A Deterministic Bases Piecewise Wind Power Forecasting Models

Abstract

Continue emphasis in mitigating the environmental impacts of fossil generated electrical energy has fuelled interest in sustainable and renewable energy; as a result of this interest, renewable energy penetration into power utilities energy mix has increased significantly. Two major issues delaying further increase of renewable energy are supply intermittency and availability. Prediction of renewable energy availability can never be over emphasized. In this paper we propose a simple nonlinear least square piecewise model to predict output power of a small Canadian wind farm. The proposed model decomposes the wind speed sweeping the wind turbine into three major speed groups, slow, moderate and fast speed. The dynamics of the wind speed in each group defines the model and the prediction error performance. We showed that the piecewise model outperformed the manufacturer’s power curve that is traditionally uses by wind farms. We present typical predictions for Fall, Winter, Spring and Summer and compared results from our proposed model to the manufacturer’s power curve. The piecewise model as well as the manufacturer’s power curve performances are both related to the skill of the wind speed estimator, accurate wind speed estimates will result to excellent forecast for both models.

Keywords

• Wind power model building, Wind power forecasting, statistical analysis

References

1. Saurabh S. Soman, Hamidreza Zareipour, Om Malik, and Paras Mandal “A Review of Wind Power and Wind Speed Forecasting Methods With Different Time Horizons
2. Y-K Wu, and J-S Hong, “A literature review of wind forecasting technology in the world,” IEEE Power Tech 2007, Lausanne , pp. 504- 509, 1-5 July 2007.
3. H. Lund, “Large-scale integration of wind power into different energy systems,” Energy, vol. 30, no. 13, pp. 2402-2412, Oct. 2005.
4. Henrik Madsen, Pierre Pinson, George Kariniotakis, Henrik Aa. Nielsen and Torben S. Nielsen “Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models” Wind Engineering Volume 29, No. 6, 2005, pp 475-489
5. M. Negnevitsky, P. Johnson, and S. Santoso, “Short term wind power forecasting using hybrid intelligent systems,” IEEE Power Engineering Society General Meeting 2007, pp.1-4, 24-28 June 2007.
6. A. Fabbri, T. G. S. Roman, J. R. Abbad, and V. H. M. Quezada, “Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market,” IEEE Trans. Power Syst., vol. 20, no.3, pp. 1440- 1446, Aug. 2005.
7. P. Pinson, C. Chevallier, and G. N. Kariniotakis, “Trading wind generation from short-term probabilistic forecasts of wind power,” IEEE Trans. Power Syst., vol. 22, no.3, pp.1148-1156, Aug. 2007.
8. S. J. Watson, L. Landberg, and J. A. Halliday, “Application of wind speed forecasting to the integration of wind energy into a large scale power system,” IEE Proc. - Gener. Transm. Distrib., vol. 141, no. 4, pp. 357- 362, July 1994.

