Improving Wind Speed Forecasting for Wind Energy Generation Optimization

1. IMPROVEMENTS IN NUMERICAL PREDICTION OF LOW LEVEL WINDS Adam Deppe

2. Motivation • U.S. Department of Energy (DOE) goal of having 20% of the nation’s electrical energy from wind by 2030 • DOE workshop report states  “…1% error in wind speed estimates for a 100-MW wind generation facility can lead to losses approaching $12,000,000 over the lifetime of that plant ..."   • To optimize wind for power generation, accurate weather forecasts are needed • Better weather forecasts lead to greater confidence and more reliance on wind energy as a reliable energy source

3. Wind Speed Forecasting Background • Typically, weather forecasting done for precipitation and temperature, not wind – until recently • Meteorologists traditionally have focused wind forecasts at the 10 m level, a height strongly influenced by surface friction • Prior wind forecasting research in the western United States has focused on flow in complex terrain (e.g. Wood 2000, Ayotte et al. 2001) • Not applicable in Iowa where low-level jets and changing surface conditions are likely to be the dominant factors • Statistical approach to predict wind speed at different levels (Huang et al. 1996, Kamal et al. 1997) – time series analysis

4. Problem Statement • With the increased growth in the wind energy sector, wind speed forecasts at turbine hub height (80 m) are now needed • Due to the lack of observations, validating forecasts at this height has been difficult and little attention has been paid to wind forecasts at 80 m in the meteorological community • In this study, an ensemble was created based on six different ways to represent drag as well as other forecasting techniques to improve wind speed forecasting at 80 m

5. “PBL Scheme” Fd(z,t) V

6. PBL Parameterizations – Different Ways to Represent Drag • PBL schemes were developed to help resolve the turbulent fluxes in the boundary layer – aka the drag force • Due to the complex nature of boundary layer, very difficult to model – changes diurnally and seasonally • Smaller BL in winter – snow pack • To parameterize the planetary boundary layer - both local and non-local parameterizations are used • Local closure - estimates unknown fluxes using known values and/or gradients at the same point (Stull 1988) • Non-local closure - estimates unknown fluxes using known values and/or gradients at many points in space (Stull 1988)

7. Model Domain • Weather forecast model tested • Weather Research and Forecasting (WRF) model • Model Forecast Period • 54 hour forecasts – first 6 hours not used - model spin-up • Six different ways to Represent Drag Force • Yonsei University Scheme (YSU) - WRF • Mellor-Yamada-Janjic (MYJ) - WRF • Quasi-Normal Scale Elimination PBL (QNSE) - WRF • Mellor-Yamada Nakanishi and Niino Level 2.5 PBL (MYNN2.5) - WRF • Mellor-Yamada Nakanishi and Niino Level 3.0 PBL (MYNN3.0) - WRF • Pleim PBL scheme (also called Asymmetric Convective Model (ACM2)) – WRF Pomeroy Iowa Wind Farm

8. Model Specifics • Driving model (Initial and lateral boundary conditions) - Regional and Global models that provide data to limited domain used in the WRF • Global Forecast System (GFS) • North American Model (NAM) • Observation data • 80 m meteorological tower on the southwest side of the Pomeroy, Iowa wind farm • Data was taken at 10 minute increments and averaged over one hour periods centered on each hour; to match model output • Evaluation period • From June 2008 through September 2010, excluding periods where missing data was observed

10. Pre-Run Modification MAE of three different GFS perturbations using the YSU and MYNN3.0 PBL schemes from 10 cases in January 2010 Ensemble - best model skill MAE associated with the wind speed at 80 m from three different initialization times from 10 cases in January 2010 Time Initialization - Higher model skill (lower MAE) than Perturbations

11. Day Ahead Market Midnight Noon Noon

12. Day Ahead Market Midnight Noon Noon

13. Larger the model spread – less confidence in forecast Day Ahead Market Midnight Noon Noon

14. Day Ahead Market Midnight Noon Noon

15. Day Ahead Market Midnight Noon Noon

16. Day Ahead Market Noon Midnight Noon

17. Pre-Run Modification Cont. Highest Model skill associated with 10 km grid spacing Computing power limited in most private companies, running 10 km model runs are much more feasible than 4 km runs MAE of wind speed at 80 m from two different grid spacings (4 km and 10 km) from 10 cases in January 2010

18. Day Ahead Market Midnight Noon Noon

19. Similar model skill – with terrain effects, like mountains – results would be much different Day Ahead Market Midnight Noon Noon

20. Post-Processing Training of the model based on day 1 results 15 cases from June 2008 to May 2009 Training approach was not a reliable method to predict wind speed as conditions change from day to day Picked Ensemble – showed best model skill only 33% of time Non-Picked Ensemble – showed best model skill 27% of time

