Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures

Gómez-Lázaro, Emilio; Bueso, María; Kessler, Mathieu; Martín-Martínez, Sergio; Zhang, Jie; Hodge, Bri-Mathias; Molina-García, Angel

doi:10.3390/en9020091

Title: Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures

Journal Article · Tue Feb 02 00:00:00 EST 2016 · Energies (Basel)

DOI:https://doi.org/10.3390/en9020091· OSTI ID:1419591

Gómez-Lázaro, Emilio; Bueso, María; Kessler, Mathieu; Martín-Martínez, Sergio; Zhang, Jie; Hodge, Bri-Mathias; Molina-García, Angel

Here, the Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.

View Journal Article

Cite

Export

Save

Research Organization:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC36-08-GO28308; AC36-08GO28308

OSTI ID:: 1419591

Alternate ID(s):: OSTI ID: 1238038

Report Number(s):: NREL/JA-5D00-65841; ENERGA; PII: en9020091

Journal Information:: Energies (Basel), Journal Name: Energies (Basel) Vol. 9 Journal Issue: 12; ISSN 1996-1073

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

Citation Metrics:

Cited by: 14 works

Citation information provided by
Web of Science

References (29)

Some Aspects of Wind Power Statistics Hennessey, Joseph P. Journal of Applied Meteorology, Vol. 16, Issue 2 https://doi.org/10.1175/1520-0450(1977)016<0119:SAOWPS>2.0.CO;2	journal	February 1977
Review of Weibull Statistics for Estimation of Wind Speed Distributions Conradsen, K.; Nielsen, L. B.; Prahm, L. P. Journal of Climate and Applied Meteorology, Vol. 23, Issue 8 https://doi.org/10.1175/1520-0450(1984)023<1173:ROWSFE>2.0.CO;2	journal	August 1984
Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case Jaramillo, O. A.; Borja, M. A. Renewable Energy, Vol. 29, Issue 10 https://doi.org/10.1016/j.renene.2004.02.001	journal	August 2004
Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application Chang, Tian Pau Applied Energy, Vol. 88, Issue 1 https://doi.org/10.1016/j.apenergy.2010.06.018	journal	January 2011
Impacts of large amounts of wind power on design and operation of power systems, results of IEA collaboration Holttinen, Hannele; Meibom, Peter; Orths, Antje Wind Energy, Vol. 14, Issue 2 https://doi.org/10.1002/we.410	journal	March 2011
A study of Weibull parameters using long-term wind observations Lun, Isaac Y. F.; Lam, Joseph C. Renewable Energy, Vol. 20, Issue 2 https://doi.org/10.1016/S0960-1481(99)00103-2	journal	June 2000
Studying the Variability and Uncertainty Impacts of Variable Generation at Multiple Timescales Ela, Erik; O'Malley, Mark IEEE Transactions on Power Systems, Vol. 27, Issue 3 https://doi.org/10.1109/TPWRS.2012.2185816	journal	August 2012
A Production Simulation Tool for Systems With Integrated Wind Energy Resources Maisonneuve, Nicolas; Gross, George IEEE Transactions on Power Systems, Vol. 26, Issue 4 https://doi.org/10.1109/TPWRS.2011.2143437	journal	November 2011
Influence of the data sampling interval in the estimation of the parameters of the Weibull wind speed probability density distribution: a case study Ramírez, Penélope; Carta, José Antonio Energy Conversion and Management, Vol. 46, Issue 15-16 https://doi.org/10.1016/j.enconman.2004.11.004	journal	September 2005
Finite Mixture Estimation Algorithm for Arbitrary Function Approximation Volk, Matej; Nagode, Marko; Fajdiga, Matija Strojniški vestnik – Journal of Mechanical Engineering, Vol. 58, Issue 2 https://doi.org/10.5545/sv-jme.2011.085	journal	February 2012
Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions Carta, J. A.; Ramírez, P. Renewable Energy, Vol. 32, Issue 3 https://doi.org/10.1016/j.renene.2006.05.005	journal	March 2007
Estimation of wind energy potential using finite mixture distribution models Akpinar, Sinan; Akpinar, Ebru Kavak Energy Conversion and Management, Vol. 50, Issue 4 https://doi.org/10.1016/j.enconman.2009.01.007	journal	April 2009
A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting Haque, Ashraf Ul; Nehrir, M. Hashem; Mandal, Paras IEEE Transactions on Power Systems, Vol. 29, Issue 4 https://doi.org/10.1109/TPWRS.2014.2299801	journal	July 2014
The REBMIX Algorithm and the Univariate Finite Mixture Estimation Nagode, Marko; Fajdiga, Matija Communications in Statistics - Theory and Methods, Vol. 40, Issue 5 https://doi.org/10.1080/03610920903480890	journal	February 2011
Mixture probability distribution functions to model wind speed distributions Kollu, Ravindra; Rayapudi, Srinivasa; Narasimham, Svl International Journal of Energy and Environmental Engineering, Vol. 3, Issue 1 https://doi.org/10.1186/2251-6832-3-27	journal	January 2012
Estimation of wind energy potential using different probability density functions Chang, Tian Pau Applied Energy, Vol. 88, Issue 5 https://doi.org/10.1016/j.apenergy.2010.11.010	journal	May 2011
Operational Adequacy Studies of Power Systems With Wind Farms and Energy Storages Wang, Peng; Gao, Zhiyong; Bertling, Lina IEEE Transactions on Power Systems, Vol. 27, Issue 4 https://doi.org/10.1109/TPWRS.2012.2201181	journal	November 2012
Influence of the level of fit of a density probability function to wind-speed data on the WECS mean power output estimation Carta, José A.; Ramírez, Penélope; Velázquez, Sergio Energy Conversion and Management, Vol. 49, Issue 10 https://doi.org/10.1016/j.enconman.2008.04.012	journal	October 2008
Methods for Estimating Wind Speed Frequency Distributions Justus, C. G.; Hargraves, W. R.; Mikhail, Amir Journal of Applied Meteorology, Vol. 17, Issue 3 https://doi.org/10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2	journal	March 1978
The Effect of Large-Scale Wind Power on System Balancing in Northern Europe Aigner, Tobias; Jaehnert, Stefan; Doorman, Gerard L. IEEE Transactions on Sustainable Energy, Vol. 3, Issue 4 https://doi.org/10.1109/TSTE.2012.2203157	journal	October 2012
Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean Akdağ, S. A.; Bagiorgas, H. S.; Mihalakakou, G. Applied Energy, Vol. 87, Issue 8 https://doi.org/10.1016/j.apenergy.2010.02.033	journal	August 2010
A review of wind speed probability distributions used in wind energy analysis Carta, J. A.; Ramírez, P.; Velázquez, S. Renewable and Sustainable Energy Reviews, Vol. 13, Issue 5 https://doi.org/10.1016/j.rser.2008.05.005	journal	June 2009
Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction Chen, Niya; Qian, Zheng; Nabney, Ian T. IEEE Transactions on Power Systems, Vol. 29, Issue 2 https://doi.org/10.1109/TPWRS.2013.2282366	journal	March 2014
Finite Mixture Modeling via REBMIX Nagode, Marko Journal of Algorithms and Optimization https://doi.org/10.5963/JAO0302001	journal	April 2015
Generation System Reliability Evaluation Incorporating Correlations of Wind Speeds With Different Distributions Qin, Zhilong; Li, Wenyuan; Xiong, Xiaofu IEEE Transactions on Power Systems, Vol. 28, Issue 1 https://doi.org/10.1109/TPWRS.2012.2205410	journal	February 2013
A Versatile Probability Distribution Model for Wind Power Forecast Errors and Its Application in Economic Dispatch Zhang, Zhao-Sui; Sun, Yuan-Zhang; Gao, David Wenzhong IEEE Transactions on Power Systems, Vol. 28, Issue 3 https://doi.org/10.1109/TPWRS.2013.2249596	journal	August 2013
Assessment of wind energy resources for electricity generation using WECS in North-Central region, Nigeria Ohunakin, Olayinka S. Renewable and Sustainable Energy Reviews, Vol. 15, Issue 4 https://doi.org/10.1016/j.rser.2011.01.001	journal	May 2011
Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis Seguro, J. V.; Lambert, T. W. Journal of Wind Engineering and Industrial Aerodynamics, Vol. 85, Issue 1 https://doi.org/10.1016/S0167-6105(99)00122-1	journal	March 2000
Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy Gualtieri, Giovanni; Secci, Sauro Renewable Energy, Vol. 62 https://doi.org/10.1016/j.renene.2013.07.003	journal	February 2014

