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Wind farm power production is known to be significantly affected by turbine wakes. When mesoscale numerical models are used to predict power production, the turbine wakes cannot be resolved directly because they are sub-grid features and therefore their effects need to be parameterized. Here we propose a new wind farm parameterization that is based on the Jensen model, a well-known analytical wake model that predicts the expansion and wind speed of an ideal wake. The Jensen parameterization is implemented and inserted into two commonly-used atmospheric numerical models: the Weather Research and Forecasting (WRF) Model and the Model for Prediction Across Scales (MPAS). In addition, the internal variability in wind speed and direction within a wind farm, the wind direction uncertainty, and the superposition of multiple wakes are taken into account with an innovative approach. The proposed approach and parameterization are tested against observational data at two offshore wind farms: Lillgrund (small in size and tightly spaced) and Anholt (large and widely spaced). Results indicate that power production is predicted more accurately with the Jensen than with the Fitch wind farm parameterization, which is the only one available in WRF. Power predictions with the Jensen parameterization are similar in WRF and MPAS. The sensitivity to grid resolution is small and the bias is generally low and negative. In conclusion, we recommend that the Jensen wind farm parameterization be used in the WRF and MPAS models, especially for coarse resolution, high turbine density, and wind directions aligned with the turbine columns.

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km−2. CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms.

Mesoscale impact of the sea surface on the performance of offshore wind farms

<strong class="journal-contentHeaderColor">Abstract.</strong> Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier-Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed. This inflow model is well suited for RANS simulations of large wind farms because it does not lead to the development of nonphysical wind farm wakes. Second, a RANS-based wind farm parametrization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation less than 1 % in terms of wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 74.4 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models; namely 89.3 % and 99.9 %, for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model (WRF), but the models are in agreement with the inflow wind speed of the downstream wind farm. The double wind farm case is also simulated with the TurbOPark engineering wake model. Similar wake shapes are reproduced by TurbOPark but the model predicts a larger wind farm wake magnitude compared to RANS and WRF. TurbOPark predicts much better results when its ground model is switched off and a wake expansion coefficient of 0.06 is used. The RANS-AD-AWF model is also validated with SCADA measurements in terms of wind farm shape; the model captures the trend of the measurements for a wide range of wind directions, although the SCADA measurements indicate more pronounced wind farm wake shapes for certain wind directions.

Wind-turbine-wake evolution during the evening transition introduces variability to wind-farm power production at a time of day typically characterized by high electricity demand. During the evening transition, the atmosphere evolves from an unstable to a stable regime, and vertical stratification of the wind profile develops as the residual planetary boundary layer decouples from the surface layer. The evolution of wind-turbine wakes during the evening transition is examined from two perspectives: wake observations from single turbines, and simulations of multiple turbine wakes using the mesoscale Weather Research and Forecasting (WRF) model. Throughout the evening transition, the wake’s wind-speed deficit and turbulence enhancement are confined within the rotor layer when the atmospheric stability changes from unstable to stable. The height variations of maximum upwind-downwind differences of wind speed and turbulence intensity gradually decrease during the evening transition. After verifying the WRF-model-simulated upwind wind speed, wind direction and turbulent kinetic energy profiles with observations, the wind-farm-scale wake evolution during the evening transition is investigated using the WRF-model wind-farm parametrization scheme. As the evening progresses, due to the presence of the wind farm, the modelled hub-height wind-speed deficit monotonically increases, the relative turbulence enhancement at hub height grows by 50%, and the downwind surface sensible heat flux increases, reducing surface cooling. Overall, the intensifying wakes from upwind turbines respond to the evolving atmospheric boundary layer during the evening transition, and undermine the power production of downwind turbines in the evening.

