Wind Farm Parameterization Research Articles

Brief communication: A simple axial induction modification to the Weather Research and Forecasting Fitch wind farm parameterization

Abstract. We propose a modification to the Fitch wind farm parameterization implemented in the Weather Research and Forecasting (WRF) model. This modification, derived from 1D momentum theory, employs a wind-speed-dependent induction factor to correct the local grid wind speed back to freestream before computing the turbine's power and thrust. While the original implementation underestimates power, the modified version shows good agreement with the power curve. We strongly recommend employing the modification for all studies that model at maximum one turbine per WRF grid cell. For simulations with more turbines per grid cell, additional inner-cell wake losses have to be considered.

A numerical simulation framework for wakes downstream of large wind farms based on equivalent roughness model

Accurate assessment of the wake effect of wind farm clusters is of great significance for the development of wind power bases, yet there is currently a deficiency in numerical simulation methods with high accuracy and simple modeling. To address this issue, a numerical simulation framework for large wind-farm wakes based on a wind farm parameterization model is proposed, and is then implemented based on the open-source CFD software OpenFOAM. In this framework, a newly proposed wind farm equivalent roughness model is adopted to parameterize wind farms and incorporated into the simulation via the wall stress model. To validate the method, the large-eddy simulation based on the actuator disk model resolving all the turbines in the wind farm is taken as a benchmark. Thirty two cases including spanwise infinite wind farms and finite wind farms with different streamwise turbine spacings, spanwise turbine spacings, wind turbine thrust coefficients, and roughness lengths of ground surface are set to analyze the sensitivity of the framework to different wind farm parameters. Results show that the proposed method predicts the velocity deficit and turbulence intensity downstream of the wind farm accurately. Compared to the classical wake superposition model, the prediction accuracy of the velocity deficit is improved by more than 30 %. Furthermore, without resolving the turbines using fine mesh, the framework shows a high potential to reduce computational costs compared with the turbine-resolved simulation.

Can mesoscale models capture the effect from cluster wakes offshore?

Long wakes from offshore wind turbine clusters can extend tens of kilometers downstream, affecting the wind resource of a large area. Given the ability of mesoscale numerical weather prediction models to capture important atmospheric phenomena and mechanisms relevant to wake evolution, they are often used to simulate wakes behind large wind turbine clusters and their impact over a wider region. Yet, uncertainty persists regarding the accuracy of representing cluster wakes via mesoscale models and their wind turbine parameterizations. Here, we evaluate the accuracy of the Fitch wind farm parameterization in the Weather Research and Forecasting model in capturing cluster-wake effects using two different options to represent turbulent mixing in the planetary boundary layer. To this end, we compare operational data from an offshore wind farm in the North Sea that is fully or partially waked by an upstream array against high-resolution mesoscale simulations. In general, we find that mesoscale models accurately represent the effect of cluster wakes on front-row turbines of a downstream wind farm. However, the same models may not accurately capture cluster-wake effects on an entire downstream wind farm, due to misrepresenting internal-wake effects.

Evaluation of wind farm parameterizations in the WRF model under different atmospheric stability conditions with high-resolution wake simulations

