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
jzack@awstruewind.com
Overview
•
•
•
•
State-of-the-Art Forecasting Tools
Forecasting Time Scales
AWST’s ERCOT Forecast System
Forecasting the Future of Forecasting
Overview of the State-of-the-Art in
Wind Power Production Forecasting
What are the tools used in forecasting?
How are they typically used?
AWST’s Forecast System
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
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.)
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
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
Predict ors
P1 ,P2 ,...
SMLR
ANN
SVM
Predict and
F
Training
Algorithm
F = f ( P1 ,P2 ,...)
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
Plant Output Models
Plant-scale Power Curve: 1 Year of Data
Hourly Data
100%
90%
Power Output (% of Capacity)
• Relationship of met
variables to power
production
• Could be physical or
statistical
• Often based on wind
speed but can consider
other variables
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
2
4
6
8
10 12 14 16 18 20 22 24 26 28 30
Met TowerWind Speed (m/s)
Desired Data from Wind Plant
Measured Power Output vs. Wind Speed
Desired Wind Plant Data for Forecasting
•
•
Power production and turbine availability
Met tower within 5km and 100m elevation of
each turbine
•
•
One met tower with two levels of data
•
•
•
Wind speed and direction at hub height and T
and P at 2m
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
100%
80%
60%
Power Curve
Reported
40%
Well-Behaved Data
20%
0%
2
4
6
8
10
12
14
16
18
20
100%
80%
60%
Power Curve
Reported
40%
One of the keys to
accurate forecasts…
High quality data
from the plant
Data with Issues
20%
0%
2
4
6
8
10
12
14
16
Avg. Met Tower Wind Speed (m/s)
18
20
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
Forecasting Time Scales
How does the wind power production
forecasting challenge vary with the
look-ahead period?
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
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
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?
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)
• Hourly increments, reported once per hour
• 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)
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
The Implementation
• Ensemble of physics-based models
• Ensemble of statistical models
• Plant output model
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
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
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
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
Intermediate-term Statistical
Met Variable Forecasting (7-48 hrs)
• Ensemble of 24 different statistical forecasts
• Separate statistical forecast model for each lookahead 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
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
Plant Output Model
• Two models
• Why two models?
– Data quality and quantity
• Forecast obtained by using
ensemble composite of met
variable forecasts as input
Plant-scale Power Curve: 1 Year of Data
Hourly Data
100%
90%
Power Output (% of Capacity)
– Plant-scale power curve
– Power curve deviation model
• Wind direction
• Atmospheric stability
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
2
4
6
8
10 12 14 16 18 20 22 24 26 28 30
Met TowerWind Speed (m/s)
Plant Output Model Issue
Turbine Availability Reporting
• Reporting of actual
turbine availability has
been very inconsistent in
other applications
• Example depicts both
missing and probably
inaccurate actual
availability data. 100%
availability was specified
for all hours on the chart
Reported PIRP Data - June 2006
Measured
100
Power (% Capacity)
• Projected (scheduled)
availability often left at
100%
Availability Data Problem Example
90
80
70
60
50
40
30
20
10
0
0
5
10
15
20
Wind speed (m/s)
25
30
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.
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
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.
The Result
• Chart depicts rolling 6hr ahead forecast of
aggregate power
production
• Period 26 Feb 12 CST
to 28 Feb 12 CST
• Red line: STWPF
• Green line: WGRPP
Forecasting the Future of Forecasting
What is being (can be) done to
improve forecast performance?
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
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: 2009-2010
– 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 30-50 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
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 physicsbased 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