This paper describes the development of a wind turbine (WT) component lifetime fatigue load variation map within an offshore wind farm. A case study on the offshore wind farm Horns Rev I is conducted with this purpose, by quantifying wake effects using the Dynamic Wake Meandering (DWM) method, which has previously been validated based on CFD, Lidar and full scale load measurements. Fully coupled aeroelastic load simulations using turbulent wind conditions are conducted for all wind directions and mean wind speeds between cut-in and cut-out using site specific turbulence level measurements. Based on the mean wind speed and direction distribution, the representative 20-year lifetime fatigue loads are calculated. It is found that the heaviest loaded WT is not the same when looking at blade root, tower top or tower base components. The blade loads are mainly dominated by the wake situations above rated wind speed and the highest loaded blades are in the easternmost row as the dominating wind direction is from West. Regarding the tower components, the highest loaded WTs are also located towards the eastern central location. The turbines with highest power production are, not surprisingly, the ones facing a free sector towards west and south. The power production results of few turbines are compared with SCADA data. The results of this paper are expected to have significance for operation and maintenance planning, where the schedules for inspection and service activities can be adjusted to the requirements arising from the varying fatigue levels. Furthermore, the results can be used in the context of remaining fatigue lifetime assessment and planning of decommissioning.

Currently, to include the effects of wind farms in the Weather Research and Forecasting (WRF) model, a common choice is to use the Fitch wind farm parametrisation (WFP). This WFP has long been implemented into the WRF's standard repository and has been the subject of several wind resource assessment studies. However, one of the disadvantages of its current (WRF version 4.4) set-up is that it is constrained to one Planetary Boundary Layer (PBL) parametrisation. The Fitch scheme is coupled to the Mellor-Yamada Nakanishi Niino (MYNN) PBL parametrisation because it can inject Turbulent Kinetic Energy (TKE) from the turbines into the atmosphere. More importantly, it is the only PBL where the TKE advection can be activated. This feature is essential since it stores the TKE from one time step to the next and prevents the high TKE concentration at the turbine's location.One way for the WFPs to become PBL-independent is to move away from focusing on the TKE source term and parametrise the turbulence in some other way. The Explicit Wake Parametrisation (EWP) is a WFP coupled to WRF that, as opposed to the Fitch scheme, does not include an explicit TKE source term and the turbulence is produced via enhanced vertical shear. The EWP is based on the assumption that the advection and diffusion terms in the RANS Navier-Stokes equations dominate the development of the wake. As a result, the drag equation is also related to the diffusion term from a 1.5 turbulence closure. The EWP then needs the turbine's information, wind speed and the turbulent diffusivity coefficient (Km) from the PBLs to calculate the wind deficit. Given the latter, the EWP can work if Km is present and comes from at least a 1.5-order turbulence closure PBL. However, studies have yet to attempt to prove this feature since it has only been used with the MYNN scheme.In this study, we demonstrate the use of the EWP in WRF when other PBL schemes are used and the implications of this approach. We demonstrate this implementation under ideal neutral conditions with similar setups and forcings (surface roughness length, Coriolis parameter and hub-height wind speed) for two local (1.5-order closure) PBL schemes. Similarly, we test the possibility of coupling EWP into two non-local PBL schemes (first-order closure). The study focuses on the wake recovery behaviour, the drag strength and the power produced by an idealized wind farm under the four PBL schemes. Early results show faster wake recovery from non-local PBls than local ones, which could be related to the diffusivity coefficient values and the PBL's mixing rates.

Abstract. With the rapid growth in offshore wind energy, it is important to understand the dynamics of offshore wind farms. Most of the offshore wind farms are currently installed in coastal regions where they are often affected by sea-breezes. In this work, we quantitatively study the recovery processes for coastal wind farms under sea-breeze conditions. We use a modified Borne's method to identify sea breeze days off the west coast of India in the Arabian Sea. For the identified sea breeze days, we simulate a hypothetical wind farm covering 50×50 km2 area using the Weather Research and Forecasting (WRF) model driven by realistic initial and boundary conditions. We use three wind farm layouts with the turbines spaced 0.5, 1, and 2 km apart. The results show an interesting power generation pattern with a peak at the upwind edge and another peak at the downwind edge due to sea breeze. Wind farms affect the circulation patterns, but the effects of these modifications are very weak compared to the sea breezes. Vertical recovery is the dominant factor with more than half of the momentum extracted by wind turbines being replenished by vertical turbulent mixing. However, horizontal recovery can also play a strong role for sparsely packed wind farms. Horizontal recovery is stronger at the edges where the wind speeds are higher whereas vertical recovery is stronger in the interior of the wind farms. This is one of the first studies to examine replenishment processes in offshore wind farms under sea breeze conditions. It can play an important role in advancing our understanding wind farm-atmospheric boundary layer interactions.

