1. © 2007 AWS Truewind, LLC Optimization of Wind Power Production Forecast Performance During Critical Periods for Grid Management John W Zack, Principal AWS Truewind, LLC 463 New Karner Road Albany, NY 12205 USA jzack@awstruewind.com Presented at the European Wind Energy Conference Milan, Italy: May 8, 2007

2. © 2007 AWS Truewind, LLC Mapping and Project Development –Utilizes AWST’s resource assessment tools: MesoMap and SiteWind –Constructed regional wind maps for over 25 countries and 50 states and regions –Been involved in over 15,000 MW of project development Forecasting –Based on AWST’s multi-model forecast system: eWind –Currently forecasting for over 3,500 MW in North America and Europe –Selected as forecast provider to several major grid operators: CAISO, ERCOT etc. European Applications through Meteosim Truewind partnership –Headquarters in Barcelona, Spain AWS Truewind Headquarters: Albany, NY, USA Mapping Energy Assessment Project Engineering Performance Evaluation Forecasting Integrated Consulting Services to the Wind Energy Industry

3. © 2007 AWS Truewind, LLC The Issue: What do We Want from a Forecast? Wind power production forecast systems are typically designed to yield the “best forecast performance” with the available data –Usually means optimization for some overall performance metric (MAE, RMSE, etc.) Users typically are more sensitive to forecast error at specific times or during particular events –Example to be considered here: large ramps (changes) in power production over short time periods Forecast systems can be customized to optimize performance and information types for a specific application –Therefore, users should take time to understand what they want and need from wind power production forecast for their application

4. © 2007 AWS Truewind, LLC How Wind Forecasts are Produced Typically from a combination of physics-based (NWP) and statistical models Based on a diverse set of input data with widely varying characteristics Forecast ensembles (sets of forecasts) are often used to model uncertainty Importance of specific models and data types vary with look- ahead period A state-of-the-art wind forecast system

5. © 2007 AWS Truewind, LLC Targeting Forecast Performance Forecast systems are generally structured to optimize performance over all events Regime-based schemes sometimes used to differentiate environmental conditions but typically not for specific events Extreme and infrequent events are often treated as “outliers” in statistical forecast models designed for overall forecasting Here, we will examine the forecasting of large ramp events (power production changes of > 50% of capacity in < 4 hrs)

6. © 2007 AWS Truewind, LLC A Closer Look at Forecasting Large Ramps in Power Production What processes cause them? How well are they forecasted now? How can forecasts be improved?

7. © 2007 AWS Truewind, LLC Processes that Cause Large Ramps: Why do We Care? Large ramps events are caused by a variety of different atmospheric and engineering processes The forecasting problem and hence its solution depends on the nature of the underlying cause A successful forecast of ramp events will likely require a multi-scheme forecast system optimized for the prediction of each type of ramp event and include the ability to automatically select between the types

8. © 2007 AWS Truewind, LLC Processes That Cause Large Ramps Large-scale weather systems (e,g. fronts) –Large scale, quasi-horizontal processes –Long life cycles (days) –Forecast problem System movement & development / decay –Forecast tools Can easily be tracked by surface met data NWP models -> good predictions, several days Onset of local or mesoscale circulations –Smaller scale, quasi-horizontal process –Shorter left cycles (a day or less) –Forecast problem Development / decay & movement –Forecast tools Sometimes can be tracked by sfc met data Remote sensing is a better tool if available NWP models -> fair-good predictions, 1-2 days

9. © 2007 AWS Truewind, LLC Processes That Cause Large Ramps Vertical mixing of momentum (dry convection) –Small-scale, vertical process –Short, often highly variable life cycles (bursts) –Forecast problem Turbulent mixing changes <-- stability, wind shear –Forecast tools Difficult to monitor with surface met data Need remote sensing tools (Doppler radar etc.) NWP models -> reliable predictions of potential only Thunderstorms (moist convection) –Small-scale horizontal & vertical process –Short life cycles (one to a few hours) –Forecast problem System development, decay and movement –Forecast tools Difficult to monitor with surface met data Need remote sensing tools (Doppler radar etc.) NWP models ->good forecast of potential for storms, not specific storm time, location, intensity

10. © 2007 AWS Truewind, LLC Processes That Cause Large Ramps Reaching turbine overspeed (cut-out) threshold –Could result from a variety of met processes –Can be very sensitive to small changes in wind speed (from just below to just above threshold) –Forecast problem Depends on nature of underlying process Often need to predict small changes in wind speed (if around threshold) –Forecast Tools Monitor wind/power production at the farm Off-site met towers and remote sensing can be useful NWP models are quite useful if large scale or mesoscale process are key factors

