A multi-fidelity approach for wind farm simulations and comparison with field data

<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.

The NOWITECH Reference Wind Farm

<strong class="journal-contentHeaderColor">Abstract.</strong> With the increasing growth of wind farm installations, the impact of wake effects from wind turbines on power output, structural loads, and revenue has become a major concern. Hence, there is a need for precise simulation tools to facilitate efficient and cost-effective design and operation of wind farms. To address this need, we present HAWC2Farm, a dynamic and versatile aeroelastic wind farm simulation methodology that combines state-of-the-art engineering models to accurately capture the complex physical phenomena in wind farms. HAWC2Farm models each turbine in a wind farm using the aeroelastic turbine simulator, HAWC2, while sharing a large, high-resolution turbulence box to model atmospheric flow field effects at the farm level, and a modified Dynamic Wake Meandering (DWM) model for capturing wake interactions. The method is computationally efficient and provides valuable insights for wind farm design. It is validated using time series field measurements from the Lillgrund wind farm, including scenarios with yaw steering and a turbine shutdown. HAWC2Farm addresses the challenges of modelling the complex dynamics in wind farms, enabling more accurate, informed and cost-effective design and operation.

The electrical systems needed for offshore wind farms to collect power from wind turbines-and transmit it to shore- will be a significant cost element of these systems. This paper describes the development of a simplified model of the cost and performance of such systems. The performance prediction accounts for losses as a function of the power produced in the wind farm and the length and size of the cables. The cost prediction is flexibly formulated so wind farm configurations can be evaluated by parameters such as the number of wind turbines, wind turbine size, turbine array configuration and spacing, and distance from shore. The collection system-the medium-voltage electrical grid within the wind farm, and the transmission system-the high-voltage electrical connection to an on-shore transmission line-are treated independently in the model. Data sources for the model and limitations of the data are discussed, and comparison is made to costs reported by others. The choice of transmission system technology is also addressed. This electrical system model is intended for integration into a more comprehensive model of offshore wind farm design, cost, and performance that will be used for parametric studies and optimization of wind farm configurations. Because some concepts for future offshore wind installations in deep water use floating platforms, this paper briefly discusses the application of submarine cable technology to nonfixed termination points, a departure from current practice. Introduction The National Wind Technology Center (NWTC) of the National Renewable Energy Laboratory (NREL) in Golden, Colorado, has undertaken a series of concept studies to evaluate the cost and performance of offshore wind farms. The product of these studies will be a comprehensive model of offshore wind farm design, cost, and performance suitable for parametric studies and optimization of wind farmconfigurations. The overall goal of this effort is to helpidentify technology pathways for offshore wind energy development and deployment in the United States. Offshore wind farms present an attractive option because they allow for larger wind turbines that operate in higher wind resources than land-based [1,2]. Offshore installations are also more expensive, so an understanding of their performance, cost, and optimal configurations is needed. This paper is an overview of one of these concept studies. It focuses on the power losses in wind farm electrical power collection and transmission systems, as well as the costs of the system components and their installation. A hypothetical system was based loosely on the Horns Rev offshore wind farm in Denmark. Inquiries were made with manufacturers about electrical and cost data on the required components, which were then compiled in a spreadsheet model. The performance prediction accounts for losses as a function of power output of the wind farm and length and size of the cables. The cost prediction is flexibly formulated so wind farm configurations can be evaluated by parameters such as the number of wind turbines, wind turbine size, turbine array configuration and spacing, and distance from shore.

We present a wind simulation framework for offshore and onshore wind farms. The simulation framework involves an automatic hybrid high-quality mesh generation process, a pre-processing to impose initial and boundary conditions, and a solver for the Reynolds Averaged Navier-Stokes (RANS) equations with two different turbulence models, a modified standard k-ϵ model and a realizable k-ϵ model in which we included the Coriolis effects. Wind turbines are modeled as actuator discs. The wind farm simulation framework has been implemented in Alya, an in-house High Performance Computing (HPC) multi-physics finite element parallel solver. An application example is shown for an onshore wind farm composed of 165 turbines.

The U.S. Department of Energy (DOE) acquired and installed a 1.5-megawatt (MW) wind turbine at the National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory. This turbine (hereafter referred to as the DOE 1.5) is envisioned to become an integral part of the research initiatives for the DOE Wind Program, such as Atmosphere to Electrons (A2e). A2e is a multiyear DOE research initiative targeting significant reductions in the cost of wind energy through an improved understanding of the complex physics governing wind flow into and through wind farms. For more information, visit http://energy.gov/eere/wind/atmosphere-electrons. To validate new and existing high-fidelity simulations, A2e must deploy several experimental measurement campaigns across different scales. Proposed experiments include wind tunnel tests, scaled field tests, and large field measurement campaigns at operating wind plants. Data of interest includes long-term atmospheric data sets, wind plant inflow, intra-wind plant flows (e.g., wakes), and rotor loads measurements. It is expected that new, high-fidelity instrumentation will be required to successfully collect data at the resolutions required to validate the high-fidelity simulations.