Sensitivity of simulated wind power under diverse spatial scales and multiple terrains using the weather research and forecasting model

High-resolution simulations are conducted with the Weather Research and Forecasting Model to evaluate the sensitivity of wake effects and power production from two wind farm parameterizations [the commonly used Fitch scheme and the more recently developed Explicit Wake Parameterization (EWP)] to the resolution at which the model is applied. The simulations are conducted for a 9-month period for a domain encompassing much of the U.S. Midwest. The two horizontal resolutions considered are 4 km × 4 km and 2 km × 2 km grid cells, and the two vertical discretizations employ either 41 or 57 vertical layers (with the latter having double the number in the lowest 1 km). Higher wind speeds are observed close to the wind turbine hub height when a larger number of vertical layers are employed (12 in the lowest 200 m vs 6), which contributes to higher power production from both wind farm schemes. Differences in gross capacity factors for wind turbine power production from the two wind farm parameterizations and with resolution are most strongly manifest under stable conditions (i.e., at night). The spatial extent of wind farm wakes when defined as the area affected by velocity deficits near to wind turbine hub heights in excess of 2% of the simulation without wind turbines is considerably larger in simulations with the Fitch scheme. This spatial extent is generally reduced by increasing the horizontal resolution and/or increasing the number of vertical levels. These results have important applications to projections of expected annual energy production from new wind turbine arrays constructed in the wind shadow from existing wind farms.

Offshore wind turbines in a wind farm are affected by wakes of upstream turbines and adjacent wind farms depending on the park layout and wind direction. As a result the power output may decrease, while structural loads are increasing. In this research a coupled numerical approach based on multi-body system and free vortex methods is used to simulate shadow effects on the Alpha Ventus wind farm. The AV5 is operating at 12 m/s wind speed at half wake conditions with enabled control system and flexible blades and tower. Results of power output, rotor speed, blade pitch and blade root moment over time and azimuth demonstrate the high impact of the half wake condition on the wind turbine performance and loads.

In this study, large-eddy simulations (LESs) were performed to investigate the effects of changing wind direction on the turbine wakes and associated power losses in the Horns Rev offshore wind farm. In the LES framework, the turbulent subgrid-scale stresses are parameterized using a tuning-free Lagrangian scale-dependent dynamic model, and the turbine-induced forces are computed using a dynamic actuator-disk model with rotation (ADM-R). This dynamic ADM-R couples blade-element theory with a turbine-specific relation between the blade angular velocity and the shaft torque to compute simultaneously turbine angular velocity and power output. A total of 67 simulations were performed for a wide range of wind direction angles. Results from the simulations show a strong impact of wind direction on the spatial distribution of turbine-wake characteristics, such as velocity deficit and turbulence intensity. This can be explained by the fact that changing the wind angle can be viewed as changing the wind farm layout relative to the incoming wind, while keeping the same wind turbine density. Of particular importance is the effect of wind direction on the distance available for the wakes to recover and expand before encountering other downwind turbines (in full-wake or partial-wake interactions), which affects the power losses from those turbines. As a result, even small changes in wind direction angle can have strong impacts on the total wind farm power output. For example, a change in wind direction of just 10° from the worst-case full-wake condition is found to increase the total power output by as much as 43%. This has important implications for the design of wind farms and the management of the temporal variability of their power output.

