Publications

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U.S. critical infrastructure assets are often designed to operate for decades, and yet long-term planning practices have historically ignored climate change. With the current pace of changing operational conditions and severe weather hazards, research is needed to improve our ability to translate complex, uncertain risk assessment data into actionable inputs to improve decision-making for infrastructure planning. Decisions made today need to explicitly account for climate change – the chronic stressors, the evolution of severe weather events, and the wide-ranging uncertainties. If done well, decision making with climate in mind will result in increased resilience and decreased impacts to our lives, economies, and national security. We present a three-tier approach to create the research products needed in this space: bringing together climate projection data, severe weather event modeling, asset-level impacts, and contextspecific decision constraints and requirements. At each step, it is crucial to capture uncertainties and to communicate those uncertainties to decision-makers. While many components of the necessary research are mature (i.e., climate projection data), there has been little effort to develop proven tools for long-term planning in this space. The combination of chronic and acute stressors, spatial and temporal uncertainties, and interdependencies among infrastructure sectors coalesce into a complex decision space. By applying known methods from decision science and data analysis, we can work to demonstrate the value of an interdisciplinary approach to climate-hazard decision making for longterm infrastructure planning.

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This paper describes how performance problems can be “masked,” or not readily evident by several causes: by photovoltaic (PV) system configuration (such as the size of the PV array capacity relative to the size of the inverter and the resultant clipped operating mode); by instrumentation design, installation, and maintenance (such as a misaligned or dirty pyranometer); by contract clauses (when operational availability is transformed to contractual availability, which excludes many factors); and by identified management and operational practices (such as reporting on a portfolio of plants rather than individually). A simple method based on a duration curve is introduced to overcome shortcomings of Performance Ratio based on nameplate capacity and Performance Index based on hourly simulation when quantifying masking effects, and inverter clipping and pyranometer soiling are presented as two examples of the new method. With a better understanding of the non-transparency of masking issues, stakeholders can better interpret performance data and deliver improved AC and DC plant conditions through PV system operation and maintenance (O&M) for improved performance, reduced O&M costs, and a more consistently delivered, and reduced, levelized cost of energy (LCOE).

PV system reliability analyses often depend on production data to evaluate the system state. However, using this information alone leads to incomplete assessments, since contextual information about potential sources of data quality issues is lacking (e.g., missing data from offline communications vs. offline production). This paper introduces a new Python-based software capability (called pvOps) for fusing production data with readily available text-based maintenance information to improve reliability assessments. In addition to details about the package development process, the general capabilities to gain actionable insights using field data are presented through a case study. These findings highlight the significant potential for continued advancements in operational assessments.

PV system reliability analyses often depend on production data to evaluate the system state. However, using this information alone leads to incomplete assessments, since contextual information about potential sources of data quality issues is lacking (e.g., missing data from offline communications vs. offline production). This paper introduces a new Python-based software capability (called pvOps) for fusing production data with readily available text-based maintenance information to improve reliability assessments. In addition to details about the package development process, the general capabilities to gain actionable insights using field data are presented through a case study. These findings highlight the significant potential for continued advancements in operational assessments.

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In this paper we consider the effects of corporate hierarchies on innovation spread across multilayer networks, modeled by an elaborated SIR framework. We show that the addition of management layers can significantly improve spreading processes on both random geometric graphs and empirical corporate networks. Additionally, we show that utilizing a more centralized working relationship network rather than a strict administrative network further increases overall innovation reach. In fact, this more centralized structure in conjunction with management layers is essential to both reaching a plurality of nodes and creating a stable adopted community in the long time horizon. Further, we show that the selection of seed nodes affects the final stability of the adopted community, and while the most influential nodes often produce the highest peak adoption, this is not always the case. In some circumstances, seeding nodes near but not in the highest positions in the graph produces larger peak adoption and more stable long-time adoption.

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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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