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

In the present paper a new streamline model for wake development based on a streamline topology is applied and compared with different approaches for modeling the wind turbine wakes. The contribution of the present work is the comparison of the streamline-and straight-wake models as well as the different wake models, e.g., Jensen and Gaussian. The models have been applied to two wind farm cases. The results from the first case are compared against SCADA measurements and computational fluid dynamics simulations (CFD). The CFD simulations are performed using Reynolds-Averaged Navier Stokes (RANS) together with the actuator disc (AD) approach. The mean percent difference in power using the different wake models ranged between 19-20%. Mean percent difference in power for the AD-RANS was approximately 20%. For the second wind farm case only wake models are compared and approximately ±1% difference exists between them. The present work shows that the streamline topology of the wind turbine wake flow as well as the wake models have an effect on the performance prediction of wind farms.

Wind energy is a promising alternate energy resource. However, the on-land wind farms are limited by space, noise, and visual pollution and, therefore, many countries build wind farms near the shore. Until now, most offshore wind farms have been built in relatively shallow water (less than 30 m) with fixed tower type wind turbines. Recently, several countries have planned to move wind farms to deep water offshore locations to find stronger and steadier wind fields as compared to near shore locations. For the wind farms in deeper water, floating platforms have been proposed to support the wind turbine. The model tests described in this paper were performed at MARIN (maritime research institute netherlands) with a model setup corresponding to a 1:50 Froude scaling. The wind turbine was a scaled model of the national renewable energy lab (NREL) 5 MW horizontal axis reference wind turbine supported by three different generic floating platforms: a spar, a semisubmersible, and a tension-leg platform (TLP). The wave environment used in the tests is representative of the offshore in the state of Maine. In order to capture coupling between the floating platform and the wind turbine, the 1st bending mode of the turbine tower was also modeled. The main purpose of the model tests was to generate data on coupled motions and loads between the three floating platforms and the same wind turbine for the operational, design, and survival seas states. The data are to be used for the calibration and improvement of the existing design analysis and performance numerical codes. An additional objective of the model tests was to establish the advantages and disadvantages among the three floating platform concepts on the basis of the test data. The paper gives details of the scaled model wind turbine and floating platforms, the setup configurations, and the instrumentation to measure motions, accelerations, and loads along with the wind turbine rpm, torque, and thrust for the three floating wind turbines. The data and data analysis results are discussed in the work of Goupee et al. (2012, “Experimental Comparison of Three Floating Wind Turbine Concepts,” OMAE 2012-83645).

As floating offshore wind turbines (FOWTs) become the most viable option for wind farms in deeper waters, it is important to investigate their dynamic response in inclement conditions when failures, such as yaw misalignment, are more likely to occur. This research uses hour-long simulations in FAST, software developed by The National Renewable Energy Lab (NREL), to analyze the effect of yaw error on anchor tensions and platform displacements in both a traditional single-line wind farm geometry, where each anchor is connected to one turbine, and an optimum multiline anchor geometry, where each anchor is connected to three turbines. NREL’s 5 MW reference turbine on a semi-submersible base is analyzed using six realizations of each combination of co-directional wind and waves, wind speed and yaw error; resulting in 2,484 simulations in total. The variability in platform displacements and mooring forces increases as wind speed increases, and as yaw errors approach critical values. The angle of incidence of the co-directional wind and waves dictates which anchor experiences the most tension for both the single-line and multiline concepts. In the multiline geometry, the greatest increases in anchor tension occurs when the downwind turbine has yaw error. Yaw error increases the maximum anchor tension by up to 43% in the single-line geometry and up to 37% in the multiline geometry. In the multiline geometry, yaw error causes the direction of the resultant anchor force to vary by up to 20°. These changes in anchor tension magnitudes and directions are governed by the platform displacements, and are a direct result of the differences in the tangential and normal coefficients of drag of the turbine blades. When designing floating offshore wind farms, the influence of yaw error on loading magnitudes and directions are to be considered when determining the necessary capacities and calculating the corresponding reliabilities for wind turbine components.

