Recent research has demonstrated exciting potential for wind plant control systems to improve the cost of energy of wind plants. Wind plant controls seek to improve global wind plant performance over control systems in which each turbine optimizes only its individual performance by accounting for the way wind turbines interact through their wakes. Although these technologies can be applied to existing wind plants, it is probable that the maximum benefit would be derived by designing wind plants with these capabilities in mind. In this paper, we use system engineering approaches to perform coupled wind plant controls and position layout optimizations of a model wind plant. Using several cost metrics, we compare the results of this optimization to the original plant and to plants in which the control or layout is optimized separately or sequentially. Results demonstrate that the benefit of this coupled optimization can be substantial, but it depends on the particular constraints of the optimization.

This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant annual energy production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses.

Wind turbines are complex systems where component‐level changes can have significant system‐level effects. Effective wind turbine optimization generally requires an integrated analysis approach with a large number of design variables. Optimizing across large variable sets is orders of magnitude more efficient with gradient‐based methods as compared with gradient‐free method, particularly when using exact gradients. We have developed a wind turbine analysis set of over 100 components where 90% of the models provide numerically exact gradients through symbolic differentiation, automatic differentiation, and adjoint methods. This framework is applied to a specific design study focused on downwind land‐based wind turbines. Downwind machines are of potential interest for large wind turbines where the blades are often constrained by the stiffness required to prevent a tower strike. The mass of these rotor blades may be reduced by utilizing a downwind configuration where the constraints on tower strike are less restrictive. The large turbines of this study range in power rating from 5–7MW and in diameter from 105m to 175m. The changes in blade mass and power production have important effects on the rest of the system, and thus the nacelle and tower systems are also optimized. For high‐speed wind sites, downwind configurations do not appear advantageous. The decrease in blade mass (10%) is offset by increases in tower mass caused by the bending moment from the rotor‐nacelle‐assembly. For low‐wind speed sites, the decrease in blade mass is more significant (25–30%) and shows potential for modest decreases in overall cost of energy (around 1–2%). Copyright © 2016 John Wiley & Sons, Ltd.

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