Award Abstract #0540342
DDDAS-TMRP: DDDAS for Autonomic Interconnected Systems: The National Energy Infrastructure

NSF Org:	CNS Division of Computer and Network Systems
Initial Amendment Date:	September 14, 2005
Latest Amendment Date:	March 19, 2007
Award Number:	0540342
Award Instrument:	Standard Grant
Program Manager:	Anita J. LaSalle CNS Division of Computer and Network Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	January 1, 2006
Expires:	December 31, 2007 (Estimated)
Awarded Amount to Date:	$200000
Investigator(s):	Thomas Downar thomas.j.downar.1@purdue.edu (Principal Investigator) Edward Coyle (Co-Principal Investigator) Athan Meliopoulos (Co-Principal Investigator) Christoph Hoffmann (Co-Principal Investigator) Oleg Wasynczuk (Co-Principal Investigator)
Sponsor:	Purdue University Young Hall West Lafayette, IN 47907 765/494-4600
NSF Program(s):	ITR-DYNAMIC DATA DRIV APP SYS
Field Application(s):	0000912 Computer Science
Program Reference Code(s):	HPCC, 9218
Program Element Code(s):	7581

ABSTRACT

The basic technological framework used to manage the national electric power grid is several generations old and is not prepared to meet the increased pressures of a growing economy, especially with the reduced

investment for transmission infrastructure in the past several decades. Experts recognize that

blackouts similar to the Northeast US - Canadian blackout of August 2003 remain a reality. The

Dynamic Data-Driven Applications (DDDAS) paradigm offers a unique framework for

methodological innovations that can revolutionize the nation's energy infrastructure, alleviate the

threat of blackouts, and assure the long term stability, reliability, and efficiency of the electric power

grid. New technology designed for the national energy infrastructure would increase the total energy utilized by

dynamically balancing power supply and demand, and by providing the ability to respond to

potentially debilitating situations before they develop into a crisis. The proposed research will

advance new methodologies for situation awareness, control flexibility, autonomic functionalities, and

self-healing that will revolutionize the state of the art in energy management. The centerpiece of the

research will be an integrated real time dynamic sensing and simulation capability for the electric

power grid. DDDAS provides the framework for the methodological innovations to achieve

seamless and robust integration of measurements and dynamic simulations across the multiple

boundaries of timescales and spatial characteristics encountered in the power grid.

To achieve its objectives the project will make advances along six integrated tasks (e.g. Simulation, Sensing, Integrative Methods, Visualization,

Computer Security, and Demonstration). The advances of this research include 1) real

time, high fidelity power grid simulation using integrated distributed heterogeneous simulation (DHS)

and neural network techniques on high end computational platforms, 2) the integration of real time

simulation and sensing using both high cost, high precision and low cost distributed sensor networks,

3) a new class of stable and robust mathematical algorithms for solving complex ill-posed problems

such as the real time power grid simulation/sensing application, 4) new approaches to real time

visualization and computer security. To demonstrate the intellectual merit and to achieve the

broadest impact of this research, the team will take advantage of existing collaborations with the

Midwest Systems Independent Operators (MISO), the first regional transmission organization

approved in the United States, and technology transfer mechanisms are essentially already in place.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

A.P. Sakis Meliopoulos, George J. Cokkinides, O. Wasynczuk, E. Coyle, M. Bell, C. Hoffmann, C. Nina-Rotaru, T. Downar, L. Tsoukalas, R. Gao. "PMU Data Characterization and Applications to Stability Monitoring," IEEE PES General Meeting, 2006, p. 1.

Emily T. Swain, Yunlin Xu, Rong Gao, Thomas J. Downar, Lefteri H. Tsoukalas. "The Application of Neural Networks to Electric Power Grid Simulation," International Conf. on Artificial Neural Networks (ICANN), 2006, p. 736.

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