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To promote the resilience and protection of infrastructure assets from an all-hazards perspective, this paper describes the progress of interdependencies modeling and integration efforts to anticipate cascading failures among critical infrastructure systems. A data-centric architecture is adopted that integrates existing and proven infrastructure models used for impact assessment analyses to aid and enhance the design of resilient infrastructure systems. The assessment framework is applicable to all types of critical infrastructure and permits (1) the integration and automation of the assessment process, including threat and hazard identification and data acquisition; (2) the estimation and projection of impact zones; (3) the simulation of the initial effects on infrastructure assets resulting from an initiating disruptive event; (4) the evaluation of propagating effects within each infrastructure system; and (5) the simulation of the influence of cascading failures across infrastructure systems. The paper presents the application of the framework to integrate two proven energy models-EPfast, for electric power, and NGfast, for natural gas-to anticipate regional and local cascading failures, and design resilient energy systems. Two state-level case studies illustrate the approach in simulating the propagation of disruptions between the natural gas and electric power systems.

and numerous other organizations with whom the three-laboratory team has interacted during this project. The project team would also like to acknowledge the Clean Energy States Alliance (CESA) for very close collaboration in implementing its energy policy and regulations database into the Energy Zones Mapping Tool, as well as Navigant Consulting for its contributions related to demand-side resources.

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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.