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Title: Framework for Modeling High-Impact, Low-Frequency Power Grid Events to Support Risk-Informed Decisions

Natural and man-made hazardous events resulting in loss of grid infrastructure assets challenge the security and resilience of the electric power grid. However, the planning and allocation of appropriate contingency resources for such events requires an understanding of their likelihood and the extent of their potential impact. Where these events are of low likelihood, a risk-informed perspective on planning can be difficult, as the statistical basis needed to directly estimate the probabilities and consequences of their occurrence does not exist. Because risk-informed decisions rely on such knowledge, a basis for modeling the risk associated with high-impact, low-frequency events (HILFs) is essential. Insights from such a model indicate where resources are most rationally and effectively expended. A risk-informed realization of designing and maintaining a grid resilient to HILFs will demand consideration of a spectrum of hazards/threats to infrastructure integrity, an understanding of their likelihoods of occurrence, treatment of the fragilities of critical assets to the stressors induced by such events, and through modeling grid network topology, the extent of damage associated with these scenarios. The model resulting from integration of these elements will allow sensitivity assessments based on optional risk management strategies, such as alternative pooling, staging and logistic strategies, andmore » emergency contingency planning. This study is focused on the development of an end-to-end HILF risk-assessment framework. Such a framework is intended to provide the conceptual and overarching technical basis for the development of HILF risk models that can inform decision-makers across numerous stakeholder groups in directing resources optimally towards the management of risks to operational continuity.« less
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Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Disaster Risk Reduction, 18:125-137
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
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Country of Publication:
United States