Publications

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Risk and resilience assessments for critical infrastructure focus on myriad objectives, from natural hazard evaluations to optimizing investments. Although research has started to characterize externalities associated with current or possible future states, incorporation of equity priorities at project inception is increasingly being recognized as critical for planning related activities. However, there is no standard methodology that guides development of equity-informed quantitative approaches for infrastructure planning activities. To address this gap, we introduce a logic model that can be tailored to capture nuances about specific geographies and community priorities, effectively incorporating them into different mathematical approaches for quantitative risk assessments. Specifically, the logic model uses a graded, iterative approach to clarify specific equity objectives as well as inform the development of equations being used to support analysis. We demonstrate the utility of this framework using case studies spanning aviation fuel, produced water, and microgrid electricity infrastructures. For each case study, the use of the logic model helps clarify the ways that local priorities and infrastructure needs are used to drive the types of data and quantitative methodologies used in the respective analyses. The explicit consideration of methodological limitations (e.g., data mismatches) and stakeholder engagements serves to increase the transparency of the associated findings as well as effectively integrate community nuances (e.g., ownership of assets) into infrastructure assessments. Such integration will become increasingly important to ensure that planning activities (which occur throughout the lifecycle of the infrastructure projects) lead to long-lasting solutions to meet both energy and sustainable development goals for communities.

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ReNCAT is a software application that suggests microgrid portfolios that reduce the impact of large-scale disruptions to power, as measured by the Social Burden Metric. ReNCAT examines a power distribution network to identify regions that can be isolated into microgrids that enable critical services to be provided even if the remainder of the study area is left without power. ReNCAT operates on a simplified representation of the power grid, one that aggregates and approximates loads and conductors. Microgrids are formed within the power network by setting switch states to split or join portions of the grid. ReNCAT identifies candidate microgrid portfolios with varying tradeoffs between cost and service availability.

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Social Infrastructure Service Burden (abbr. Social Burden) is defined as the burden to a population for attaining services needed from infrastructure. Infrastructure services represent opportunities to acquire things that people need, such as food, water, healthcare, financial services, etc. Accessing services requires effort, disruption to schedules, expenditure of money, etc. Social Burden represents the relative hardship people experience in the process of acquiring needed services. Social Burden is comprised of several components. One component is the effort associated with travel to a facility that provides a needed service. Another component of burden is the financial impact of acquiring resources once at the providing location. We are applying Social Burden as a resilience metric by quantifying it following a major disruption to infrastructure. Specifically, we are most interested in quantifying this metric for events in which energy systems are a major component of the disruption. We do not believe this is the only such use of the Social Burden metric, and therefore we will also be exploring its use to describe blue-sky conditions of a society in the future. Furthermore, while the construct can be applied to a dynamically changing situation, we are applying it statically, directly following a disruption. This notably ignores recovery dynamics that are a key capability of resilient systems. This too will be explored in future research.

Community, corporate, and government organizations are being targeted by disinformation attacks at an unprecedented rate. These attacks interrupt the ability of organizations to make high-consequence decisions and can lower their confidence in datasets and analytics. New interdisciplinary research approaches are being actively developed to expand resilience theory applications to organizations, and to determine the metrics and mitigations needed to increase resilience against disinformation. This paper presents initial ideas on adapting resilience methodologies for organizations and disinformation, highlighting key areas that require further exploration in this emerging field of research.

Sandia National Laboratories' (Sandia) Resilient Energy Systems (RES) Strategic Initiative is establishing a strategic vision for U.S. energy systems' resilience through threat-informed research and development, enabling energy and interdependent infrastructure systems to successfully adapt in an environment of accelerating change. A key challenge in promoting energy systems resilience lies in developing rigorous resilience analysis methodologies to quantify system performance. Resilience analysis methodologies should enable evaluation of the consequences of various disruptions and the relative effectiveness of potential mitigations. To address this challenge, RES synthesized the common components of Sandia's resilience frameworks into an integrated methodology for energy and infrastructure resilience analysis. This report documents, demonstrates, and extends this methodology.

