Simulating wind power forecast error distributions for spatially aggregated wind power plants
Abstract
Dispersion and aggregation of wind power plants lower the uncertainty of wind power by reducing wind power forecasting errors. Using quantitative methods, this paper studies the dispersion's impact on the uncertainty of the aggregated wind power production. A method to simulate day-ahead forecast error distributions at different dispersion and forecasting skill scenarios is presented. The proposed method models the uncertainty of wind power forecasting on an annual basis and at different levels of production. As a result, the uncertainty in the forecasting of spatially dispersed wind power plants is modelled using two continuous distributions: Laplace and beta distribution. The analysis shows that even the production level uncertainty can be modelled in various dispersion and forecasting skill scenarios. The model for aggregated forecast error distributions requires only four variables: capacity-weighted distance of the total wind power plant fleet, mean of the site-specific mean absolute errors (MAEs), number of aggregated wind power plants, and the mean variability of the elevations from the proximities of the aggregated wind power plants. Here, the results are especially promising when the number of aggregated wind power plants exceeds five, the terrain complexity is low or moderate, and the aggregation region is large. This is demonstrated throughmore »
- Authors:
-
[1];
[1];
[2]
- Technical Research Centre of Finland, Espoo (Finland). Design and Operation of Energy Systems VTT
- National Renewable Energy Lab. (NREL), Denver, CO (United States). Power System Design and Studies; Univ. of Colorado, Boulder, CO (United States). Computer and Energy Engineering
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- OSTI Identifier:
- 1571902
- Report Number(s):
- NREL/JA-5D00-75244
Journal ID: ISSN 1095-4244
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Wind Energy
- Additional Journal Information:
- Journal Volume: 23; Journal Issue: 1; Journal ID: ISSN 1095-4244
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 17 WIND ENERGY; wind power forecasting; wind power; aggregated wind power production
Citation Formats
Miettinen, Jari, Holttinen, Hannele, and Hodge, Bri‐Mathias S. Simulating wind power forecast error distributions for spatially aggregated wind power plants. United States: N. p., 2019.
Web. doi:10.1002/we.2410.
Miettinen, Jari, Holttinen, Hannele, & Hodge, Bri‐Mathias S. Simulating wind power forecast error distributions for spatially aggregated wind power plants. United States. https://doi.org/10.1002/we.2410
Miettinen, Jari, Holttinen, Hannele, and Hodge, Bri‐Mathias S. Wed .
"Simulating wind power forecast error distributions for spatially aggregated wind power plants". United States. https://doi.org/10.1002/we.2410. https://www.osti.gov/servlets/purl/1571902.
@article{osti_1571902,
title = {Simulating wind power forecast error distributions for spatially aggregated wind power plants},
author = {Miettinen, Jari and Holttinen, Hannele and Hodge, Bri‐Mathias S.},
abstractNote = {Dispersion and aggregation of wind power plants lower the uncertainty of wind power by reducing wind power forecasting errors. Using quantitative methods, this paper studies the dispersion's impact on the uncertainty of the aggregated wind power production. A method to simulate day-ahead forecast error distributions at different dispersion and forecasting skill scenarios is presented. The proposed method models the uncertainty of wind power forecasting on an annual basis and at different levels of production. As a result, the uncertainty in the forecasting of spatially dispersed wind power plants is modelled using two continuous distributions: Laplace and beta distribution. The analysis shows that even the production level uncertainty can be modelled in various dispersion and forecasting skill scenarios. The model for aggregated forecast error distributions requires only four variables: capacity-weighted distance of the total wind power plant fleet, mean of the site-specific mean absolute errors (MAEs), number of aggregated wind power plants, and the mean variability of the elevations from the proximities of the aggregated wind power plants. Here, the results are especially promising when the number of aggregated wind power plants exceeds five, the terrain complexity is low or moderate, and the aggregation region is large. This is demonstrated through a case study for Texas, United States.},
doi = {10.1002/we.2410},
journal = {Wind Energy},
number = 1,
volume = 23,
place = {United States},
year = {Wed Sep 11 00:00:00 EDT 2019},
month = {Wed Sep 11 00:00:00 EDT 2019}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Cited by: 10 works
Citation information provided by
Web of Science
Web of Science
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
General statistics of geographically dispersed wind power
journal, April 2010
- Hasche, Bernhard
- Wind Energy, Vol. 13, Issue 8
Wind power forecasting error distributions over multiple timescales
conference, July 2011
- Hodge, Bri-Mathias; Milligan, Michael
- 2011 IEEE Power & Energy Society General Meeting, 2011 IEEE Power and Energy Society General Meeting
Variability in large-scale wind power generation: Variability in large-scale wind power generation
journal, October 2015
- Kiviluoma, Juha; Holttinen, Hannele; Weir, David
- Wind Energy, Vol. 19, Issue 9
Statistical Analysis of Wind Power Forecast Error
journal, August 2008
- Bludszuweit, H.; Dominguez-Navarro, J. A.; Llombart, A.
