Dynamic Network Flow Model for Power Grid Systemic Risk Assessment and Resilience Enhancement
Publication: Journal of Infrastructure Systems
Volume 28, Issue 2
Abstract
Power infrastructure networks are susceptible to performance disruptions induced by natural or anthropogenic hazard events. For example, extreme weather events or cyberattacks can disrupt the functionality of multiple network components concurrently or sequentially, resulting in a chain of cascading failures throughout the network. Mitigating the impacts of such system-level cascading failures (systemic risks) requires analyzing the entire network considering the physics of its dynamic power flow. This study focuses on the draw-down phase of power infrastructure network resilience—assessing the power grid vulnerability and robustness, through simulating cascading failure propagations using a dynamic cascading failure physics-based model. The study develops and demonstrates the utility of a link vulnerability index to construct power transmission line vulnerability maps, as well as a node importance index for power (sub)station ranking according to the resulting cascading failure size. Overall, understanding the criticality of different network components provides stakeholders with the insights essential for building resilience and subsequently managing it within the context of power grids and supports policymakers and regulators in making informed decisions pertaining to the tolerable degree of systemic risk constrained by available resources.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. The Ontario power grid data, based on the IESO Summer 2010 base case scenario, were used to demonstrate the application of the model. Requests for such data may be made directly to IESO.
Acknowledgments
This research was supported by the Canadian Nuclear Energy Infrastructure Resilience under Systemic Risk (CaNRisk)—Collaborative Research and Training Experience (CREATE) program of the Natural Science and Engineering Research Council (NSERC) of Canada. Additional support through the INTERFACE Institute and the INViSiONLab of McMaster University is acknowledged.
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Received: Apr 18, 2021
Accepted: Dec 14, 2021
Published online: Feb 7, 2022
Published in print: Jun 1, 2022
Discussion open until: Jul 7, 2022
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