Wake Steering Optimization Under Uncertainty

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.