integrating wind into the transmission grid
DESCRIPTION
Integrating Wind into the Transmission Grid. Michael C Brower, PhD AWS Truewind LLC Albany, New York [email protected]. Providing integrated consulting services to the wind industry Responsible for the Irish Wind Atlas (with ESBI, initiated by SEI) - PowerPoint PPT PresentationTRANSCRIPT
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Integrating Wind into the Transmission Grid
Michael C Brower, PhDAWS Truewind LLCAlbany, New York
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About AWS Truewind• Providing integrated consulting services to the
wind industry
• Responsible for the Irish Wind Atlas (with ESBI, initiated by SEI)
• Forecasting for 2000+ MW of wind plant in US and Europe
• Conducted wind integration studies in US
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Time Scales – Electric Power
• Regulation: seconds to minutes
• Load following: minutes to hours
• Unit commitment: hours to days
• Reliability: months to years
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Time Scales – Wind
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53 MW Capacity
Wind and Wind Plant VariabilityNot the Same
+33%
+10%
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Propagation of Gusts Through a Wind Farm
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Mean Change in Power vsNumber of Turbines at Flat
Rock
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Spatial Diversity of Turbine Output
Correlation coefficient of power change for different average times over the distance
From Ernst et al, 1999
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Typical 4-Hr PIRP Forecast PerformanceSan Gorgonio Pass, California - May 2003
Reported vs Forecasted Hourly Energy Output
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
110%
Date (Label is 1 AM PDT)
Reported 4-Hr Forecast
Wind Forecasting
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Forecast Accuracy Vs Time
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Forecast Accuracy Vs Output
3-Hour Ahead Forecasts
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
0% 20% 40% 60% 80% 100% 120%
Forecast Output (% of Capacity)
Mea
n A
bsol
ute
Err
or (
% o
f C
apac
ity)
Central US
San Gorgonio
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New York Integration Study
• Evaluating 3300 MW of wind on a 33,000 MW system
• Time scales from seconds to days
• AWS Truewind provided wind data
• GE PSEC performing grid analysis (from AGC to day-ahead scheduling)
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NY Study: The Challenge
• How to simulate the behavior of 3300 MW of wind with little site data?– Must capture spatial and temporal
correlations– Met stations often not in windy areas and
exhibit wrong diurnal pattern
• Solution: Mesoscale modeling
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NY Study: Tasks• Selected 33 potential project sites with
50-300 MW capacity• Used a mesoscale weather model to
simulate hourly wind speed, direction, temperature for 5 continuous years
• Sampled 1-min and 1-sec data to synthesize sub-hourly fluctuations
• Created statistical model to synthesize plant forecasts – based on actual forecasts
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16
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Forecasting
0
50
100
150
200
250
300
350
-0.8
-0.6
-0.4
-0.2 0
0.2
0.4
0.6
0.8
Error Distribution
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Validation of Dynamic BehaviorMean Absolute Deviation
0.000
0.050
0.100
0.150
0.200
0.250
0 2 4 6 8 10 12 14Hours Ahead
Per
cen
t o
f R
ated
Cap
acit
y
MADISON TURBINE
MADISON PLANT
SIM POINT
SIM 11.5MW PLANT
SIM 50 MW PLANT
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0
500
1000
1500
2000
2500
3000
0 60 120 180
9/14/2002 6:00 8/2/2001 5:00 5/25/2002 19:00 10/20/2003 16:00 4/12/2002 13:00
“Extreme” Wind Events
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0
5000
10000
15000
20000
6:00:00 7:00:00 8:00:00
-500.00
0.00
500.00
Total NY Load September Wind AGC MW w / Load OnlyAGC MW w / Load & Wind Rate Limited MW w / Load Only Rate Limited MW w / Load & Wind
“Extreme” Event System Response
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Conclusions• Wind, turbine, and wind plant variability are
not the same
• The more spatial diversity, the less temporal variability
• Mesoscale modeling provides a powerful tool for analyzing scenarios of large wind penetration