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Martínez-Arellano, G; Nolle, L; Cant, R; Lotfi, A; Windmill, C (2014)
Publisher: Springer
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics
Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution Numerical Weather Prediction (NWP) models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power out- put that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using Genetic Programming (GP) and Quantile Regression Forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. WWEA, World wind energy half-year report 2012, Tech. rep.World Wind Energy Association (2012).
    • 2. Ferreira, C., Gama, J., Matias, L., Botterud, A. and Wang, J. A Survey on Wind Power Ramp Forecasting, Tech. rep. ARL, DIS-10-13 (2010).
    • 3. Pinson, P. Catalogue of complex extreme situations.Technical Report, EU Project SafeWind, Deliverable Dc1.2, (2009).
    • 4. Giebel, G. The State of the Art in Short-Term Prediction of Wind Power: A Literature Overview, 2nd Edition, Project ANEMOS. Available at http://www.safewind.eu/images/Articles/Deliverables/ swind deliverable dp-1.4 sota v1.1.pdf [Accessed on September 17, 2013].
    • 5. Kanamitsu, M. and Alpert, J.C. and Campana, K. A. and Caplan, P.M. and Deaven, D.G. and Iredell, M. and Katz, B. and Pan, H. L. and Sela, J. and White, G. H., Recent Changes Implemented into the Global Forecast System at NMC, Weather and Forecasting, Vol.6, 1991, pp. 425-435.
    • 6. Landberg, L., Short-term prediction of the power production from wind farms, Journal of Wind Engineering and Industrial Aerodynamics, Vol. 80, 1999, pp. 207-220.
    • 7. Landberg, L., Short-term prediction of local wind conditions, Journal of Wind Engineering and Industrial Aerodynamics, vol. 89, 2001, pp. 235-245.
    • 8. Constantinescu, E.M., Zavala, E.M., Rocklin, M., Sangmin Lee, Anitescu, M., A computational framework for un1 certainty quanti cation and stochastic optimization in unit commitment with wind power generation, IEEE Transactions on Power Systems, vol. 26, 2011, pp. 431- 441.
    • 9. Skamarock, W.C. and Klemp, J.B. and Dudhia, J. and O. Gill, D. and Barker, D. M. and Wang, W. and Powers, J. G., A Description of the Advanced Research WRF Version 2, AVAILABLE FROM NCAR, Vol.88, 2001, pp. 7-25.
    • 10. Alexiadis, MC., Dokopoulos, PS., Sahsamanoglou, H., Manousaridis, IM., Short-term forecasting of wind speed and related electrical power, Solar Energy, Vol. 63(1),1998, pp. 61-68.
    • 11. Lazic, L. and Pejanovic, G. and Zivkovic, M., Wind forecasts for wind power generation using Eta model, Renewable Energy, Vol.35, No.6, 2010, pp.1236-1243.
    • 12. C. Monteiro, R. Bessa, V. Miranda, A. Botterud, J. Wang, G. Conzelmann, I. Porto, et al., Wind power forecasting: state-of-the-art 2009., Tech. rep., Argonne National Laboratory (ANL) (2009).
    • 13. Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H. 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 (2008).
    • 14. A. M. Foley, P. G. Leahy, A. Marvuglia, E. J. McKeogh, Current methods and advances in forecasting of wind power generation, Renewable Energy, vol. 37, no. 1, pp. 1 - 8 (2012). http://dx.doi.org/10.1016/j.renene.2011.05.033.
    • 15. Sweeney, C.P. and Lynch, P., Adaptative post-processing of short-term wind forecasts for energy applications, Wind Energy, doi. 10.1002/we.420 (2010).
    • 16. Zhao, P. and Wang, J. and Xia, J. and Dai, Y. and Sheng, Y. and Yue, J. Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China, Renewable Energy, Vol.43, pp. 234-241(2012).
    • 17. Delle Monache, L. and Nipen, T. and Liu, Y. and Roux, G. and Stull, R. Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions, Monthly Weather Review, Vol.139, No.11, pp. 3554-3570 (2011).
    • 18. Cassola, F. and Burlando, M., Wind speed and wind energy forecast through Kalman ltering of Numerical Weather Prediction model output, Applied Energy, Vol.99, pp. 154-166 (2012).
    • 19. Salcedo-Sanz, S. and Ortiz-Garc a, E. G. and PortillaFigueras, A. and Prieto, L. and Paredes, D, Hybridizing the fth generation mesoscale model with arti cial neural networks for short-term wind speed prediction.Renewable Energy, vol.34, no.6, pp.1451-1457 (2009).
