Generalized Spectral Clustering of Low-Inertia Power Networks

TL;DR

This paper introduces a spectral clustering method for partitioning low-inertia power networks into coherent subsystems, enhancing distributed control strategies amid increasing system complexity.

Contribution

It establishes a spectral embedding approach based on the network's linearized dynamics, connecting it to spectral clustering and demonstrating robustness on a test system.

Findings

Spectral embedding effectively decomposes power networks into coherent clusters.

The method is robust to perturbations in steady-state operating points.

Application on IEEE 30-bus system validates the approach.

Abstract

Large-scale integration of distributed energy resources has led to a rapid increase in the number of controllable devices and a significant change in system dynamics. This has necessitating the shift towards more distributed and scalable control strategies to manage the increasing system complexity. In this work, we address the problem of partitioning a low-inertia power network into dynamically coherent subsystems to facilitate the utilization of distributed control schemes. We show that an embedding of the power network using the spectrum of the linearized synchronization dynamics matrix results in a natural decomposition of the network. We establish the connection between our approach and the broader framework of spectral clustering using the Laplacian matrix of the admittance network. The proposed method is demonstrated on the IEEE 30-bus test system. We consider the robustness of the clusters by analyzing the sensitivity of the small eigenvalues and their corresponding eigenspaces to perturbations caused by variation in the steady-state operating points of the network.