Worst-Case Load Shedding in Electric Power Networks

TL;DR

This paper addresses the complex problem of identifying the worst-case line outages in electric power networks to maximize power disruption, using an innovative iterative decomposition algorithm that is scalable and guarantees convergence.

Contribution

The paper introduces a novel iterative decomposition algorithm for the nonlinear AC power flow model, enabling scalable and convergent solutions for worst-case load shedding problems.

Findings

Algorithm effectively identifies critical line outages in IEEE test cases.

Approach guarantees convergence to a critical point with decreasing objective value.

Scalable method applicable to large power networks.

Abstract

We consider the worst-case load-shedding problem in electric power networks where a number of transmission lines are to be taken out of service. The objective is to identify a pre-specified number of line outage that leads to the maximum interruption of power generation and load at the transmission level, subject to the AC power flow model, the load and generation capacity of the buses, and the phase angle limit across the transmission lines. For this nonlinear model with binary constraints, we show that all decision variables are separable except for the nonlinear power flow equations. We develop an iterative decomposition algorithm, which converts the worst-case load shedding problem into a sequence of small subproblems. We show that the subproblems are either convex problems that can be solved efficiently or nonconvex problems that have closed-form solutions. Consequently, our approach is scalable for large networks. Furthermore, we prove global convergence of our algorithm to a critical point and the objective value is guaranteed to decrease throughout the iterations. Numerical experiments with IEEE test cases demonstrate the effectiveness of the developed approach.