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The most critical element of the nation's energy infrastructure is our electricity generation, transmission, and distribution system known as the "power grid." Computer simulation is an effective tool that can be used to identify vulnerabilities and predict the system response for various contingencies. However, because the power grid is a very large-scale nonlinear system, such studies are presently conducted "open loop" using predicted loading conditions months in advance and, due to uncertainties in model parameters, the results do not provide grid operators with accurate "real time" information that can be used to avoid major blackouts such as were experienced on the East Coast in August of 2003. However, the paradigm of Dynamic Data-Driven Applications Systems (DDDAS) provides a fundamentally new framework to rethink the problem of power grid simulation. In DDDAS, simulations and field data become a symbiotic feedback control system and this is refreshingly different from conventional power grid simulation approaches in which data inputs are generally fixed when the simulation is launched. The objective of the research described herein was to utilize the paradigm of DDDAS to develop a marriage between sensing, visualization, and modelling for large-scale simulation with an immediate impact on the power grid. Our research has focused on methodological innovations and advances in sensor systems, mathematical algorithms, and power grid simulation, security, and visualization approaches necessary to achieve a meaningful large-scale real-time simulation that can have a significant impact on reducing the likelihood of major blackouts.

DDDAS for Autonomic Interconnected Systems: The National Energy Infrastructure