Hong Liu CanWEA presentation

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

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Hong Liu CanWEA presentation

  1. 1. 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 Exhibition Montreal, Quebec – November 1, 2010
  2. 2. Synergizing Two NWP Models to Improve Hub-Height Wind Speed Forecasts • Drivers • Methodology • Evaluation Criteria • Data Source • Results • Discussions
  3. 3. 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
  4. 4. 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)
  5. 5. Methodology (1) Continental Scale NWP Meso-scale NWP Wind Forecast On-line Wind / Power Data High Resolution Geography Nested Meso-scale NWP Site Specific Physical 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.
  6. 6. Methodology (2) GEM (15-km) Forecast Model Optimal Combination Improved Forecast NAM (12-km) Forecast Model
  7. 7. Methodology (3) Vertical Level k+1 Vertical Level k (i,j,k+1) (i,j,k) (i+4,j+4,k+1) (i+4,j+4,k) H d(i,j) d(i,j) Z(i+4,j+4,k+1) Z(i+4,j+4,k) (XT,YT)    N ji m N ji m TT jid jid HjiU HYXU , , ),( 1 ),( ),,( ),,( 2 *)11(*1 NAMGEM NAMGEM FF FWFWIF   
  8. 8. Methodology (3) • Relative improvement of combined forecast (Nielsen et al, 2007): • Weight on the best of two (Nielsen et al, 2007): 1 12 2 2 1; 1)11(2)11( 1 1        I IRI R IP 1)11(2)11( )11(1 1 2    IRI IR W
  9. 9. 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) Improvement 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 Weight(W1) I1=0% I1=5% I1=10% I1=15% W1(I1=0%) W1(I1=5%) W1(I1=10%) W1(I1=15%)
  10. 10. Evaluation Criteria • Root Mean Squared Error (RMSE, Lange,2003) • Improvement RMSE N e e e e x x r x x x x i i N i pred meas pred meas pred meas           1 2 1 2 1 2 2 2 2      ( ) ( ) ( )( ( , )) ( ( ) ( )) (%)(%) / / NAMGEM NAMGEMcombined RMSE RMSERMSE IP  
  11. 11. Data Sources (NWPs)
  12. 12. Data Sources (Measurements) Onshore Met Masts near Great Lakes – Site1 (80-m) – Site2 (60-m) – Site3 (80-m) – Site4 (60-m)
  13. 13. 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) RMSE(m/s) GEM NAM GEM+NAM
  14. 14. 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) RMSE(m/s) GEM NAM GEM+NAM
  15. 15. 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) RMSE(m/s) GEM NAM GEM+NAM
  16. 16. 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) RMSE(m/s) GEM NAM GEM+NAM
  17. 17. 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(%RMSE) Site1 Site2 site3 Site4
  18. 18. 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(%RMSE) Site1 Site2 site3 Site4
  19. 19. 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 WindSpeed(m/s) Measurement GEM NAM GEM+NAM
  20. 20. Discussions • Importance of forecast aspects – Trading – Unit commitment & scheduling – O&M • Next step is to see if this approach could improve the ramp forecasts
  21. 21. 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.
  22. 22. 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 you CanWEA 2010, 26th Annual Conference and Exhibition Montreal, Quebec – November 1, 2010

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