Abstract. Wind farm parameterizations (WFPs) are used in mesoscale models for predicting wind farm power production and its impact on wind resources while considering the variability of the regional wind climate. However, the performance of WFPs is influenced by various factors including atmospheric stability. In this study, we compared two widely used WFPs in the Weather Research and Forecasting (WRF) model to large-eddy simulations (LES) of turbine wakes performed with the same model. The Fitch WFP and the explicit wake parameterization were evaluated for their ability to represent wind speed and turbulent kinetic energy (TKE) in a two-turbine wind farm layout under neutral, unstable, and stable atmospheric stability conditions. To ensure a fair comparison, the inflow conditions were kept as close as possible between the LES and mesoscale simulations for each type of stability condition, and the LES results were spatially aggregated to align with the mesoscale grid spacing. Our findings indicate that the performance of WFPs varies depending on the specific variable (wind speed or TKE) and the area of interest downwind of the turbine when compared to the LES reference. The WFPs can accurately depict the vertical profiles of the wind speed deficit for either the grid cell containing the wind turbines or the grid cells in the far wake, but not both simultaneously. The WFPs with an explicit source of TKE overestimate TKE values at the first grid cell containing the wind turbine; however, for downwind grid cells, agreement improves. On the other hand, WFPs without a TKE source underestimate TKE in all downwind grid cells. These agreement patterns between the WFPs and the LES reference are consistent under the three atmospheric stability conditions. However, the WFPs resemble less the wind speed and TKE from the LES reference under stable conditions than that under neutral or unstable conditions.

Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation

Abstract. With increasing number and proximity of wind farms, it becomes crucial to consider wind farm effects (WFEs) in the numerical weather prediction (NWP) models used to forecast power production. Furthermore, these WFEs are also expected to affect other weather-related parameters at least locally. Thus, we implement the explicit wake parameterization (EWP) in the NWP model HARMONIE–AROME (hereafter HARMONIE) along-side the existing wind farm parameterization (WFP) by Fitch et al. (2012) (FITCH). We evaluate and compare the two WFPs against research flight measurements as well as against similar simulations performed with the Weather Research and Forecasting (WRF) model using case studies. The case studies include a case for WFEs above a wind farm as well as two cases for WFEs at hub height in the wake of farms. The results show that EWP and FITCH have been correctly implemented in HARMONIE. For the simulated cases, EWP underestimates the WFEs on wind speed and strongly underestimates the effect on turbulent kinetic energy (TKE). FITCH agrees better with the observations, and WFEs on TKE are particularly well captured by HARMONIE–FITCH. After this successful evaluation, simulations with all wind turbines in Europe will be performed with HARMONIE and presented in the second part of this paper series.

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

Assessment of Five Wind-Farm Parameterizations in the Weather Research and Forecasting Model: A Case Study of Wind Farms in the North Sea

Abstract To simulate the large-scale impacts of wind farms, wind turbines are parameterized within mesoscale models in which grid sizes are typically much larger than turbine scales. Five wind-farm parameterizations were implemented in the Weather Research and Forecasting (WRF) Model v4.3.3 to simulate multiple operational wind farms in the North Sea, which were verified against a satellite image, airborne measurements, and the FINO-1 meteorological mast data on 14 October 2017. The parameterization by Volker et al. underestimated the turbulence and wind speed deficit compared to measurements and to the parameterization of Fitch et al., which is the default in WRF. The Abkar and Porté-Agel parameterization gave close predictions of wind speed to that of Fitch et al. with a lower magnitude of predicted turbulence, although the parameterization was sensitive to a tunable constant. The parameterization by Pan and Archer resulted in turbine-induced thrust and turbulence that were slightly less than that of Fitch et al., but resulted in a substantial drop in power generation due to the magnification of wind speed differences in the power calculation. The parameterization by Redfern et al. was not substantially different from Fitch et al. in the absence of conditions such as strong wind veer. The simulations indicated the need for a turbine-induced turbulence source within a wind-farm parameterization for improved prediction of near-surface wind speed, near-surface temperature, and turbulence. The induced turbulence was responsible for enhancing turbulent momentum flux near the surface, causing a local speed-up of near-surface wind speed inside a wind farm. Our findings highlighted that wakes from large offshore wind farms could extend 100 km downwind, reducing downwind power production as in the case of the 400-MW Bard Offshore 1 wind farm whose power output was reduced by the wakes of the 402-MW Veja Mate wind farm for this case study. Significance Statement Because wind farms are smaller than the common grid spacing of numerical weather prediction models, the impacts of wind farms on the weather have to be indirectly incorporated through parameterizations. Several approaches to parameterization are available and the most appropriate scheme is not always clear. The absence of a turbulence source in a parameterization leads to substantial inaccuracies in predicting near-surface wind speed and turbulence over a wind farm. The impact of large clusters of offshore wind turbines in the wind field can exceed 100 km downwind, resulting in a substantial loss of power for downwind turbines. The prediction of this power loss can be sensitive to the chosen parameterization, contributing to uncertainty in wind-farm economic planning.

