hong liu canwea presentation
TRANSCRIPT
![Page 1: Hong Liu CanWEA presentation](https://reader035.vdocuments.mx/reader035/viewer/2022070520/58f08e831a28ab792f8b45d7/html5/thumbnails/1.jpg)
Synergizing Two NWP Models to Improve Hub-Height Wind
Speed Forecasts
Hong Liu, Ph.D., ORTECH Power Peter Taylor, Ph.D., Prof., York
University
CanWEA 2010, 26th Annual Conference and ExhibitionMontreal, Quebec – November 1, 2010
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Synergizing Two NWP Models to Improve Hub-Height Wind Speed Forecasts
• Drivers• Methodology• Evaluation Criteria• Data Source• Results• Discussions
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ORTECH Power
• An engineering/consulting firm that specialized in getting renewable energy projects completed, from project management to permitting to financial analysis onto commissioning.
• ORTECH helps;– investors buy Wind Farms– developers build Wind Farms
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Drivers• Two forecast paradigms:
– Statistical– Physical
• Forecast errors dictated by phase error (Lange, 2003; Liu, 2009 )
• Refined NWP modelling limited by data availability (Giebel, 2003, Yu, et al, 2008, Liu, 2009)
• Ensemble forecasts constrained by computational resources (Cutler, et al, 2008, Mohrlen, 2004)
• Synergizing outputs from more than 1 NWP model as an alternative (Marti, 2006, Nielsen et al, 2007)
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Methodology (1)Continental Scale
NWP
Meso-scale NWP
Wind Forecast
On-line Wind / Power Data
High Resolution Geography
Nested Meso-scale NWP
Site SpecificPhysical Models
Power Model Wind Farm Specifications
Power Forecast
MOS
MOS
Statistical Models to Replace: Physical Downscaling; Extrapolation of Wind Speed to Hub Height; Conversion of Wind Speed to Power; Spatial Upscaling from a Reference Wind Farm; and MOS.
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Methodology (2)
GEM (15-km)
Forecast Model
Optimal Combination
Improved Forecast
NAM(12-km)
Forecast Model
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Methodology (3)
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Methodology (3)• Relative improvement of
combined forecast (Nielsen et al, 2007):
• Weight on the best of two (Nielsen et al, 2007):
1
122
2
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11
IIRI
RIP
1)11(2)11()11(11 2
IRIIRW
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Methodology (4)
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Correlation (R)
Impr
ovem
ent
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
Wei
ght (
W1)
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I1=5%
I1=10%
I1=15%
W1(I1=0%)
W1(I1=5%)
W1(I1=10%)
W1(I1=15%)
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Evaluation Criteria• Root Mean Squared Error (RMSE, Lange,2003)
• Improvement
RM SEN
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e x x r x x x x
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p red m eas pred m eas pred m eas
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2 2
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( ) ( )( ( , )) ( ( ) ( ) )
(%)(%)/
/
NAMGEM
NAMGEMcombined
RMSERMSERMSE
IP
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Data Sources (NWPs)
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Data Sources (Measurements)
Onshore Met Masts near Great Lakes
– Site1 (80-m)– Site2 (60-m)– Site3 (80-m)– Site4 (60-m)
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Results (Site1)
1
1.5
2
2.5
3
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
RM
SE (m
/s)
GEMNAMGEM+NAM
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Results (Site2)
1
1.5
2
2.5
3
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
RM
SE (m
/s)
GEMNAMGEM+NAM
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Results (Site3)
1
1.5
2
2.5
3
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
RM
SE (m
/s)
GEMNAMGEM+NAM
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Results (Site4)
1
1.5
2
2.5
3
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
RM
SE (m
/s)
GEMNAMGEM+NAM
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Results (IP - GEM)
-40%
-30%
-20%
-10%
0%
10%
20%
3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
IP (%
RM
SE)
Site1Site2site3Site4
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Results (IP - NAM)
-40%
-30%
-20%
-10%
0%
10%
20%
3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
IP (%
RM
SE)
Site1Site2site3Site4
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Which forecast is better?
0
2
4
6
8
10
12
14
13/07/2008 0:00 13/07/2008 12:00 14/07/2008 0:00 14/07/2008 12:00 15/07/2008 0:00
Time
Win
d Sp
eed
(m/s)
MeasurementGEMNAMGEM+NAM
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Discussions
• Importance of forecast aspects– Trading– Unit commitment & scheduling– O&M
• Next step is to see if this approach could improve the ramp forecasts
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References• Cutler, N., Kepert, J. D., Outhred, H. R. and MacGill, I. F.,
2008, Characterizing Wind Power Forecast Uncertainty with numerical Weather Prediction Spatial Fields, Wind Engineering, 32, 509-524.
• Giebel, G., 2003, The State-of-the-Art in Short-Term Prediction of wind Power - A Literature Overview, Project ANEMOS, Risø National Laboratory.
• Lange, M., 2003, Analysis of the Uncertainty of Wind Power Predictions, PhD Thesis, University Oldenburg, Oldenburg, Germany.
• Liu, H., 2009, Wind Speed Forecasting for Wind Energy Applications, PhD Thesis, York University, Toronto, Ontario, Canada.
• Marti, I., 2006, Evaluation of Advanced Wind Power Forecasting Models – Results of the Anemos Project, European Wind Energy Conference, Athens, Greek.
• Mohrlen, C., 2004, Uncertainty in wind energy forecasting, PhD Thesis, University College Cork, National University of Ireland.
• Nielsen, H. A., Nielsen, T. S. and Madsen H., 2007, Optimal Combination of wind Power Forecasts, Wind Energy, 10: 471-482
• Yu, W, Plante, A., Chardon, L., Benoit, R., Glazer, A., Tran, L. D., Gauthier, F., Petrucci, F., Forcione, A. and Roberge, G., 2008, A Wind Forecasting System for Application in Wind Power Management – Results from One-year Real-Time Tests in Quebec, CanWEA 2008 Annual Conference, Vancouver, Canada.
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Synergizing Two NWP Models to Improve Hub-Height Wind Speed
Forecasts
Hong Liu, Ph.D., ORTECH Power Peter Taylor, Ph.D., Prof., York
University
Thank youCanWEA 2010, 26th Annual Conference and
ExhibitionMontreal, Quebec – November 1, 2010