Electric power is crucial to the function of other infrastructures, as well as to the stability of the economy and the social order. Disruption of commercial electric power service, even for brief periods of time, can create significant consequences to the function of other sectors, and make living in some environments untenable. This analysis, conducted in 2017 for the United States Department of Energy (DOE) as part of the Grid Modernization Laboratory Consortium (GMLC) Initiative, focuses on describing the function of each of the other infrastructure sectors and subsectors, with an eye towards those elements of these sectors that depend on primary electric power service through the commercial electric power grid. It leverages the experience of Sandia analysts in analyzing historical disruptive events, and from the development of capabilities designed to identify the physical, logical, and geographic connectivity between infrastructures. The analysis goes on to identify alternatives for the provision of primary electric power service, and the redundancy of said alternatives, to provide a picture of the sector’s ability to withstand an extended disruption.
Sandia's Academic Alliance (SAA) program takes a deliberate approach to building partnerships with universities that combine strengths in key academic disciplines, contain sizable portfolios of relevant research capabilities, and demonstrate a strong institutional commitment to national security. The SAA Program aims to solve significant problems that Sandia could not address alone, sustain and enrich Sandia's talent pipeline, and accelerate the commercialization and adoption of new technologies.
Simulation models can improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, then on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.
This report documents the key findings from the Reservoir Maintenance and Development (RM&D) Task of the U.S. Department of Energy's (DOE), Geothermal Technologies Office (GTO) Geothermal Vision Study (GeoVision Study). The GeoVision Study had the objective of conduc ting analyses of future geothermal growth based on sets of current and future geothermal technology developments. The RM&D Task is one of seven tasks within the GeoVision Study with the others being, Exploration and Confirmation, Potential to Penetration, Institutional Market Barriers, Environmental and Social Impacts, Thermal Applications, and Hybrid Systems. The full set of findings and the details of the GeoVision Study can be found in the final GeoVision Study report on the DOE-GTO website. As applied here, RM&D refers to the activities associated with developing, exploiting, and maintaining a known geothermal resource. It assumes that the site has already been vetted and that the resource has been evaluated to be of sufficient quality to move towards full-scale development. It also assumes that the resource is to be developed for power generation, as opposed to low-temperature or direct use applications. This document presents the key factors influencing RM&D from both a technological and operational standpoint and provides a baseline of its current state. It also looks forward to describe areas of research and development that must be pursued if the development geothermal energy is to reach its full potential.
Alaska oil fields provide an important, but diminishing, portion of the crude oil processed by Alaska and U.S. West Coast refineries. Production of crude oil in Alaska is being stressed by declining production in mature fields, high costs for developing and producing new fields, increasing competition from tight oil production in the Lower 48 states, and low global oil prices. The National Transportation Fuel Model is a network model of petroleum infrastructure in the Lower 48 states and portions of Canada developed at Sandia National Laboratories. It provides a simulation capability for analysis of system-wide responses to stressing events. Until now, however, this model did not explicitly include the petroleum infrastructure of Alaska and the transport of crude oil by marine shipments from Alaska to West Coast refineries. This paper describes the methods and information requirements for adding Alaska infrastructure to the National Transportation Fuel Model, provides an overview of the new Alaska portion of the model, and presents an example simulation of a closure of a large San Francisco refinery.
This report presents a methodology for estimating the impacts of events that damage or disrupt liquid fuels infrastructure. The impact of a disruption depends on which components of the infrastructure are damaged, the time required for repairs, and the position of the disrupted components in the fuels supply network. Impacts are estimated for seven stressing events in regions of the United States, which were selected to represent a range of disruption types. For most of these events the analysis is carried out using the National Transportation Fuels Model (NTFM) to simulate the system-level liquid fuels sector response. Results are presented for each event, and a brief cross comparison of event simulation results is provided.
This report has been written for the Department of Energy’s Energy Policy and Systems Analysis Office to inform their writing of the Quadrennial Energy Review in the area of energy resilience. The topics of measuring and increasing energy resilience are addressed, including definitions, means of measuring, and analytic methodologies that can be used to make decisions for policy, infrastructure planning, and operations. A risk-based framework is presented which provides a standard definition of a resilience metric. Additionally, a process is identified which explains how the metrics can be applied. Research and development is articulated that will further accelerate the resilience of energy infrastructures.
A natural gas network model was used to assess the likely impact of a scenario San Andreas Fault earthquake on the natural gas network. Two disruption scenarios were examined. The more extensive damage scenario assumes the disruption of all three major corridors bringing gas into southern California. If withdrawals from the Aliso Canyon storage facility are limited to keep the amount of stored gas within historical levels, the disruption reduces Los Angeles Basin gas supplies by 50%. If Aliso Canyon withdrawals are only constrained by the physical capacity of the storage system to withdraw gas, the shortfall is reduced to 25%. This result suggests that it is important for stakeholders to put agreements in place facilitating the withdrawal of Aliso Canyon gas in the event of an emergency.
This report provides the results of a scoping study evaluating the potential risk reduction value of a hypothetical, earthquake early-warning system. The study was based on an analysis of the actions that could be taken to reduce risks to population and infrastructures, how much time would be required to take each action and the potential consequences of false alarms given the nature of the action. The results of the scoping analysis indicate that risks could be reduced through improving existing event notification systems and individual responses to the notification; and production and utilization of more detailed risk maps for local planning. Detailed maps and training programs, based on existing knowledge of geologic conditions and processes, would reduce uncertainty in the consequence portion of the risk analysis. Uncertainties in the timing, magnitude and location of earthquakes and the potential impacts of false alarms will present major challenges to the value of an early-warning system.