A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling

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.

A coupled turbine-interaction wind farm parameterization in the Weather Research and Forecasting model

The wind farm parameterization in the mesoscale numerical weather prediction (NWP) model is a valuable approach to exploring the performance and atmospheric impact of the large-scale wind farm. However, the sub-grid interactions between turbines are not resolved in the current parameterization method. In this study, we propose an improved wind farm parameterization by coupling a novel sub-grid wind turbine interference model. It is incorporated in the state-of-the-art Weather Research and Forecasting (WRF) model. To verify the model, the coupled model is applied in a real offshore wind farm in China for comparing the model performances with the original parameterization. The findings demonstrate that the proposed approach improves the wind simulation and has minimal sensitivity to horizontal grid resolution, hence strengthening the model’s robustness. Additionally, it is discovered that the sub-grid interaction reduces the turbine-produced turbulence and shortens the wake length by more than 30% compared to the original parameterization. Importantly, the power output prediction of turbine cells is modified, approximately resulting in a 1 % reduction of the entire wind farm relative to the original model. The effects of sub-grid turbine interaction are effectively captured in the proposed model and it is recommended for further wind farm simulations.

Inter-farm cluster interaction of the operational and planned offshore wind power base

The inter-farm cluster interaction is one of the most difficult aspects of building and planning ten million-kilowatt offshore wind power bases. This study is an attempt to assess the wake and production interactions in the substantial offshore wind farms in Jiangsu Province of China under operational (2020) and planned (2025) scenarios using the updated Weather Research and Forecasting model coupled with the 3D Price-Weller-Pinkel ocean model and the wind farm parameterization model. The results indicate a larger wind deficit of −0.73 ∼ −1.30 m/s in the planned levels of 2025 than that of −0.52 m/s in 2020, with the wake meandering enlarged from 30 km to 90–100 km. Moreover, the newly added power capacity path in the future wind farm development has an impact on power production. The capacity factor of the wind power base will decrease if the average number of turbines in a wind farm is increased or the turbine capacity is increased. It is interesting to note that the planned development mode of positive turbine capacity with conservative farm capacity has relatively low inter-farm cluster wake interference and can be recommended as a priority path for the offshore wind power industry to achieve carbon neutrality and cleaner production.

The Jensen wind farm parameterization

Abstract. 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 (herein referred to as just “WRF”) 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 wind farm parameterization 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 WRF and MPAS, especially for coarse resolution, high turbine density, and wind directions aligned with the turbine columns.

Evaluation of the Fitch Wind-Farm Wake Parameterization with Large-Eddy Simulations of Wakes Using the Weather Research and Forecasting Model

Abstract Wind-farm parameterizations in weather models can be used to predict both the power output and farm effects on the flow; however, their correctness has not been thoroughly assessed. We evaluate the wind-farm parameterization of the Weather Research and Forecasting Model with large-eddy simulations (LES) of the wake performed with the same model. We study the impact on the velocity and turbulence kinetic energy (TKE) of inflow velocity, roughness, resolution, number of turbines (one or two), and inversion height and strength. We compare the mesoscale with the LES by spatially averaging the LES within areas correspondent to the mesoscale horizontal spacing: one covering the turbine area and two downwind. We find an excellent agreement of the velocity within the turbine area between the two types of simulations. However, within the same area, we find the largest TKE discrepancies because in mesoscale simulations, the turbine-added TKE has to be highest at the turbine position to be advected downwind. Within the downwind areas, differences between velocities increase as the wake recovers faster in the LES, whereas for the TKE both types of simulations show similar levels. From the various configurations, the impact of inversion height and strength is small for these heights and inversion levels. The highest impact for the one-turbine simulations appears under the low-speed case due to the higher thrust, whereas the impact of resolution is low for the large-eddy simulations but high for the mesoscale simulations. Our findings demonstrate that higher-fidelity simulations are needed to validate wind-farm parameterizations.

