characteristics of the power system, including the distribution of power load, the distribution of distributed energy resources, and the topology structure of the power supply network. Furthermore, this study delved into the core principles of the K-means algorithm and discussed conventional methods for enhancing its performance. To address the limitations of traditional K-means algorithms and make them better suited for various data distribution patterns, we proposed an improved algorithm. This enhanced algorithm incorporates an adaptive learning rate strategy, which enables it to dynamically adjust to different data distribution types. The outcomes of our experiments provide compelling evidence for the superiority of the improved K-means algorithm. Specifically, we assessed the algorithm's performance using the Davies-Bouldin index, a widely accepted metric for clustering quality. The results demonstrated a noteworthy contrast between the traditional K-means algorithm and our improved version. The average Davies-Bouldin index for the improved K-means algorithm was found to be 22.56, while the traditional K-means algorithm yielded an average index of 78.53. This discrepancy underscores the considerable improvement achieved by our approach, as the lower Davies-Bouldin index indicates superior clustering quality. Future research directions can include further optimizing algorithms to adapt to more complex power system scenarios, as well as applying algorithms to actual power systems for verification.