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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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HT v2.01 - 2/23Mississauga, June 16th, 2009

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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Strongautocorrelation


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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DiurnalPatterns


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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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



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

wpsc 20090616 modeling

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