Dynamic Modeling of Components on the Electrical Grid
Bailey YoungWofford College
Dr. Steven Fernandez and Dr. Olufemi Omitaomu Computational Sciences and Engineering Division
August 2009
2 Managed by UT-Battellefor the U.S. Department of Energy
Overview
• Introduction and background
• Research objectives
• Methods
• Results and validation
• Conclusion
• Future research
• Acknowledgments
3 Managed by UT-Battellefor 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
4 Managed by UT-Battellefor 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
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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 Managed by UT-Battellefor 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
7 Managed by UT-Battellefor the U.S. Department of Energy
Methods
Number of customers in 2008 per county is found by using the equations
__________________ House2000 + Firms2000
Pop2000CF =
CF = Correction Factor Pop2000 = Population in 2000Pop2008 = Population in 2008House2000 = Nighttime population in 2000Firms 2000 = Firms in 2000Customers = 2008 customers
= Customers_________CF
Pop2008
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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 Managed by UT-Battellefor 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-locatedpopulation data
Geo-locatedsubstation data
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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 Managed by UT-Battellefor 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
12 Managed by UT-Battellefor the U.S. Department of Energy
Results and validation Showing Correction Factor Vs. Counties
0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
800.0
900.0
1 to
1.4
1.4
to 1
.5
1.5
to 1
.6
1.6
to 1
.7
1.7
to 1
.8
1.8
to 1
.9
1.9
to 2
.0
2.0
to 2
.1
2.1
to 2
.2
2.2
to 2
.3
2.3
to 2
.4
2.4
to 2
.5
2.5
to 2
.6
2.6
to 2
.7
2.7
to 2
.8
2.8
to 2
.9
2.9
to 3
.0
3.0
to 3
.1
3.1
to 3
.2
3.2
to 3
.3
3.3
to 3
.4
3.4
to 3
.5
3.5
to 3
.6
Correction Factors Between
Nu
mb
er o
f C
ou
nti
es
Legend
counties_test
Correction Factor
1.256757 - 1.679397
1.679398 - 1.880786
1.880787 - 1.986974
1.986975 - 2.072005
2.072006 - 2.151424
2.151425 - 2.236053
2.236054 - 2.339809
2.339810 - 2.494391
2.494392 - 2.738462
2.738463 - 3.150787
3.150788 - 4.660821
Correction factor vs. counties
Nu
mb
er
of
Co
un
ties
Correction Factors
• Shows representation of correction factor by county.
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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
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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
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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 Managed by UT-Battellefor 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