Dynamic Modeling of Components on the Electrical Grid Bailey Young Wofford College Dr. Steven...
-
Upload
augustine-floyd -
Category
Documents
-
view
218 -
download
1
Transcript of Dynamic Modeling of Components on the Electrical Grid Bailey Young Wofford College Dr. Steven...
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
5 Managed by UT-Battellefor 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 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
8 Managed by UT-Battellefor 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
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
10 Managed by UT-Battellefor 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
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.
13 Managed by UT-Battellefor 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
14 Managed by UT-Battellefor 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
15 Managed by UT-Battellefor 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 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.