DOI QR코드

DOI QR Code

Nonlinear Kalman filter bias correction for wind ramp event forecasts at wind turbine height

  • Xu, Jing-Jing (International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics, Chinese Academy of Science) ;
  • Xiao, Zi-Niu (State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences) ;
  • Lin, Zhao-Hui (International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics, Chinese Academy of Science)
  • 투고 : 2019.06.17
  • 심사 : 2020.03.28
  • 발행 : 2020.04.25

초록

One of the growing concerns of the wind energy production is wind ramp events. To improve the wind ramp event forecasts, the nonlinear Kalman filter bias correction method was applied to 24-h wind speed forecasts issued from the WRF model at 70-m height in Zhangbei wind farm, Hebei Province, China for a two-year period. The Kalman filter shows the remarkable ability of improving forecast skill for real-time wind speed forecasts by decreasing RMSE by 32% from 3.26 m s-1 to 2.21 m s-1, reducing BIAS almost to zero, and improving correlation from 0.58 to 0.82. The bias correction improves the forecast skill especially in wind speed intervals sensitive to wind power prediction. The fact shows that the Kalman filter is especially suitable for wind power prediction. Moreover, the bias correction method performs well under abrupt weather transition. As to the overall performance for improving the forecast skill of ramp events, the Kalman filter shows noticeable improvements based on POD and TSS. The bias correction increases the POD score of up-ramps from 0.27 to 0.39 and from 0.26 to 0.38 for down-ramps. After bias correction, the TSS score is significantly promoted from 0.12 to 0.26 for up-ramps and from 0.13 to 0.25 for down-ramps.

키워드

과제정보

연구 과제번호 : Technology and application of wind power / photovoltaic power prediction for promoting renewable energy consumption

연구 과제 주관 기관 : State Grid Corporation of China

This research was supported by National Key R&D Program of China (Technology and application of wind power / photovoltaic power prediction for promoting renewable energy consumption, 2018YFB0904200) and eponymous Complement S&T Program of State Grid Corporation of China (SGLNDKOOKJJS1800266).

