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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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https://doi.org/10.5194/wes-2019-72
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-72
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 14 Oct 2019

Submitted as: research article | 14 Oct 2019

Review status
A revised version of this preprint was accepted for the journal WES.

Wake steering optimization under uncertainty

Julian Quick1,2, Jennifer King2, Ryan N. King2, Peter E. Hamlington1, and Katherine Dykes2 Julian Quick et al.
  • 1University of Colorado, Boulder, CO, USA
  • 2National Renewable Energy Laboratory, Golden, CO, USA

Abstract. Turbines in wind power plants experience significant power losses when wakes from upstream turbines affect the energy production of downstream turbines. A promising plant-level control strategy to reduce these losses is wake steering, where upstream turbines are yawed to direct wakes away from downstream turbines. However, there are significant uncertain- ties in many aspects of the wake steering problem. For example, in-field sensors do not give perfect information and inflow to the plant is complex and difficult to forecast with available information, even over short time periods. Here, we formulate and solve an optimization under uncertainty (OUU) problem for determining optimal plant-level wake steering strategies in the presence of uncorrelated uncertainties in the direction, speed, turbulence intensity, and shear of the incoming wind, as well as in turbine yaw positions. The OUU wake steering strategy is first examined for a two-turbine test case to explore the impacts of different types of inflow uncertainties, and is then demonstrated for a more realistic 11-turbine wind power plant. Of the sources of uncertainty considered, we find that wake steering strategies are most sensitive to uncertainties in the wind speed and direction. The OUU strategy also tends to favor smaller yaw angles when maximizing expected power production. Ultimately, the plant-level wake steering strategy formulated using the OUU approach yields 0.48 % more expected annual energy production than the deterministic strategy when considering stochastic inputs. Thus, not only does the present OUU strategy produce more power in realistic conditions, it also reduces risk by prescribing strategies that call for less extreme yaw angles.

Julian Quick et al.

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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

Julian Quick et al.

Julian Quick et al.

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Latest update: 16 Feb 2020
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Short summary
We investigate the tradeoffs 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.
We investigate the tradeoffs in optimization of wake steering strategies, where upstream...
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