Validation of a methodology for post-construction Energy Yield Assessment of an operational wind farm

Authors

  • M. Costa MEGAJOULE S.A. Rua do Divino Salvador de Moreira, 255; 4470-105 Maia (Portugal) Author
  • T. Rocha INESC TEC, Centre for Power and Energy Systems Rua Dr. Roberto Frias, 4200-465 Porto (Portugal) Author
  • J. Mendonça SIIS, Instituto Politécnico do Porto, R. Dr. Roberto Frias, 4200-465 Porto (Portugal) Author
  • R. Pilão CIETI, ISEP Instituto Politécnico do Porto Rua Dr. António Bernardino de Almeida, 341; 4200-072 Porto (Portugal) Author
  • P. Pinto MEGAJOULE S.A. Rua do Divino Salvador de Moreira, 255; 4470-105 Maia (Portugal) Author

DOI:

https://doi.org/10.52152/4118

Keywords:

Wind energy, post-construction Energy Yield Assessment, linear regression.

Abstract

The uncertainty associated with the prospective Energy Yield Assessment (EYA) of a wind farm may be reduced by re-estimating the energy yield after it enters normal operation. This study aims to validate a simple methodology for conducting post-construction EYA of an operational wind farm. The proposed methodology derives a linear relationship between a historical source of wind speed data and the observed wind farm production on a monthly basis.In the first stage, the impact of different data sources on the accuracy of the Long-Term energy yield estimate was assessed. Results suggest that the determination coefficient R is a reliable indicator for selecting the most adequate source of historical wind speed data to be used in the Long-Term energy yield estimate.In the second stage, the model was validated from a statistical point of view by testing the premises of the linear regression model, namely the significance of the linear correlation (ANOVA test), and the normally-distributed (Shapiro-Wilk test), non-self-correlated (Durbin-Watson), and homoscedastic (Breusch-Pagan test) residuals. Results show these premises are verified for most test cases, indicating that the model is statistically robust for most test cases.

References

J. H. O. U. K. V. Johannes Lindvall, “Post-construction production assessment of wind farms - Assessment and optimization of the energy production of operational wind farms: Part 1,” ENERGIFORSK, 2016.

R. Gelaro, W. McCarty, M. J. Suarez, R. Todling, A. Molod, L. Takacs, C. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, K. Wargan, L. Coy, R. Cullather, C. Draper, S. Akella, V. Buchard, A. Conaty, A. da Silva, W. Gu, G.-K. Kim, R. Koster, R. Lucchesi, D. Merkova, J. E. Nielsen, G. Partyka, S. Pawson, W. Putman, M. Rienecker, S. D. Schubert, M. Sienkiewicz, B. Zhao, "The modern-era retrospective analysis for research and applications, version 2 (MERRA-2)," J. Clim., vol. 30, pp. 5419–5454, 2017.

R. McKenna, S. Pfenninger, H. Heinrichs, J. Schmidt, I. Staffell, C. Bauer, K. Gruber, A. N. Hahmann, M. Jansen, M. Klingler, N. Landwehr, X. Guo Larsen, J. Lilliestam, B. Pickering, M. Robinius, T. Trondle, O. Turkovska, S. Wehrle, J. Michael Weinand, J. Wohland, “High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs,” Renewable Energy, Elsevier, vol. 164, pp. 659-684, 2022.

H-Y Kim, “Analysis of variance (ANOVA) comparing means of more than two groups,” Department of Dental Laboratory Science and Engineering, College of Health Science & Department of Public Health Science, Korea, 2014.

N. M. Razali, B. W. Yap, “Power Comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling Tests,” Journal of Statistical Modeling and Analytics, vol. 2, no. 1, pp. 21-33, 2011.

J. Durbin, G. S. Watson, “Testing for Serial Correlation in Least Squares Regression: I,” Biometrika, vol. 37, no. 3/4, pp. 409-428, 1950.

T. S. Breusch, A. R. Pagan, “A simple test for heteroscedasticity and random coefficient variation,” Econometrica, vol. 47, pp. 1287-1294, 1979.

Downloads

Published

2024-11-13

Issue

Section

Articles