New developments in wind energy forecasting

An overview of new and current developments in wind power forecasting is given where the focus lies upon practical implementations and experiences concerning the operational systems in Europe. In general, modern short-term wind power prediction systems use either statistical or physical approaches to determine the anticipated wind power based on numerical weather forecasts. As an example the physical system Previento is described in detail. The typical accuracy of the forecasts for single wind farms as well as the aggregated production is shown. One focus of this paper is the intelligent use of multiple input from numerical weather models to improve the accuracy of the power forecast. The two main approaches that are applied operationally are ensemble predictions from one weather model and the combination of different numerical weather models. A weather-dependent combination tool that exploits the capabilities of numerical models of different weather services is described in detail. In the future, wind power predictions will be embedded deeper in the processes of grid operators and traders. An example shown here are highly localized predictions for specific grid points which can directly be used as input for power flow calculations, grid management or day-ahead congestion forecasts (DACF). In addition, the market integration of wind energy is pushed in a number of countries such that trading platforms which convert fluctuating wind power production into electricity products become more important.

[1]  Corinna Möhrlen,et al.  FORECASTING WIND POWER IN HIGH WIND PENETRATION MARKETS, USING MULTI-SCHEME ENSEMBLE PREDICTION METHODS , 2006 .

[2]  T. Palmer.  Predicting uncertainty in forecasts of weather and climate , 2000 .

[3]  Detlev Heinemann,et al.  Influence of Thermal Stratification on Wind Profiles for Heights up to 140 m , 2003 .

[4]  M. Lange,et al.  Physical Approach to Short-Term Wind Power Prediction , 2005 .

[5]  Detlev Heinemann,et al.  Relating the uncertainty of short-term wind speed predictions to meteorological situations with methods from synoptic climatology , 2004 .

[6]  Matthias Lange,et al.  Anemos : development of a next generation wind power forecasting system for the large-scale integration of onshore & offshore wind farms , 2003 .

[7]  Lars Landberg,et al.  Short-term prediction of the power production from wind farms , 1999 .

[8]  Gregor Giebel,et al.  State-of-the-Art on Methods and Software Tools for Short-Term Prediction of Wind Energy Production , 2003 .

[9]  K. Droegemeier,et al.  Objective Verification of the SAMEX ’98 Ensemble Forecasts , 2001 .

[10]  M. Lange.  On the Uncertainty of Wind Power Predictions—Analysis of the Forecast Accuracy and Statistical Distribution of Errors , 2005 .

[11]  Matthias Lange,et al.  Analysis of the uncertainty of wind power predictions , 2003 .

[12]  M. Lange,et al.  Previento-A Wind Power Prediction System with an Innovative Upscaling Algorithm , 2001 .

[13]  Detlev Heinemann,et al.  PREVIENTO REGIONAL WIND POWER PREDICTION WITH RISK CONTROL , 2002 .

[14]  Pierre Pinson,et al.  On‐line assessment of prediction risk for wind power production forecasts , 2003 .

[15]  M. Lange,et al.  Forecasting offshore wind speeds above the North Sea , 2005 .

[16]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[17]  H. Aa,et al.  PREDICTION OF WIND POWER USING TIME-VARYING COEFFICIENT-FUNCTIONS , 2002 .

[18]  Rebecca J. Barthelmie,et al.  'Copenhagen Offshore Wind 2005' Topic: Wind turbine technology - for offshore appli cation. Wind resources and wake effects Short-term forecasting of wind speeds in the offsho re environment , 2005 .

[19]  Ulrich Focken,et al.  Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects , 2002 .