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ENERGY MARKETS
A view from below of one of the
sixty-six GE SLE 1.5MW turbines on
the Erie Shores Wind Farm,
Ontario, Canada
Source: http://www.powerauthority.on.ca
Modeling
ofWind Energy
Wind Power
Standing Committee
June 16, 2009
Mississauga
Hans J.H. [email protected]
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ENERGY MARKETS The Company Assets
OPGs generating portfolio has a total capacity of22,000 megawatts (MW) making us one of the largestpower generators in North America. Our generationassets include:
3 nuclear generating stations5 fossil generating stations64 hydroelectric generating stations
In 2008, OPG generated 107.8 terawatt hours (TWh) ofelectricity, supplying approximately 75% of Ontariodemand. Revenues of 6,082 $M.
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ENERGY MARKETS Agenda
Preliminaries OPG Ontario Generation Mix Impact of Wind Generation
Characteristics of Wind Speed Intermittent, diurnal, seasonal,
auto- and spatial correlation Stochastic Model
Wind Speed to MW Power Curves Turbines
Data, Calibration & Validation
Simulation Results
Case Study
Conclusion
Credits
4700MW2020
1260MW2009
472MW2008
Wind Farms in OntarioName Plate Capacity
OPG approx. 10MWNo new developments as
per shareholder mandate.
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ENERGY MARKETS
Actual Wind Speed for Prince
0
5
10
15
20
25
30
1 322 643 964 1285 1606 1927 2248 2569 2890 3211 3532 3853 4174 4495 4816 5137 5458 5779 6100 6421 6742 7063 7384 7705 8026 8347 8668
Hour
Wind
Speed
Characteristics of Wind Speed
Time Series of Hourly Wind-speeds for one Year in the Prince location
8760 hours
WindSpeed[m/s]
Intermittent
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ENERGY MARKETS Characteristics of Wind Speed
WeibullDistribution
Actual Wind Speed forPrince
0
5
10
15
20
25
30
1 322 643 964 1285 1606 1927 2248 2569 2890 3211 3532 3853 4174 4495 4816 5137 5458 5779 6100 6421 6742 7063 7384 7705 8026 8347 8668
Hour
WindSpee
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ENERGY MARKETS Characteristics of Wind Speed
Strongautocorrelation
Actual Wind Speed forPrince
0
5
10
15
20
25
30
1 322 643 964 1285 1606 1927 2248 2569 2890 3211 3532 3853 4174 4495 4816 5137 5458 5779 6100 6421 6742 7063 7384 7705 8026 8347 8668
Hour
WindSpee
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ENERGY MARKETS Characteristics of Wind Speed
DiurnalPatterns
Actual Wind Speed forPrince
0
5
10
15
20
25
30
1 322 643 964 1285 1606 1927 2248 2569 2890 3211 3532 3853 4174 4495 4816 5137 5458 5779 6100 6421 6742 7063 7384 7705 8026 8347 8668
Hour
WindSpee
SeasonalPatterns
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ENERGY MARKETS Characteristics of Wind Speed
Spatial
Correlation
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ENERGY MARKETS Model Requirements
Weibull distribution
Diurnal Patterns
Seasonal Patterns
Auto correlation
Spatial correlation
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ENERGY MARKETS Mathematical Model
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ENERGY MARKETS
Raw windspeeds are Weibull
Backout Exponential
Transform to Uniform
Transform to underlying Markov chain
Back out AR(1) model
Estimate covariance matrix
Mathematical Model
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ENERGY MARKETS Mathematical Model
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ENERGY MARKETS Wind Speed to MW
Power curves are calibrated to a parametric, family of continuous
curves and provide a close fit to the (discrete) power curves provided
by the manufacturer. Much faster conversion of wind speed to power.