The sensitivity of the Fitch wind farm parameterization to a three-dimensional planetary boundary layer scheme

Abstract. Wind plant wake impacts can be estimated with a number of simulation methodologies, each with its own fidelity and sensitivity to model inputs. In turbine-free mesoscale simulations, hub-height wind speeds often significantly vary with the choice of a planetary boundary layer (PBL) scheme. However, the sensitivity of wind plant wakes to a PBL scheme has not been explored because, as of the Weather Research and Forecasting model v4.3.3, wake parameterizations were only compatible with one PBL scheme. We couple the Fitch wind farm parameterization with the new NCAR 3DPBL scheme and compare the resulting wakes to those simulated with a widely used PBL scheme. We simulate a wind plant in pseudo-steady states under idealized stable, neutral, and unstable conditions with matching hub-height wind speeds using two PBL schemes: MYNN and the NCAR 3DPBL. For these idealized scenarios, average hub-height wind speed losses within the plant differ between PBL schemes by between −0.20 and 0.22 m s−1, and correspondingly, capacity factors range between 39.5 %–53.8 %. These simulations suggest that PBL schemes represent a meaningful source of modeled wind resource uncertainty; therefore, we recommend incorporating PBL variability into future wind plant planning sensitivity studies as well as wind forecasting studies.

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

The impact of the ocean on the performance of offshore wind farms is a challenging study in the wind energy field. In the present study, by coupling a mesoscale numerical weather prediction model, Weather Research and Forecasting (WRF), with a sea surface model and a refined wind farm parameterization, a systematic atmosphere-sea surface-wind farm simulation method is established. The question of how the ocean-atmosphere interaction changes the wake effect and power output of wind farms based on the method is explored. The results show that the momentum deficit due to the operating wind turbines is remarkable, and the wind speed deficit has a strong horizontal diffusion on a sea surface. Wake from the offshore wind farm is prone to mix and extends with a shorter length than that on the flat land. The wake mixing enhances the interference from upwind to downwind turbines, resulting in the power outputs of downwind turbines being decreased by 10%. Besides, the increasing wind shear due to the ocean waves decreases the wind power by around 3.5%. Accordingly, it is significant for applying such a coupled air-sea surface-wind farm simulation method when assessing the offshore wind farm performance and the impact of the wind farms on the marine atmospheric boundary layer.

Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling

Abstract. As part of the ongoing X-Wakes research project, a 5-month wake-measurement campaign was conducted using a scanning lidar installed amongst a cluster of offshore wind farms in the German Bight. The main objectives of this study are (1) to demonstrate the performance of such a system and thus quantify cluster wake effects reliably and (2) to obtain experimental data to validate the cluster wake effect simulated by the flow models involved in the project. Due to the lack of free wind flow for the wake flow directions, wind speeds obtained from a mesoscale model (without any wind farm parameterization) for the same time period were used as a reference to estimate the wind speed deficit caused by the wind farm wakes under different wind directions and atmospheric stabilities. For wind farm waked wind directions, the lidar data show that the wind speed is reduced up to 30 % at a wind speed of about 10 m s−1, depending on atmospheric stability and distance to the wind farm. For illustrating the spatial extent of cluster wakes, an airborne dataset obtained during the scanning wind lidar campaign is used and compared with the mesoscale model with wind farm parameterization and the scanning lidar. A comparison with the results of the model with a wind farm parameterization and the scanning lidar data reveals a relatively good agreement in neutral and unstable conditions (within about 2 % for the wind speed), whereas in stable conditions the largest discrepancies between the model and measurements are found. The comparative multi-sensor and model approach proves to be an efficient way to analyze the complex flow situation in a modern offshore wind cluster, where phenomena at different length scales and timescales need to be addressed.

