Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing
Abstract
Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind-energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to 12h of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80m wind speed observations from towers in Boulder, Colorado, and near the Columbia River gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of rampmore »
- Authors:
-
- Univ. of Colorado, Boulder, CO (United States). Dept. of Atmospheric and Oceanic Sciences
- Univ. of Colorado, Boulder, CO (United States). Cooperative Inst. for Research in the Environmental Sciences; NOAA Earth System Research Lab. (ESRL), Boulder, CO (United States). Physical Sciences Division
- NOAA Earth System Research Lab. (ESRL), Boulder, CO (United States). Physical Sciences Division
- Univ. of Colorado, Boulder, CO (United States). Dept. of Atmospheric and Oceanic Sciences; National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE; National Oceanic and Atmospheric Administration (NOAA) (United States)
- OSTI Identifier:
- 1475521
- Report Number(s):
- NREL/JA-5000-72520
Journal ID: ISSN 2366-7451
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Wind Energy Science (Online)
- Additional Journal Information:
- Journal Name: Wind Energy Science (Online); Journal Volume: 3; Journal Issue: 1; Journal ID: ISSN 2366-7451
- Publisher:
- European Wind Energy Association - Copernicus
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 17 WIND ENERGY; wind power forecasting; ramp events
Citation Formats
Worsnop, Rochelle P., Scheuerer, Michael, Hamill, Thomas M., and Lundquist, Julie K. Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing. United States: N. p., 2018.
Web. doi:10.5194/wes-3-371-2018.
Worsnop, Rochelle P., Scheuerer, Michael, Hamill, Thomas M., & Lundquist, Julie K. Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing. United States. https://doi.org/10.5194/wes-3-371-2018
Worsnop, Rochelle P., Scheuerer, Michael, Hamill, Thomas M., and Lundquist, Julie K. Thu .
"Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing". United States. https://doi.org/10.5194/wes-3-371-2018. https://www.osti.gov/servlets/purl/1475521.
@article{osti_1475521,
title = {Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing},
author = {Worsnop, Rochelle P. and Scheuerer, Michael and Hamill, Thomas M. and Lundquist, Julie K.},
abstractNote = {Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind-energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to 12h of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80m wind speed observations from towers in Boulder, Colorado, and near the Columbia River gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method with regard to predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake shuffle method yields the highest skill at predicting ramp events for these datasets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO, site using any of the multivariate methods because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.},
doi = {10.5194/wes-3-371-2018},
journal = {Wind Energy Science (Online)},
number = 1,
volume = 3,
place = {United States},
year = {Thu Jun 14 00:00:00 EDT 2018},
month = {Thu Jun 14 00:00:00 EDT 2018}
}
Works referenced in this record:
Probabilistic wind power forecasts with an inverse power curve transformation and censored regression: The inverse power curve transformation and censored regression
journal, September 2013
- Messner, Jakob W.; Zeileis, Achim; Broecker, Jochen
- Wind Energy, Vol. 17, Issue 11
The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs
journal, October 2015
- Wilczak, James; Finley, Cathy; Freedman, Jeff
- Bulletin of the American Meteorological Society, Vol. 96, Issue 10
Probabilistic forecasts, calibration and sharpness
journal, April 2007
- Gneiting, Tilmann; Balabdaoui, Fadoua; Raftery, Adrian E.
- Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 69, Issue 2
Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression
journal, April 2010
- Thorarinsdottir, Thordis L.; Gneiting, Tilmann
- Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 173, Issue 2
Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time
journal, March 2013
- Budischak, Cory; Sewell, DeAnna; Thomson, Heather
- Journal of Power Sources, Vol. 225
Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach
journal, January 1984
- Dawid, A. P.
- Journal of the Royal Statistical Society. Series A (General), Vol. 147, Issue 2
Spatial ensemble post-processing with standardized anomalies: Spatial Ensemble Post-Processing
journal, January 2017
- Dabernig, Markus; Mayr, Georg J.; Messner, Jakob W.
- Quarterly Journal of the Royal Meteorological Society, Vol. 143, Issue 703
Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts
journal, January 2006
- Nielsen, Henrik Aalborg; Madsen, Henrik; Nielsen, Torben Skov
- Wind Energy, Vol. 9, Issue 1-2
Evaluating the quality of scenarios of short-term wind power generation
journal, August 2012
- Pinson, P.; Girard, R.
- Applied Energy, Vol. 96
Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space–Time Method
journal, September 2006
- Gneiting, Tilmann; Larson, Kristin; Westrick, Kenneth
- Journal of the American Statistical Association, Vol. 101, Issue 475
A comparison of a few statistical models for making quantile wind power forecasts
journal, January 2006
- Bremnes, John Bjørnar
- Wind Energy, Vol. 9, Issue 1-2
Strictly Proper Scoring Rules, Prediction, and Estimation
journal, March 2007
- Gneiting, Tilmann; Raftery, Adrian E.
- Journal of the American Statistical Association, Vol. 102, Issue 477
A Wind Energy Ramp Tool and Metric for Measuring the Skill of Numerical Weather Prediction Models
journal, August 2016
- Bianco, Laura; Djalalova, Irina V.; Wilczak, James M.
