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Visualization of Energy Resources for Emergency Response Planning

This study presents a dynamic modeling approach for electrical grid components, aiding in outage predictions and emergency responses. Using the VERDE system, it simulates the electric grid and provides real-time data on transmission lines, weather overlays, and more to enhance situational awareness. The research objectives include determining electrical customers, translating code, and comparing outputs to improve prediction accuracy. Results show the effectiveness of correction factors in customer population estimation, with future research focusing on material loss predictions during outages.

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Visualization of Energy Resources for Emergency Response Planning

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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

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