Stratification-based wind power forecasting in a high penetration wind power system using a hybrid model with charged system search algorithm

This work proposes a novel stratification-based wind power forecasting method, and develops a hybrid forecasting model at different stratifications by using charged system search algorithm. The proposed model applies the concept of segmentation from the theory of optimal stratification to forecast short-term wind power outputs. Additionally, the proposed method elucidates different weighting values of each individual model at different segmentation blocks. Based on the forecasting results, the proposed stratification-based hybrid model outperforms traditional stand-alone models and un-stratified hybrid models in terms of forecasting accuracy, which verifies the proposed forecasting model for accurate wind power forecasting.

[1]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[2]  Paras Mandal,et al.  A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting , 2014, IEEE Transactions on Power Systems.

[3]  J. Kain,et al.  A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization , 1990 .

[4]  Wei Huang,et al.  A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results , 2004 .

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

[6]  Alexander Khain,et al.  Microphysics, Radiation and Surface Processes in the Goddard Cumulus Ensemble (GCE) Model , 2003 .

[7]  P. Varaiya,et al.  Bringing Wind Energy to Market , 2012, IEEE Transactions on Power Systems.

[8]  Yongqian Liu,et al.  Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features , 2014, IEEE Transactions on Smart Grid.

[9]  V M F Mendes,et al.  Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Wind Power Forecasting in Portugal , 2011, IEEE Transactions on Sustainable Energy.

[10]  Men-Shen Tsai,et al.  Application of Novel Charged System Search With Real Number String for Distribution System Loss Minimization , 2013, IEEE Transactions on Power Systems.

[11]  Duehee Lee,et al.  Short-Term Wind Power Ensemble Prediction Based on Gaussian Processes and Neural Networks , 2014, IEEE Transactions on Smart Grid.

[12]  R. Singh,et al.  Approximately Optimum Stratification on the Auxiliary Variable , 1971 .

[13]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[14]  Kit Po Wong,et al.  Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.

[15]  Chongqing Kang,et al.  Modeling Conditional Forecast Error for Wind Power in Generation Scheduling , 2014, IEEE Transactions on Power Systems.

[16]  Yuan-Kang Wu,et al.  A literature review of wind forecasting technology in the world , 2007, 2007 IEEE Lausanne Power Tech.

[17]  P. McSharry,et al.  Short-Term Load Forecasting Methods: An Evaluation Based on European Data , 2007, IEEE Transactions on Power Systems.

[18]  Xiaofeng Meng,et al.  Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction , 2014, IEEE Transactions on Power Systems.

[19]  Mark O'Malley,et al.  Impact of Wind Forecast Error Statistics Upon Unit Commitment , 2012, IEEE Transactions on Sustainable Energy.

[20]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[21]  Joao P. S. Catalao,et al.  A review of short-term wind power forecasting approaches , 2013 .

[22]  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.

[23]  Kenneth Bruninx,et al.  A Statistical Description of the Error on Wind Power Forecasts for Probabilistic Reserve Sizing , 2014, IEEE Transactions on Sustainable Energy.

[24]  Zongxiang Lu,et al.  A Consideration of the Wind Power Benefits in Day-Ahead Scheduling of Wind-Coal Intensive Power Systems , 2013, IEEE Transactions on Power Systems.

[25]  Jianxue Wang,et al.  Review on probabilistic forecasting of wind power generation , 2014 .

[26]  Gerard Doorman,et al.  The effect of large-scale wind power on system balancing in Northern Europe , 2013, 2013 IEEE Power & Energy Society General Meeting.

[27]  Jordan G. Powers,et al.  A Description of the Advanced Research WRF Version 2 , 2005 .

[28]  S. N. Singh,et al.  AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network , 2012, IEEE Transactions on Sustainable Energy.

[29]  Pinar Karagoz,et al.  A Novel Wind Power Forecast Model: Statistical Hybrid Wind Power Forecast Technique (SHWIP) , 2015, IEEE Transactions on Industrial Informatics.

[30]  J. Dudhia,et al.  A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes , 2006 .