Skip to main content

Advertisement

SpringerLink
Log in

A universal power-law model for wind speed uncertainty

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The uncertainty is a significant characteristic of wind speed in wind engineering field. Especially, it has brought much more problems to the grid in safe and efficient utilization of large scale wind power. And there is urgent need of systematic and perfect models that can describe windspeed uncertainty in grid scheduling and controlling. In this paper, a universal power-law model is proposed for properly depicting the uncertainty of both wind speed and wind power. According to the turbulence nature of wind uncertainty, the uncertainty model of wind speed is firstly obtained by using wavelet multi-scale transform algorithm for its tight supporting characteristic, which is more reasonable than the traditional algorithm of getting the mean valve and the variance valve of the time series. And the turbulent intensity model is further improved by a power-law model, which is suitable for much more kinds of turbulence on complex geographical conditions than that proposed in current international IEC standard with the sufficient actual data. In physically speaking, the model improvement with three parameters is consistent with turbulence development mechanism. Moreover, the uncertainty modeling method of wind power is developed based on the universal power-law model, which is not only suitable for the power of single wind turbine, but also suitable for the power of whole wind farm. It’s very importance that the wind speed uncertainty model is extended to model the power uncertainty of wind turbine and farm, in especial its or their power output is usually limited for human adjustment control. It has a certain significance to the real-time dispatch and optimal control of the renewable energy power system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. Simiu, E., Scanlan, R.H.: Wind effects on structures. Wiley, Hoboken (1996)

    Google Scholar 

  2. Leung, D.Y.C., Yang, Y.: Wind energy development and its environmental impact: a review. Renew. Sustain. Energy Rev. 16(1), 1031–1039 (2012)

    Article  Google Scholar 

  3. Ayhan, D., Sağlam, Ş.: A technical review of building-mounted wind power systems and a sample simulation model. Renew. Sustain. Energy Rev. 16(1), 1040–1049 (2012)

    Article  Google Scholar 

  4. Hansen, A.D., Altin, M., Margaris, I.D., et al.: Analysis of the short-term overproduction capability of variable speed wind turbines. Renew. Energy 68, 326–336 (2014)

    Article  Google Scholar 

  5. IEC 61400-1 third edition 2005-08 Wind turbines – Part 1: Design requirements, International Electrotechnical Commission, IEC, (2005)

  6. Burton, T., Jenkins, N., Sharpe, D.: Wind energy handbook. Wiley, Hoboken (2011)

    Book  Google Scholar 

  7. Domínguez-García, J.L., Gomis-Bellmunt, O., Bianchi, F.D., et al.: Power oscillation damping supported by wind power: a review. Renew. Sustain. Energy Rev. 16(7), 4994–5006 (2012)

    Article  Google Scholar 

  8. Barthelmie, R.J., Frandsen, S.T., Nielsen, M.N., et al.: Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm. Wind Energy 10(6), 517–528 (2007)

    Article  Google Scholar 

  9. Lubitz, W.D.: Impact of ambient turbulence on performance of a small wind turbine. Renew. Energy 61, 69–73 (2014)

    Article  Google Scholar 

  10. Kuik, G.V., Ummels, B., Hendriks, R.: Sustainable Energy Technologies. Springer, Amsterdam (2007)

    Google Scholar 

  11. Pinson, P.: Estimation of the uncertainty in wind power forecasting. École Nationale Supérieure des Mines de Paris, (2006)

  12. Van der Hoven, I.: Power spectrum of horizontal wind speed in the frequency range from 0.0007 to 900 cycles per hour. J. Meteorol. 14(2), 160–164 (1957)

    Article  Google Scholar 

  13. Welfonder, E., Neifer, R., Spanner, M.: Development and experimental identification of dynamic models for wind turbines. Control Eng. Pract. 5(1), 63–73 (1997)

    Article  Google Scholar 

  14. Freudenreich, K.A.K.: The new standard IEC 61400-1: 2005 and its effect on the load level of wind turbines.//Proceedings of Deutsche Wind Energie Konferenz. (2006)

  15. Carpman, N.: Turbulence intensity in complex environments and its influence on small wind turbines. (2011)

  16. Sørensen, J.D., Frandsen, S., Tarp-Johansen, N.J.: Effective turbulence models and fatigue reliability in wind farms. Probab. Eng. Mech. 23(4), 531–538 (2008)

    Article  Google Scholar 

  17. Chamorro, L.P., Porté-Agel, F.: A wind-tunnel investigation of wind-turbine wakes: boundary-layer turbulence effects. Boundary-layer Meteorol 132(1), 129–149 (2009)

    Article  Google Scholar 

  18. Wu, Y.T., Porté-Agel, F.: Atmospheric turbulence effects on wind-turbine wakes: an LES study. Energies 5(12), 5340–5362 (2012)

    Article  Google Scholar 

  19. Kim, Y., Xie, Z.T.: Modelling the effect of freestream turbulence on dynamic stall of wind turbine blades. Comput. Fluids 129, 53–66 (2016)

    Article  MathSciNet  Google Scholar 

  20. Stevens, R.J.A.M., Meneveau, C.: Flow structure and turbulence in wind farms. Annu. Rev. Fluid Mech. 49(1), 311–339 (2017)

