A coupledwind‐wave modeling system is used to simulate 23 years of storms and estimate offshore extreme wind statistics. In this system, the atmospheric Weather Research and Forecasting (WRF) model and Spectral Wave model for Near shore (SWAN) are coupled, through a wave boundary layer model (WBLM) that is implemented in SWAN. The WBLM calculates momentum and turbulence kinetic energy budgets, using them to transfer wave‐induced stress to the atmospheric modeling. While such coupling has a trivial impact on the wind modeling for 10‐m wind speeds less than 20 ms−1, the effect becomes appreciable for stronger winds—both compared with uncoupled WRF modeling and with standard parameterization schemes for roughness length. The coupled modeling output is shown to be satisfactory compared with measurements, in terms of the distribution of surface‐drag coefficient with wind speed. The coupling is also shown to be important for estimation of extreme winds offshore, where the WBLM‐coupled results match observations better than results from noncoupled modeling, as supported by measurements from a number of stations.

Lateral boundaries can have a large effect on the introduction of external large‐scale structures in limited area models. This case study of a midlatitude cyclone using the advanced Weather Research and Forecasting (WRF) model examines challenges in simulating the storm intensity (characterised by sea level pressure, relative vorticity and wind speed) when a storm centre enters close to the lateral boundary corner in the outermost model domain. A domain shift, nudging techniques, adjustments of the WRF relaxation layer and the influence of the boundary condition update frequency are investigated as possible solutions. The update frequency of the lateral boundary conditions is found to be the most efficient in improving the storm intensity, while adjustments to the relaxation layer or nudging techniques did not overcome the lack of sufficiently updated lateral boundary conditions. This suggests that the modelling of the storm intensification requires sufficiently high temporal resolution.

Majority of the severe variability in power production of an offshore wind farm occurs when open cellular convection (OCC) is observed. With a diameter of 10-80 km, the open cells are essentially the main drivers of hour-scale wind fluctuations passing through the wind farm. Here we aim to quantify the impact of the OCC on Horns Rev-I offshore wind farm located in the North Sea, in terms of variance in the power production and turbulence intensity. Using mesoscale simulations, met-mast measurements and high frequency (1 Hz) SCADA data from all the operating turbines, the behaviour of power deficit and added turbulence intensity is explored comparatively with and without presence of open cells. The investigation is a case study performed on a ‘day-to-day’ basis with an in depth analysis of the in-farm effects, such as the wake behaviour and smaller scale atmospheric structures. For the investigated event, the study shows striking difference in wind farm operation under the open cell structures and underlines the importance of taking local mesoscale phenomena into account for wind farm operation monitoring and control, short-term wake estimation, forecasting and market participation.

No abstract

The Global Atlas for Siting Parameters project compiles a suite of models into a complex modeling system, uses up-to-date global datasets, and creates global atlases of siting parameters at a spatial resolution of 275 m. These parameters include the 50-year wind, turbulence, and turbine class recommendations based on relevant generic turbines. The suite of models includes the microscale Linear Computational Model (LINCOM), a statistical, spectral correction method here revised for strong convective areas and tropical cyclone affected areas, two turbulence models with four setups, and load models. To this complexity, an uncertainty model was developed to classify the various sources of uncertainties for both the extreme wind and the turbulence calculations, and accordingly, atlases of uncertainty classification were created. Preliminary validation of the global calculations of the 50-year wind and turbulence is done through comparisons with measurements, and the results are promising. This is the first time the siting parameters are obtained with such a high spatial resolution and shared on open data portal. It is expected to benefit the global wind energy planning and development.

Abstract. 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.

<div> <p>Energy scenarios envision installation of up to 230 GW of wind capacity over available areas within the German onshore by 2050. The associated technical wind energy potential is typically derived assuming that the electricity generated by the wind turbines does not affect the wind resource. Consequently, future capacity factors, the ratio of generation to installed capacity, are implicitly assumed to be independent of the extent to which the wind resource is depleted. However, capacity factors reduce as wind capacity is increased. This is because kinetic energy (KE) removal lowers wind speeds that result in lower generation from the turbines. To assess the relevance of this resource depletion effect on capacity factors, we simulated electricity generation by wind turbines with a range of hypothetical and planned deployment scenarios using the Weather Research and Forecasting (WRF) model that incorporates the effects of atmosphere - turbine interactions and compared these to estimates derived from a simple, momentum-balance approach (VKE). Despite potential biases in modelled wind speeds, we find that for a typical planned scenario of ~200 GW deployed over 13.8% of land area, mean annual wind speeds reduce by an average of 0.4 m s<sup>-1</sup> compared to the case where the impact of atmospheric - turbine interactions is excluded. Associated reductions in capacity factor are up to 20% in regions of high installed capacities. To isolate the key atmospheric influence, we compare the simulated range of wind speeds and capacity factors with those from the VKE model that only accounts for KE removal effects. We find that the KE removal effects play the dominant role in shaping the reductions in wind speeds and capacity factors, thus providing a simple tool to capture these effects.  We conclude that with increased deployment of wind energy in the context of the energy transition, these wind resource depletion effects need to be taken into account, but this can be done in a comparatively simple and physical way.</p> </div>

Abstract. 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 and tested for the application of RANS simulations of large wind farms. Second, a RANS-based wind farm parameterization 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 of less than 1 % in terms of the wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 75.1 % 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, 92.3 % and 99.9 % for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. If the wind farm thrust and power coefficient inputs are derived from RANS-AD simulations, then the CPU time reduction is still 82.7 % for the wind farm cluster case. 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, but the models are in agreement with the inflow wind speed of the downstream wind farm. The RANS-AD-AWF model is also validated with measurements in terms of wind farm wake shape; the model captures the trend of the measurements for a wide range of wind directions, although the measurements indicate more pronounced wind farm wake shapes for certain wind directions.