11. © 2007 AWS Truewind, LLC Potential Complexity of Ramp Events: March 22-23, 2005 Ramp Case San Gorgonio Pass of Southern California, USA ~350 MW of capacity in the Pass (mostly on the eastern end of the Pass) 270 MW downward ramp in 2 hrs (1800-2000 PST ) Followed by a 250 MW upward ramp in 4 hrs (2100 to 0100 PST) with 200 MW in 1 hr

12. © 2007 AWS Truewind, LLC 50 m Wind Speeds in the Pass 50 m winds in the central part of the Pass (upstream from most of the wind farms) remain high throughout the event 50 m winds in the eastern part of the Pass (location of wind farms) experience a sharp deceleration followed by an acceleration Wind Is from the west (left to right) at both locations

13. © 2007 AWS Truewind, LLC What is Happening in This Case? Difficult to understand with measured wind data alone Use supplementary measured and simulated data –Doppler radar reflectivity (rain) and radial wind (wind speed) data –Physics-based model simulation data (not in forecast mode) 2100 PST 22 March 2005 50 m AGL wind speed (m/s) 2100 PST 22 March 2005 Wind Speed (m/s) at ~1500 m AMSL (~1000 m AGL over the Pass) Simulated

14. © 2007 AWS Truewind, LLC Putting all of the pieces together... Rain-cooling of near-surface air causes stabilization of boundary layer Stabilization cuts off mixing and wind speeds suddenly drop at 50 m Rain stops, shear increases -> high winds mix back to 50 m level

15. © 2007 AWS Truewind, LLC Ramp Prediction Tools: Autoregressive vs Physics-Based Models Difficult for purely autoregressive model to forecast large ramps (recent trends are not a good predictor) Physics-based model adds considerable skill in 4-hr ahead forecast of significant ramps Very large ramps are rare and difficult to forecast

16. © 2007 AWS Truewind, LLC Ramp Forecast Evaluation Standard Forecast System Use Event-based Evaluation Approach (Yes/No) Ramp Event Definition –Change in production > 50% of capacity within a 4 hr period –No overlapping periods Forecast Success Criteria –Ramp event in hourly forecast data within +/- 2 hrs, > 80% amplitude Forecast Production –Standard AWST eWind system (no optimization for ramp forecasting) –Power production and met time series data from wind farms –Output data from regional physics-based (NWP) model simulations –No off-site or remotely-sensed data in vicinity of wind farms Evaluation Specifications –3 wind farm aggregates in California, USA (~ 350 MW capacity each) –4-hr and next day (next calendar day) ahead forecasts

17. © 2007 AWS Truewind, LLC Evaluation Results Standard Forecast System Event-based Forecasts 57% of large ramp events forecasted by day-ahead mode ~64% forecasted in 4-hr ahead mode MAE is substantially higher during ramp events Skill of 4-hr over day- ahead mode is less during ramp events

18. © 2007 AWS Truewind, LLC Ramp-event Forecasting System: Under Development Multi-scheme event-based system Separate scheme for each process type of ramp event –Different predictor data selected for each event type –Statistical classification models employed (ANN,SVM) –Ensemble approach (set of forecasts from perturbed forecast system) Composite of individual event forecast for overall forecast Deterministic and probabilistic forecasts Preliminary results indicate value in this approach –Tested with standard forecast system measurement and NWP data –10% to 15% improvement in event-based performance scores (hit rate, false alarm rate, critical success index etc.) –Most improvement associated with targeted use of NWP data –Need better offsite measurement data for better hours-ahead prediction Especially for vertically oriented processes 3-D remote sensing data will be extremely valuable

19. © 2007 AWS Truewind, LLC What We Have Learned About Large Ramp Event Forecasting Physics-based (NWP) models often have clues about ramp events but miss exact time and/or amplitude of the event Purely autoregressive forecast tools often do not perform well during ramp periods (Typically not a result of recent trends) External (to wind farms) data is critical! –Physics-based model output –3-D off-site meteorological data, especially remotely sensed data Must be aware of differences in ramp-causing processes –Caused by several different horizontal and/or vertical processes! –Forecast system should select predictors based on type of ramp-event –Need multi-scheme approach Event-based forecasting is most promising approach –Yes/No prediction of occurrence in a specific time window (deterministic) –Probability of occurrence in a specific time window (probabilistic)