Proceedings of ESCAPE 35ISSN: 2818-4734
Volume: 4 (2025)
Table of Contents
LAPSE:2025.0162
Published Article
LAPSE:2025.0162
Kernel-based estimation of wind farm power probability density considering wind speed and wake effects due to wind direction
Samuel Martínez-Gutiérrez, Daniel Sarabia, Alejandro Merino
June 27, 2025
Abstract
This study compares the probability density function (PDF) of the power generated by a wind farm obtained analytically with the PDF considering the wake effect between wind turbines, a phenomenon that reduces the power generation capacity of wind farms. Instead of considering the wake effect in the analytical method, which is complex and difficult to solve, it has been proposed to use kernel estimators to obtain the PDF. To calculate it, a wind farm power output data set has been used. This data set was generated using historical wind speed and direction data and the Katic multiple wake model. Discrepancies between the analytical PDF and PDF fitted with the kernel estimators, can lead to an overstatement of the annual available energy by 4 an 9 %, depending on the complexity of the wind farm layout. These inconsistencies can have significant implications for production planning, wind farm design, and integration of wind power into the grid. Therefore, this analysis underscores the necessity of incorporating the wake effect in wind farm modelling, to guarantee more precise projections of generation and energy availability.
Keywords
kernel estimators, Wake effect, wind farm power distribution
Suggested Citation
Martínez-Gutiérrez S, Sarabia D, Merino A. Kernel-based estimation of wind farm power probability density considering wind speed and wake effects due to wind direction. Systems and Control Transactions 4:73-78 (2025) https://doi.org/10.69997/sct.128956
Author Affiliations
Martínez-Gutiérrez S: University of Burgos, Department of Digitalization, Area of Systems Engineering and Automatic Control, Burgos, Burgos, Spain
Sarabia D: University of Burgos, Department of Digitalization, Area of Systems Engineering and Automatic Control, Burgos, Burgos, Spain
Merino A: University of Burgos, Department of Digitalization, Area of Systems Engineering and Automatic Control, Burgos, Burgos, Spain
Journal Name
Systems and Control Transactions
Volume
4
First Page
73
Last Page
78
Year
2025
Publication Date
2025-07-01
Version Comments
Original Submission
Other Meta
PII: 0073-0078-1229-SCT-4-2025, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2025.0162
This Record
External Link

https://doi.org/10.69997/sct.128956
Article DOI
Download
Files
Jun 27, 2025
Main Article
License
CC BY-SA 4.0
Meta
Record Statistics
Record Views
1363
Version History
[v1] (Original Submission)
Jun 27, 2025
 
Verified by curator on
Jun 27, 2025
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2025.0162
 
Record Owner
PSE Press
Links to Related Works
Directly Related to This Work
Article DOI
References Cited
  1. Mathew S. Wind energy: Fundamentals, resource analysis and economics. Springer Berlin Heidelberg (2007) https://doi.org/10.1007/3-540-30906-3
  2. Herrero-Novoa C, Pérez IA, Sánchez ML, García MÁ, Pardo N, Fernández-Duque B. Wind speed description and power density in northern Spain. Energy 138:967-976 (2017) https://doi.org/10.1016/j.energy.2017.07.127
  3. Villanueva D, Feijóo A. Wind power distributions: A review of their applications. Renew Sustain Energy Rev 14:1490-1495 (2010) https://doi.org/10.1016/j.rser.2010.01.005
  4. Louie H. Characterizing and modeling aggregate wind plant power output in large systems. IEEE PES General Meeting, PES 2010. (2010) https://doi.org/10.1109/PES.2010.5589286
  5. Feijoo A, Villanueva D. Wind farm power distribution function considering wake effects. IEEE Trans Power Syst 32:3313-3314 (2017) https://doi.org/10.1109/TPWRS.2016.2614883
  6. Jensen NO. A note on wind generator interaction. Risø National Laboratory, vol. No 2411. (1983) Accessed: Feb. 15, 2023
  7. Katic I, Højstrup J, Jensen NO. A simple model for cluster efficiency. In: EWEC'86. Proceedings. Vol. 1. Ed: Raguzzi A, Palz W, Sesto E. (1987) pp. 407-410
  8. Sobolewski RA, Feijóo AE. Estimation of wind farms aggregated power output distributions. Int J Electr Power Energy Syst 46:241-249 (2013) https://doi.org/10.1016/j.ijepes.2012.10.032
  9. Betz A. Das Maximum der theoretisch möglichen Ausnützung des Windes durch Windmotoren. Z für das gesamte Turbinenwesen 26:307-309 (1920)
  10. Jones MC. Simple boundary correction for kernel density estimation. Stat Comput 3:135-146 (1993) https://doi.org/10.1007/BF00147776
  11. The MathWorks Inc, "Statistical and Machine Learning Toolbox version: 24.1 (R2024a)," 2024
  12. The MathWorks Inc., "MATLAB version: 24.1 (R2024a)," 2024, The MathWorks Inc., Natick, Massachusetts, United States
  13. Hahmann AN, et al. The making of the New European Wind Atlas - Part 1: Model sensitivity. Geosci Model Dev 13:5053-5078 (2020) https://doi.org/10.5194/gmd-13-5053-2020
  14. Dörenkämper M, et al. The Making of the New European Wind Atlas - Part 2: Production and evaluation. Geosci Model Dev 13:5079-5102 (2020) https://doi.org/10.5194/gmd-13-5079-2020
  15. New European Wind Atlas. https://map.neweuropeanwindatlas.eu/
  16. Hellman G. Über die Bewegung der Luft in den untersten Schichten der Atmosphere. Meteorol Z 273:34 (1916)
(0.09 seconds)

[0.09 s]