9. G.N. Kariniotakis, I. Marti, T.S. Nielsen, G. Giebel, J. Tambke, I. Waldl, J. Usasla, R. Brownsword, G. Kallas, U. Focken, I. Sanchez, N. Hatziargyriou, A. M. Pa;omares, P. Frayssinet “Advanced Short-term Forecasting of Wind Generation-Anemos” IEEE Trans. On Power Systems, invited paper to special section on Power System Performance Issues Associated with Wind Energy [10]
10. N. R. Ullah, K. Bhattacharya, and T. Thiringer, “Wind farms as reactive power ancillary service providers — technical and economic issues,” IEEE Trans. Energy Convers., vol. 24, no. 3, pp. 661-672, Sept. 2009. [11]
11. M. Lei, L. Shiyan, J. Chuanwen, Liu Hongling, and Z. Yan, “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, vol. 13, no. 4, pp. 915-920, May 2009. [12]
12. Audun Botterud, Jianhui Wang, laudio Monterio and Vladimiro Miranda “Wind Power Forecasting and Electricity Market Operations” U.S Department of Energy Office of Science Laboratory, Contract no DE- AC02-06CH11357 [13]
13. Goudong Liu and Kevin Tomsovic “Quantifying Spinning Reserve in Systems With Significant Wind Power Penetration” IEEE Yransactions on Power Systems, Vol. 27,No 4, Nov. 2012 [14]
14. A. Costa, A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitosa, “A review on the young history of the wind power short-term prediction,” Renewable and Sustainable Energy Reviews, vol. 12, no. 6, pp. 1725- 1744, Aug. 2008. [15]
15. C. W. Potter, and M. Negnevitsky, “Very short-term wind forecasting for Tasmanian power generation,” IEEE Trans. Power Syst., vol. 21, no. 2, pp. 965- 972, May 2006. [16]
16. T.S. Nielsen, H. Aa. Nielsen and H. Madsen “Prediction of Wind Power Using Time-Varying Coefficient-Functions” 15th Triennial World Congress, Barcelona, Spain, 2002 IFAC [17]
17. M. Milligan, M. Schwartz, and Y. Wan, “Statistical wind power forecasting models: Results for U.S. wind farms,” in Proc. Wind Power, Austin, TX, May 18–21, 2003, NREL/CP-500-33 956 Rep. [18]
18. M. Lange, and U. Focken, “New developments in wind energy forecasting,” IEEE Power and Energy Society General Meeting 2008 - Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1-8, 20-24 July 2008. [19]
19. B. Candy, S. J. English, and S. J. Keogh, “A Comparison of the impact of QuikScat and WindSat wind vector products on met office analyses and forecasts,” IEEE Trans. Geosci. Remote Sens., vol. 47, no.6, pp. 1632-1640, June 2009. [20]
20. Pierre Pinson, Henrik Madsen, Henrik Aa. Nielsen, George Papaefthymiou, and Bernd Klockl “From Probabilistic Forecasts to Statistical Scenarios of Short- term Wind Power Production” Wind Energy 2008 [21]
21. E. Cadenas, and W. Rivera, “Wind speed forecasting in the South Coast of Oaxaca, Mexico,” Renewable Energy, vol. 32, no. 12, pp. 2116-2128, Oct. 2007. [22]
22. Niya Chen, Zheng Qian, Xiaofeng Meng and Ian T. Nabney “Short-Term Wind Power Forecasting Using Gaussian Processes” Proceeding of the Twenty-Third International Joint Conference on Artificial Intelligence, pp 2790 - [23]
23. E. Cadenas, O.A. Jaramillo, and W. Rivera, “Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method,” Renewable Energy, vol. 35, no. 5, pp. 925-930, May 2010. [24]
24. P. S. Chang, and L. Li, “Ocean surface wind speed and direction retrievals from the SSM/I,” IEEE Trans. Geosci. Remote Sens., vol. 36, no. 6, pp. 1866-1871, Nov 1998. [25]
25. M. C. Alexiadis, P. S. Dokopoulos, and H. S. Sahsamanoglou, “Wind speed and power forecasting based on spatial correlation models,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 836-842, Sept. 1999. [26]
26. I. G. Damousis, M. C. Alexiadis, J. B. Theocharis, and P. S. Dokopoulos, “A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation,” IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 352- 361, June 2004. [27]
27. H. Liu, H-Q Tian, C. Chen, and Y. Li, “A hybrid statistical method to predict wind speed and wind power,” Renewable Energy, vol. 35, no. 8, pp. 1857- 1861, Aug. 2010. [28]
28. G. Sideratos, and N. D. Hatziargyriou, “An Advanced Statistical
29. Forecasting,” IEEE Trans. Power Syst., vol. 22, no. 1, pp. Method 258-265, Feb. 2007. for Wind Power [29]
30. I. J. Ramirez-Rosado, L. A. Fernandez-Jimenez, C. Monteiro, J. Sousa, and R. Bessa, “Comparison of two new short-term wind-power forecasting systems,” Renewable Energy, Vol.34, Issue 7, pp. 1848- 1854, Jul. 2009. [30]
31. G. H. Riahy, and M. Abedi, “Short term wind speed forecasting for wind turbine applications using linear prediction method,” Renewable Energy, vol. 33, no. 1, pp. 35-41, January 2008. [31]
32. M. S. Miranda, and R. W. Dunn, “One-hour-ahead wind speed prediction using a Bayesian methodology,” IEEE Power Engineering Society General Meeting 2006, pp. 1-6. [32]
33. Zeno Farkas “Considering Air Density in Wind arXiv.org Power arXiv:1103.2198v1 > physics >