Increased wind power forecast skill due to improved NWP in the last decade
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1 Increased wind power forecast skill due to improved NWP in the last decade Lueder von Bremen ForWind Center for Wind Energy Research of the Universities Oldenburg, Hannover und Bremen EWEA Workshop on Wind Power Forecasting 3-4 December 2013, Rotterdam
2 Outline Inspiration Complexity of clean experiments for wind power Clean NWP impact on long term fc errors Issues not validating meteorological variables Saturated fc error smoothing (in Germany) Conclusions (2) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
3 Inspiration what is the impact of each single factor for improved regional wind power forecast? Control zone Germany (3) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen overall improvement: 25% better NWP increased wind power capacity and more disperse distribution better WPP models & combination Source: Powering Europe: Wind energy and the electricity grid EWEA, Nov 2010
4 Diagnozing an impact factor in clean experiments change only one component to pindown the impact of changes consistent individual verification data (at single sites) of constant quality long time series validate meteorological (model) variables variables are usually not spatially aggregated when verified (4) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
5 Diagnozing an impact factor in clean experiments change only one component to pindown the impact of changes consistent individual verification data (at single sites) of constant quality long time series validate meteorological (model) variables variables are usually not spatially aggregated when verified Can be avoided by computing own verification data set! (5) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
6 Diagnozing an impact factor in clean experiments change only one component to pindown the impact of changes consistent individual verification data (at single sites) of constant quality long time series validate meteorological (model) variables variables are usually not spatially aggregated when verified Can be avoided by computing own verification data set! (6) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
7 Simulation of wind power forecast improvement (in Germany) ECMWF ECMWF model level winds from Jan 2001 Dec 2012 Forecasts up to +72h Analysis as verification data (6 hourly) Horizontal resolution has changed from T511 (~40 km) to T1279 (~16 km) DWD model level winds from Jan 2007 Dec 2012 Forecasts up to +72h (hourly, but used 6 hourly when comparing with ECMWF) Analysis as verification data (hourly, but used 6 hourly) Constant horizontal resolution (7 km, ) Simplified power curve model (TradeWind PC) for each grid box at (wind power weighted) average hub in each grid box (7) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
8 Transformation of wind speed distribution to power distribution by power curve Wind speed distribution with σ=1 m/s Manufactor TradeWind (EU-Project) Simplify the effect of error amplification by power curves: Use only one power curve (TradeWind) (8) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
9 Simulated day-ahead forecast root mean square error relative to produced power T511 ~ 40km T799 ~ 25km T1279 ~ 16km D+2 42% rel. improv. What is required to extract the clean impact due to NWP? (9) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
10 Simulated day-ahead forecast root mean square error (seperate clean NWP impact) T511 ~ 40km T799 ~ 25km T1279 ~ 16km D+2 Clean NWP share at improvement (wind power distribution of Jan 2001 used) Further decrease due to changed capacity distr. Clean NWP improvement 40% between 2001 and 2012 (10) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
11 Simulated forecast root mean square error relative to produced power T511 ~ 40km T799 ~ 25km T1279 ~ 16km D+3 D+2 D+1 Clean Exp. Full lines Jan 2001distr. Dashed lines real distribution Rel. improvement 42 % (D+1), 31 % (D+2), 27 % (D+3) in 10y (clean exp.) (11) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
12 German load factor and hub height speed simulated by ECMWF Dashed lines: real development Full lines: capacity distribution frozen in Jan 2007 Load factor Average wind speed (12) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
13 Wind power RMSE relative to prod. power Dependence of wind power forecast error on mean wind speed and wind Gaussian speed error normalized with produced power Weibull distribution at each wind speed class/bin (shape factor=2) (13) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen Decrease of wind power error with average wind speed (~0.1 per m/s)
14 Wind power RMSE relative to rated power Dependence of wind power forecast error on mean wind speed and wind Gaussian speed error normalized with installed power Weibull distribution at each wind speed class/bin (shape factor=2) (14) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen Increase of wind power error with average wind speed (~ per m/s)
