Efficient restoration of the electric grid from significant disruptions – both natural and manmade – that lead to the grid entering a failed state is essential to maintaining resilience under a wide range of threats. Restoration follows a set of black start plans, allowing operators to select among these plans to meet the constraints imposed on the system by the disruption. Restoration objectives aim to restore power to a maximum number of customers in the shortest time. Current state-of-the-art for restoration modeling breaks the problem into multiple parts, assuming a known network state and full observability and control by grid operators. These assumptions are not guaranteed under some threats. This paper focuses on a novel integration of modeling and analysis capabilities to aid operators during restoration activities. A power flow-informed restoration framework, comprised of a restoration mixed-integer program informed by power flow models to identify restoration alternatives, interacts with a dynamic representation of the grid through a cognitive model of operator decision-making, to identify and prove an optimal restoration path. Application of this integrated approach is illustrated on exemplar systems. Validation of the restoration is performed for one of these exemplars using commercial solvers, and comparison is made between the steps and time involved in the commercial solver, and that required by the restoration optimization in and of itself, and by the operator model in acting on the restoration optimization output. Publications and proposals developed under this work, along with a path forward for additional expansion of the work, and summary of what was achieved, are also documented.
Complex networks of information processing systems, or information supply chains, present challenges for performance analysis. We establish a mathematical setting, in which a process within an information supply chain can be analyzed in terms of the functionality of the system's components. Principles of this methodology are rigorously defended and induce a model for determining the reliability for the various products in these networks. Our model does not limit us from having cycles in the network, as long as the cycles do not contain negation. It is shown that our approach to reliability resolves the nonuniqueness caused by cycles in a probabilistic Boolean network. An iterative algorithm is given to find the reliability values of the model, using a process that can be fully automated. This automated method of discerning reliability is beneficial for systems managers. As a systems manager considers systems modification, such as the replacement of owned and maintained hardware systems with cloud computing resources, the need for comparative analysis of system reliability is paramount. The model is extended to handle conditional knowledge about the network, allowing one to make predictions of weaknesses in the system. Finally, to illustrate the model's flexibility over different forms, it is demonstrated on a system of components and subcomponents.
This document summarizes the findings of a review of published literature regarding the potential impacts of electromagnetic pulse (EMP) and geomagnetic disturbance (GMD) phenomena on oil and gas pipeline systems. The impacts of telluric currents on pipelines and their associated cathodic protection systems has been well studied. The existing literature describes implications for corrosion protection system design and monitoring to mitigate these impacts. Effects of an EMP on pipelines is not a thoroughly explored subject. Most directly related articles only present theoretical models and approaches rather than specific analyses and in-field testing. Literature on SCADA components and EMP is similarly sparse and the existing articles show a variety of impacts to control system components that range from upset and damage to no effect. The limited research and the range of observed impacts for the research that has been published suggests the need for additional work on GMD and EMP and natural gas SCADA components.
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.
The NetFlow Dynamics (NFD) model was developed for estimating the availability of a commodity supplied by a national- or regional-scale infrastructure following unexpected disruption of one or more of its components. The large scope of the disruptions of interest produce changes in availability lasting days to weeks. Consequently, the model does not resolve daily variations in system state and does not include the long-term processes that cause infrastructures to evolve as assets are added and removed according to owners ’planning decisions. NFD simulates fluid flow, including petroleum and other incompressible fluids, as well as natural gas and other compressible fluids, through pipeline networks characterized by limits on transmission capacity and storage. It was designed to enable efficient exploration of possible transmission system responses to large-scale disruptions lasting for days or longer. The model formulation reflects constraints on transmission and storage capacity imposed by the physical system assets. Those capacity limits are input parameters and are not derived from more basic system properties such as pipeline diameters and compressor power. A system’s response to a large disruption is controlled by operational decisions as well as damage to physical assets. The NFD model formulation allows users to efficiently consider alternative scenarios about the way remaining capacity might be used so that the analysis result appropriately reflects uncertainties about operator response.
Distributed Energy Resources (DER) are being added to the nation's electric grid, and as penetration of these resources increases, they have the potential to displace or offset large-scale, capital-intensive, centralized generation. Integration of DER into operation of the traditional electric grid requires automated operational control and communication of DER elements, from system measurement to control hardware and software, in conjunction with a utility's existing automated and human-directed control of other portions of the system. Implementation of DER technologies suggests a number of gaps from both a security and a policy perspective. This page intentionally left blank.
