Optimization Under Uncertainty for Wake Steering Strategies: Preprint

Julian Quick, Ryan King, Paul Fleming, Andrew Ning, Katherine Dykes

Research output: Contribution to conferencePaper


Wind turbines in a wind power plant experience significant power losses because of aerodynamic interactions between turbines. One control strategy to reduce these losses is known as 'wake steering,' in which upstream turbines are yawed to direct wakes away from downstream turbines. Previous wake steering research has assumed perfect information, however, there can be significant uncertainty in many aspects of the problem, including wind inflow and various turbine measurements. Uncertainty has significant implications for performance of wake steering strategies. Consequently, the authors formulate and solve an optimization under uncertainty (OUU) problem for finding optimal wake steering strategies in the presence of yaw angle uncertainty. The OUU wake steering strategy is demonstrated on a two-turbine test case and on the utility-scale, offshore Princess Amalia Wind Farm. When we accounted for yaw angle uncertainty in the Princess Amalia Wind Farm case, inflow-direction-specific OUU solutions produced between 0% and 1.4% more power than the deterministically optimized steering strategies, resulting in an overall annual average improvement of 0.2%. More importantly, the deterministic optimization is expected to perform worse and with more downside risk than the OUU result when realistic uncertainty is taken into account. Additionally, the OUU solution produces fewer extreme yaw situations than the deterministic solution.
Original languageAmerican English
Number of pages12
StatePublished - 2017
EventWake Conference 2017 - Visby, Sweden
Duration: 30 May 20171 Jun 2017


ConferenceWake Conference 2017
CityVisby, Sweden

NREL Publication Number

  • NREL/CP-5000-68185


  • control
  • optimization under uncertainty
  • wake steering
  • wind energy
  • yaw


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