Probabilistic $N$-$k$ Failure-Identification for Power Systems
Abstract
This paper considers a probabilistic generalization of the - failure-identification problem in power transmission networks, where the probability of failure of each component in the network is known a priori and the goal of the problem is to find a set of components that maximizes disruption to the system loads weighted by the probability of simultaneous failure of the components. The resulting problem is formulated as a bilevel mixed-integer nonlinear program. Convex relaxations, linear approximations, and heuristics are developed to obtain feasible solutions that are close to the optimum. A general cutting-plane algorithm is proposed to solve the convex relaxation and linear approximations of the - problem. Extensive numerical results corroborate the effectiveness of the proposed algorithms on small-, medium-, and large-scale test instances, the test instances include the IEEE 14-bus system, the IEEE single-area and three-area RTS96 systems, the IEEE 118-bus system, the WECC 240-bus test system, the 1354-bus PEGASE system, and the 2383-bus Polish winter-peak test system.
Cite
@article{arxiv.1704.05391,
title = {Probabilistic $N$-$k$ Failure-Identification for Power Systems},
author = {Kaarthik Sundar and Carleton Coffrin and Harsha Nagarajan and Russell Bent},
journal= {arXiv preprint arXiv:1704.05391},
year = {2021}
}
Comments
17 pages, Networks, 2018