Albert Betz(18851968)
Waloddi Weibull(18871979)
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ENERGY MARKETS
457.70 MW
199.65 MW
39.60 MW
76.00 MW
487.50 MW
Total MW
Kingsbridge II (2.3 MW 69)
Kruger (2.3 MW 44)
Wolfe Island (2.3 MW 86)
Cut-in: 4 m/s
Cut-out: 25 m/s
Siemens SWT-2.3-82
Leader A & B (1.65 MW 121)Cut-in: 3.5 m/s
Cut-out: 20 m/s
Vestas V82 -1.65 MW
Kingsbridge (1.8 MW 22)Cut-in: 4 m/s
Cut-out: 25 m/s
Vestas V80 -1.8 MW
Ripley ( 2 MW 38)Cut-in: 2 m/s
Cut-out: 28 m/s
ENERCON E82
Prince I & II(1.5 MW 126)ErieShores (1.5 MW 66)
Melancthon I & II (1.5 MW 133)
Cut-in: 3.5 m/sCut-out: 25 m/s
GE sle 1.5
Wind Farm(s)Cut-in speed andCut-out speed
Wind Turbine
Wind Speed to MW
GE sle 1.5 MW
ENERCON E82
Vestas V80-1.8 MW
Vestas V82 -1.65 MW
Siemens SWT-2.3-82
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ENERGY MARKETS
Autocorrelations
and DistributionalCharacteristics
50 m5 sites,
unrelated tocurrent wind
developments
Every 10
minutes
1-2 yearsOPG Metered
data
Spatial Correlations
and Annual
Fluctuations
Average
of 0-30
mb above
groundlevel
20 km for all
of Ontario
Every 3
hours
25 years
(1978-
2006)
NOAA dataset(National
Oceanic and
Atmospheric
Administration)
Diurnal and
Seasonal Patterns
80 m1 km
resolution for
all of Ontario
Intra day and
monthly
averages
20 years
(1984-
2003) but
annualaverages
only
Ontario Wind
Atlas
Best FeaturesAltitudeSpacialResolution
TemporalResolution
HistorySource
Data used for Calibration
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ENERGY MARKETS Ontario wind atlas:1km resolution, average wind speed at 80m
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ENERGY MARKETSSimulated wind-speeds
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ENERGY MARKETSSimulated wind-speeds
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ENERGY MARKETS
Erie Shores: Actual versus Theoretical Capacity
Validation: Wind speed to MW
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ENERGY MARKETS
0
0.1
0.2
0.3
0.4
0.5
0.6
Erie Shore
Sep06
Nov06
Jan07
Feb07
Apr07
Jun07
Jul07
Sep07
Nov07
Dec07
Actual Production
Simulated Production
0
0.1
0.2
0.3
0.4
0.5
0.6
Kingsbridge
Sep06
Nov06
Jan07
Feb07
Apr07
Jun07
Jul07
Sep07
Nov07
Dec07
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7Melancthon
Sep06
Nov06
Jan07
Feb07
Apr07
Jun07
Jul07
Sep07
Nov07
Dec07
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7Prince Farms
Sep06
Nov06
Jan07
Feb07
Apr07
Jun07
Jul07
Sep07
Nov07
Dec07
Validation: Monthly Capacity Factors
Major outage
in December
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ENERGY MARKETS
In summer, at peak electricity demand,7-12% of installed capacity will be generating, at a 50% confidence level, and
this drops to 1-3%, at a 90% confidence level.
In winter, at peak electricity demand,22-41% of installed capacity will be generating, at a 50% confidence level, and
this drops to 3-6%, at a 90% confidence level.
Case Study
50%90%50%
Winter
90%
Summer
Available Wind Capacity at a Specified Confidence Level
41%6%11%3%Top 4 Hours, max PD day
7%
7%
9%
12%
12%
1%
1%
2%
2%
2%
22%3%Top 10% PD Hours
22%3%Top 5% PD Hours
27%3%Top 1% PD Hours
40%6%Top 4 Hours, max PD day
38%5%Top PD Hours
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ENERGY MARKETS
The planned increase in wind generation in Ontario by 2020,will have significant impacts on the power system.
Wind is a good energy resource, however its pattern is notwell matched with the timing of Ontarios energy requirements.
Wind generation has limited benefit in meeting peak demand,although geographic diversity helps.
There is large uncertainty in wind generation on all timescales:annually, monthly, weekly and hourly.
The stochastic and historical models, developed withinthe Energy Markets division of OPG, allow the companyto assess and plan for the impact of new wind generationon OPGs assets.
Conclusion
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ENERGY MARKETS Credits
Over the last few years, several people within the Planning
and Analysis group, that is part of the Energy Markets
division of OPG, were instrumental in making the wind
model operational. This involved procuring and processingthe different data sets, designing and implementing the
wind simulation process, and conducting the various case
studies.
In particular, the contributions of Eva Janossy, Alan
Leung*, Hai Doan, and Derek Hardinge are acknowledged.
* Now at the Ontario Power Authority