Articles | Volume 3, issue 1
Wind Energ. Sci., 3, 371–393, 2018
https://doi.org/10.5194/wes-3-371-2018
Wind Energ. Sci., 3, 371–393, 2018
https://doi.org/10.5194/wes-3-371-2018
Research article
14 Jun 2018
Research article | 14 Jun 2018

Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

Rochelle P. Worsnop et al.

Related authors

Mountain waves can impact wind power generation
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci., 6, 45–60, https://doi.org/10.5194/wes-6-45-2021,https://doi.org/10.5194/wes-6-45-2021, 2021
Short summary

Related subject area

Design methods, reliability and uncertainty modelling
Effectively using multifidelity optimization for wind turbine design
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022,https://doi.org/10.5194/wes-7-991-2022, 2022
Short summary
Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling
Benjamin Sanderse, Vinit V. Dighe, Koen Boorsma, and Gerard Schepers
Wind Energ. Sci., 7, 759–781, https://doi.org/10.5194/wes-7-759-2022,https://doi.org/10.5194/wes-7-759-2022, 2022
Short summary
Fast yaw optimization for wind plant wake steering using Boolean yaw angles
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022,https://doi.org/10.5194/wes-7-741-2022, 2022
Short summary
A simplified, efficient approach to hybrid wind and solar plant site optimization
Charles Tripp, Darice Guittet, Jennifer King, and Aaron Barker
Wind Energ. Sci., 7, 697–713, https://doi.org/10.5194/wes-7-697-2022,https://doi.org/10.5194/wes-7-697-2022, 2022
Short summary
Influence of wind turbine design parameters on linearized physics-based models in OpenFAST
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022,https://doi.org/10.5194/wes-7-559-2022, 2022
Short summary

Cited articles

A2E: WFIP2 Wind Forecast Improvement Project 2, available from: https://a2e.energy.gov/projects/wfip2, last access: 30 October 2017. 
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R., Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A., Lin, H., Peckham, S. E., Smith, T. L., Moninger, W. R., Kenyon, J. S., and Manikin, G. S.: A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh, Mon. Weather Rev., 144, 1669–1694, https://doi.org/10.1175/MWR-D-15-0242.1, 2015. 
Bianco, L., Djalalova, I. V., Wilczak, J. M., Cline, J., Calvert, S., Konopleva-Akish, E., Finley, C., and Freedman, J.: A Wind Energy Ramp Tool and Metric for Measuring the Skill of Numerical Weather Prediction Models, Weather Forecast., 31, 1137–1156, https://doi.org/10.1175/WAF-D-15-0144.1, 2016. 
Bossavy, A., Girard, R., and Kariniotakis, G.: Forecasting ramps of wind power production with numerical weather prediction ensembles, Wind Energy, 16, 51–63, https://doi.org/10.1002/we.526, 2013. 
Bremnes, J. B.: A comparison of a few statistical models for making quantile wind power forecasts, Wind Energy, 9, 3–11, https://doi.org/10.1002/we.182, 2006. 
Download
Short summary
This paper uses four statistical methods to generate probabilistic wind speed and power ramp forecasts from the High Resolution Rapid Refresh model. The results show that these methods can provide necessary uncertainty information of power ramp forecasts. These probabilistic forecasts can aid in decisions regarding power production and grid integration of wind power.