scholarly journals Optimal control of energy extraction in wind-farm boundary layers

2015 ◽  
Vol 768 ◽  
pp. 5-50 ◽  
Author(s):  
Jay P. Goit ◽  
Johan Meyers
Keyword(s):  
Boundary Layer ◽  
Optimal Control ◽  
Boundary Layers ◽  
Wind Turbines ◽  
Wind Farm ◽  
Large Eddy Simulations ◽  
Energy Extraction ◽  
Reynolds Stresses ◽  
Turbulent Dissipation ◽  
Large Eddy

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 %.

scholarly journals Large-Eddy Simulations of Stratified Atmospheric Boundary Layers: Comparison of Different Subgrid Models

2020 ◽  
Author(s):  
Srinidhi N. Gadde ◽  
Anja Stieren ◽  
Richard J. A. M. Stevens
Keyword(s):  
Boundary Layer ◽  
Boundary Layers ◽  
Large Scale ◽  
Wind Farm ◽  
Research Area ◽  
Large Eddy Simulations ◽  
Turbulence Energy ◽  
Atmospheric Boundary Layers ◽  
Smagorinsky Model ◽  
Large Eddy

Abstract The development and assessment of subgrid-scale (SGS) models for large-eddy simulations of the atmospheric boundary layer is an active research area. In this study, we compare the performance of the classical Smagorinsky model, the Lagrangian-averaged scale-dependent (LASD) model, and the anisotropic minimum dissipation (AMD) model. The LASD model has been widely used in the literature for 15 years, while the AMD model was recently developed. Both the AMD and the LASD models allow three-dimensional variation of SGS coefficients and are therefore suitable to model heterogeneous flows over complex terrain or around a wind farm. We perform a one-to-one comparison of these SGS models for neutral, stable, and unstable atmospheric boundary layers. We find that the LASD and the AMD models capture the logarithmic velocity profile and the turbulence energy spectra better than the Smagorinsky model. In stable and unstable boundary-layer simulations, the AMD and LASD model results agree equally well with results from a high-resolution reference simulation. The performance analysis of the models reveals that the computational overhead of the AMD model and the LASD model compared to the Smagorinsky model is approximately 10% and 30% respectively. The LASD model has a higher computational and memory overhead because of the global filtering operations and Lagrangian tracking procedure, which can result in bottlenecks when the model is used in extensive simulations. These bottlenecks are absent in the AMD model, which makes it an attractive SGS model for large-scale simulations of turbulent boundary layers.

scholarly journals The Aerodynamics of the Curled Wake: A Simplified Model in View of Flow Control

2018 ◽  
Author(s):  
Luis A. Martínez-Tossas ◽  
Jennifer Annoni ◽  
Paul A. Fleming ◽  
Matthew J. Churchfield
Keyword(s):  
Boundary Layer ◽  
Steady State ◽  
Wind Farm ◽  
Stokes Equations ◽  
Large Eddy Simulations ◽  
Simplified Model ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Actuator Disk ◽  
Large Eddy

Abstract. When a wind turbine is yawed, the shape of the wake changes and a curled wake profile is generated. The curled wake has drawn a lot of interest because of its aerodynamic complexity and applicability to wind farm controls. The main mechanism for the creation of the curled wake has been identified in the literature as a collection of vortices that are shed from the rotor plane when the turbine is yawed. This work extends that idea by using aerodynamic concepts to develop a control-oriented model for the curled wake based on approximations to the Navier-Stokes equations. The model is tested and compared to large-eddy simulations using actuator disk and line models. The model is able to capture the curling mechanism for a turbine under uniform inflow and in the case of a neutral atmospheric boundary layer. The model is then tested inside the FLOw Redirection and Induction in Steady State framework and provides excellent agreement with power predictions for cases with two and three turbines in a row.

scholarly journals Evaluation of the the wind farm wake parametrization with large-eddy simulations of wakes in WRF

2021 ◽  
Author(s):  
Alfredo Peña ◽  
Jeffrey Mirocha
Keyword(s):  
Boundary Layer ◽  
Wind Farm ◽  
Wrf Model ◽  
Wind Farms ◽  
Large Eddy Simulations ◽  
Disk Model ◽  
Mesoscale Models ◽  
Boundary Layer Height ◽  
Wind Speeds ◽  
Large Eddy

