Articles | Volume 3, issue 1
https://doi.org/10.5194/wes-3-371-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-3-371-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jun 2018
Research article |
| 14 Jun 2018
| 14 Jun 2018
Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing
Rochelle P. Worsnop
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, University of Colorado
Boulder, Boulder, Colorado, USA
Michael Scheuerer
Cooperative Institute for Research in the Environmental Sciences,
University of Colorado Boulder, Boulder, Colorado, USA
NOAA/ESRL, Physical Sciences Division, Boulder, Colorado, USA
Thomas M. Hamill
NOAA/ESRL, Physical Sciences Division, Boulder, Colorado, USA
Julie K. Lundquist
Department of Atmospheric and Oceanic Sciences, University of Colorado
Boulder, Boulder, Colorado, USA
National Renewable Energy Laboratory, Golden, Colorado, USA
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Cited
16 citations as recorded by crossref.
- Case study of a bore wind-ramp event from lidar measurements and HRRR simulations over ARM Southern Great Plains Y. Pichugina et al. 10.1063/5.0161905
- Characterisation of intra-hourly wind power ramps at the wind farm scale and associated processes M. Pichault et al. 10.5194/wes-6-131-2021
- Predicting power ramps from joint distributions of future wind speeds T. Muschinski et al. 10.5194/wes-7-2393-2022
- Cholesky-based multivariate Gaussian regression T. Muschinski et al. 10.1016/j.ecosta.2022.03.001
- Generative machine learning methods for multivariate ensemble postprocessing J. Chen et al. 10.1214/23-AOAS1784
- Covariance structures for high-dimensional energy forecasting J. Browell et al. 10.1016/j.epsr.2022.108446
- Extended-Range Probabilistic Fire-Weather Forecasting Based on Ensemble Model Output Statistics and Ensemble Copula Coupling R. Worsnop et al. 10.1175/MWR-D-19-0217.1
- Data-driven method for the improving forecasts of local weather dynamics T. Krivec et al. 10.1016/j.engappai.2021.104423
- Gumbel copula based multi interval ramp product for power system flexibility enhancement S. Sreekumar et al. 10.1016/j.ijepes.2019.05.018
- Generative emulation of weather forecast ensembles with diffusion models L. Li et al. 10.1126/sciadv.adk4489
- Data-driven energy management of isolated power systems under rapidly varying operating conditions S. Chapaloglou et al. 10.1016/j.apenergy.2022.118906
- A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems E. Ahn & J. Hur 10.3390/en15072676
- Hybrid model of the near-ground temperature profile J. Kocijan et al. 10.1007/s00477-019-01736-5
- Simulation and detection of wind power ramps and identification of their causative atmospheric circulation patterns A. Dalton et al. 10.1016/j.epsr.2020.106936
- Wind Ramp Events Validation in NWP Forecast Models during the Second Wind Forecast Improvement Project (WFIP2) Using the Ramp Tool and Metric (RT&M) I. Djalalova et al. 10.1175/WAF-D-20-0072.1
- Evaluating ensemble post‐processing for wind power forecasts K. Phipps et al. 10.1002/we.2736
16 citations as recorded by crossref.
- Case study of a bore wind-ramp event from lidar measurements and HRRR simulations over ARM Southern Great Plains Y. Pichugina et al. 10.1063/5.0161905
- Characterisation of intra-hourly wind power ramps at the wind farm scale and associated processes M. Pichault et al. 10.5194/wes-6-131-2021
- Predicting power ramps from joint distributions of future wind speeds T. Muschinski et al. 10.5194/wes-7-2393-2022
- Cholesky-based multivariate Gaussian regression T. Muschinski et al. 10.1016/j.ecosta.2022.03.001
- Generative machine learning methods for multivariate ensemble postprocessing J. Chen et al. 10.1214/23-AOAS1784
- Covariance structures for high-dimensional energy forecasting J. Browell et al. 10.1016/j.epsr.2022.108446
- Extended-Range Probabilistic Fire-Weather Forecasting Based on Ensemble Model Output Statistics and Ensemble Copula Coupling R. Worsnop et al. 10.1175/MWR-D-19-0217.1
- Data-driven method for the improving forecasts of local weather dynamics T. Krivec et al. 10.1016/j.engappai.2021.104423
- Gumbel copula based multi interval ramp product for power system flexibility enhancement S. Sreekumar et al. 10.1016/j.ijepes.2019.05.018
- Generative emulation of weather forecast ensembles with diffusion models L. Li et al. 10.1126/sciadv.adk4489
- Data-driven energy management of isolated power systems under rapidly varying operating conditions S. Chapaloglou et al. 10.1016/j.apenergy.2022.118906
- A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems E. Ahn & J. Hur 10.3390/en15072676
- Hybrid model of the near-ground temperature profile J. Kocijan et al. 10.1007/s00477-019-01736-5
- Simulation and detection of wind power ramps and identification of their causative atmospheric circulation patterns A. Dalton et al. 10.1016/j.epsr.2020.106936
- Wind Ramp Events Validation in NWP Forecast Models during the Second Wind Forecast Improvement Project (WFIP2) Using the Ramp Tool and Metric (RT&M) I. Djalalova et al. 10.1175/WAF-D-20-0072.1
- Evaluating ensemble post‐processing for wind power forecasts K. Phipps et al. 10.1002/we.2736
Latest update: 18 Apr 2024
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.
This paper uses four statistical methods to generate probabilistic wind speed and power ramp...
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