Improving Power Systems Controllability via Edge Centrality Measures

TL;DR

This paper introduces a control-theoretic edge centrality matrix to identify and modify critical power network lines, significantly enhancing controllability and stability in complex power systems.

Contribution

It proposes a novel ECM approach that considers dynamic behavior for better identification and control of influential network edges in power systems.

Findings

ECM effectively identifies critical network edges.

Edge modifications improve controllability and damping.

Validated on IEEE benchmarks with positive results.

Abstract

Improving the controllability of power networks is crucial as they are highly complex networks operating in synchrony; even minor perturbations can cause desynchronization and instability. To that end, one needs to assess the criticality of key network components (buses and lines) in terms of their impact on system performance. Traditional methods to identify the key nodes/edges in power networks often rely on static centrality measures based on the network's topological structure ignoring the network's dynamic behavior. In this paper, using multi-machine power network models and a new control-theoretic edge centrality matrix (ECM) approach, we: (i) quantify the influence of edges (i.e., the line susceptances) in terms of controllability performance metrics, (ii) identify the most influential lines, and (iii) compute near-optimal edge modifications that improve the power network controllability. Employing various IEEE power network benchmarks, we validate the effectiveness of the ECM-based algorithm and demonstrate improvements in system reachability, control, and damping performance.