- Weather and Forecasting, Vol. 31, Issue 4
From probabilistic forecasts to statistical scenarios of short-term wind power production
journal, January 2009
- Pinson, Pierre; Madsen, Henrik; Nielsen, Henrik Aa.
- Wind Energy, Vol. 12, Issue 1
A Similarity-Based Implementation of the Schaake Shuffle
journal, May 2016
- Schefzik, Roman
- Monthly Weather Review, Vol. 144, Issue 5
Forecasting ramps of wind power production with numerical weather prediction ensembles: Forecasting ramps of wind power with ensembles
journal, February 2012
- Bossavy, Arthur; Girard, Robin; Kariniotakis, George
- Wind Energy, Vol. 16, Issue 1
Variability of interconnected wind plants: correlation length and its dependence on variability time scale
journal, April 2015
- St. Martin, Clara M.; Lundquist, Julie K.; Handschy, Mark A.
- Environmental Research Letters, Vol. 10, Issue 4
Using Conditional Kernel Density Estimation for Wind Power Density Forecasting
journal, March 2012
- Jeon, Jooyoung; Taylor, James W.
- Journal of the American Statistical Association, Vol. 107, Issue 497
A method for preferential selection of dates in the Schaake shuffle approach to constructing spatiotemporal forecast fields of temperature and precipitation: SELECTING DATES IN THE SCHAAKE SHUFFLE
journal, April 2017
- Scheuerer, Michael; Hamill, Thomas M.; Whitin, Brett
- Water Resources Research, Vol. 53, Issue 4
Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation
journal, May 2005
- Gneiting, Tilmann; Raftery, Adrian E.; Westveld, Anton H.
- Monthly Weather Review, Vol. 133, Issue 5
Turbine Inflow Characterization at the National Wind Technology Center
journal, May 2013
- Clifton, Andrew; Schreck, Scott; Scott, George
- Journal of Solar Energy Engineering, Vol. 135, Issue 3
Wind turbine power production and annual energy production depend on atmospheric stability and turbulence
journal, January 2016
- St. Martin, Clara M.; Lundquist, Julie K.; Clifton, Andrew
- Wind Energy Science, Vol. 1, Issue 2
Analog-Based Ensemble Model Output Statistics
journal, July 2015
- Junk, Constantin; Delle Monache, Luca; Alessandrini, Stefano
- Monthly Weather Review, Vol. 143, Issue 7
The Schaake Shuffle: A Method for Reconstructing Space–Time Variability in Forecasted Precipitation and Temperature Fields
journal, February 2004
- Clark, Martyn; Gangopadhyay, Subhrendu; Hay, Lauren
- Journal of Hydrometeorology, Vol. 5, Issue 1
A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh
journal, April 2016
- Benjamin, Stanley G.; Weygandt, Stephen S.; Brown, John M.
- Monthly Weather Review, Vol. 144, Issue 4
Long-term research challenges in wind energy – a research agenda by the European Academy of Wind Energy
journal, January 2016
- van Kuik, G. A. M.; Peinke, J.; Nijssen, R.
- Wind Energy Science, Vol. 1, Issue 1
From wind ensembles to probabilistic information about future wind power production ¿ results from an actual application
conference, June 2006
- Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik
- 2006 International Conference on Probabilistic Methods Applied to Power Systems
Understanding wind ramp events through analysis of historical data
conference, April 2010
- Kamath, Chandrika
- IEEE PES T&D 2010
Associating weather conditions with ramp events in wind power generation
conference, March 2011
- Kamath, Chandrika
- 2011 IEEE/PES Power Systems Conference and Exposition (PSCE)
Using Conditional Kernel Density Estimation for Wind Power Density Forecasting
journal, March 2012
- Jeon, Jooyoung; Taylor, James W.
- Journal of the American Statistical Association, Vol. 107, Issue 497
Bayesian Model Averaging for Wind Speed Ensemble Forecasts Using Wind Speed and Direction
journal, December 2017
- Eide, Siri Sofie; Bremnes, John Bjørnar; Steinsland, Ingelin
- Weather and Forecasting, Vol. 32, Issue 6
Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space–Time Method
journal, September 2006
- Gneiting, Tilmann; Larson, Kristin; Westrick, Kenneth
- Journal of the American Statistical Association, Vol. 101, Issue 475
Probabilistic wind speed forecasting on a grid based on ensemble model output statistics
journal, September 2015
- Scheuerer, Michael; Möller, David
- The Annals of Applied Statistics, Vol. 9, Issue 3
Long-term research challenges in wind energy – a research agenda by the European Academy of Wind Energy
journal, January 2016
- van Kuik, G. A. M.; Peinke, J.; Nijssen, R.
- Wind Energy Science, Vol. 1, Issue 1
Works referencing / citing this record:
Hybrid model of the near-ground temperature profile
journal, October 2019
- Kocijan, Juš; Perne, Matija; Mlakar, Primož
- Stochastic Environmental Research and Risk Assessment, Vol. 33, Issue 11-12
Evaluating Ensemble Post-Processing for Wind Power Forecasts
text, January 2020
- Phipps, Kaleb; Lerch, Sebastian; Andersson, Maria
- arXiv