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

Veeramany, Arun¹  Unwin, Stephen D.¹  Coles, Garill A.¹  Dagle, Jeffery E.¹  Millard, W. David¹  Yao, Juan¹  Glantz, Clifford S.¹  Gourisetti, Sri Nikhil Gup¹ 

[1] Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

关键词: Power Grid;    Hazards;    Probabilistic Risk Assessment;    High-Impact;    Low-Frequency;    HILF;    Initiating Event;    Fragility;    NERC;    Monte Carlo;    Infrastructure;    Importance Analysis;    Decision-Making;    Hazard Characterization;    Hazard Curve;    Risk Framework;    Elicitation;    Informed Opinion;   

DOI  :  10.2172/1228355
RP-ID  :  PNNL-24673
PID  :  OSTI ID: 1228355
Others  :  Other: TE1104000

学科分类：数学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

Natural and man-made hazardous events resulting in loss of grid infrastructure assets challenge the electric power grid???s security and resilience. 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 problematic as there exists an insufficient statistical basis to directly estimate the probabilities and consequences of their occurrence. Since 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 can inform where resources are most rationally and effectively expended. The present effort is focused on development of a 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 sectors. The North American Electric Reliability Corporation (NERC) 2014 Standard TPL-001-4 considers severe events for transmission reliability planning, but does not address events of such severity that they have the potential to fail a substantial fraction of grid assets over a region, such as geomagnetic disturbances (GMD), extreme seismic events, and coordinated cyber-physical attacks. These are beyond current planning guidelines. As noted, the risks associated with such events cannot be statistically estimated based on historic experience; however, there does exist a stable of risk modeling techniques for rare events that have proven of value across a wide range of engineering application domains. There is an active and growing interest in evaluating the value of risk management techniques in the State transmission planning and emergency response communities, some of this interest in the context of grid modernization activities. The availability of a grid HILF risk model, integrated across multi-hazard domains which, when interrogated, can support transparent, defensible and effective decisions, is an attractive prospect among these communities. In this report, we document an integrated HILF risk framework intended to inform the development of risk models. These models would be based on the systematic and comprehensive (to within scope) characterization of hazards to the level of detail required for modeling risk, identification of the stressors associated with the hazards (i.e., the means of impacting grid and supporting infrastructure), characterization of the vulnerability of assets to these stressors and the probabilities of asset compromise, the grid???s dynamic response to the asset failures, and assessment of subsequent severities of consequence with respect to selected impact metrics, such as power outage duration and geographic reach. Specifically, the current framework is being developed to;1. Provide the conceptual and overarching technical paradigms for the development of risk models; 2. Identify the classes of models required to implement the framework - providing examples of existing models, and also identifying where modeling gaps exist; 3. Identify the types of data required, addressing circumstances under which data are sparse and the formal elicitation of informed judgment might be required; and 4. Identify means by which the resultant risk models might be interrogated to form the necessary basis for risk management.