21. Bias Correction of the Model • Bias corrections developed from 30 cases (all seasons) from June 2008 to Jan. 2010 • Applied to Case study from Oct. 11, 2009 to Nov. 11, 2009 • Wind Speed bias correction seen as best way to improve forecast (green box) • Non bias correction showed worst results (red box)

22. Day Ahead Market Model over-prediction Nighttime Model under-prediction Daytime Noon Noon Midnight

23. Operational Model Development Green boxes show highest model skill • Bias corrections for 00Z and 18Z time initializations and NAM and GFS initial boundary conditions over a period from Aug. 14-28, 2009 • Six schemes that showed best model skill (lowest MAE) formed operational model • Five out of six scheme either YSU or Pleim – both non-local turbulent closure schemes • Four out of six scheme use the 00Z time initialization • Four out of six scheme use the GFS initial boundary conditions

24. Operational Model Results • Tested over 25 cases during the summer and fall of 2010 • Best model skill seen in Operational Model after wind speed bias correction (Green Box) • Largest standard deviation (measure of model spread) in operation model ensemble • Deterministic forecast is the best individual model found from the period studied

25. Ramp Events Sensitive Area – Ramp events not important above or below this area Ramp event - changes in wind power of 50% or more of total capacity in four hours or less (Greaves et al. 2009) Approximated using a typical wind turbine power curve such that any wind speed increase or decrease of more than 3 ms-1 within the 6-12 ms-1window in four hours or less was considered a ramp Fifty eight cases spanning 116 days from June 2008 through June 2009 were validated – Models all used GFS initial boundary conditions

26. Ramp Event Results Event was considered a ramp event if change in wind power was 50% or more of total capacity in four hours or less - wind speed increase or decrease of more than 3 m/s within the 6-12 m/s All PBL schemes under-predict number of ramp events Number of ramp events during Day 1 for GFS initial boundary conditions (06-30 hours after model start up) Fewer Ramp events forecasted on Day 2 Number of ramp events during Day 2 for GFS initial boundary conditions (30-54 hours after model start up)

27. Ramp Events Results Cont. Average amplitude of ramp up and ramp down events for GFS initial boundary conditions • Amplitude was over-predicted by all six PBL schemes for ramp-up events • Obs. show on average over 4m/s ramp event • If ramp-up event occurred at 6m/s and went to 10m/s within 4hrs • – Power increase from 216 kW to 1000 kW

28. Sensitive Area – Ramp events not important above or below this area

29. Three hour averaged diurnal cycle of ramp-up events using the midpoint of the ramp event • Peak at 01Z – LLJ related • Peak at 16Z – Growth of BL • Three hour averaged diurnal cycle of ramp-down events using the midpoint of the ramp event • Less noticeable trend

30. Summary of Results • To forecast winds, we have focused on drag (six different schemes) • However, explored other methods to improve forecasts • Perturbations of GFS model • Low skill • Varied Time Initializations • High skill • Grid Spacing • Little difference as terrain is flat – less computing power with 10km • Training of the Model • Not a reliable method as conditions change from day to day • Bias Correction • Noticed a diurnal bias in the model data • Investigated whether other biases existed – wind speed bias correction • Combination of techniques yields a model that is significantly more skillful

31. Summary of Results • All six PBL schemes tested underestimated the number of ramp-up and ramp-down events • Average ramp-up events around 4m/s increase • Increase in power produced from 216 kW to 1000 kW • For example, caused a blackout in Texas • Modeled ramp-up events occurred most often between 22 UTC and 01 UTC - closely matched observed ramp-up events (occurred most frequently around 01 UTC)

32. Great Plains Low Level Jet (LLJ) • LLJs - first described in the late 1930’s by Goualt (1938) and Farquharson (1939) • Areas of relatively fast-moving winds in the lower atmosphere, LLJs were first studied because of their roll in transporting warm, moist air from the Gulf of Mexico into the Great Plains, leading to convective events (Stensrud 1996) • Most well-known LLJs occur over the Great Plains of the United States, although found around the world - Europe, Africa, and Australia (Stensrud 1996) • Maximum winds during nocturnal LLJ events over the Great Plains are between 10 ms-1 and 30 ms-1(Whiteman et al. 1997)

33. Great Plains LLJ Cont. • Whiteman (1997) classified two years of LLJs in northern Oklahoma and discovered that LLJs occur: • 47% of the time during the warm season • 45% of the time during the cold season • Whiteman (1997) found approximately 50% of the maximum wind speeds during LLJ events occurred less than 500 m above the surface • With the potential for wind turbine hub heights to increase from 80 m to 120 m or higher, LLJ interaction with wind turbines could largely affect the power performance of wind farms (Schwartz and Elliot, 2005).