참고문헌

  1. Bradford, K.T., Carpenter, R.L. and Shaw, B. (2010), "Forecasting southern plains wind ramp events using the WRF model at 3-km", Proceedings of the 9th Annual Student Conference, Atlanta, U.S.A, January.
  2. Burton, T., Jenkins, N., Sharpe, D. and Bossanyi, E. (2001), Wind Energy Handbook, John Wiley & Sons, Ltd, Chichester, West Sussex, England.
  3. Chen, F. and Dudhia, J. (2001), "Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: Model description and implementation", Mon. Wea. Rev., 129(4), 569-585. https://doi.org/10.1175/15200493(2001)129<0569:CAALSH>2.0.CO;2.
  4. Chen T.H. and Tran, V.T. (2015), "Prospects of wind energy on Penghu Island, Taiwan", Wind Struct., 20(1), 1-13. https://doi.org/10.12989/was.2015.20.1.001.
  5. Cheng, X.L., Li, J., Hu, F., Xu, J. and Zhu, R. (2015), "Refined numerical simulation in wind resource assessment", Wind Struct., 20(1), 59-74. http://dx.doi.org/10.12989/was.2015.20.1.059.
  6. Chou M.D. and Suarez, M.J. (1994), "An efficient thermal infrared radiation parameterization for use in general circulation models", NASA Tech. Memo., NASA, U.S.A.
  7. Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H. and Feitosa, E. (2008), "A review on the young history of the wind power short-term prediction", Renew. Sustain. Energy Rev., 12(6), 1725-1744. https://doi.org/10.1016/j.rser.2007.01.015.
  8. Crochet, P. (2004), "Adaptive Kalman filtering of 2-m temperature and 10-m wind-speed forecasts in Iceland", Meteor. Appl., 11, 173-187. https://doi.org/10.1017/S1350482704001252
  9. Delle Monache, L., Nipen, T., Deng, X., Zhou, Y. and Stull, R. (2006), "Ozone ensemble forecasts: 2. A Kalman-filter predictor bias correction", J. Geophys. Res., 111, D05308, https://doi.org/10.1029/2005JD006311.
  10. Delle Monache, L., Nipen, T., Liu, Y., Roux, G. and Stull, R. (2011), "Kalman filter and analog schemes to post-process numerical weather predictions", Mon. Weather. Rev., 139, 3554-3570. https://doi.org/10.1175/2011MWR3653.1.
  11. Monache, L.D., Wilczak, J., McKeen, S., Grell, G., Pagowski, M., Peckham, S., Stull, R., Mchenry, J. and McQueen, J. (2008), "A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone", Tellus, 60(2), 238-249. https://doi.org/10.1111/j.1600-0889.2007.00332.x.
  12. Deppe, A.J., Gallus, W.A. and Takle, E.S. (2013), "A WRF ensemble for improved wind speed forecasts at turbine height", Wea. Forecasting, 28(1), 212-228. https://doi.org/10.1175/WAF-D-11-00112.1.
  13. Dhunny, A., Lollchund, M. and Rughooputh, S.D.D.V. (2015), "A high-resolution mapping of wind energy potentials for Mauritius using Computational Fluid Dynamics (CFD)", Wind Struct., 20(4), 565-578. http://dx.doi.org/10.12989/was.2015.20.4.565.
  14. Francis, N. (2008), Predicting Sudden Changes in Wind Power Generation, North American Windpower, October.
  15. Freedman, J., Markus, M. and Penc, R. (2008), "Analysis of west Texas wind plant ramp-up and ramp-down events", AWS Truewind Report, AWS Truepower, U.S.A.
  16. Galanis, G., Louka, P., Katsafados, P., Pytharoulis, I. and Kallos, G. (2006), "Applications of Kalman filters based on non-linear functions to numerical weather predictions", Ann. Geophys., 24, 2451-2460. https://doi.org/10.5194/angeo-24-2451-2006
  17. Giebel, G. (2001), "On the benefits of distributed generation of wind energy in Europe", Ph. D. Dissertation, University of Oldenburg, Dusseldorf, Germany.
  18. Glahn, H.R. and Lowry, D.A. (1972), "The use of model output statistics (MOS) in objective weather forecasting", J. Appl. Meteor., 11(8), 1203-1211. https://doi.org/10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2.
  19. Greaves, B., Collins, J., Parkes, J. and Tindal, A. (2009), "Temporal forecast uncertainty for ramp events", Wind Eng., 33(4), 309-320. https://doi.org/10.1260/030952409789685681
  20. Gunter, W.S., Schroeder, J.L., Weiss, C.C. and Bruning, E.C. (2017), "Surface measurements of the 5 June 2013 damaging thunderstorm wind event near Pep, Texas", Wind Struct., 24(2), 185-204. https://doi.org/10.12989/was.2017.24.2.185.
  21. Hacker, J.P. and Rife, D.L. (2007), "A practical approach to sequential estimation of systematic error on near-surface mesoscale grids", Wea. Forecasting, 22(6), 1257-1273. https://doi.org/10.1175/2007WAF2006102.1.
  22. Homleid, M. (1995), "Diurnal corrections of short-term surface temperature forecasts using Kalman filter", Wea. Forecasting, 10(4), 689-707. https://doi.org/10.1175/1520-0434(1995)010<0689:DCOSTS>2.0.CO;2.
  23. Hu, X.M., Klein, P.M. and Xue, M. (2013), "Evaluation of the updated YSU planetary boundary layer scheme within WRF for wind resource and air quality assessments", J. Geophys. Res., 118(18), 10490-10505. https://doi.org/10.1002/jgrd.50823.
  24. Janjic, Z.I. (1994), "The step-mountain Eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes", Mon. Weather Rev., 122(5), 927-945. https://doi.org/10.1175/15200493(1994)122<0927:TSMECM>2.0.CO;2