Cited By (1)

Economic Dispatch of the Low-Carbon Green Certificate with Wind Farms Based on Fuzzy Chance Constraints Wang, Xiuyun; Wang, Jian; Tian, Biyuan Energies, Vol. 11, Issue 4 https://doi.org/10.3390/en11040943	journal	April 2018

Similar Records

Method for including wind-generated power within electric utility generation scheduling

Thesis/Dissertation · Sat Jan 01 00:00:00 EST 1983 · OSTI ID:1419591

Davarya, F

On Model Selection Criteria in Multimodel Analysis

Journal Article · Fri Mar 21 00:00:00 EDT 2008 · Water Resources Research, 44(3):Art. no. W03428 · OSTI ID:1419591

Ye, Ming; Meyer, Philip D; Neuman, Shlomo P

{sup 18}F-FLT uptake kinetics in head and neck squamous cell carcinoma: A PET imaging study

Journal Article · Tue Apr 15 00:00:00 EDT 2014 · Medical Physics · OSTI ID:1419591

Liu, Dan; Fenwick, John D.; Chalkidou, Anastasia; +2 more

Related Subjects

17 WIND ENERGY
24 POWER TRANSMISSION AND DISTRIBUTION
wind power generation
Weibull distributions
Weibull mixtures
Akaike information criterion
AIC
Bayesian information criterion
BIC

Title: Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures

Citation Formats

References (29)

Cited By (1)

Similar Records

Related Subjects