1. AWST’s Implementation of Wind Power Production Forecasting for ERCOT
ERCOT Workshop
Austin, TX
March 17, 2008
AWST’s Implementation of Wind Power Production Forecasting for ERCOT
John W. Zack
AWS Truewind LLC
Albany, New York

2. Overview State-of-the-Art Forecasting Tools Forecasting Time Scales
AWST’s ERCOT Forecast System
Forecasting the Future of Forecasting

3. Overview of the State-of-the-Art in Wind Power Production Forecasting
What are the tools used in forecasting?
How are they typically used?

4. AWST’s Forecast System

5. Physics-based Models
Differential equations for basic physical principles are solved on a 3-D grid
Must specify initial values of all variables at each grid point and properties of earth’s surface
Simulates the evolution of the atmosphere in a 3-D volume
Many different models
Eta, GFS, MM5, WRF, MASS etc.
Same basic equations with subtle but critical differences
Can be customized for an application

6. Physics-based Simulation Example
Forecast of the evolution of the 50 m winds over southern Texas during a 66-hr period beginning 6 PM CST on 26 Feb 2008
Requires ~ 9.4 trillion operations (add, multiply etc.)

7. Physics-based Models Key Performance Factors
Initial values for all prognostic variables must be specified for every grid cell
Boundary values must be specified for all boundary cells (usually from another model with a larger domain)
Grid has finite resolution - some processes are at the “sub-grid” scale and feedback to affect grid scale
Surface properties (roughness, heat capacity etc.) of the earth must be specified or modeled

8. Statistical Models
Empirical equations are derived from historical predictor and predictand data (“training sample”)
Current predictor data and empirical equations is then used to make forecasts
Many types of models
Time series, MLR, ANN, SVR
More sophisticated does not always mean better performance

9. Statistical Models: Performance Factors
Type & configuration of the statistical model and training algorithm
Size, quality and representativeness of the training sample
Input variables made available for training
The type of relationships that actually exist
Issue: difficult to understand the reasons for observed performance

10. Plant Output Models Relationship of met variables to power production
Could be physical or statistical
Often based on wind speed but can consider other variables

11. High quality data from the plant
Desired Data from Wind Plant
Desired Wind Plant Data for Forecasting
Power production and turbine availability
Met tower within 5km and 100m elevation of each turbine
Wind speed and direction at hub height and T and P at 2m
One met tower with two levels of data
T and P at 2m and hub height
Wind speed and direction at hub height and hub height - 30m
Consistent monitoring and calibration of data
Well-Behaved Data
One of the keys to accurate forecasts…
High quality data from the plant
Data with Issues

12. Forecast Ensembles Uncertainty present in any forecast method due to
Input data
Model type and configuration
Approach: perturb input data and model parameters within their range of uncertainty and produce a set of forecasts
Benefits
Ensemble composite is often the best forecast
Spread of ensemble provides a case-specific measure of uncertainty

13. Forecasting Time Scales
How does the wind power production forecasting challenge vary with the look-ahead period?

14. Hours-ahead Forecasts
Must forecast small scale weather features
Large eddies, local-scale circulations
Rapidly-changing, short life-times
e.g. cloud features, mountain circulations, sea/land breezes
Typically poorly defined by current observing systems
Tools:
Difficult to use physics-based models
Autoregressive statistical models on wind farm time series data
Supplement with offsite predictor data
Errors grow rapidly with increasing look-ahead time

15. Days Ahead Forecasts Little skill in forecasting small-scale features
Forecast skill mostly from medium and large scale weather systems
Well-defined by current sensing networks
Tools:
Physics-based model simulations are the best tool
Statistical models used to adjust physics-based output (MOS)
Regional & continental scale weather data are most important
Errors grow slowly with increasing look-ahead time

16. The ERCOT Forecast System
What input data is used?
How is AWST’s system configured for the ERCOT application?
Why was it configured that way?
What products are delivered?

17. Forecast System Input Production data (from ERCOT)
Hourly increments, reported once per hour
Power production (MW)
Historical turbine availability
Planned outages
WGR Met data (from WGRs)
Parameters: Wind speed, wind direction, temperature and pressure
At whatever height available
Regional weather data (from NWS and other sources)
NWP data (from US NWS and Environment Canada)

18. General Approach & Philosophy
Apply AWST’s extensively used eWind system
Forecast met variables with physics and statistical models
Use plant output model to obtain power production forecast
Employ sophisticated quality control of input data
Configure and customize physics-based and statistical models for optimum performance in Texas
Employ an ensemble forecasting scheme
Ensemble members created by varying factors that are most significant in producing uncertainty in forecasts in Texas
Input data
Model parameters
Construct an optimal composite forecast based on recent performance of all ensemble members

19. The Implementation Ensemble of physics-based models
Ensemble of statistical models
Plant output model

20. Physics-based Simulations Types
Two physics-based models
MASS: Mesoscale Atmospheric Simulation System
Developed and maintained by MESO, Inc (AWST partner)
Has a 25-year history of development and application
Customized by AWST/MESO for wind energy forecasting in 1990’s
WRF: Weather Research and Forecasting model
New community model developed by US consortium including NCAR & NWS
In widespread use for several years
Eight types of physics-based simulations
Two models: MASS and WRF
Four Initializations: US NAM, US GFS, US RUC, EC Global GEM
Output used as input into statistical models