  25. Jolliffe, I.T. and Stephenson, D.B. (2012), Forecast Verification: A Practitioner's Guide in Atmospheric Science, (2th Edition), John Wiley & Sons, Ltd, West Sussex, England.
  26. Kain, J.S. (2004), "The Kain-Fritsch convective parameterization: An update", J. Appl. Meteor., 43(1), 170-181. https://doi.org/10.1175/15200450(2004)043%3C0170:TKCPAU%3E2.0.CO;2.
  27. Kalman, R.E. (1960), "A new approach to linear filtering and prediction problems", J. basic Eng., 82(1), 35-45. https://doi.org/10.1115/1.3662552.
  28. Kalman, R.E. and Bucy, R.S. (1961), "New results in linear filtering and prediction theory", J. basic Eng., 83(1), 95-108. https://doi.org/10.1115/1.3658902.
  29. Kalnay, E. (2002), Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, Cambridge, Cambridgeshire, U.K.
  30. Lange, M. and Focken, U. (2006), Physical Approach to Short-term Wind Power Prediction, Springer, Berlin, Heidelberg, Germany.
  31. Lin, Y.L., Farley, R.D. and Orville, H.D. (1983), "Bulk parameterization of the snow field in a cloud model", J. Climate Appl. Meteor., 22(6), 1065-1092. https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.
  32. Louka, P., Galanis, G., Siebert, N., Kariniotakis, G., Katsafados, P., Pytharoulis, I. and Kallos, G. (2008), "Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering", J. Wind Eng. Ind. Aerod., 96(12), 2348-2362. https://doi.org/10.1016/j.jweia.2008.03.013.
  33. McCollor, D. and Stull, R. (2008), "Hydrometeorological accuracy enhancement via post-processing of numerical weather forecasts in complex terrain", Wea. Forecasting, 23(1), 131-144. https://doi.org/10.1175/2007WAF2006107.1.
  34. Mellor, G.L. and Yamada, T. (1982), "Development of a turbulence closure model for geophysical fluid problems", Rev. Geophys. Space Phys., 20(4), 851-875. https://doi.org/10.1029/RG020i004p00851.
  35. Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J. and Clough, S.A. (1997), "Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave", J. Geophys. Res., 102(D14), 16663-16682. https://doi.org/10.1029/97JD00237.
  36. Muller, M.D. (2011), "Effects of model resolution and statistical postprocessing on shelter temperature and wind forecasts", J. Appl. Meteor., 50(8), 1627-1636. https://doi.org/10.1175/2011JAMC2615.1.
  37. Powers, J.G. (2007), "Numerical prediction of an Antarctic severe wind event with the Weather Research and Forecasting (WRF) Model", Mon. Weather. Rev., 135(9), 3134-3157. https://doi.org/10.1175/MWR3459.1.
  38. Rife, D.L. and Davis, C.A. (2005), "Verification of temporal variations in mesoscale numerical wind forecasts", Mon. Wea. Rev., 133(11), 3368-3381. https://doi.org/10.1175/MWR3052.1.
  39. Rincon, A., Jorba, O. and Baldasano, J.M. (2010), "Development of a short-term irradiance prediction system using post-processing tools on WRF-ARW meteorological forecasts in Spain", 10th EMS Annual Meeting, Zurich, Switzerland, September.
  40. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D. M., Duda, M.G., Huang, X., Wang, W. and Powers, J.G. (2008), "A description of the Advanced Research WRF version 3", NCAR Technical Note, NCAR, U.S.A.
  41. Stensrud, D.J. and Yussouf, N. (2003), "Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England", Mon. Weather Rev., 131(10), 2510-2524. https://doi.org/10.1175/15200493(2003)131%3C2510:SEPOMT%3E2.0.CO;2.
  42. Xu, J.J., Hu, F., Xiao, Z.N. and Li, J. (2013), "Analog bias correction of numerical model on wind power prediction", J. Appl. Meteorol. Sci. (In Chinese), 24(6), 731-740.
  43. Xu, J.J., Hu, F., Xiao, Z.N. and Cheng, X.L. (2014), "Bias correction to wind direction forecast by the circular - circular regression method", Atmos. Oceanic Sci. Lett., 7, 87-91. https://doi.org/10.3878/j.issn.1674-2834.13.0057
  44. Yang, Q., Berg, L.K., Pekour, M., Fast, J.D., Newsom, R.K., Stoelinga, M. and Finley, C. (2013), "Evaluation of WRF-predicted near-hub-height winds and ramp events over a Pacific Northwest site with complex terrain", J. Appl. Meteor. Climatol., 52(8), 1753-1763. https://doi.org/10.1175/JAMC-D-12-0267.1
  45. Yim, S.H., Fung, J.C., Lau, A.K. and Kot, S.C. (2007), "Developing a high-resolution wind map for a complex terrain with a coupled MM5 CALMET system", J. Geophys. Res., 112(D5), https://doi.org/10.1029/2006JD007752.
  46. Zack, J.W. (2007), "Optimization of wind power production forecast performance during critical periods for grid management", In Proceedings of the European Wind Energy Conference EWEC, Milano. Italy.
  47. Zhang, H., Pu, Z. and Zhang, X. (2013), "Examination of errors in near-surface temperature and wind from WRF numerical simulations in regions of complex terrain", Wea. Forecasting, 28(3), 893-914. https://doi.org/10.1175/WAF-D-12-00109.1.