<p>Mesoscale models, such as the Weather Research and Forecasting (WRF) model, are now commonly used to predict wind resources, and in recent years their outputs are being used as inputs to wake models for the prediction of the production of wind farms. Also, wind farm parametrizations have been implemented in the mesoscale models but their accuracy to reproduce wind speeds and turbulent kinetic energy fields within and around wind farms is yet unknown. This is partly because they have been evaluated against wind farm power measurements directly and, generally, a lack of high-quality observations of the wind field around large wind farms. Here, we evaluate the in-built wind farm parametrization of the WRF model, the so-called Fitch scheme that works together with the MYNN2 planetary boundary layer (PBL) scheme against large-eddy simulations (LES) of wakes using a generalized actuator disk model, which was also implemented within the same WRF version. After setting both types of simulations as similar as possible so that the inflow conditions are nearly identical, preliminary results show that the velocity deficits can differ up to 50% within the same area (determined by the resolution of the mesoscale run) where the turbine is placed. In contrast, within that same area, the turbine-generated TKE is nearly identical in both simulations. We also prepare an analysis of the sensitivity of the results to the inflow wind conditions, horizontal grid resolution of both the LES and the PBL run, number of turbines within the mesoscale grid cells, surface roughness, inversion strength, and boundary-layer height.</p>

scholarly journals Wake properties and power output of very large wind farms for different meteorological conditions and turbine spacings: A large-eddy simulation case study for the German Bight

2021 ◽  
Author(s):  
Oliver Maas ◽  
Siegfried Raasch
Keyword(s):  
Boundary Layer ◽  
Power Density ◽  
Boundary Layers ◽  
Power Output ◽  
German Bight ◽  
Wind Farm ◽  
Wind Farms ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Large Wind

Abstract. Germany’s expansion target for offshore wind power capacity of 40 GW by the year 2040 can only be reached if large portions of the Exclusive Economic Zone in the German Bight are equipped with wind farms. Because these wind farm clusters will be much larger than existing wind farms, it is unknown how they affect the boundary layer flow and how much power they will produce. The objective of this large-eddy-simulation study is to investigate the wake properties and the power output of very large potential wind farms in the German Bight for different turbine spacings, stabilities and boundary layer heights. The results show that very large wind farms cause flow effects that small wind farms do not. These effects include, but are not limited to, inversion layer displacement, counterclockwise flow deflection inside the boundary layer and clockwise flow deflection above the boundary layer. Wakes of very large wind farms are longer for shallower boundary layers and smaller turbine spacings, reaching values of more than 100 km. The wake in terms of turbulence intensity is approximately 20 km long, where longer wakes occur for convective boundary layers and shorter wakes for stable boundary layers. Very large wind farms in a shallow, stable boundary layer can excite gravity waves in the overlying free atmosphere, resulting in significant flow blockage. The power output of very large wind farms is higher for thicker boundary layers, because thick boundary layers contain more kinetic energy than thin boundary layers. The power density of the energy input by the geostrophic pressure gradient limits the power output of very large wind farms. Because this power density is very low (approximately 2 W m−2), the installed power density of very large wind farms should be small to achieve a good wind farm efficiency.

scholarly journals The aerodynamics of the curled wake: a simplified model in view of flow control

2019 ◽  
Vol 4 (1) ◽  
pp. 127-138 ◽  
Author(s):  
Luis A. Martínez-Tossas ◽  
Jennifer Annoni ◽  
Paul A. Fleming ◽  
Matthew J. Churchfield
Keyword(s):  
Boundary Layer ◽  
Wind Farm ◽  
Stokes Equations ◽  
Large Eddy Simulations ◽  
Simplified Model ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Actuator Disk ◽  
Large Eddy ◽  
Good Agreement

