A Robust Optimization Approach for Terminating the Cascading Failure of Power Systems

TL;DR

This paper introduces a robust optimization method using a Markov chain model to predict and prevent cascading failures in power systems by optimally shedding load, accounting for uncertainties and stochastic factors.

Contribution

It develops a novel Markov chain-based framework combined with robust optimization and Dykstra's algorithm for effective power system protection against cascading blackouts.

Findings

Successfully predicts cascading failure paths in power systems.

Provides a lower bound for blackout prevention probability.

Validated on IEEE 118 bus system case study.

Abstract

Due to uncertainties and the complicated intrinsic dynamics of power systems, it is difficult to predict the cascading failure paths once the cascades occur. This makes it challenging to achieve the effective power system protection against cascading blackouts. By incorporating uncertainties and stochastic factors of the cascades, a Markov chain model is developed in this paper to predict the cascading failure paths of power systems. The transition matrix of Markov chain is dependent on the probability of branch outage caused by overloads or stochastic factors. Moreover, a robust optimization formulation is proposed to deal with the cascading blackouts by shedding load optimally for multiple cascading failure paths with relatively high probabilities. Essentially, it can be converted to the best approximation problem. Thus, an efficient numerical solver based on Dykstra's algorithm is employed to deal with the robust optimization problem. In theory, we provide a lower bound for the probability of preventing the cascading blackouts of power systems. Finally, the proposed approach for power system protection is verified by a case study of IEEE 118 bus system.