PowerPredict Wind Power Forecasting September 2011

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PowerPredict Wind Power Forecasting September 2011 For further information please contact: Dr Geoff Dutton, Energy Research Unit, STFC Rutherford Appleton Laboratory, Didcot, Oxon OX11 0QX E-mail: geoff.dutton@stfc.ac.uk Tel: 01235 445823

PowerPredict Background and history EC More CARE (FP5) Meteo wind power forecasting (stand alone) for Ireland EPSRC Forecasting study (2001-2004) EC-funded ANEMOS project (2002-2006) New techniques, benchmarking against best in Europe Formation of spin-out company PowerPredict (2007) Development of geographical interface (2007/08) Westmill wind farm trial using upgraded software (2009)

PowerPredict Why do wind power forecasting? System operation : Power trading : scheduling reduce spinning reserve reduce exposure in markets Forecast horizons : 1 72 (120) hours ahead

PowerPredict Why do wind power forecasting? 3 regimes : seconds/minutes (operation) 2 approaches : hours/days (scheduling, trading) days/weeks (maintenance) time series ( black box ) methods NWP (numerical weather prediction) from mechanics, thermodynamics

Time series : NWP : Theory (1) Data Explanatory variables Wind speed forecast Downscaling Optimisation algorithm Turbine power curve Power forecast Power forecast

HIRLAM forecast Forecasting - NWP methods Theory (2) Geostrophic drag Log profile MOS : v a(, t...) b(, t...) hub v local Surface wind WA S P Orography Roughness Power Curve Local wind MOS Power forecast

Theory (3) Forecasting possible time series methods AR(MA) : P t k a P a P a P 1 t 2 t k 3 t 2k... Neural net : x 1, w 1 x 2, w 2 Σ α f ( j x j w j ) x j, w j Fuzzy logic : IF temperature is high THEN wind is low

Power / MW RMS forecast error / MW Theory (4) beating Persistence Persistence the value of a forecast variable P at time t + k will be the same as at current time t : 6 5 4 P t Pt k Pt k 3 2.5 2 Rated power : 6.45 MW 3 1.5 2 1 1 0.5 0-10 0 10 20 30 0 0 6 12 18 24 30 36 Look-ahead time k / hours Look-ahead time / hours

Theory (5) Forecasting errors Various measures : Mean Error (ME) (Bias) Mean Absolute Error (MAE) Root Mean Square Error (RMSE) Normalized MAE Normalized RMS Accuracy depends on complexity of terrain, look ahead time, level of power production, and the NWP parameters

Real-time wind power forecasting software: 5 years supported operation at EirGrid (ESB) control centre Long term wind power forecast using wind speed/direction from NWP forecast Short term wind power forecast using statistical approach based on most recent wind power measurements Robustness of initialisation and operation Scaleable output to allow for uninstrumented wind farms Flexibility to add new wind farms (MySQL database) Meteo Achievements

Long term Met-based wind power forecast Forecast method / power curve interpretation selectable by wind farm Persistence Simple power curve Manufacturer s power curve Derived power curve

Long term manufacturer s power curve Meteo database Wind turbine types Wind clusters Wind farm

Long term derived power curve

POWER PREDICT : Major data inputs (minimum requirement) Wind turbine types Wind clusters SCADA Data interval: 15 mins. Numerical weather prediction Time delay: ~ 10 s. Data interval: 1 hour Look ahead: 48 hours Time delay: 3 hours Manufacturer, model name Cluster name Rated (nominal) power (kw) Wind turbine type Cut-in speed (m/s) Rated speed (m/s) Cut-out speed (m/s) Hub-height (m) Rotor diameter (m) Power curve [speed (m/s), power (kw)] Number of wind turbines Rated power of cluster (MW) Wind farm (Measured wind speed) (m/s) (Measured wind direction) Availability Output power (MW) New download every 15 mins. Predicted wind speed (m/s) Predicted wind direction New download every 6 hours Name Latitude, Longitude Number of clusters Rated (nominal) power (MW) Maximum power (MW) N w wind farms Control variables Timesteps, Update intervals, Look ahead times Number of: wind farms, wind farms with Scada data, wind farms with weather prediction Surface roughness (Z0) Scaling factors Wind farm static data ACCESS database User interface Initial input Update only as required

POWER PREDICT : Major data inputs (optimum) Wind turbine types Wind clusters SCADA Data interval: 15 mins. Numerical weather prediction Time delay: ~ 10 s. Data interval: 1 hour Look ahead: 48 hours Time delay: 3 hours Manufacturer, model name Cluster name Rated (nominal) power (kw) Wind turbine type Cut-in speed (m/s) Rated speed (m/s) Cut-out speed (m/s) Hub-height (m) Rotor diameter (m) Power curve [speed (m/s), power (kw)] Number of wind turbines Rated power of cluster (MW) Wind farm (Measured wind speed) (m/s) (Measured wind direction) Availability Output power (MW) New download every 15 mins. Predicted wind speed (m/s) Predicted wind direction New download every 6 hours Historic data Name Latitude, Longitude Number of clusters Rated (nominal) power (MW) Maximum power (MW) N w wind farms Control variables Timesteps, Update intervals, Look ahead times Number of: wind farms, wind farms with Scada data, wind farms with weather prediction Surface roughness (Z0) Scaling factors Wind farm static data ACCESS database User interface [ predicted wind speed (m/s), measured output power (MW)] Initial input Update only as required

PowerPredict: Development version Improved algorithms based on EPSRC forecasting project and EC ANEMOS project Combined statistical and meteorological forecast Requires minimum of 20 days historic data On-line updating of derived power curves High wind speed warning Incorporation of availability and maintenance scheduling Under development Interactive, web-based agency service

RMS error PowerPredict Development version performance (base year/aggregated wind farms) Persistence Met-based (F2) Combined forecast Look ahead (hrs)

PowerPredict Development version performance (Westmill)

PowerPredict Conclusions Improved performance from merged forecasting algorithm Inclusion of availability and maintenance scheduling (in progress) Web-based agency service at www.powerpredict.co. uk real-time trial in progress PowerPredict development version v. Meteo RMS error Met-based (F2) Combined forecast Persistence Look ahead (hrs)

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