The transformation of the distribution grid from a centralized to decentralized architecture, with bi-directional power and data flows, is made possible by a surge in network intelligence and grid automation. While changes are largely beneficial, the interface between grid operator and automated technologies is not well understood, nor are the benefits and risks of automation. Quantifying and understanding the latter is an important facet of grid resilience that needs to be fully investigated. The work described in this document represents the first empirical study aimed at identifying and mitigating the vulnerabilities posed by automation for a grid that for the foreseeable future will remain a human-in-the-loop critical infrastructure. Our scenario-based methodology enabled us to conduct a series of experimental studies to identify causal relationships between grid-operator performance and automated technologies and to collect measurements of human performance as a function of automation. Our findings, though preliminary, suggest there are predictive patterns in the interplay between human operators and automation, patterns that can inform the rollout of distribution automation and the hiring and training of operators, and contribute in multiple and significant ways to the field of grid resilience.
Research into modeling of the quantification and prioritization of resources used in the recovery of lifeline critical infrastructure following disruptive incidents, such as hurricanes and earthquakes, has shown several factors to be important. Among these are population density and infrastructure density, event effects on infrastructure, and existence of an emergency response plan. The social sciences literature has a long history of correlating the population density and infrastructure density at a national scale, at a country-to-country level, mainly focused on transportation networks. This effort examines whether these correlations can be repeated at smaller geographic scales, for a variety of infrastructure types, so as to be able to use population data as a proxy for infrastructure data where infrastructure data is either incomplete or insufficiently granular. Using the best data available, this effort shows that strong correlations between infrastructure density for multiple types of infrastructure (e.g. miles of roads, hospital beds, miles of electric power transmission lines, and number of petroleum terminals) and population density do exist at known geographic boundaries (e.g. counties, service area boundaries) with exceptions that are explainable within the social sciences literature. The correlations identified provide a useful basis for ongoing research into the larger resource utilization problem.
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.
Concepts from Complexity Science are valuable and allow a simulation approach for critical infrastructures that is flexible and has wide ranging applications.
Economists, systems analysts, engineers, regulatory specialists, and other experts were assembled from academia, the national laboratories, and the energy industry to discuss present restoration practices (many have already been defined to the level of operational protocols) in the sectors of the energy infrastructure as well as other infrastructures, to identify whether economics, a discipline concerned with the allocation of scarce resources, is explicitly or implicitly a part of restoration strategies, and if there are novel economic techniques and solution methods that could be used help encourage the restoration of energy services more quickly than present practices or to restore service more efficiently from an economic perspective. AcknowledgementsDevelopment of this work into a coherent product with a useful message has occurred thanks to the thoughtful support of several individuals:Kenneth Friedman, Department of Energy, Office of Energy Assurance, provided the impetus for the work, as well as several suggestions and reminders of direction along the way. Funding from DOE/OEA was critical to the completion of this effort.Arnold Baker, Chief Economist, Sandia National Laboratories, and James Peerenboom, Director, Infrastructure Assurance Center, Argonne National Laboratory, provided valuable contacts that helped to populate the authoring team with the proper mix of economists, engineers, and systems and regulatory specialists to meet the objectives of the work.Several individuals provided valuable review of the document at various stages of completion, and provided suggestions that were valuable to the editing process. This list of reviewers includes Jeffrey Roark, Economist, Tennessee Valley Authority; James R. Dalrymple, Manager of Transmission System Services and Transmission/Power Supply, Tennessee Valley Authority; William Mampre, Vice President, EN Engineering; Kevin Degenstein, EN Engineering; and Patrick Wilgang, Department of Energy, Office of Energy Assurance.With many authors, creating a document with a single voice is a difficult task. Louise Maffitt, Senior Research Associate, Institute for Engineering Research and Applications at New Mexico Institute of Mining & Technology (on contract to Sandia National Laboratories) served a vital role in the development of this document by taking the unedited material (in structured format) and refining the basic language so as to make the flow of the document as close to a single voice as one could hope for. Louise's work made the job of reducing the content to a readable length an easier process. Additional editorial suggestions from the authors themselves, particularly from Sam Flaim, Steve Folga, and Doug Gotham, expedited this process.
