Performance of offshore wind farms built in the nearshore region will be affected by onshore terrain with higher turbulence in the flow when wind is blowing from land toward sea. Current study employs large-eddy simulation to investigate the effect of coastal terrain on the performance of large nearshore offshore wind farms. At first, two atmospheric boundary layer (ABL) simulation cases are defined to investigate the evolution of an internal boundary layer (IBL) during the sea-to-land and land-to-sea transition of the flow. The growth rate of the IBL was similar for both ABL simulation cases. However, the mean velocity-based definition of IBL heights, which essentially are the equilibrium layer, were half the height of shear stress-based IBLs. The first wind farm simulation case only considers an offshore surface, while the second case includes the region with land-to-sea transition upstream of the wind farm. Better wake recovery is observed in the case that considers the effect of onshore terrain. This is attributed to the higher inflow turbulence level, which resulted in higher entrainment of kinetic energy from the flow above. The farm-induced IBL for a land-to-sea transition case shows rapid growth for the first few turbine rows, while the offshore only case shows gradual growth. However, the difference between the two IBLs decreases with downstream distance, implying that for sufficiently long wind farms, both IBLs will converge. Total power output of the land-to-sea transition case is 17% higher than the offshore only case for the farm layout and roughness heights considered in this study.

In wind energy, the general practice in assessing the wind resource of a site is to employ a 10-min averaging to measured wind data. However, small wind turbines (SWTs) with rotor diameters <15 m will have a shorter response time scale; thus, an averaging time window of 10 min is too long for accurate wind resource assessments. The current study investigates the effect of averaging time windows on wind resource assessment and power estimation of SWTs. To that end, wind data from a 1-year measurement campaign is analysed using two short-term averaging windows of 30 and 60 s and two long-term averaging windows of 5 and 10 min. Although the average wind speed over the 1-year period is 1.3 m/s with all four windows, when wind speeds higher than 3 m/s are considered, the average wind speed obtained using a 30-s averaging window is 8% higher than that obtained using a 10-min window. Power prediction using the 10 kW SWT shows that the predicted power using a 30-s window is almost twice as large compared to that estimated using a 10-min window. Finally, the rotational speed of the SWT obtained with short-term averaging shows better agreement with that computed by direct modelling of the turbine with the measured wind speed as the input to the model. The study thus shows that the performance of SWTs will be better predicted if short-term averaging windows are used.

LiDAR-based wind speed measurements have seen a significant increase in interest in wind energy. However, reconstruction of wind speed vector from a LiDAR-measured radial wind speed is still a challenge. Furthermore, for extensive application of LiDAR technology, it can be used as a means to validate simulation and analytical models. To that end, this study employed scanning Doppler LiDAR for assessment of wind fields at an offshore site and compared Weather Research and Forecasting (WRF)-based mesoscale simulations and several wake models with the measurements. Firstly, the effect of carrier-to-noise-ratio (CNR) and data availability on the quality of scanning LiDAR measurements was evaluated. Analysis of vertical profiles show that the average wind speed is higher for wind blowing from the sea than that blowing from the land. Furthermore, profiles obtained from the WRF simulation also show a similar tendency in the LiDAR measurements in general, though it overestimates the wind speeds at higher altitudes. A method for reconstruction of wind fields from plan-position indicator (PPI) and range height indicator (RHI) scans of LiDAR-measured line of sight velocities was then proposed and first used to investigate the effect of coastal terrain. An internal boundary layer with strong shear could be observed to develop from the coastline. Finally, the flow field around wind turbine was measured using PPI scan and used to validate wake models.

This study evaluates scanning Doppler Lidar-based wind field measurement and analysis techniques for wind energy applications. The Lidar measurements are first validated against measurements from existing V1 Lidar. It is found that the availability of 20% (30 data per 10 minutes) is sufficient to produce fairly good 10 minutes averaged wind speed and direction. The vertical profile measurements for wind blowing from land and that from sea are then performed using DBS configuration. The averaged wind speed for the former is lower than for the later. Two more scan modes, RHI and PPI, are employed to investigate the effect of coastal terrain on the near shore velocity profiles and to characterize the wind turbine wake. An internal boundary layer develops from the shore and persists up to 2000 m offshore. Lidar measurement data are also used to validate numerical simulations by a mesoscale model. The measured and predicted wind speeds agree well up to the height of 500 m. Finally, measurements of flow field around wind turbine shows the velocity deficit in the wind turbine wake and agree with those predicted by a wake model.

