Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Concepts from Complexity Science are valuable and allow a simulation approach for critical infrastructures that is flexible and has wide ranging applications.

Abstract not provided.

Abstract not provided.

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.

Abstract not provided.

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.