Wake steering optimization under uncertainty

Abstract

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.

Wind farm yaw control set-point optimization under model parameter uncertainty

“…2 is discretized to solve using the analytic wake model 9,13 . While other expectation calculation methods could be used, such as polynomial chaos expansion 15 , direct quadrature is used in this study for simplicity and due to the computational efficiency of the gradient-based set-point optimization 5 . The discretization, along with the prescribed wind condition uncertainty 13 , become additional hyperparameters.…”

“…The influence of wind direction uncertainty on optimal set-point values has been characterized in previous studies 13,15 . Given uncertainty or variability in the wind direction, small changes in α as a function of time would result in suboptimal γ * s for instantaneous wind condition realizations.…”

“…Simley et al (2020) 14 extended the robust set-point optimization to include natural yaw misalignment variability due to slowly evolving yaw control systems. Finally, Quick et al (2020) 15 used polynomial chaos expansion to solve the yaw set-point optimization under uncertainty problem with the addition of stochastic turbulence intensity and shear and found that the uncertain wind direction had the largest impact on the set-point optimization results.…”