Abstract. When a wind turbine is yawed, the shape of the wake changes and a curled wake profile is generated. The curled wake has drawn a lot of interest because of its aerodynamic complexity and applicability to wind farm controls. The main mechanism for the creation of the curled wake has been identified in the literature as a collection of vortices that are shed from the rotor plane when the turbine is yawed. This work extends that idea by using aerodynamic concepts to develop a control-oriented model for the curled wake based on approximations to the Navier–Stokes equations. The model is tested and compared to time-averaged results from large-eddy simulations using actuator disk and line models. The model is able to capture the curling mechanism for a turbine under uniform inflow and in the case of a neutral atmospheric boundary layer. The model is then incorporated to the FLOw Redirection and Induction in Steady State (FLORIS) framework and provides good agreement with power predictions for cases with two and three turbines in a row.

scholarly journals Comparison of Field Measurements and Large Eddy Simulations of the Scaled Wind Farm Technology (SWiFT) Site

2019 ◽  
Author(s):  
Myra L. Blaylock ◽  
Brent C. Houchens ◽  
David C. Maniaci ◽  
Thomas Herges ◽  
Alan Hsieh ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Wind Farm ◽  
Field Measurements ◽  
Operating Conditions ◽  
Large Eddy Simulations ◽  
Department Of Energy ◽  
Large Eddy ◽  
In Series ◽  
The Impact

Abstract Power production of the turbines at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at the Texas Tech University’s National Wind Institute Research Center was measured experimentally and simulated for neutral atmospheric boundary layer operating conditions. Two V27 wind turbines were aligned in series with the dominant wind direction, and the upwind turbine was yawed to investigate the impact of wake steering on the downwind turbine. Two conditions were investigated, including that of the leading turbine operating alone and both turbines operating in series. The field measurements include meteorological evaluation tower (MET) data and light detection and ranging (lidar) data. Computations were performed by coupling large eddy simulations (LES) in the three-dimensional, transient code Nalu-Wind with engineering actuator line models of the turbines from OpenFAST. The simulations consist of a coarse precursor without the turbines to set up an atmospheric boundary layer inflow followed by a simulation with refinement near the turbines. Good agreement between simulations and field data are shown. These results demonstrate that Nalu-Wind holds the promise for the prediction of wind plant power and loads for a range of yaw conditions.

An Investigation of Wind Farm Power Production for Various Atmospheric Boundary Layer Heights

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
A. Al Sam ◽  
R. Szasz ◽  
J. Revstedt
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Wind Shear ◽  
Wind Farm ◽  
Power Production ◽  
Large Eddy Simulations ◽  
Power Coefficient ◽  
Higher Power ◽  
Large Eddy ◽  
Aerodynamic Roughness

The dependency of the atmospheric boundary layer (ABL) characteristics on the ABL’s height is investigated by using large eddy simulations (LES). The impacts of ABL’s height on the wind turbine (WT) power production are also investigated by simulating two subsequent wind turbines using the actuator line method (ALM). The results show that, for the same driving pressure forces and aerodynamic roughness height, the wind velocity is higher at deeper ABL, while the wind shear and the wind veer are not affected by the depth. Moreover, the turbulence intensity, kinetic energy, and kinematic shear stress increase with the ABL’s height. Higher power production and power coefficient are obtained from turbines operating at deeper ABL.

scholarly journals Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 41
Author(s):  
Zexia Zhang ◽  
Christian Santoni ◽  
Thomas Herges ◽  
Fotis Sotiropoulos ◽  
Ali Khosronejad
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Power Production ◽  
Velocity Fields ◽  
Large Eddy Simulations ◽  
Wake Flow ◽  
Surface Model ◽  
Flow Conditions ◽  
Large Eddy ◽  
Turbine Wake

A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are conducted using an actuator surface model to simulate the turbine structures to produce training and validation datasets of the CNN. The simulations are validated using the SpinnerLidar measurements of turbine wakes at the SWiFT site and the instantaneous and time-averaged velocity fields from the training LES are used to train the CNN. The trained CNN is then applied to predict 3D realizations of time-averaged velocity in the wake of the SWiFT turbines under flow conditions different than those for which the CNN was trained. LES results for the validation cases are used to evaluate the performance of the CNN predictions. Comparing the validation LES results and CNN predictions, we show that the developed CNN autoencoder model holds great potential for predicting time-averaged flow fields and the power production of wind turbines while being several orders of magnitude computationally more efficient than LES.

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