Visible to the public Network Theory Based Power Grid Criticality Assessment

TitleNetwork Theory Based Power Grid Criticality Assessment
Publication TypeConference Paper
Year of Publication2018
AuthorsNasiruzzaman, A. B. M., Akter, M. N., Mahmud, M. A., Pota, H. R.
Conference Name2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)
ISBN Number978-1-5386-9316-2
KeywordsAustralia, blackout, composability, critical transmission lines identification, energy management system, energy management systems, flow network, Generators, Linear programming, linear programming duality, linear programming problem, maximum flow problem, Metrics, network flow, network flow problem, network theory, network theory (graphs), power grid connectivity monitoring, power grid control center, power grid criticality assessment, power grid vulnerability, power grid vulnerability analysis, power grids, power transmission lines, pubcrawl, resilience, Resiliency, standard IEEE test cases, Transmission line measurements, vulnerability assessment
Abstract

A process of critical transmission lines identification in presented here. The criticality is based on network flow, which is essential for power grid connectivity monitoring as well as vulnerability assessment. The proposed method can be utilized as a supplement of traditional situational awareness tool in the energy management system of the power grid control center. At first, a flow network is obtained from topological as well as functional features of the power grid. Then from the duality property of a linear programming problem, the maximum flow problem is converted to a minimum cut problem. Critical transmission lines are identified as a solution of the dual problem. An overall set of transmission lines are identified from the solution of the network flow problem. Simulation of standard IEEE test cases validates the application of the method in finding critical transmission lines of the power grid.

URLhttps://ieeexplore.ieee.org/document/8707572
DOI10.1109/PEDES.2018.8707572
Citation Keynasiruzzaman_network_2018