Identification of Multi-Microgrid Clusters Using Terminal Spectral Clustering Algorithm

Abstract

In response to the increasing impact of extreme weather on power distribution networks (PDNs), prioritizing resilience is imperative. This study introduces an innovative k-means spectral clustering algorithm to define the boundaries of microgrids (MGs) within a multi-microgrid (MMG) system. The aim is to improve reliability by clustering PDNs into resilient MGs. The power systems are modeled with nodes representing buses, and connections are represented as edges. The analysis involves computing the adjacency matrix, degree matrix, Laplacian matrix, and applying k-means clustering to group buses based on terminal point features. Silhouette coefficients (SC) are calculated to assess the quality of the clustering. The proposed method is tested on three IEEE distribution systems: IEEE 33, 69, and 118 bus systems. Findings reveal distinct clusters within each system with SC values above 0.68, particularly emphasizing the significance of terminal points as the basis for assisting power engineers in decision-making for predetermined grid partitioning.