hong liu canwea presentation

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

Synergizing Two NWP Models to Improve Hub-Height Wind Speed Forecasts

• Drivers• Methodology• Evaluation Criteria• Data Source• Results• Discussions

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

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)

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.

Methodology (2)

GEM (15-km)

Forecast Model

Optimal Combination

Improved Forecast

NAM(12-km)

Forecast Model

Methodology (3)

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Vertical Level k

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d(i,j)

Z(i+4,j+4,k+1)

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NAMGEM

NAMGEM

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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

1;1)11(2)11(

11

IIRI

RIP

1)11(2)11()11(11 2

IRIIRW

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)

I1=0%

I1=5%

I1=10%

I1=15%

W1(I1=0%)

W1(I1=5%)

W1(I1=10%)

W1(I1=15%)

Evaluation Criteria• Root Mean Squared Error (RMSE, Lange,2003)

• Improvement

RM SEN

e

e e

e x x r x x x x

ii

N

i

p red m eas pred m eas pred m eas

1

2 1

2

1

2 2

2 2

( )

( ) ( )( ( , )) ( ( ) ( ) )

(%)(%)/

/

NAMGEM

NAMGEMcombined

RMSERMSERMSE

IP

Data Sources (NWPs)

Data Sources (Measurements)

Onshore Met Masts near Great Lakes

– Site1 (80-m)– Site2 (60-m)– Site3 (80-m)– Site4 (60-m)

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

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

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

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

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

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

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

Discussions

• Importance of forecast aspects– Trading– Unit commitment & scheduling– O&M

• Next step is to see if this approach could improve the ramp forecasts

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.

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