Identifying Critical Risks of Cascading Failures in Power Systems
For power system operators, this work provides a method to identify critical risks of cascading failures, though it is incremental as it applies optimal control theory to a known problem.
The paper formulates the identification of critical elements causing worst-case cascading blackouts in power systems as a dynamic optimization problem and proposes an algorithm (ICRA) based on the maximum principle. Simulations on IEEE 9-Bus and 14-Bus systems demonstrate effectiveness, but no quantitative performance numbers are provided.
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.