&amp;lt;p&amp;gt;Wind farm far wakes are of particular interest for offshore installations, as turbulence intensity, which is the main driver for wake dissipation, is much lower over the ocean than over land. Therefore, wakes behind offshore wind turbines and wind parks are expected to be much longer than behind onshore parks.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In situ measurements of the far wakes were missing before the initiation of the research project WIPAFF (WInd PArk Far Fields) in 2015. The main results of which are reported here. WIPAFF has been funded by the German Federal Ministry for Economic Affairs and Energy and ran from November 2015 to April 2019. &amp;amp;#160;The main goal of WIPAFF was to perform a large number of in situ measurements from aircraft operations at hub height behind wind parks in the German Bight (North Sea), to evaluate further SAR images and to update and validate existing meso-scale and industrial models on the basis of the observations to enable a holistic coverage of the downstream wakes.&amp;lt;br&amp;gt;&amp;amp;#160;&amp;lt;br&amp;gt;A &amp;amp;#160;unique &amp;amp;#160;dataset &amp;amp;#160;from &amp;amp;#160;airborne in situ data, &amp;amp;#160;remote sensing &amp;amp;#160;by &amp;amp;#160;laser &amp;amp;#160;scanner &amp;amp;#160;and &amp;amp;#160;SAR &amp;amp;#160;gained &amp;amp;#160;during &amp;amp;#160;the WIPAFF &amp;amp;#160;project &amp;amp;#160;proves &amp;amp;#160;that &amp;amp;#160;wakes &amp;amp;#160;up to &amp;amp;#160;several &amp;amp;#160;tens of kilometers exist downstream of offshore wind farms during stable conditions, while under neutral/unstable conditions, the wake length amounts to 15 km or less. Turbulence occurs at the lateral boundaries of the wakes, due to shear between the reduced wind speed inside the wake and the undisturbed flow. Data also indicates that a denser wind park layout increases the wake length additionally due to a higher initial wind speed deficit. The recovery of the decelerated flow in the wake can be modeled as a first order approximation by an exponential function. The project could also reveal that wind-farm parameterizations in the numerical meso-scale WRF model show a feasible agreement with the observations.&amp;amp;#160;&amp;lt;/p&amp;gt;

Wind is one of the cleanest renewable energy resources. Through the “Thousand Wind Turbines Project”, Taiwan is planning to increase the proportion of power generation from renewable energy and has set a target of 5.7 GW for offshore wind by 2025. The effects of future offshore wind farms (OWFs) over the Taiwan Strait on the atmospheric environment have not been evaluated. This study examined the potential effects of proposed OWFs on the atmospheric environment if the OWFs had existed during Tropical Storm Haitang (2017) by using Weather Research and Forecasting (WRF) model. A small set of ensemble simulations was conducted for studying the sensitivity of the ambient conditions in the region to the wind farm locations, the number and density of the turbines, and the initial time of simulations. Following the landfall and northward movement of Tropical Storm Haitang, a series of complex interactions between the typhoon circulation and the wind farm emerged, including small time slots of wake effect and mountain blocking effect. The combination of these rapidly changing OWFs-related effects contributed to a weak reduction in precipitation (− 1.08 mm) and hub-height wind speed (− 0.25 m s−1), as well as minimal warming near the surface (+ 0.13 °C) over southern Taiwan.

This paper discusses a data-driven, cooperative control strategy to maximize wind farm power production. Conventionally, every wind turbine in a wind farm is operated to maximize its own power production without taking into account the interactions between the wind turbines in a wind farm. Because of wake interference, such greedy control strategy can significantly lower the power production of the downstream wind turbines and, thus, reduce the overall wind farm power production. As an alternative to the greedy control strategy, we study a cooperative wind farm control strategy that determines and executes the optimum coordinated control actions for maximizing the total wind farm power production. To determine the optimum coordinated control actions of the wind turbines, we employ a data-driven optimization method that seeks to find the optimum control actions using only the power measurement data collected from the wind turbines in a wind farm. In particular, we employ the Bayesian Ascent (BA) algorithm, a probabilistic optimization method constructed based on Gaussian Process regression and the trust region concept. Wind tunnel experiments using 6 scaled wind turbine models are conducted to assess (1) the effectiveness of the cooperative control strategy in improving the power production; and (2) the efficiency of the BA algorithm in determining the optimum control actions of the wind turbines using only the input control actions and the output power measurement data.