© 2007 aws truewind, llc optimization of wind power production forecast performance during critical...

© 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


• 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


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


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


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)


A Closer Look at ForecastingLarge Ramps in Power Production

• What processes cause them?

• How well are they forecasted now?

• How can forecasts be improved?


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


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



• 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



• 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


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


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


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


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


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


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


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%


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


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)

© 2007 aws truewind, llc optimization of wind power production forecast performance during critical...

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