Computation and Analysis of an Offshore Wind Power Forecast: Towards a Better Assessment of Offshore Wind Power Plant Aerodynamics

For the first time, the Weather Research and Forecast (WRF) model with the Wind Farm Parameterization (WFP) modeling method is utilized for a short-range wind power forecast simulation of 48 h of an offshore wind farm with 100 turbines located on the east coast of the China Yellow Sea. The effects of the horizontal multi-grid downsize method were deployed and investigated on this simulation computation. The simulation was validated with the field data from the Supervisory Control and Data Acquisition (SCADA) system, and the results showed that the horizontal mesh downsize method improved the accuracy of wind speed and then wind power forecast. Meanwhile, the wind power plant aerodynamics with turbine wake and sea–land shore effects were investigated, where the wake effects from the wind farm prolonged several miles downstream, evaluated at two wind speeds of 7 m/s and 10 m/s instances captured from the 48 h of simulation. At the same time, it was interesting to find some sea–land atmospheric effects with wind speed oscillation, especially at the higher wind speed condition. Finally, the research results show that the WRF + WFP model for the wind power forecast for production operation may not be ready at this stage; however, they show that the methodology helps to evaluate the wind power plant aerodynamics with wake effects and micrometeorology of the sea–land interconnection region. This plant aerodynamics study set the stage for a wake turbine interaction study in the future, such as one utilizing the NREL FAST.FARM tool.

Comparison of individual versus ensemble wind farm parameterizations inclusive of sub‐grid wakes for the WRF model

AbstractWind turbine wakes can be predicted somewhat accurately with mesoscale numerical models, such as the Weather Research and Forecast (WRF) model, via a wind farm parameterization (WFP) that treats the effects of the wakes, which are sub‐grid features, on power production and the environment. A few WFPs have been proposed in the literature, but none has been able to properly account for the individual wakes within a grid cell or the effects of overlapping wakes from multiple turbines. A solution to these two issues is a WFP that includes both a wake model, which is a simplified analytical model of the wind speed (or wind power) deficit caused by a wake, and a wake superposition model, which accounts for overlapping wakes. Several such WFPs are developed here for the WRF model—based on the Jensen, the Geometric, and the Gaussian wake models coupled with two wake superposition methods (based on a squared deficit and a squared velocity superposition)—and tested individually, as well as combined together in an ensemble (EWFP), at two modern offshore wind farms. Most WFPs perform satisfactorily alone, but the EWFP generally outperforms them at both farms. The issue of resolved versus sub‐grid wakes is explored for single‐ and multi‐cell cases and for directions of alignment and non‐alignment between the wind direction and the turbine columns. Although different combinations of wake loss and wake superposition models might be preferred at other wind farms, the general findings and detailed performance statistics given here might provide useful guidance in their selection.

Surface impacts of large offshore wind farms

Future offshore wind farms around the world will be built with wind turbines of size and capacity never seen before (with diameter and hub height exceeding 150 and 100 m, respectively, and rated power exceeding 10 MW). Their potential impacts at the surface have not yet been studied. Here we conduct high-resolution numerical simulations using a mesoscale model with a wind farm parameterization and compare scenarios with and without offshore wind farms equipped with these ‘extreme-scale’ wind turbines. Wind speed, turbulence, friction velocity, and sensible heat fluxes are slightly reduced at the surface, like with conventional wind turbines. But, while the warming found below the rotor in stable atmospheric conditions extends to the surface with conventional wind turbines, with extreme-scale ones it does not reach the surface, where instead minimal cooling is found. Overall, the surface meteorological impacts of large offshore wind farms equipped with extreme-scale turbines are statistically significant but negligible in magnitude.

Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models

Abstract. Numerical wind resource modelling across scales from the mesoscale to the turbine scale is of increasing interest due to the expansion of offshore wind energy. Offshore wind farm wakes can last several tens of kilometres downstream and thus affect the wind resources of a large area. So far, scale-specific models have been developed but it remains unclear how well the different model types can represent intra-farm wakes, farm-to-farm wakes as well as the wake recovery behind a farm. Thus, in the present analysis the simulation of a set of wind farm models of different complexity, fidelity, scale and computational costs are compared among each other and with SCADA data. In particular, two mesoscale wind farm parameterizations implemented in the mesoscale Weather Research and Forecasting model (WRF), the Explicit Wake Parameterization (EWP) and the Wind Farm Parameterization (FIT), two different high-resolution RANS simulations using PyWakeEllipSys equipped with an actuator disk model, and three rapid engineering wake models from the PyWake suite are selected. The models are applied to the Nysted and Rødsand II wind farms, which are located in the Fehmarn Belt in the Baltic Sea. Based on the performed simulations, we can conclude that both WRF + FIT (BIAS = 0.52 m s−1) and WRF + EWP (BIAS = 0.73 m s−1) compare well with wind farm affected mast measurements. Compared with the RANS simulations, baseline intra-farm variability, i.e. the wind speed deficit in between turbines, can be captured reasonably well with WRF + FIT using a resolution of 2 km, a typical resolution of mesoscale models for wind energy applications, while WRF + EWP underestimates wind speed deficits. However, both parameterizations can be used to estimate median wind resource reduction caused by an upstream farm. All considered engineering wake models from the PyWake suite simulate peak intra-farm wakes comparable to the high fidelity RANS simulations. However, they considerably underestimate the farm wake effect of an upstream farm although with different magnitudes. Overall, the higher computational costs of PyWakeEllipSys and WRF compared with those of PyWake pay off in terms of accuracy for situations when farm-to-farm wakes are important.

Climatic Impacts of Wind-Wave-Wake Interactions in Offshore Wind Farms

Accurate wind resource assessments are necessary for cost effective offshore wind energy developments. The wind field offshore depends on the sea state. In coastal areas, where wind farms are usually built today, wind and waves are often not in full balance. In addition, wind farms modify their surrounding wind and turbulence field, especially downwind. These wind farm wakes, in turn, interact with the wave field, creating a complex dynamical system. To fully capture the dynamics in such a system in a realistic way, a coupled atmosphere-wave modelling system equipped with a wind farm parameterization should be applied. However, most conventional resource assessment relies on standalone atmosphere model simulations. We compare the wind-wave-wake climate predicted from a coupled modelling system, to one predicted from a standalone atmosphere model. Using a measurement-driven statistical-dynamical downscaling method, we show that about 180 simulation days are enough to represent the wind- and wave-climate, as well as the relation between those two, for the German Bight. We simulate these representative days with the atmosphere-wave coupled and the uncoupled modelling system. We perform simulations both without wind farms as well as parameterizing the existing wind farms as of July 2020. On a climatic average, wind resources derived from the coupled modelling system are reduced by 1% in 100 m over the sea compared to the uncoupled modelling system. In the area surrounding the wind farm the resources are further reduced. While the climatic reduction is relatively small, wind speed differences between the coupled and uncoupled modelling systems differ by more than ±20% on a 10-min time-scale. The turbulent kinetic energy derived from the coupled system is higher, which contributes to a more efficient wake dissipation on average and thus slightly smaller wake-affected areas in the coupled system. Neighbouring wind farms reduce wind resources of surrounding farms by up to 10%. The wind farm wakes reduce significant wave height by up to 3.5%. The study shows the potential of statistical-dynamical downscaling and coupled atmosphere-wave-wake modelling for offshore wind resource assessment and physical environmental impact studies.