Abstract
In order to incorporate large amounts of wind power into the electric grid, it is necessary to provide grid operators with wind power forecasts for the day ahead, especially when managing extreme situations: rapid changes in power output of a wind farm. These so-called ramp events are complex and difficult to forecast. Hence, they introduce a high risk of instability to the power grid. Therefore, the development of reliable ramp prediction methods is of great interest to grid operators. Forecasting ramps for the day ahead requires wind power forecasts, which usually involve numerical weather prediction models at very high resolutions. This is resource and time consuming. This paper introduces a novel approach for short-term wind power prediction by combining the Weather Research and Forecasting—advanced Research WRF model (WRF-ARW) with genetic programming. The latter is used for the final downscaling step and as a prediction technique, estimating the total hourly power output for the day ahead at a wind farm located in Galicia, Spain. The accuracy of the predictions is above 85 % of the total power capacity of the wind farm, which is comparable to computationally more expensive state-of-the-art methods. Finally, a ramp detection algorithm is applied to the power forecast to identify the time and magnitude of possible ramp events. The proposed method clearly outperformed existing ramp prediction approaches.
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References
WWEA, World wind energy half-year report 2012, Tech. rep.World Wind Energy Association, 2012.
Wood, A.J. Power Generation, Operation and Control. Wiley-Interscience, 1996.
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.
Pinson, P. Catalogue of complex extreme situations.Technical Report, EU Project SafeWind, Deliverable Dc1.2, 2009.
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.
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.
Landberg, L., Short-term prediction of local wind conditions, Journal of Wind Engineering and Industrial Aerodynamics, vol. 89, 2001, pp. 235–245.
Constantinescu, E.M., Zavala, E.M., Rocklin, M., Sangmin Lee, Anitescu, M., A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation, IEEE Transactions on Power Systems, vol. 26, 2011, pp. 431–441.
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.
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.
Lazić, L. and Pejanović, G. and Živković, M., Wind forecasts for wind power generation using Eta model, Renewable Energy, Vol. 35, No. 6, 2010, pp. 1236–1243.
Sweeney, C. P. and Lynch, P. and Nolan, P. Reducing errors of wind speed forecasts by an optimal combination of post-processing methods, Meteorological Applications, doi.10.1002/met.294, 2011.
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.
Salcedo-Sanz, S. and Ortiz-García, E. G. and Portilla-Figueras, A. and Prieto, L. and Paredes, D, Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction.Renewable Energy, Vol. 34, No. 6, 2009, pp. 1451–1457.
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, 2012, pp. 234–241.
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, 2011, pp. 3554–3570.
Cassola, F. and Burlando, M., Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output, Applied Energy, Vol. 99, 2012, pp. 154–166.
Salcedo-Sanz, S. and Ortiz-García, E. G. and Pérez-Bellido, 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, 2011, pp. 4052–4057.
Greaves, B., Collins, J., Parkes, J., Tindal, A. Temporal forecast uncertainty for ramp events. Wind Engineering, Vol. 33, No. 11, 2009, pp. 309–319.
Bossavy, A., Girard, R. and Kariniotakis, G., Forecasting ramps of wind power production with numerical weather prediction ensembles, Wind Energy, Vol. 16, No. 1, 2013, pp. 51–63.
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 fields. Wind Energy, Vol. 12, No. 6, 2009, pp. 542–555.
Sotavento Galicia Experimental Wind Farm, sotaventogalicia.com, accesed on 29 April, 2013.
Koza, J.R., Genetic Programming: on the programming of computers by means of natural selection, MIT Press, 1992.
Poli, R. and Langdon, B. and McPhee, N. F., A field guide to genetic programming with contributions by J. R. Koza, Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008
Kotanchek, M. E. and Vladislavleva, E. Y. and Smits, G. F., Genetic Programming Theory and Practice VII, Springer US, 2010.
Martinez-Arellano, Giovanna, Nolle, Lars and Bland, John. Improving WRF-ARW Wind Speed Predictions using Genetic Programming. SGAI Conf. 2012: 347–360.
Tindal, A., Johnson, C., LeBlanc, M., Harman, K., Rareshide, E. and Graves, A. Site-especific adjustments to wind turbine power curves. AWEA Wind Power Conf., 2008.
Vladislavleva, E., Friedrich, T., Neumann, F. and Wagner, M., Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables and their Correlation. Renewable Energy, Vol. 50, 2013, pp. 236–243.
Louka, P., Galanis, G., Siebert, N., Kariniotakis, G., Katsafados, P., Pytharoulis, I., Kallos, G., Improvements in wind speed forecasts for wind power prediction purposes using Kalman FIltering. Journal of Wind Engineering and Industrial Aerodynamics. Vol. 96, No. 12, 2008, pp. 2348–2362.
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Martínez-Arellano, G., Nolle, L. (2013). Genetic Programming for Wind Power Forecasting and Ramp Detection. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_30
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DOI: https://doi.org/10.1007/978-3-319-02621-3_30
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