Identifying Critical Risks of Cascading Failures in Power Systems

TL;DR

This paper presents a novel method to identify critical electrical elements in power systems whose initial disturbances can cause maximum cascading failures, using a dynamic optimization approach and optimal control theory.

Contribution

It introduces a dynamic optimization framework and an algorithm based on the maximum principle to identify critical risks in power system cascading failures.

Findings

The proposed algorithm effectively identifies critical elements in test systems.

Simulation results demonstrate the method's ability to predict worst-case failures.

The approach provides a systematic way to assess cascading failure risks.

Abstract

Potential critical risks of cascading failures in power systems can be identified by exposing those critical electrical elements on which certain initial disturbances may cause maximum disruption to power transmission networks. In this work, we investigate cascading failures in power systems described by the direct current (DC) power flow equations, while initial disturbances take the form of altering admittance of elements. The disruption is quantified with the remaining transmission power at the end of cascading process. In particular, identifying the critical elements and the corresponding initial disturbances causing the worst-case cascading blackout is formulated as a dynamic optimization problem (DOP) in the framework of optimal control theory, where the entire propagation process of cascading failures is put under consideration. An Identifying Critical Risk Algorithm (ICRA) based on the maximum principle is proposed to solve the DOP. Simulation results on the IEEE 9-Bus and the IEEE 14-Bus test systems are presented to demonstrate the effectiveness of the algorithm.