21. Physics-based Forecast Simulations Configurations
Nested grids over Texas and vicinity
6 to10 km horizontal grid cell size for highest resolution nest
Initialized every 6 hrs
Length of simulations: 72 hrs
3-D grid point output data saved every hour

22. Short-term (0-6 hrs) Statistical Met Variable Forecasting
Ensemble of 12 different statistical methods
Separate statistical forecast model for each look-ahead hour for each forecast methods
Ensemble based on varying several factors (emphasis on statistical models and onsite and offsite data)
Type of statistical algorithm (Linear regression, ANN etc.)
Training sample size and time period (regime-based)
Type and amount (# of variables) of input data
Time series of met and power data
Off-site met tower or remotely sensed data
Physics-based model output data
Statistical procedure used to construct an optimal composite forecast from ensemble members based on recent performance

23. Short-term forecasting (0-6 hrs) Customization for Texas
Use offsite data types available in Texas
Analyze time series of data from Texas wind farms
Determine autocorrelation structure
Use most appropriate statistical procedures
Identify significant offsite time-lagged spatial relationships for each forecast site
Analyze patterns and relationships in high-resolution numerical simulations and observed data
Define Texas-specific regimes for statistical modeling

24. Intermediate-term Statistical Met Variable Forecasting (7-48 hrs)
Ensemble of 24 different statistical forecasts
Separate statistical forecast model for each look-ahead hour for each forecast method
Ensemble based on varying several factors (emphasis on physics-based models)
Type of statistical algorithm
Training sample size and time period
Type of physics-based model
Source of physics-based model initialization and boundary data
Statistical procedure used to construct an optimal composite forecast from ensemble members based on recent performance

25. Intermediate-term Forecasting Customization for Texas
Customize surface property databases for Texas
Standard databases often have misrepresentations
Customize physics-based model to optimally simulate phenomena important in Texas
Low level jets (reverse turbulence profile)
Shallow cold air surges
Intense thunderstorms
Configure model grids to have high grid resolution in areas critical to wind farm wind variation
Texas-specific regimes for statistics

26. Plant Output Model Two models Why two models?
Plant-scale power curve
Power curve deviation model
Wind direction
Atmospheric stability
Why two models?
Data quality and quantity
Forecast obtained by using ensemble composite of met variable forecasts as input

27. Plant Output Model Issue
Turbine Availability Reporting
Reporting of actual turbine availability has been very inconsistent in other applications
Projected (scheduled) availability often left at 100%
Example depicts both missing and probably inaccurate actual availability data. 100% availability was specified for all hours on the chart

28. Short Term Wind Power Forecast (STWPF)
The STWPF is a forecast of the most likely value of power production.
STWPF forecasts are created for individual WGRs and the aggregate of all ERCOT WGRs.
The STWPF is produced each hour and extends 48 hours.

29. Wind Generation Resource Power Potential (WGRPP)
80% Probability of Exceedence (POE) forecast is calculated for the aggregate output of all WGRs.
WGRPP for the aggregate must be the sum of the WGRPP for individual WGRs.
The WGRPP for individual WGRs is calculated by disaggregating the aggregate WGRPP forecast.
The WGRPP is produced each hour and extends 48 hours

30. WGRPP Disaggregation
The 80% POE forecast for the aggregate is larger than the sums of the 80% POE forecasts for the individual WGRs.
Aggregate WGRPP forecast is disaggregated to create WGRPP forecasts for each WGR.
Disaggregation is based on the error correlation between the WGRs.

31. The Result
Chart depicts rolling 6-hr ahead forecast of aggregate power production
Period 26 Feb 12 CST to 28 Feb 12 CST
Red line: STWPF
Green line: WGRPP

32. Forecasting the Future of Forecasting
What is being (can be) done to improve forecast performance?

33. How will forecasts be improved? (Top Three List)
(3) Improved physics-based/statistical models
Improved physics-based modeling of sub-grid and surface processes
Better data assimilation techniques for physics-based models
Learning theory advances: how to extract more relevant info from data
(2) More effective use of models
Enabled by more computational power
Higher resolution, more frequent physics-based model runs
More sophisticated use of ensemble forecasting
Use of more advanced statistical models and training methods
(1) More/better data
Expanded availability and use of “off-site” data in the vicinity of wind plants, especially from remote sensors
A leap in quality/quantity of satellite-based sensor data

34. New Remote Sensing Technology
Low-power, low-cost, dual-polarization phased array Doppler radars on cellular towers being developed by CASA (Center for Adaptive Sensing of the Atmosphere)
Target price: $50 K per unit
Commercial availability:
Small enough to mount on cell towers;
Will measure atmosphere below 1 km that is not visible to current National Weather Service NEXRAD Doppler radar (72% of atmosphere below 1 km is not visible to current NWS system)
Provide winds with resolution in 100’s of meters out to km
New data every few minutes
Attempt currently being made to organize a field project in Tehachapi Pass in California to evaluate the value of this technology to short-term wind energy forecasting

35. Summary AWST’s Approach to Forecasting: eWind
Widely used and verified for wind power productions forecasts in North America
Based on an ensemble of forecasts from several physics-based and statistical models using different datasets
Extensive customization for forecasting in Texas
General Points About Forecasting
Forecast quality has strong dependence on quality and quantity of data from the wind generation facilities
Forecast systems can and should be customized to meet the requirements of a particular application
Forecast technology is changing rapidly - need system/team that can keep pace with the evolution of forecasting technology