Articles | Volume 5, issue 1
Wind Energ. Sci., 5, 413–426, 2020
Wind Energ. Sci., 5, 413–426, 2020
Research article
30 Mar 2020
Research article | 30 Mar 2020

Wake steering optimization under uncertainty

Julian Quick et al.

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Cited articles

Adams, B. M., Ebeida, M. S., Eldred, M. S., Geraci, G., Jakeman, J. D., Maupin, K. A., Monschke, J. A., Stephens, J. A., Swiler, L. P., Vigil, D. M., Wildey, T. M., Bohnhoff, W. J., Dalbey, K. R., Eddy, J. P., Frye, J. R., Hooper, R. W., Hu, K. T., Hough, P. D., Khalil, M., Ridgway, E. M., Winokur, J. G., and Rushdi, A.: Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.8 Theory Manual, 2014. a, b, c, d
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Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of wind turbine wakes in yawed conditions, J. Fluid Mech., 806, 506–541,, 2016. a
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Bossanyi, E. and Jorge, T.: Optimisation of wind plant sector management for energy and loads, in: Control Conference (ECC), 2016 European, Aalborg, Denmark, IEEE, 922–927,, 29 June–1 July 2016. a
Short summary
We investigate the trade-offs in optimization of wake steering strategies, where upstream turbines are positioned to deflect wakes away from downstream turbines, with a probabilistic perspective. We identify inputs that are sensitive to uncertainty and demonstrate a realistic optimization under uncertainty for a wind power plant control strategy. Designing explicitly around uncertainty yielded control strategies that were generally less aggressive and more robust to the uncertain input.