    • 20. Salcedo-Sanz, S. and Ortiz-Garc a, E. G. and PerezBellido, A. M. and Portilla-Figueras, A. and Prieto, L., Short term wind speed prediction based on evolutionary support vector regression algorithms, Expert Systems with Applications, vol.38, no.4, pp. 4052-4057 (2011).
    • 21. Bourke, W. Performance of the ECMWF and the BoM Ensemble Systems in the Southern Hemisphere. Monthly Weather Review, vol. 132, pp. 2338-2357 (2004).
    • 22. Toth, Z. and Kalnay, E. Ensemble Forecasting at NMC: The Generation of Perturbation. Bulletin of the American Meteorological Society, vol. 74, pp. 2317-2330 (1993)..
    • 23. Arribas, A., Robertson, K. B. and Mylne, K. R. Test of a Poor Man's Ensemble Prediction System for Short-Range Probability Forecasting. Monthly Weather Review, vol. 133, pp. 1825-1839.
    • 24. Greaves, B., Collins, J., Parkes, J., Tindal, A. Temporal forecast uncertainty for ramp events. Wind Engineering, vol. 33, no. 11, pp. 309-319 (2009).
    • 25. Bossavy, A., Girard, R. and Kariniotakis, G., Forecasting ramps of wind power production with numerical weather prediction ensembles, Wind Energy, vol. 16, no. 1, pp. 51- 63 (2013).
    • 26. Cutler, N.J., Outhred, H.R., MacGill, I.F., Kepert, J.D., Characterizing future large, rapid changes in aggregated wind power using numerical weather prediction spatial elds. Wind Energy, vol. 12, no. 6, pp. 542-555 (2009).
    • 27. Gallego, C., Costa, A., Cuerva, A., Landberg, L., Greaves, B and Collins, J. A wavelet-based approach for large wind power ramp characteristaion. Wind Energy, vol. 16, no. 2, pp. 257-278 (2013).
    • 28. Mart nez-Arellano, G. and Nolle, L. Genetic Programming for Wind Power Forecasting and Ramp Detection. Research and Development in Intelligent Systems XXX, pp. 403 - 417 (2013).
    • 29. Koza, J.R., Genetic Programming: on the programming of computers by means of natural selection, MIT Press (1992).
    • 30. Poli, R. and Langdon, B. and McPhee, N. F., A eld guide to genetic programming with contributions by J. R. Koza, Published via http://lulu.com and freely available at http://www.gp- eld-guide.org.uk, (2008).
    • 31. Kotanchek, M. E. and Vladislavleva, E. Y. and Smits, G. F., Genetic Programming Theory and Practice VII, Springer US ( 2010).
    • 32. Skamarock, W.C. and Klemp, J.B. and Dudhia, J. and O. Gill, D. and Barker, D. M. and Wang, W. and Powers, J. G., A Description of the Advanced Research WRF Version 2, AVAILABLE FROM NCAR, Vol.88, pp. 7-25 (2001).
    • 33. J. Michalakes, M. Vachharajani, GPU Acceleration of Numerical Weather Prediction, Parallel Processing Letters, Vol. 18 No. 4 . World Scienti c. Dec. (2008) 531 { 548.
    • 34. Mart nez-Arellano, G., Nolle, L. and Bland, J. Improving WRF-ARW Wind Speed Predictions using Genetic Programming. SGAI'12 Conf. pp 347-360 (2012).
    • 35. Tindal, A., Johnson, C., LeBlanc, M., Harman, K., Rareshide, E. and Graves, A. Site-especi c adjustments to wind turbine power curves. AWEA Wind Power Conf. (2008).
    • 36. Sotavento Galicia Experimental Wind Farm, sotaventogalicia.com, accessed on 29 April, 2013.
    • 37. Pinson, P. Estimation of the uncertainty in wind power forecasting, PhD Thesis, Ecole des Mines de Paris, Paris, France (2006).
    • 38. G.Kariniotakis and co authors. What performance can be expected by short-term wind power prediction models depending on site characteristics? In CD-Proc. of the 2004 European Wind Energy Conference, EWEC04, London, United Kingdom, November 2004
    • 39. Meinshausen, N. Quantile Regression Forests. Journal of Machine Learning Research. Vol. 7, pp. 983 - 999 (2006).
    • 40. L. A. Zadeh, Fuzzy Sets, Information and Control, New York: Academic Press, vol. 8, pp. 338-353 (1965)
    • 41. Zhang, Y., Wang, J. and Wang, X. Review on probabilistic forecasting of wind power generation. Renewable and Sustainable Energy Reviews, vol. 32, pp. 255 { 270 (2014).
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