Publications

Backus, George A.; Trucano, Timothy G.; Robinson, David G.; Adams, Brian M.; Richards, Elizabeth H.; Siirola, John D.; Boslough, Mark B.; Taylor, Mark A.; Conrad, Stephen H.; Kelic, Andjelka; Roach, Jesse D.; Warren, Drake E.; Ballantine, Marissa D.; Stubblefield, W.A.; Snyder, Lillian A.; Finley, Ray E.; Horschel, Daniel S.; Ehlen, Mark E.; Klise, Geoffrey T.; Malczynski, Leonard A.; Stamber, Kevin L.; Tidwell, Vincent C.; Vargas, Vanessa N.; Zagonel, Aldo A.

Abstract not provided.

Barton, Dianne C.; Eidson, Eric D.; Schoenwald, David A.; Stamber, Kevin L.; Reinert, Rhonda K.

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.

Barton, Dianne C.; Stamber, Kevin L.

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.