As the major part of new wind turbines are installed in clusters or wind farms, there is a strong need for reliable and accurate tools for predicting the increased loadings due to wake operation and the associated reduced power production. The dynamic wake meandering (DWM) model has been developed on this background, and the basic physical mechanisms in the wake—i.e., the velocity deficit, the meandering of the deficit, and the added turbulence—are modeled as simply as possible in order to make fast computations. In the present paper, the DWM model is presented in a version suitable for full integration in an aeroelastic model. Calibration and validation of the different parts of the model is carried out by comparisons with actuator disk and actuator line (ACL) computations as well as with inflow measurements on a full-scale 2 MW turbine. It is shown that the load generating part of the increased turbulence in the wake is due almost exclusively to meandering of the velocity deficit, which causes “apparent” turbulence when measuring the flow in a fixed point in the wake. Added turbulence, originating mainly from breakdown of tip vortices and from the shear of the velocity deficit, has only a minor contribution to the total turbulence and with a small length scale in the range of 10–25% of the ambient turbulence length scale. Comparisons of the calibrated DWM model with ACL results for different downstream positions and ambient turbulence levels show good correlation for both wake deficits and turbulence levels. Finally, added turbulence characteristics are compared with correlation results from literature.

<strong class="journal-contentHeaderColor">Abstract.</strong> As a consequence of the rapid growth of the globally installed offshore wind energy capacity, the size of individual wind farms is increasing. This poses a challenge to models that predict energy production. For instance, the current generation of wake models has mostly been calibrated on existing wind farms of much smaller size. This work analyses annual energy production and wake losses for future multi-gigawatt wind farms with atmospheric large-eddy simulation. To that end, one year of actual weather has been simulated for a suite of hypothetical four-gigawatt offshore wind farm scenarios. The scenarios differ in terms of applied turbine type, installed capacity density, and layout. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger, while keeping the total installed capacity the same. Even for turbine types with similar rated power, but slightly different power curves, significant differences in production were found. Although wind speed was identified as the most dominant factor determining the aerodynamic losses, a clear impact of atmospheric stability has been identified. By analyzing losses of the first-row turbines, the yearly average global-blockage effect is estimated between 2 to 3 %, but it can reach levels over 10 % for stably stratified conditions and wind speeds around 8 ms^&minus;1. Using a high-fidelity modeling technique, the present work provides insights in the performance of future, multi-gigawatt wind farms for a full year of realistic weather conditions.

We present a novel meshing and simulation approach for wind farms, featuring realignment and mesh adaptation. The turbines are modeled with actuator discs, which are discretized by means of an adaptation process to represent a level set function. The level-set-based simulation framework is combined with an adaptation cycle to capture both the solution and the actuator discs. In addition, we devise a turbine realignment process which takes into account the actual flow in the actuator disc configuration. Several results are presented to highlight the features of the approach. First, the adaptive simulation approach is validated, fulfilling the theoretical convergence rates and improving the accuracy of the boundary tight representations. Second, the adaptive simulation process is applied to a full wind farm configuration featuring 219 turbines, illustrating that is it well devised for complex wind farm configurations. Third, the turbine reorientation process is validated in a one turbine scenario. Finally, the realignment simulation framework is applied in a wind farm featuring 115 turbines. The presented results outline the significance of the proposed work, enabling turbine realignment and mesh adaptation to perform accurate simulations of complex wind farm configurations.

An insight on the key factors influencing the accuracy of the actuator line method for use in vertical-axis turbines: Limitations and open challenges

In this work, we numerically investigated the effects of yaw angle on the wake and power characteristics of two National Renewable Energy Laboratory (NREL) 5 MW wind turbines based on actuator line method (ALM) and large eddy simulation (LES) under a neutral atmospheric boundary layer (ABL) with specified offshore surface roughness. The turbines are placed in tandem, with a spacing of seven rotor diameters, and the yaw angles range from 0° to 30°. The results indicate that under coordinated yaw conditions, the wakes of the two turbines significantly shift with increasing yaw angles, encroaching on the trailing edge of the turbines. The expansion of the wakes also gradually weakens, leading to a reduction in width. The superposition of the wake generated by the downstream turbine diminishes, leading to both turbines exhibiting approximately comparable physical characteristics within their respective wakes. As the wake of the upstream turbine propagates downstream, a secondary low-speed region emerges between the primary low-speed zone of the wake of downstream turbine and the surrounding atmosphere. With the increase in yaw angle, this secondary low-speed region significantly enhances the rate of wake recovery while also inducing a more pronounced deflection of the wake, thereby demonstrating a stronger entrainment effect. Regarding load characteristics, the time history of power characteristics and the power spectral density (PSD) spectra indicate a good turbine response to the inflow. The power characteristics of the upstream turbine exhibit a scaling law is closely related to the yaw angle. The quantitative relationship is established between yaw angle and the power distribution of the turbines, alongside a proposed correlation between the yaw angle and the cos 2(γ) scaled power curve. The power of upstream turbine decreases and the power of downstream turbine gradually increases with the increase in yaw angle. It is further found that the downstream turbine demonstrates optimal performance at a yaw angle of 20°due to the influence of the yawed upstream turbine. These analyses provide insights into the characteristics of wind turbine arrays under yaw conditions from the perspective of unsteady wake features, interactions, and aerodynamic performance, which can aid in wind farm unit planning and control strategies.