34. Problem Statement • Few studies have examined the performance of forecasting models during LLJ events, or the sensitivity to how surface drag is represented in models • In this study, the ability of the WRF model to accurately reproduce vertical wind structure during LLJ events was evaluated using six different drag schemes in the WRF model to observations from the Lamont, OK wind profiler site

35. Observed Data Location • Observed data from the U. S. Department of Energy ARM project located at Lamont, OK. • 915-MHz wind profiler - measure wind speeds below 500 m, unlike the NOAA 404-MHz profilers • Observed data ranged from 96 m - 2462 m above the surface; vertical resolution of 60 m • 30 LLJ cases and 30 non-LLJ cases were chosen between June 2008 and May 2010 • Same forecasts model was used as in Pomeroy – different dates, different heights evaluated, different location

36. LLJ Maximum Wind Speeds All PBL schemes under-predict max LLJ wind speed Average Maximum LLJ Wind Speed over 30 LLJ events for GFS initial boundary conditions Lowest predicted max LLJ wind speed occurred in YSU scheme P-values of the YSU PBL scheme vs. other PBL schemes for maximum LLJ wind speed Null hypothesis was rejected in favor of the alternative hypothesis, indicating that the under-prediction of the wind speed in the YSU PBL scheme was highly significant Null hypothesis - difference between the maximum LLJ wind speed of the YSU scheme (u1) will be equal to the maximum LLJ wind speed of the other PBL schemes (u2)

37. Height of LLJ Maximum Wind Speed All PBL schemes under-predict height of LLJ max Average Maximum LLJ Wind Speed over 30 LLJ events for GFS initial boundary conditions Highest predicted height of LLJ max occurred in YSU scheme P-values of the YSU PBL scheme vs. other PBL schemes for height of LLJ maximum Null hypothesis was rejected in favor of the alternative hypothesis, indicating that the higher height predicted by the YSU PBL scheme was highly significant Null hypothesis - difference between the height of LLJ maximum of the YSU scheme (u1) will be equal to the height of the LLJ maximum of the other PBL schemes (u2)

38. June 26, 2008 LLJ Maximum

39. LLJ Case Study – March 24, 2009Wind Speed Under-prediction of wind speed maximum in YSU Scheme LLJ structure not present in YSU scheme LLJ feature present in all other PBL schemes

40. Eddy Viscosity YSU scheme - eddy viscosity value five times larger than any other scheme Larger eddy viscosity - more mixing and turbulence Higher speeds occurred above and below the jet core, with higher momentum air being mixed closer to the surface – resulting in substantially weaker LLJ with a higher elevation of the maximum

41. Occurrence of LLJ Maximum • All six PBL schemes showed the maximum LLJ wind speed occurring near or just after midnight • Observed maximum LLJ wind speeds occurred a little later, with dual peaks at 08 UTC (2 am LST) and 10 UTC (4 am LST) • Overall, the PBL schemes appeared to predict the timing of the peak LLJ occurrence reasonably well with perhaps a small early bias

42. Influences on Wind Energy 96 m: Wind speed 1.2 m/s stronger during LLJ events – power increase of 117 kW LLJ vs. Non-LLJ event comparison 157 m: Wind speed 2.1 m/s stronger during LLJ events – power increase of 496 kW Speed shear – difference between 157 m and 96 m wind speed - is almost double during LLJ events • Number of kW to power an average home per day = 50-70 kW • Speed shear is present at current hub height (80m) • Project this summer will focus on speed shear from 10m to 80m and from 40m to 120m (entire reach of wind turbine blades)

43. 96 and 157 m Wind Speed All six PBL schemes over-predicted the wind speed during LLJ events Bias and MAE associated with 96 m wind speed forecasts during LLJ events for GFS initial boundary conditions YSU scheme showed the lowest MAE, while the highest was observed with the MYNN 2.5 scheme All six PBL schemes over-predicted the wind speed during LLJ events – although YSU small positive bias Bias and MAE associated with 157 m wind speed forecasts during LLJ events for GFS initial boundary conditions YSU scheme showed the lowest MAE, while the highest was observed with the MYNN 2.5 scheme

44. Summary of Results - LLJ • LLJ maximum wind speeds were under-predicted by all PBL schemes - largest under-prediction occurred with the YSU scheme – larger drag present • All the PBL schemes except the YSU scheme under-predicted the height of the LLJ maximum by more than 125 m • YSU scheme - likely cause of the under-predicted wind speed and higher jet elevation - result of the strong eddy viscosity occurring during stable conditions • Increased mixing - LLJs in the YSU scheme were substantially under-predicted and momentum was spread out over a deeper layer of the atmosphere • Timing or temporal trends of the LLJ maximum - models had wind speed maxima occurring near or just after midnight (06-08 UTC), typically a few hours before observed LLJs (08-10 UTC)