15 Simulated forecast root mean square error relative to rated power T511 ~ 40km T799 ~ 25km T1279 ~ 16km D+3 D+2 D+1 Clean Exp. Full lines Jan 2001distr. Rel. improvement 29 % (D+1), 17 % (D+2), 12 % (D+3) in 10y (clean exp.) (15) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen Dashed lines real distribution
16 Simulated forecast root mean square error relative to rated power DWD: 6y, ECMWF: 12y using Jan 2007 distribution DWD (D+3) D+3 D+2 D+1 ECMWF (D+1) Rel. in 10 y D+1 D+2 D+3 (16) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
17 How to properly assess how much the skill in wind power forecasts has improved? Possible solution: evaluate wind speed (here: weighted with Jan 2007 wind power distribution) DWD (D+3) ECMWF (D+1) Rel. in 10 y (17) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen D+1 D+2 D+3
18 Change in wind power capacity [MW/160km^2] from Jan 1995 to Dec 2011 (18) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
19 Skill improvement by enhanced spatial forecast error smoothing (ECMWF) NWP year= 2011 normalized with rated power Day+2 Day+1 Day+3 Saturation reached with respect to rated power in 2001/2002 (19) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
20 Conclusions Thank you for your attention Wind power fc error improvement depends on used error metric Normalization with rated power yields similar results as improvements in wind speed NWP (ECMWF) rel. improvement alone is 16 % (12 %) in 10y for D+2 (D+3) (normalized with rated power) (DWD: 24 % (20 %)) Saturation for fc error smoothing was reached in 2001/2002 (relative to rated capacity) However, there is still potential for further wind power fc error smoothing (combine with offshore) Outlook: How to assess the impact of NWP improvements using real wind power data? Consistent time series of wind power are required! Acknowledgement: This work was supported by the Federal Ministry for Science and Culture of Lower Saxony. Contact: lueder.von.bremen@forwind.de (20) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
21 Skill in average German wind speed Jan 2007 wind power distribution all German grid points (incl. offshore) Still potential for enhanced wind fc error smoothing in Germany (21) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
22 day-ahead wind power forecast error in Germany (Meta-Forecast by TSOs) Normalized with rated capacity Normalized with prod. wind power -30% -25% (22) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
23 Average yearly speed and load factor stratified by wind speed class Fig mean speed and power should be flat (real av. Speed and load factor also in th.idl is prepared (23) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
24 Wind power error D+3 for various wind speed classes Normalized with rated power (capacity distribution Jan 2007) Wind speed classes: 3-4, 4-5, 5-6, 6-7, 7-8, 8-9 m/s relative (24) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
25 Load factor in Germany Data from TSO-Websites (25) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
26 day-ahead wind power forecast error in Germany (Meta-Forecast by TSOs) Normalized with rated capacity Normalized with prod. wind power -30% -25% (26) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
27 What is fc error smoothing? Cross-Correlation of forecast error (D+2) at FINO1 Year 2008 (29) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
28 Impact of spatial forecast error smoothing (DWD) NWP year= 2011 normalized with rated power Hub height=real Day+1 Day+3 Day+2 Saturation level much lower compared to ECMWF smoothing stronger at Day 3 than at Day 2 (35) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
29 Impact of spatial forecast error smoothing (DWD) NWP year= 2012 normalized with rated power Hub height=real Day+1 Day+3 Day+2 Now smoothing at Day 3 is weakest (as expected) (36) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
30 How to assess varying mean wind speed in evaluation of long term WPP skill properly? (37) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
31 What is the perspecitve for future spatial forecast error smoothing, e.g. hub height of 140m? NWP year= 2011 Hub height=140m normalized with rated power normalized with prod. power Saturation level is a bit higher (~30 % for rated power) (38) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
32 Simulated day-ahead forecast root mean square error (seperate clean NWP impact) T511 ~ 40km T799 ~ 25km T1279 ~ 16km 10m winds (with neutral Profile to hub height) 10 m winds are no option at all! Difference due to changed capacity distr.: Forecast error smoothing (39) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
33 Published day-ahead wind power forecast error Normalized with rated capacity Normalized with prod. wind power year (40) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
34 Cross-Correlation of forecast error (D+2) at FINO1 D+3 D+1 Year 2008 (41) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
35 Cross-Correlation of forecast error (D+2) at FINO1 Reduction of RMSE D+3 (D+2) D+1 (42) EWEA Workshop on Wind Power Forecasting 3 December 2013, Rotterdam - Lueder von Bremen
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