Infrastructure Modeling

Research on infrastructure protection at NMC includes development of new models, infrastructure stablility and vulnerablilty, and system optimization. Collaborating NMC scientists are focused on increasing the stability of the national energy infrastructure. This research includes modeling regional water distribution and understanding electrical grid behavior.

New Jersey Regional Water Distribution Model Development, Maintenance, and Deployment

Tim McPherson, NMC Affiliate Scientist, LANL Staff Scientist
William Daniel, LANL Staff Scientist
David Judi, LANL Staff Scientist
Steve Linger, LANL Staff Scientist
Brett Okhysen, LANL Staff Scientist
This project is developing a regional water distribution model for the New Jersey Exit 14 Area.  As infrastructure ages or as purveyors upgrade infrastructure components, system performance is expected to change. This project incorporates these changes into an updated regional distribution model. Funding for this project comes from the New Jersey Office of Homeland Security and Preparedness.

Power Grid Spectroscopy

Michael Chertkov, NMC Affiliate Research Scientist, LANL Staff Scientist
Scott Beckhaus, NMC Affiliate Research Scientist, LANL Staff Scientist

The goal of this project is to develop a better fundamental understanding of electrical grid behavior by applying approaches from the fields of nonlinear systems, applied mathematics, statistical physics, and signal analysis to study the dynamics of voltage, power flow, and frequency disturbances on large-scale transmission grids. The May 2012 publication in Physics Today, Getting a grip on the electrical grid, gives a physicist's view of the electrical grid to enable our understanding grid behavior. Scientists and engineers must first understand the grid's behavior over a broad spatiotemporal scale. In this article the authors outline the physics and phenomena associated with grid behavior. A knowledge of the physics of the electrical grid needs to be coupled with complementary methods from operations research, computer science, control theory, machine learning, and electrical power engineering. Along with these disciplines, this article serves to enable the development of better methods of monitoring and controlling the grid as it becomes smarter and more autonomous. Funding for this project comes from the National Science Foundation.

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