Research on Power Flow Interface Oriented Regional Wind Power Forecasting Method

Abstract:

Accurate regional wind power forecasting guarantees the security and economics of the power system integrated with large scale of wind power. Aiming at the gross wind power output of the whole regional grid area, existing regional wind power forecasting methods fails to characterize the locally gross output power of the wind farm aggregation forming a power flow interface with specified flow restraints. In this paper, the work flow of the power flow oriented regional wind power forecasting method based on whole-grid regional wind power forecasting methods was presented first. Then, the data preparation, data preprocessing and the mathematical description of the algorithm for our method were presented. Finally, the case study proved the feasibility and effectiveness of our method. The conclusion indicates that the method presented in this paper implements a multiple temporal and spatial scale regional win power forecasting technology, which can obviously improve the accuracy of regional wind power forecasting, relieve the pressure for the grid side and improve the utilization rate of wind power.

• Periodical: Applied Mechanics and Materials (Volume 483)
• Pages: 275-279
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.483.275
• Online since: December 2013
• Authors: Yun Yu*, Bo Yang, Fu Jiang Ge
• Keywords: Decision Supporting, Power Flow Interface, Regional Wind Power Forecasting, Smooth Effect

References

[1] Giebel G., Landberg L., Kariniotakis G., in: State-of-the-art on methods and software tools for short-term prediction of wind energy production, European Wind Energy Conference & Exhibition, Madrid, Spain (2003).

[2] Kariniotakis G., Mayer D., Moussafir J. ANEMOS, in: development of a next generation wind power forecasting system for the large-scale integration of onshore & offshore wind farms, European Wind Energy Conference & Exhibition, Madrid, Spain (2003).

[3] Focken U., Lange M., Waldl H.P. Previento: A Wind Power Prediction System with an Innovative Upscaling Algorithm. (2002).

[4] Focken U., Lange M., Mönnich K., Waldl H.P., Beyer H.G., Luig A., in: 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, Journal of wind Engineering and Industrial Aerodynamics Vol. 90(3) (2002).

DOI: 10.1016/s0167-6105(01)00222-7

[5] Pinson P., Siebert N., Kariniotakis G., in: Forecasting of Regional Wind Generation by a Dynamic Fuzzy-Neural Networks Based Upscaling Approach, European Wind Energy Conference & Exhibition EWEC 2003, Madrid, Spain (2003).

[6] Feng Shuang-lei, Wang Wei-sheng, Liu Chun, Dai Hui-zhu, in: Study on the Physical Approach to Wind Power Prediction, Proceedings of the CSEE Vol. 30(2) (2010), pp.1-6.