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

2Managed by UT-Battelle for the U.S. Department of Energy Overview Introduction and background Research objectives Methods Results and validation Conclusion Future research Acknowledgments

3Managed by UT-Battelle for the U.S. Department of Energy 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

4Managed by UT-Battelle for the U.S. Department of Energy 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 Streaming analysis Weather overlay Wide-area power grid situational awareness Impact models

5Managed by UT-Battelle for the U.S. Department of Energy 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 LandScan 1 USA population database 1. Bhaduri et al., 2002; Dobson et al., 2000

6Managed by UT-Battelle for the U.S. Department of Energy 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

7Managed by UT-Battelle for the U.S. Department of Energy Methods Number of customers in 2008 per county is found by using the equations __________________ House Firms 2000 Pop 2000 CF = CF = Correction Factor Pop 2000 = Population in 2000 Pop 2008 = Population in 2008 House 2000 = Nighttime population in 2000 Firms 2000 = Firms in 2000 Customers = 2008 customers = Customers _________ CF Pop 2008

8Managed by UT-Battelle for the U.S. Department of Energy 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

9Managed by UT-Battelle for the U.S. Department of Energy 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 Supply DataDemand DataCombined Data Inputs Geo-located population data Geo-located substation data

10Managed by UT-Battelle for the U.S. Department of Energy 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

11Managed by UT-Battelle for the U.S. Department of Energy 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

12Managed by UT-Battelle for the U.S. Department of Energy Results and validation Correction factor vs. counties Number of Counties Correction Factors Shows representation of correction factor by county.

13Managed by UT-Battelle for the U.S. Department of Energy 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

14Managed by UT-Battelle for the U.S. Department of Energy 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

15Managed by UT-Battelle for the U.S. Department of Energy 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 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.

16Managed by UT-Battelle for the U.S. Department of Energy 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