Critical Infrastructures are formed by a large number of components that interact within complex networks. As a rule, infrastructures contain strong feedbacks either explicitly through the action of hardware/software control, or implicitly through the action/reaction of people. Individual infrastructures influence others and grow, adapt, and thus evolve in response to their multifaceted physical, economic, cultural, and political environments. Simply put, critical infrastructures are complex adaptive systems. In the Advanced Modeling and Techniques Investigations (AMTI) subgroup of the National Infrastructure Simulation and Analysis Center (NISAC), we are studying infrastructures as complex adaptive systems. In one of AMTI's efforts, we are focusing on cascading failure as can occur with devastating results within and between infrastructures. Over the past year we have synthesized and extended the large variety of abstract cascade models developed in the field of complexity science and have started to apply them to specific infrastructures that might experience cascading failure. In this report we introduce our comprehensive model, Polynet, which simulates cascading failure over a wide range of network topologies, interaction rules, and adaptive responses as well as multiple interacting and growing networks. We first demonstrate Polynet for the classical Bac, Tang, and Wiesenfeld or BTW sand-pile in several network topologies. We then apply Polynet to two very different critical infrastructures: the high voltage electric power transmission system which relays electricity from generators to groups of distribution-level consumers, and Fedwire which is a Federal Reserve service for sending large-value payments between banks and other large financial institutions. For these two applications, we tailor interaction rules to represent appropriate unit behavior and consider the influence of random transactions within two stylized networks: a regular homogeneous array and a heterogeneous scale-free (fractal) network. For the stylized electric power grid, our initial simulations demonstrate that the addition of geographically unrestricted random transactions can eventually push a grid to cascading failure, thus supporting the hypothesis that actions of unrestrained power markets (without proper security coordination on market actions) can undermine large scale system stability. We also find that network topology greatly influences system robustness. Homogeneous networks that are 'fish-net' like can withstand many more transaction perturbations before cascading than can scale-free networks. Interestingly, when the homogeneous network finally cascades, it tends to fail in its entirety, while the scale-free tends to compartmentalize failure and thus leads to smaller, more restricted outages. In the case of stylized Fedwire, initial simulations show that as banks adaptively set their individual reserves in response to random transactions, the ratio of the total volume of transactions to individual reserves, or 'turnover ratio', increases with increasing volume. The removal of a bank from interaction within the network then creates a cascade, its speed of propagation increasing as the turnover ratio increases. We also find that propagation is accelerated by patterned transactions (as expected to occur within real markets) and in scale-free networks, by the 'attack' of the most highly connected bank. These results suggest that the time scale for intervention by the Federal Reserve to divert a cascade in Fedwire may be quite short. Ongoing work in our cascade analysis effort is building on both these specific stylized applications to enhance their fidelity as well as embracing new applications. We are implementing markets and additional network interactions (e.g., social, telecommunication, information gathering, and control) that can impose structured drives (perturbations) comparable to those seen in real systems. Understanding the interaction of multiple networks, their interdependencies, and in particular, the underlying mechanisms for their growth/evolution is paramount. With this understanding, appropriate public policy can be identified to guide the evolution of present infrastructures to withstand the demands and threats of the future.
This report describes the features of Aspen-EE (Electricity Enhancement), a new model for simulating the interdependent effects of market decisions and disruptions in the electric power system on other critical infrastructures in the US economy. Aspen-EE extends and modifies the capabilities of Aspen, an agent-based model previously developed by Sandia National Laboratories. Aspen-EE was tested on a series of scenarios in which the rules governing electric power trades were changed. Analysis of the scenario results indicates that the power generation company agents will adjust the quantity of power bid into each market as a function of the market rules. Results indicate that when two power markets are faced with identical economic circumstances, the traditionally higher-priced market sees its market clearing price decline, while the traditionally lower-priced market sees a relative increase in market clearing price. These results indicate that Aspen-EE is predicting power market trends that are consistent with expected economic behavior.
US infrastructures provide essential services that support the economic prosperity and quality of life. Today, the latest threat to these infrastructures is the increasing complexity and interconnectedness of the system. On balance, added connectivity will improve economic efficiency; however, increased coupling could also result in situations where a disturbance in an isolated infrastructure unexpectedly cascades across diverse infrastructures. An understanding of the behavior of complex systems can be critical to understanding and predicting infrastructure responses to unexpected perturbation. Sandia National Laboratories has developed an agent-based model of critical US infrastructures using time-dependent Monte Carlo methods and a genetic algorithm learning classifier system to control decision making. The model is currently under development and contains agents that represent the several areas within the interconnected infrastructures, including electric power and fuel supply. Previous work shows that agent-based simulations models have the potential to improve the accuracy of complex system forecasting and to provide new insights into the factors that are the primary drivers of emergent behaviors in interdependent systems. Simulation results can be examined both computationally and analytically, offering new ways of theorizing about the impact of perturbations to an infrastructure network.