AWSTs Implementation of Wind Power Production Forecasting for ERCOTJohn W. ZackAWS Truewind LLCAlbany, New Yorkjzack@awstruewind.comERCOT WorkshopAustin, TX March 17, 2008

OverviewState-of-the-Art Forecasting ToolsForecasting Time ScalesAWSTs ERCOT Forecast SystemForecasting the Future of Forecasting

Overview of the State-of-the-Art inWind Power Production ForecastingWhat are the tools used in forecasting?How are they typically used?

AWSTs 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 earths 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 50m winds over southern Texas during a 66-hr period beginning 6PM CST on 26Feb 2008 Requires ~ 9.4 trillion operations (add, multiply etc.)

Physics-based Models Key Performance FactorsInitial values for all prognostic variables must be specified for every grid cellBoundary 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

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 existIssue: difficult to understand the reasons for observed performance

Plant Output ModelsRelationship of met variables to power productionCould be physical or statisticalOften based on wind speed but can consider other variables

Desired Data from Wind PlantPower production and turbine availabilityMet tower within 5km and 100m elevation of each turbineWind speed and direction at hub height and T and P at 2mOne met tower with two levels of dataT and P at 2m and hub heightWind speed and direction at hub height and hub height - 30mConsistent monitoring and calibration of data Data with IssuesWell-Behaved DataDesired Wind Plant Data for Forecasting

Forecast EnsemblesUncertainty present in any forecast method due toInput dataModel type and configurationApproach: perturb input data and model parameters within their range of uncertainty and produce a set of forecasts BenefitsEnsemble composite is often the best forecastSpread of ensemble provides a case-specific measure of uncertainty

Forecasting Time ScalesHow does the wind power production forecasting challenge vary with the look-ahead period?

Hours-ahead ForecastsMust forecast small scale weather featuresLarge eddies, local-scale circulationsRapidly-changing, short life-timese.g. cloud features, mountain circulations, sea/land breezesTypically 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 ForecastsLittle skill in forecasting small-scale featuresForecast skill mostly from medium and large scale weather systemsWell-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 AWSTs system configured for the ERCOT application? Why was it configured that way? What products are delivered?

Forecast System InputProduction data (from ERCOT)Hourly increments, reported once per hourPower production (MW)Historical turbine availabilityPlanned outagesWGR Met data (from WGRs)Hourly increments, reported once per hour Parameters: Wind speed, wind direction, temperature and pressureAt whatever height availableRegional weather data (from NWS and other sources)NWP data (from US NWS and Environment Canada)

General Approach & PhilosophyApply AWSTs extensively used eWind systemForecast met variables with physics and statistical modelsUse plant output model to obtain power production forecastEmploy sophisticated quality control of input dataConfigure and customize physics-based and statistical models for optimum performance in TexasEmploy an ensemble forecasting schemeEnsemble members created by varying factors that are most significant in producing uncertainty in forecasts in TexasInput dataModel parametersConstruct an optimal composite forecast based on recent performance of all ensemble members

The ImplementationEnsemble of physics-based modelsEnsemble of statistical modelsPlant output model

Physics-based SimulationsTypesTwo physics-based modelsMASS: Mesoscale Atmospheric Simulation SystemDeveloped and maintained by MESO, Inc (AWST partner)Has a 25-year history of development and applicationCustomized by AWST/MESO for wind energy forecasting in 1990sWRF: Weather Research and Forecasting modelNew community model developed by US consortium including NCAR & NWSIn widespread use for several yearsEight types of physics-based simulationsTwo models: MASS and WRFFour Initializations: US NAM, US GFS, US RUC, EC Global GEM Output used as input into statistical models

Physics-based Forecast SimulationsConfigurationsNested grids over Texas and vicinity6 to10 km horizontal grid cell size for highest resolution nestInitialized every 6 hrsLength of simulations: 72 hrs3-D grid point output data saved every hour

Short-term (0-6 hrs) Statistical Met Variable Forecasting Ensemble of 12 different statistical methodsSeparate statistical forecast model for each look-ahead hour for each forecast methodsEnsemble 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 dataTime series of met and power dataOff-site met tower or remotely sensed dataPhysics-based model output dataStatistical procedure used to construct an optimal composite forecast from ensemble members based on recent performance

Short-term forecasting (0-6 hrs)Customization for TexasUse offsite data types available in TexasAnalyze time series of data from Texas wind farmsDetermine autocorrelation structureUse most appropriate statistical proceduresIdentify significant offsite time-lagged spatial relationships for each forecast siteAnalyze patterns and relationships in high-resolution numerical simulations and observed dataDefine Texas-specific regimes for statistical modeling

Intermediate-term Statistical Met Variable Forecasting (7-48 hrs)Ensemble of 24 different statistical forecastsSeparate statistical forecast model for each look-ahead hour for each forecast methodEnsemble based on varying several factors (emphasis on physics-based models)Type of statistical algorithmTraining sample size and time periodType of physics-based modelSource of physics-based model initialization and boundary dataStatistical procedure used to construct an optimal composite forecast from ensemble members based on recent performance

Intermediate-term Forecasting Customization for TexasCustomize surface property databases for TexasStandard databases often have misrepresentationsCustomize physics-based model to optimally simulate phenomena important in TexasLow level jets (reverse turbulence profile)Shallow cold air surgesIntense thunderstormsConfigure model grids to have high grid resolution in areas critical to wind farm wind variationTexas-specific regimes for statistics

Plant Output ModelTwo modelsPlant-scale power curvePower curve deviation modelWind directionAtmospheric stabilityWhy two models?Data quality and quantityForecast obtained by using ensemble composite of met variable forecasts as input

Plant Output Model Issue 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 chartTurbine Availability Reporting

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 DisaggregationThe 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 ResultChart depicts rolling 6-hr ahead forecast of aggregate power productionPeriod 26 Feb 12CST to 28 Feb 12CST Red line: STWPFGreen line: WGRPP

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

How will forecasts be improved?(Top Three List)(3) Improved physics-based/statistical modelsImproved physics-based modeling of sub-grid and surface processes Better data assimilation techniques for physics-based modelsLearning theory advances: how to extract more relevant info from data(2) More effective use of modelsEnabled by more computational powerHigher resolution, more frequent physics-based model runsMore sophisticated use of ensemble forecastingUse of more advanced statistical models and training methods(1) More/better dataExpanded availability and use of off-site data in the vicinity of wind plants, especially from remote sensorsA leap in quality/quantity of satellite-based sensor data

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 100s 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

New Remote Sensing Technology

SummaryAWSTs Approach to Forecasting: eWindWidely used and verified for wind power productions forecasts in North AmericaBased on an ensemble of forecasts from several physics-based and statistical models using different datasetsExtensive customization for forecasting in TexasGeneral Points About Forecasting Forecast quality has strong dependence on quality and quantity of data from the wind generation facilitiesForecast systems can and should be customized to meet the requirements of a particular applicationForecast technology is changing rapidly - need system/team that can keep pace with the evolution of forecasting technology