© 2007 aws truewind, llc optimization of wind power production forecast performance during critical...
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© 2007 AWS Truewind, LLC
Optimization of Wind Power Production Forecast
Performance During Critical Periods for
Grid ManagementJohn W Zack, Principal
AWS Truewind, LLC463 New Karner Road
Albany, NY 12205 [email protected]
Presented at the European Wind Energy Conference
Milan, Italy: May 8, 2007
© 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 TruewindHeadquarters: Albany, NY, USA
• Mapping• Energy Assessment• Project Engineering• Performance Evaluation• Forecasting
IntegratedConsulting
Services to the Wind Energy
Industry
© 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
© 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
© 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 oflarge ramp events (power production changes of > 50% of capacity in < 4 hrs)
© 2007 AWS Truewind, LLC
A Closer Look at ForecastingLarge Ramps in Power Production
• What processes cause them?
• How well are they forecasted now?
• How can forecasts be improved?
© 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
© 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
© 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
© 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
© 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
San Gorgonio Regional Power Production
0
50
100
150
200
250
300
350
3/22/05 12:00 3/22/05 18:00 3/23/05 0:00 3/23/05 6:00
Time (PST)
Hou
rly A
vera
ge G
en
era
tion
(M
W)
Hourly Average Power Production
© 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
© 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 200550 m AGL wind speed (m/s)
2100 PST 22 March 2005Wind Speed (m/s) at ~1500 m AMSL
(~1000 m AGL over the Pass)
Simulated Simulated
© 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
© 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
© 2007 AWS Truewind, LLC
Ramp Forecast EvaluationStandard 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
© 2007 AWS Truewind, LLC
Evaluation ResultsStandard Forecast SystemEvent-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
Parameter Units 4-Hr Ahead Day Ahead
Events Count 107 107
Hits Count 68 61
% of Events 63.6% 57.0%
Misses Count 39 46
% of Events 36.4% 43.0%
False Alarms Count 47 54
% of Fcsts 40.9% 47.0%
MAE-events % of Cap 15.8% 17.6%
MAE-overall % of Cap 9.1% 13.4%
© 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
© 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)