Changes in the nature, intensity, and frequency of climate-related extreme events have imposed a higher risk of failure on energy systems, especially those at the community level. Furthermore, the evolving energy demand patterns and transition towards renewable and localised energy supply can affect energy system resilience. How can an energy system be planned and reconfigured to address these challenges without compromising the system's resilience against chronic stresses and extreme events? Unlike energy system reliability, resilience is neither a common nor an explicit consideration in energy master planning at the community level. In addition, there is no universally agreed-upon method or metrics for measuring or estimating resilience and defining mitigation strategies. This paper introduces a multi-layered energy resilience framework and set of metrics for energy master planning of communities, including the new generation of district energy systems. The potential system disturbances and their short and long-term impacts on various components of the energy system are discussed for commonly expected and extreme events. Three layers of energy resilience are discussed: engineering-designed resilience, operational resilience, and community-societal resilience. A starting set of energy resilience metrics to support engineering design and energy master planning for communities is identified. Implications for future research and practice are noted.

The continued operation of Land Ports of Entry (LPOE), managed by the Customs and Border Protection (CBP) and General Services Administration% is vital to the U.S. economy and security. Border faculties are included in the Department of Homeland Security (DHS) Government Facilities Sector2, one of the 16 critical infrastructures "whose assets, systems, and networks, whether physical or virtual, are considered so vital to the United States that their incapacitation or destruction would have a debilitating effect on security, national economic security, national public health or safety, or any combination thereof.'" Specifically, disruptions to the flow of border crossing traffic, in the form of closures or increased border crossing wait times, impact the economy and security of all countries involved. This paper describes a process for analyzing and improving the resilience of U.S. Land Ports of Entry. For LPOE, the team believes that energy resilience is the primary objective due to the complete reliance on the e-manifest system and the increasing use of Multi-Energy Portals (MEPs). Emanifests are part of CPB's Automated Commercial Environment (ACE). They document several key pieces of information about cargo vehicles wishing to cross the border into the United States and are submitted before arriving at the port. Vehicles can be flagged for more invasive inspection based on the content of the e-manifest. MEPs are a non-intrusive inspection (NII) technology used to scan the contents of the cargo. Together MEPs and ACE serve an important role in aiding CBP with their mission to protect "the public from dangerous people and materials", and "enabling legitimate trade and travel.'" To analyze resilience of a port, the team would need to understand the port's current energy usage, which systems depend on energy and what backup systems exist, and any emergency operation plans that dictate how systems are operated in the event of a power outage. The team would also need to determine the design basis threats (DBTs) for the LPOE which could include natural disasters, manmade events, and accidents. The magnitudes of the DBTs are calculated and are then translated to expected impacts on the infrastructure and systems at the port. With this information gathered, existing LPOE models developed here at Sandia National Laboratories could be extended to support decisions about resilience. Current models are implemented in FlexSim, a 3rd party discrete event simulator. FlexSim provides 3-D visuals of physical layout that can reveal valuable insights, allows input to be variable (e.g. time it takes to interact with the CBP officer at primary inspection can vary) so that a whole range of possibilities can be captured in the results, and can be used to collect user-defined output metrics. Current LPOE models focus on cargo vehicle traffic, and process changes caused by the installation of new drive-through MEPs. Extending them to address resilience questions would require the addition of key pieces of information learned during the resilience analysis including critical systems, failure rates, and process changes for when failures occur. The primary output metric for current models is border crossing wait time. Additional metrics would also be added to the model to gain a more complete understanding of impacts related to resilience, for example, MEP scan rate. Once complete, the model could be used to analyze the effectiveness of mitigation strategies representing some future state.

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An analysis of microgrids to increase resilience was conducted for the island of Puerto Rico. Critical infrastructure throughout the island was mapped to the key services provided by those sectors to help inform primary and secondary service sources during a major disruption to the electrical grid. Additionally, a resilience metric of burden was developed to quantify community resilience, and a related baseline resilience figure was calculated for the area. To improve resilience, Sandia performed an analysis of where clusters of critical infrastructure are located and used these suggested resilience node locations to create a portfolio of 159 microgrid options throughout Puerto Rico. The team then calculated the impact of these microgrids on the region's ability to provide critical services during an outage, and compared this impact to high-level estimates of cost for each microgrid to generate a set of efficient microgrid portfolios costing in the range of 218-917M dollars. This analysis is a refinement of the analysis delivered on June 01, 2018.

