An Integrated Wind Power Forecasting Methodology: Interval Estimation Of Wind Speed, Operation Probability Of Wind Turbine, And Conditional Expected Wind Power Output Of A Wind Farm

The article presents a novel quantitative methodology for wind farm management. The methodology starts by forecasting the time series mean and volatility of wind speed. The forecasting of wind speed mean and its volatility is built on an autoregressive moving average model with a generalized autoregressive conditional heteroscedasticity process, namely an ARMA-GARCH model. With the prediction of wind speed mean and its volatility, the article establishes the interval estimation of wind speed which makes the prediction of wind speed more accurate and reliable. To facilitate the quantitative management of wind farm, the operation probability (OP) of wind turbine is formulated according to the interval estimation of wind speed. Based on the characteristics power curve of wind turbine, the article develops the conditional expected wind power output equation (CEWPOE). The interval estimation of wind speed, the OP of wind turbine, and the CEWPOE thus comprise an integrated methodology for the quantitative management of wind farm operations.

[1]  Vladimiro Miranda,et al.  ‘Good’ or ‘bad’ wind power forecasts: a relative concept , 2011 .

[2]  Julian Meng,et al.  Short-Term Wind Speed Forecasting Based On Fuzzy Artmap , 2011 .

[3]  Xiuli Qu,et al.  Short-Term Wind Power Generation Forecasting: Direct Versus Indirect Arima-Based Approaches , 2011 .

[4]  J.B. Theocharis,et al.  A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation , 2004, IEEE Transactions on Energy Conversion.

[5]  Massimiliano Caporin,et al.  Modelling and Forecasting Wind Speed Intensity for Weather Risk Management , 2009, Comput. Stat. Data Anal..

[6]  D. Rubinfeld,et al.  Econometric models and economic forecasts , 2002 .

[7]  S. Satchell,et al.  Forecasting Volatility in Financial Markets : A Review , 2004 .

[8]  Xiuli Qu,et al.  Bivariate Modeling of Wind Speed and Air Density Distribution for Long-Term Wind Energy Estimation , 2010 .

[9]  J. E. Payne,et al.  Further Evidence on Modeling Wind Speed and Time-Varying Turbulence , 2009 .

[10]  L. Kamal,et al.  Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan , 1997 .

[11]  Enrique Sentana Quadratic Arch Models , 1995 .

[12]  Ibrahim Dincer,et al.  Analysis of Some Exergoeconomic Parameters of Small Wind Turbine System , 2009 .

[13]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[14]  J. E. Payne,et al.  Modeling Wind Speed and Time-varying Turbulence in Geographically Dispersed Wind Energy Markets in China , 2009 .

[15]  Philip Hans Franses,et al.  Financial Volatility: An Introduction , 2002 .

[16]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[17]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[18]  Azmi Zakaria,et al.  Wind characteristics of Oman , 2002 .

[19]  Marno Verbeek,et al.  A Guide to Modern Econometrics , 2000 .

[20]  Gregor Giebel,et al.  The State-Of-The-Art in Short-Term Prediction of Wind Power. A Literature Overview , 2003 .

[21]  Heping Liu,et al.  Comprehensive evaluation of ARMA–GARCH(-M) approaches for modeling the mean and volatility of wind speed , 2011 .

[22]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[23]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[24]  Lamine Thiaw,et al.  A neural network based approach for wind resource and wind generators production assessment , 2010 .

[25]  W. Rivera,et al.  Wind speed forecasting in the South Coast of Oaxaca, México , 2007 .

[26]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[27]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[28]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[29]  Károly Tar,et al.  A statistical model for estimating electricity produced by wind energy , 2011 .

[30]  V. Miranda,et al.  Entropy and Correntropy Against Minimum Square Error in Offline and Online Three-Day Ahead Wind Power Forecasting , 2009, IEEE Transactions on Power Systems.

[31]  Jamie Brown Kruse,et al.  Analysis of Time-varying Turbulence in Geographically-dispersed Wind Energy Markets , 2008 .

[32]  M. J. Stevens,et al.  The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes , 1979 .

[33]  Heping Liu,et al.  Prediction of wind speed time series using modified Taylor Kriging method , 2010 .

[34]  Jamie B. Kruse,et al.  Time series analysis of wind speed with time‐varying turbulence , 2006 .

[35]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[36]  M. Negnevitsky,et al.  Short term wind power forecasting using hybrid intelligent systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[37]  J. Torres,et al.  Forecast of hourly average wind speed with ARMA models in Navarre (Spain) , 2005 .

[38]  Shan Gao,et al.  Wind speed forecast for wind farms based on ARMA-ARCH model , 2009, 2009 International Conference on Sustainable Power Generation and Supply.