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Dynamic Modeling of Components on the Electrical Grid
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  1. Dynamic Modeling of Components on the Electrical Grid Bailey Young Wofford College Dr. Steven Fernandez and Dr. Olufemi Omitaomu Computational Sciences and Engineering Division August 2009

  2. Overview • Introduction and background • Research objectives • Methods • Results and validation • Conclusion • Future research • Acknowledgments

  3. Introduction and background • FEMA needs a tool to display outage areas in large storms, to predict electrical customer outages • Use electrical outage predictions to • Create better emergency responses • Protection for critical infrastructures • VERDE : Visualizing Energy Resources Dynamically on Earth

  4. VERDE system • Simulates the electric grid • Provides common operating picture for FEMA emergency responses. • Uses Google earth as platform • Provides real-time status of transmission lines • Provides real-time weather overlay Wide-area power grid situational awareness Streaming analysis Impact models Weather overlay

  5. Energy infrastructure situational awareness • Coal delivery and rail lines • Refinery and off-shore production platforms • Natural gas pipelines • Transportation and evacuation routes • Population impacts from LandScan1 USA population database 1. Bhaduri et al., 2002; Dobson et al., 2000

  6. Research objectives • Determine electrical customers for counties of US • Translate MATLAB code to Java • Program to estimate electrical substation service and outage areas • Compare with geospatial metrics, MATLAB output with Java output • Determine reliability of Java code

  7. Pop2000 __________________ House2000 + Firms2000 CF = Pop2008 _________ CF = Customers Methods Number of customers in 2008 per county is found by using the equations CF = Correction Factor Pop2000 = Population in 2000 Pop2008 = Population in 2008 House2000 = Nighttime population in 2000 Firms 2000 = Firms in 2000 Customers = 2008 customers

  8. Methods • Compare customer estimates from correction factor with known customer data • Translate modified Moore-based algorithm in MATLAB to Java code • Use program to predict electrical substation area • Use researched geospatial metrics to compare substation’s area in MATLAB and Java • Use electrical substation locations given by CMS energy of Michigan area

  9. Methods • Modified Moore-based algorithm • Peak substation demand data from commercial data sets and population data from LandScan • Substation geographic location and peak demand from Energy Visuals • Cells approximately 1km • Demand and supply per cell Inputs Geo-located population data Geo-located substation data Demand Data Supply Data Combined Data

  10. Results and validation • Receive output from Java code • Compare MATLAB and Java outputs using Michigan substation locations Sample output of code implementation provided by Dr. Omitaomu Each color represents a different substation service area

  11. Results and validation • Compare customer estimates from correction factor with known customer data • Use known customer data in seven Florida and Maryland counties • Have non-disclosure agreements with these utility companies • These companies only electrical provider for these counties • Use ratio of predicted to actual customer estimates from the correction factor in these counties: 100.7% ± 13.6% • Utility companies use this number to convert population to customers

  12. Correction factor vs. counties Results and validation • Shows representation of correction factor by county. Number of Counties Correction Factors

  13. Conclusions • Correction factor data is effective in predicting customer population • Correction factor data imported into real time VERDE data • Reliability of Java code to be determined after verification • Used within the VERDE system to help with emergency response and outage prediction

  14. Future Research • Predict materials possibly lost in substation outage areas • Poles • Wires • Compare Java output with actual substation service data • Use substation locations to get service area • Compare service area with actual geographic areas provided through a non-disclosure agreement

  15. References • Omitaomu, O.A. and Fernandez, S.J. (2009). A methodology for enhancing emergency preparedness during extreme weather conditions. Proceedings of the 3rd Annual AFIT/WSU Mid-West CBRNE Symposium, Wright-Peterson Air Force Base, September 22-23. • Sabesan, A, Abercrombie, K. , Ganguly, A.R., Bhaduri, B., Bright, E. , and Coleman, P. (2007) Metrics for the comparative analysis of geospatial datasets with applications to high-resoluttion grid-based population data. GeoJournal. • Bhaduri, B. Bright, E., Coleman, P., and Dobson, J. (2002): LandScan: locating people is what matters, Geoinformatics, 5(2):34-37.

  16. Acknowledgements • Department of Energy • UT Battelle • Oak Ridge National Laboratory • Research Alliance in Math and Science Program and Debbie McCoy • Dr. Steven Fernandez and Dr. Olufemi Omitaomu