Publications

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Resilience has been defined as a priority for the US critical infrastructure. This paper presents a process for incorporating resiliency-derived metrics into security system evaluations. To support this analysis, we used a multi-layer network model (MLN) reflecting the defined security system of a hypothetical nuclear power plant to define what metrics would be useful in understanding a system's ability to absorb perturbation (i.e., system resilience). We defined measures focusing on the system's criticality, rapidity, diversity, and confidence at each network layer, simulated adversary path, and the system as a basis for understanding the system's resilience. For this hypothetical system, our metrics indicated the importance of physical infrastructure to overall system criticality, the relative confidence of physical sensors, and the lack of diversity in assessment activities (i.e., dependence on human evaluations). Refined model design and data outputs will enable more nuanced evaluations into temporal, geospatial, and human behavior considerations. Future studies can also extend these methodologies to capture respond and recover aspects of resilience, further supporting the protection of critical infrastructure.

Performance measures commonly used in systems security engineering tend to be static, linear, and have limited utility in addressing challenges to security performance from increasingly complex risk environments, adversary innovation, and disruptive technologies. Leveraging key concepts from resilience science offers an opportunity to advance next-generation systems security engineering to better describe the complexities, dynamism, and non-linearity observed in security performance—particularly in response to these challenges. This article introduces a multilayer network model and modified Continuous Time Markov Chain model that explicitly captures interdependencies in systems security engineering. The results and insights from a multilayer network model of security for a hypothetical nuclear power plant introduce how network-based metrics can incorporate resilience concepts into performance metrics for next generation systems security engineering.

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Ongoing operations and maintenance (O&M) are needed to ensure photovoltaic (PV) systems continue to operate and meet production targets over the lifecycle of the system. Although average costs to operate and maintain PV systems have been decreasing over time, reported costs can vary significantly at the plant level. Estimating O&M costs accurately is important for informing financial planning and tracking activities, and subsequently lowering the levelized cost of electricity (LCOE) of PV systems. This report describes a methodology for improving O&M planning estimates by using empirically-derived failure statistics to capture component reliability in the field. The report also summarizes failure patterns observed for specific PV components and local environmental conditions observed in Sandia's PV Reliability, Operations & Maintenance (PVROM) database, a collection of field records across 800+ systems in the U.S. Where system-specific or fleet-specific data are lacking, PVROM-derived failure distribution values can be used to inform cost modeling and other reliability analyses to evaluate opportunities for performance improvements.

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