    Article  MathSciNet  Google Scholar 

  21. Wang, J., Heng, J., Xiao, L.: Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting. Energy 125, 591–613 (2017)

    Article  Google Scholar 

  22. Wang, Q., Wu, H., Florita, A.R., et al.: The value of improved wind power forecasting: grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales. Appl. Energy 184, 696–713 (2016)

    Article  Google Scholar 

  23. Bludszuweit, H., Dominguez-Navarro, J.A., Llombart, A.: Statistical analysis of wind power forecast error. IEEE Trans. Power Syst. 23(3), 983–991 (2008)

    Article  Google Scholar 

  24. Hu, Q., Zhang, S., Xie, Z., et al.: Noise model based \(\nu \)-support vector regression with its application to short-term wind speed forecasting. Neural Netw. 57, 1–11 (2014)

    Article  Google Scholar 

  25. Liang, Z., Liang, J., Wang, C., et al.: Short-term wind power combined forecasting based on error forecast correction. Energy Convers. Manag. 119, 215–226 (2016)

    Article  Google Scholar 

  26. Hu, Q., Zhang, S., Yu, M., et al.: Short-term wind speed or power forecasting with heteroscedastic support vector regression. IEEE Trans. Sustain. Energy 7(1), 241–249 (2017)

    Article  Google Scholar 

  27. Herrero-Novoa, C., Pérez, I.A., Sánchez, M.I.: Wind speed description and power density in northern Spain. Energy 138, 967–976 (2017)

    Article  Google Scholar 

  28. Jung, C., Schindler, D., Laible, J., et al.: Introducing a system of wind speed distributions for modeling properties of wind speed regimes around the world. Energy Convers. Manag. 144, 181–192 (2017)

    Article  Google Scholar 

  29. Mix, D.F., Olejniczak, K.J.: Elements of wavelets for engineers and scientists. Wiley, Hoboken (2003)

    Book  Google Scholar 

  30. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. Pattern Anal. Mach. Intell. IEEE Trans. 11(7), 674–693 (1989)

    Article  Google Scholar 

  31. Frandsen, S.T., Thøgersen, M.L.: Integrated fatigue loading for wind turbines in wind farms by combining ambient turbulence and wakes. Wind Eng. 23, 327–339 (1999)

    Google Scholar 

  32. Frandsen, S.T., Madsen, P.H.: Spatially average of turbulence intensity inside large wind turbine arrays[C]//European Seminar Offshore Wind Energy in Mediterranean and Other European Seas : 97–106 (2012)

  33. Frandsen, S,T.: Turbulence and turbulence-generated structural loading in wind turbine clusters. Risø National Laboratory, (2007)

  34. Jimenez, A., Crespo, A., Migoya, E., et al.: Advances in large-eddy simulation of a wind turbine wake. J. Phys. 75(1), 012041 (2007)

    Google Scholar 

  35. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    Google Scholar 

  36. Vapnik, V., Golowich, S.E., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing.//Advances in Neural Information Processing Systems 9. (1996)

  37. Schölkopf, B., Smola, A.J., Williamson, R.C., et al.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000)

    Article  Google Scholar 

  38. Chalimourda, A., Schölkopf, B., Smola, A.J.: Experimentally optimal \(\nu \) in support vector regression for different noise models and parameter settings. Neural Netw. 17(1), 127–141 (2004)

    Article  Google Scholar 

  39. Ren, G., Liu, J.: Measurement and statistical analysis of wind speed intermittency. Energy 118, 632–643 (2016)

    Article  Google Scholar 

  40. Liu, J., Ren, G., Wan, J., et al.: Variogram time-series analysis of wind speed. Renew. Energy 99, 483–491 (2016)

    Article  Google Scholar 

  41. Pope, S.B.: Turbulent flows. Cambridge University Press, Cambridge (2000)

    Book  Google Scholar 

  42. Frisch, U.: Turbulence. Cambridge University Press, Cambridge (1995)

    Book  Google Scholar 

Download references

Acknowledgements

This work was supported by the Key R&D Project of China under Grant 2017YFB0902100 and the National Natural Science Foundation of China under Grant No. 51676054.

Author information

Authors and Affiliations

  1. Postdoctoral Research Station of Electrical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China

    Jie Wan & Jilai Yu

  2. School of Electrical Engineering&Automation, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China

    Jie Wan, Yufeng Guo & Jilai Yu

  3. School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China

    Jinfu Liu, Guorui Ren & Daren Yu

  4. Heilongjiang Electric Power Research Institute, Harbin, Heilongjiang, 150001, China

    Wenbo Hao

Authors
  1. Jie Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinfu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guorui Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yufeng Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenbo Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jilai Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daren Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jinfu Liu or Yufeng Guo.

Ethics declarations

Conflicts of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wan, J., Liu, J., Ren, G. et al. A universal power-law model for wind speed uncertainty. Cluster Comput 22 (Suppl 4), 10347–10359 (2019). https://doi.org/10.1007/s10586-017-1350-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1350-1

Keywords

Search

Navigation