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This report describes the application of an approach for determining grid modernization investments that can best improve the resilience of communities. Under the direction of the US Department of Energy's Grid Modernization Laboratory Consortium, Sandia National Laboratories (Sandia) and Los Alamos National Laboratory (Los Alamos) collaborated with community stakeholders in New Orleans, Louisiana on grid modernization strategies for resilience. Past disruptions to the electric grid in New Orleans have contributed to an inability to provide citizens with adequate access to a wide range of infrastructure services. Using a performance-based resilience metric, Sandia and Los Alamos performed analysis on how to improve access to infrastructure services across New Orleans after a major disruption using a system of resilience nodes. Resilience nodes rely on a combination of urban planning with grid investment planning for resilience in order to design clustered infrastructure assets with highly resilient electrical supply. Results of the analysis led to suggestion of 22 draft resilience node locations that can provide a wide range of infrastructure services equitably to New Orleans citizens. This report serves as a proof-of-concept for the Urban Resilience Planning Process, and describes several gaps that should be overcome in order to integrate resilience planning between electric utilities and local governments.

As system of systems (SoS) models become increasingly complex and interconnected a new approach is needed to capture the effects of humans within the SoS. Many real-life events have shown the detrimental outcomes of failing to account for humans in the loop. This research introduces a novel and cross-disciplinary methodology for modeling humans interacting with technologies to perform tasks within an SoS specifically within a layered physical security system use case. Metrics and formulations developed for this new way of looking at SoS termed sociotechnical SoS allow for the quantification of the interplay of effectiveness and efficiency seen in detection theory to measure the ability of a physical security system to detect and respond to threats. This methodology has been applied to a notional representation of a small military Forward Operating Base (FOB) as a proof-of-concept.

As system of systems (SoS) models become increasingly complex and interconnected a new approach is needed to capture the effects of humans within the SoS. Many real-life events have shown the detrimental outcomes of failing to account for humans in the loop. This research introduces a novel and cross-disciplinary methodology for modeling humans interacting with technologies to perform tasks within an SoS specifically within a layered physical security system use case. Metrics and formulations developed for this new way of looking at SoS termed sociotechnical SoS allow for the quantification of the interplay of effectiveness and efficiency seen in detection theory to measure the ability of a physical security system to detect and respond to threats. This methodology has been applied to a notional representation of a small military Forward Operating Base (FOB) as a proof-of-concept.

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Modern systems, such as physical security systems, are often designed to involve complex interactions of technological and human elements. Evaluation of the performance of these systems often overlooks the human element. A method is proposed here to expand the concept of sensitivity—as denoted by d’—from signal detection theory (Green & Swets 1966; Macmillan & Creelman 2005), which came out of the field of psychophysics, to cover not only human threat detection but also other human functions plus the performance of technical systems in a physical security system, thereby including humans in the overall evaluation of system performance. New in this method is the idea that probabilities of hits (accurate identification of threats) and false alarms (saying “threat” when there is not one), which are used to calculate d’ of the system, can be applied to technologies and, furthermore, to different functions in the system beyond simple yes-no threat detection. At the most succinct level, the method returns a single number that represents the effectiveness of a physical security system; specifically, the balance between the handling of actual threats and the distraction of false alarms. The method can be automated, and the constituent parts revealed, such that given an interaction graph that indicates the functional associations of system elements and the individual probabilities of hits and false alarms for those elements, it will return the d’ of the entire system as well as d’ values for individual parts. The method can also return a measure of the response bias* of the system. One finding of this work is that the d’ for a physical security system can be relatively poor in spite of having excellent d’s for each of its individual functional elements.

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System-of-systems modeling has traditionally focused on physical systems rather than humans, but recent events have proved the necessity of considering the human in the loop. As technology becomes more complex and layered security continues to increase in importance, capturing humans and their interactions with technologies within the system-of-systems will be increasingly necessary. After an extensive job-task analysis, a novel type of system-ofsystems simulation model has been created to capture the human-technology interactions on an extra-small forward operating base to better understand performance, key security drivers, and the robustness of the base. In addition to the model, an innovative framework for using detection theory to calculate d’ for individual elements of the layered security system, and for the entire security system as a whole, is under development.