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Title: Probabilistic N-k failure-identification for power systems

Abstract

This work considers a probabilistic generalization of the N-k 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 k components that maximizes disruption to the system loads weighted by the probability of simultaneous failure of the k 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 N-k 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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1463479
Report Number(s):
LA-UR-17-23601
Journal ID: ISSN 0028-3045
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Networks
Additional Journal Information:
Journal Volume: 71; Journal Issue: 3; Journal ID: ISSN 0028-3045
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; 97 MATHEMATICS AND COMPUTING; Interdiction, convex optimization, N-k, component-failure probabilities; power network resilience; network interdiction; N-k vulnerability; nonlinear optimization; convex relaxation; cutting-plane algorithm; network flow; DC power flow; AC power flow

Citation Formats

Sundar, Kaarthik, Coffrin, Carleton, Nagarajan, Harsha, and Bent, Russell. Probabilistic N-k failure-identification for power systems. United States: N. p., 2018. Web. doi:10.1002/net.21806.
Sundar, Kaarthik, Coffrin, Carleton, Nagarajan, Harsha, & Bent, Russell. Probabilistic N-k failure-identification for power systems. United States. doi:10.1002/net.21806.
Sundar, Kaarthik, Coffrin, Carleton, Nagarajan, Harsha, and Bent, Russell. Mon . "Probabilistic N-k failure-identification for power systems". United States. doi:10.1002/net.21806. https://www.osti.gov/servlets/purl/1463479.
@article{osti_1463479,
title = {Probabilistic N-k failure-identification for power systems},
author = {Sundar, Kaarthik and Coffrin, Carleton and Nagarajan, Harsha and Bent, Russell},
abstractNote = {This work considers a probabilistic generalization of the N-k 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 k components that maximizes disruption to the system loads weighted by the probability of simultaneous failure of the k 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 N-k 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.},
doi = {10.1002/net.21806},
journal = {Networks},
number = 3,
volume = 71,
place = {United States},
year = {2018},
month = {1}
}

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