A PDE-based optimization framework is presented that allows optimization of turbulent wind-farm boundary layers. It consists of a state-of-the-art large-eddy simulation code that allows the time-resolved simulation of the three-dimensional turbulent flow in the atmospheric boundary layer, together with the adjoint (backward) sensitivity equations to this nonlinear system of PDEs (i.e. the incompressible Navier-Stokes equations). Both the forward and the backward system are efficiently parallelized for supercomputing, and are combined with state-of-the-art gradient-based optimization methods. We use this tool to investigate the use of optimal coordinated control of wind-farm boundary-layer interaction with the aim of increasing the total energy extraction in wind farms. The individual wind turbines are considered as flow actuators and their energy extraction is dynamically regulated in time so as to optimally influence the flow field. Earlier work on wind-farm optimal control in the fully developed regime (Goit & Meyers 2015, J. Fluid Mech. 768, 550) is discussed, and extended towards wind farms in which inflow effects are important.

We investigate the use of optimal coordinated control techniques in large eddy simulations of wind farm boundary layer interaction with the aim of increasing the total energy extraction in wind farms. The individual wind turbines are considered as flow actuators, and their energy extraction is dynamically regulated in time, so as to optimally influence the flow field. We extend earlier work on wind farm optimal control in the fully-developed regime (Goit and Meyers 2015, J. Fluid Mech. 768, 5-50) to a finite' wind farm case, in which entrance effects play an important role. For the optimal control, a receding horizon framework is employed in which turbine thrust coefficients are optimized in time and per turbine. Optimization is performed with a conjugate gradient method, where gradients of the cost functional are obtained using adjoint large eddy simulations. Overall, the energy extraction is increased 7% by the optimal control. This increase in energy extraction is related to faster wake recovery throughout the farm. For the first row of turbines, the optimal control increases turbulence levels and Reynolds stresses in the wake, leading to better wake mixing and an inflow velocity for the second row that is significantly higher than in the uncontrolled case. For downstream rows, the optimal control mainly enhances the sideways mean transport of momentum. This is different from earlier observations by Goit and Meyers (2015) in the fully-developed regime, where mainly vertical transport was enhanced.

In very large wind farms, the vertical interaction with the atmospheric boundary layer plays an important role, i.e. the total energy extraction is governed by the vertical transport of kinetic energy from higher regions in the boundary layer towards the turbine level. In the current study, we investigate optimal control of wind-farm boundary layers, considering the individual wind turbines as flow actuators, whose energy extraction can be dynamically regulated in time so as to optimally influence the flow field and the vertical energy transport. To this end, we use large-eddy simulations of a fully developed pressure-driven wind-farm boundary layer in a receding-horizon optimal control framework. For the optimization of the wind-turbine controls, a conjugate-gradient optimization method is used in combination with adjoint large-eddy simulations for the determination of the gradients of the cost functional. In a first control study, wind-farm energy extraction is optimized in an aligned wind farm. Results are accumulated over one hour of operation. We find that the energy extraction is increased by 16% compared to the uncontrolled reference. This is directly related to an increase of the vertical fluxes of energy towards the wind turbines, and vertical shear stresses increase considerably. A further analysis, decomposing the total stresses into dispersive and Reynolds stresses, shows that the dispersive stresses increase drastically, and that the Reynolds stresses decrease on average, but increase in the wake region, leading to better wake recovery. We further observe also that turbulent dissipation levels in the boundary layer increase, and overall the outer layer of the boundary layer enters into a transient decelerating regime, while the inner layer and the turbine region attain a new statistically steady equilibrium within approximately one wind-farm through-flow time. Two additional optimal control cases study penalization of turbulent dissipation. For the current wind-farm geometry, it is found that the ratio between wind-farm energy extraction and turbulent boundary-layer dissipation remains roughly around 70 %, but can be slightly increased by a few per cent by penalizing the dissipation in the optimization objective. For a pressure-driven boundary layer in equilibrium, we estimate that such a shift can lead to an increase in wind-farm energy extraction of 6 %.

In the present work our focus is to improve the performance of a wind farm by coordinated control of all turbines with the aim to increase the overall energy extraction by the farm. To this end, we couple flow simulations performed using Large Eddy Simulations (LES) with gradient based optimization to control individual turbines in a farm. The control parameters are the disk-based thrust coefficient of individual turbines as a function of time. They indirectly represent the effect of control actions that would correspond to blade-pitching of the turbines. We employ a receding-horizon predictive control setting and solve the optimization problem iteratively at each time horizon based on the gradient information obtained from the evolution of the flow field and the adjoint computation. We find that the extracted farm power increases by approximately 16% for a cost functional that is based on total energy extraction. However, this energy is gained from a slow deceleration of the boundary layer which is sustained for approximately 1 hour. We further analyze the turbulent stresses and compare to wind farms without optimal control.