- IEEE Transactions on Power Systems, Vol. 23, Issue 3
Modeling Conditional Forecast Error for Wind Power in Generation Scheduling
journal, May 2014
- Zhang, Ning; Kang, Chongqing; Xia, Qing
- IEEE Transactions on Power Systems, Vol. 29, Issue 3
Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry
journal, September 2017
- Bessa, Ricardo; Möhrlen, Corinna; Fundel, Vanessa
- Energies, Vol. 10, Issue 9
Characterizing and Modeling Wind Power Forecast Errors from Operational Systems for Use in Wind Integration Planning Studies
journal, October 2012
- Hodge, Bri-Mathias S.; Ela, Erik G.; Milligan, Michael
- Wind Engineering, Vol. 36, Issue 5
A Statistical Description of the Error on Wind Power Forecasts for Probabilistic Reserve Sizing
journal, July 2014
- Bruninx, Kenneth; Delarue, Erik
- IEEE Transactions on Sustainable Energy, Vol. 5, Issue 3
Characteristics of day-ahead wind power forecast errors in Nordic countries and benefits of aggregation: Characteristics of day ahead forecast errors in Nordic countries
journal, November 2016
- Miettinen, Jari Johannes; Holttinen, Hannele
- Wind Energy, Vol. 20, Issue 6
Very-short-term probabilistic forecasting of wind power with generalized logit-normal distributions
journal, February 2012
- Pinson, P.
- Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 61, Issue 4
The Wind Integration National Dataset (WIND) Toolkit
journal, August 2015
- Draxl, Caroline; Clifton, Andrew; Hodge, Bri-Mathias
- Applied Energy, Vol. 151, p. 355-366
Development of a Next-Generation Regional Weather Research and Forecast Model
conference, November 2011
- Michalakes, J.; Chen, S.; Dudhia, J.
- Proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology, Developments in Teracomputing
A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets
journal, November 2011
- Tewari, Saurabh; Geyer, Charles J.; Mohan, Ned
- IEEE Transactions on Power Systems, Vol. 26, Issue 4
The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales
journal, December 2016
- Wang, Qin; Wu, Hongyu; Florita, Anthony R.
- Applied Energy, Vol. 184
Optimization of the Regional Spatial Distribution of Wind Power Plants to Minimize the Variability of Wind Energy Input into Power Supply Systems
journal, December 2008
- Cassola, Federico; Burlando, Massimiliano; Antonelli, Marta
- Journal of Applied Meteorology and Climatology, Vol. 47, Issue 12
Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power
journal, August 2007
- Pinson, Pierre; Chevallier, Christophe; Kariniotakis, George N.
- IEEE Transactions on Power Systems, Vol. 22, Issue 3
Statistical distribution for wind power forecast error and its application to determine optimal size of energy storage system
journal, February 2014
- Wu, Junli; Zhang, Buhan; Li, Hang
- International Journal of Electrical Power & Energy Systems, Vol. 55
Setting the Operating Reserve Using Probabilistic Wind Power Forecasts
journal, May 2011
- Matos, Manuel A.; Bessa, R. J.
- IEEE Transactions on Power Systems, Vol. 26, Issue 2
The challenge of integrating offshore wind power in the U.S. electric grid. Part I: Wind forecast error
journal, April 2017
- Archer, C. L.; Simão, H. P.; Kempton, W.
- Renewable Energy, Vol. 103
The variability of interconnected wind plants
journal, August 2010
- Katzenstein, Warren; Fertig, Emily; Apt, Jay
- Energy Policy, Vol. 38, Issue 8
Validation of Power Output for the WIND Toolkit
report, September 2014
- King, J.; Clifton, A.; Hodge, B.
Current methods and advances in forecasting of wind power generation
journal, January 2012
- Foley, Aoife M.; Leahy, Paul G.; Marvuglia, Antonino
- Renewable Energy, Vol. 37, Issue 1
Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions
journal, October 2017
- Andrade, Jose R.; Bessa, Ricardo J.
- IEEE Transactions on Sustainable Energy, Vol. 8, Issue 4