45. Summary of Results - LLJ • LLJ impacts at 96 m and 157 m - increased wind speeds and speed shear during LLJ events compared to non-LLJ events • Implies that wind production would increase during LLJ events however, wind turbine durability would need to be improved to accommodate the increased shear • At 96 and 157 m, the YSU PBL scheme showed significantly better skill (lower MAE) than the other schemes

46. General Conclusions • Non-local PBL schemes appear to show better model skill overall, however no one scheme is the answer for predicting low level winds • Example - YSU • High model skill at predicting 80 m wind speeds • High model skill during LLJ events at 96 m and 157 m • Ramp events are poorly predicted • LLJ max wind speeds are significantly under-predicted • As a result, no one scheme performed considerably better than any other and all showed room for improvement.

47. Questions?

48. References • Arribas A., K. B. Robertson, and K. R. Mylne, 2005: Test of a poor man’s ensemble prediction system for short-range probability forecasting. Mon. Wea. Rev., 133, 1825–1839. • Ayotte, K.W., R.J. Davy and P.A. Coppin, 2001: A simple temporal and spatial analysis of flow in complex terrain in the context of wind energy modeling. Bound.-Layer Meteorol.,98, 275–295. • Bélair, Stéphane, Jocelyn Mailhot, J. Walter Strapp, J. Ian MacPherson, 1999: An examination of Local versus Nonlocal Aspects of a TKE-Based Boundary Layer Scheme in Clear Convective Conditions. J. Appl. Meteor., 38, 1499–1518. • Bowler, N. E., A. Arribas, and K. R. Mylne, 2008: The benefits of multianalysis and poor man’s ensembles. Mon. Wea. Rev., 136, 4113–4129. • Bradford, K. T., R. L. Carpenter, and B. Shaw, 2010: Forecasting Southern Plains wind ramp events using the WRF model at 3km. Ninth Annual Student Conference, Atlanta, GA, Amer. Meteor. Soc., [Available online at http://ams.confex.com/ams/90annual/techprogram/paper_166661.htm]. • Dalcher, A., E. Kalnay, and R. N. Hoffman, 1988: Medium range lagged average forecasts. Mon. Wea. Rev., 116, 402–416. • Department of Energy, 2008: 20% wind energy by 2030. Energy Efficiency and Renewable Energy Rep. DOE/GO-102008-2567, 1-2. • Schreck, S, J. Lundquist, and W. Shaw, 2008: U.S. Department of Energy Workshop Report—Research needs for wind resource characterization. NREL Rep. TP-500-43521, 81-82. • Shin, H.H., S. Hong, 2011: Intercomparison of Planetary Boundary-Layer Parametrizations in the WRF Model for a Single Day from CASES-99. Boundary-Layer Meteor., 139, 261-281. • Stensrud, David J., Jian-Wen Bao, Thomas T. Warner, 2000: Using Initial Condition and Model Physics Perturbations in Short-Range Ensemble Simulations of Mesoscale Convective Systems. Mon. Wea. Rev., 128, 2077–2107. • Stull, R.B., 1998: An Introduction to Boundary Layer Meteorology. Boston, Massachusetts: Kluwer Academic Publishers. 197-242 pp. • Sukoriansky S, Galperin B, Perov V (2005) Application of a new spectral theory of stable stratified turbulence to the atmospheric boundary layer over sea ice. Boundary-Layer Meteorol. 117, 231–257. • Theis, S. E., A. Hense and U. Damrath, 2005: Probabilistic precipitation forecasts from a deterministic model: a pragmatic approach. Meteorological Applications, 12, 257-268. • Walser, A., D. Lüthi, and C. Schär, 2004: Predictability of precipitation in a cloud-resolving model. Mon. Wea. Rev., 132, 560–577. • Wei, M., Z. Toth, R.Wobus, Y. Zhu, C. H. Bishop and X. Wang 2006: Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP, Tellus58A, 28-44. • Whitaker, Jeffrey S., Andrew F. Loughe, 1998: The Relationship between ensemble Spread and Ensemble Mean Skill. Mon. Wea. Rev., 126, 3292–3302. • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467. • Wood, N., 2000: Wind flow over complex terrain: A historical perspective and the prospect for large-eddy modeling. Bound.-Layer Meteor., 96, 11–32. • Xiu, A., and J. E. Pleim, 2001: Development of a land surface Model. Part I: Application in a mesoscale meteorological model. J. Appl. Meteor., 40, 192–209. • Zack, John W., 2007: Optimization of Wind Power Production Forecast Performance During Critical Periods for Grid Management. AWEA Windpower 2007.