The development of predictive tools for emergency management has recently become a subject of major consideration among emergency responders, especially at the federal level. Often the news of an impending high-consequence threat causes significant stress on these agencies because of their inability to apprise management of probable impacts with sufficient certainty. This paper documents Argonne National Laboratory's effort to demonstrate the predictive capability of its newly enhanced tool called EPfast in estimating the impacts of postulated events on our power system. Specifically, the study focuses on EPfast's ability to estimate power outage areas resulting from random system contingencies. The San Diego September 8, 2011, blackout that affected most of southern California was selected for simulation using EPfast. Results showed agreement with actual reported impacts in both spatial and quantitative terms. The method, assumptions, and data used are presented here, and results showing their potential application to emergency planning are discussed.

This paper describes the capabilities, calculation logic, and foundational assumptions of EPfast, a new simulation and impact analysis tool developed by Argonne National Laboratory. The purpose of the model is to explore the tendency of power systems to spiral into uncontrolled islanding triggered by either man-made or natural disturbances. The model generates a report that quantifies the megawatt reductions in all affected substations, as well as the number, size, and spatial location of the formed island grids. The model is linear and is intended to simulate the impacts of high-consequence events on large-scale power systems. The paper describes a recent application of the model to examine the effects of a high-intensity New Madrid seismic event on the U.S. Eastern Interconnection (USEI). The model's final upgrade and subsequent application to the USEI were made possible via funding from

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