Large-scale benchmarking of wake models for offshore wind farms

Nygaard, Nicolai; Poulsen, Louise K.; Svensson, E; Pedersen, Jesper Grønnegaard

doi:10.1088/1742-6596/2265/2/022008

Cited by 16 publications

(38 citation statements)

References 9 publications

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“…For this reason, the median is used to limit the influence of the most extreme outliers. In addition, the residual offset must be corrected via northing calibration 24,37 . To this end, we follow Nygaard and Hansen 38 and use a constant yaw offset of −1.88° for the data period that we consider (July 2016 to December 2017).…”

Section: Methodsmentioning

confidence: 99%

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Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data

2023

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The expected growth in wind energy capacity requires efficient and accurate models for wind farm layout optimization, control, and annual energy predictions. Although analytical wake models are widely used for these applications, several model components must be better understood to improve their accuracy. To this end, we propose a Bayesian uncertainty quantification framework for physics‐guided data‐driven model enhancement. The framework incorporates turbulence‐related aleatoric uncertainty in historical wind farm data, epistemic uncertainty in the empirical parameters, and systematic uncertainty due to unmodeled physics. We apply the framework to the wake expansion parametrization in the Gaussian wake model and employ historical power data of the Westermost Rough Offshore Wind Farm. We find that the framework successfully distinguishes the three sources of uncertainty in the joint posterior distribution of the parameters. On the one hand, the framework allows for wake model calibration by selecting the maximum a posteriori estimators for the empirical parameters. On the other hand, it facilitates model validation by separating the measurement error and the model error distribution. In addition, the model adequacy and the effect of unmodeled physics are assessable via the posterior parameter uncertainty and correlations. Consequently, we believe that the Bayesian uncertainty quantification framework can be used to calibrate and validate existing and upcoming physics‐guided models.

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Section: Methodsmentioning

confidence: 99%

“…In addition, the residual offset must be corrected via northing calibration. 24,37 To this end, we follow Nygaard and Hansen 38 and use a constant yaw offset of À1.88 for the data period that we consider (July 2016 to December 2017). In what follows, this main wind direction is also denoted as the streamwise direction.…”

Section: Consistent Measurement Errormentioning

confidence: 99%

Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data

2023

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“…The Gaussian profile represents the velocity deficit in the wake much more accurately than, e.g., a top-hat profile, as comparisons with LES data and wind tunnel measurements have shown (Bastankhah and Porté-Agel, 2014). Accounting for the local TI, i.e., the sum of the ambient TI and the turbine generated TI, results in a more realistic wake expansion rate than accounting for only the ambient TI (Lissaman, 1979;Nygaard et al, 2022). Both wake models are based on the momentum-conserving velocity deficit model for a single turbine wake proposed by Bastankhah and Porté-Agel (2014).…”

Section: Description Of the Analytical Wake Modelsmentioning

confidence: 99%

“…In the TurbOPark model the superposition of wakes is performed by a quadratic sum of the velocity deficits, which conserves the kinetic energy (Katic et al, 1987;Nygaard et al, 2022). The turbine generated turbulence is modeled according to Frandsen (2007).…”

Section: Description Of the Analytical Wake Modelsmentioning

confidence: 99%

Large-eddy simulation of a 15 GW wind farm: Flow effects, energy budgets and comparison with wake models

Maas

2023

Front. Mech. Eng.

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Planned offshore wind farm clusters have a rated capacity of more than 10 GW. The layout optimization and yield estimation of wind farms is often performed with computationally inexpensive, analytical wake models. As recent research results show, the flow physics in large (multi-gigawatt) offshore wind farms are more complex than in small (sub-gigawatt) wind farms. Since analytical wake models are tuned with data of existing, sub-gigawatt wind farms they might not produce accurate results for large wind farm clusters. In this study the results of a large-eddy simulation of a 15 GW wind farm are compared with two analytical wake models to demonstrate potential discrepancies. The TurbOPark model and the Niayifar and Porté-Agel model are chosen because they use a Gaussian wake profile and a turbulence model. The wind farm has a finite size in the crosswise direction, unlike as in many other large-eddy simulation wind farm studies, in which the wind farm is effectively infinitely wide due to the cyclic boundary conditions. The results show that new effects like crosswise divergence and convergence occur in such a finite-size multi-gigawatt wind farm. The comparison with the wake models shows that there are large discrepancies of up to 40% between the predicted wind farm power output of the wake models and the large-eddy simulation. An energy budget analysis is made to explain the discrepancies. It shows that the wake models neglect relevant kinetic energy sources and sinks like the geostrophic forcing, the energy input by pressure gradients and energy dissipation. Taking some of these sources and sinks into account could improve the accuracy of the wake models.

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“…In this context, the wind farm interaction with the atmospheric boundary layer (ABL) has emerged as an increasingly important aspect to account for and model in order to yield accurate power predictions. Its existence uncovered new phenomena that conventional models either fail to capture, such as the wind deceleration upstream of a turbine cluster (Bleeg et al 2018), or model only partially, such as farm-scale wake effects (Nygaard et al 2022). The latter are produced by the interacting turbine wakes that merge eventually to form an extended region of reduced momentum downstream of the wind farm.…”

Section: Introductionmentioning

confidence: 99%

A shear stress parametrization for arbitrary wind farms in conventionally neutral boundary layers

Stipa,

Allaerts,

Brinkerhoff

2024

J. Fluid Mech.

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In the context of large off-shore wind farms, power production is influenced greatly by the turbine array's interaction with the atmospheric boundary layer. One of the most influencing manifestations of such complex interaction is the increased level of shear stress observed within the farm. This leads to higher momentum fluxes that affect the wind speed at the turbine locations and in the cluster wake. At the wind farm entrance, an internal boundary layer (IBL) grows due to the change in effective roughness imposed by the wind turbines, and for large enough clusters, this can reach the unperturbed boundary layer height in what is referred to as the fully developed regime. Downwind, a second IBL starts growing, while the shear stress profile decays exponentially to its unperturbed state. In the present study, we propose a simple analytical model for the vertical profile of the horizontal shear stress components in the three regions identified above. The model builds upon the top-down model of Meneveau (J. Turbul., vol. 13, 2012, N7), and assumes that the flow develops in a conventionally neutral boundary layer. The proposed parametrization is verified successfully against large-eddy simulations, demonstrating its ability to capture the vertical profile of horizontal shear stress, and its evolution both inside and downwind of the wind farm. Our findings suggest that the developed model can prove extremely useful to enhance the physical grounds on which new classes of coupled wind farm engineering models are based, leading to a better estimation of meso-scale phenomena affecting the power production of large turbine arrays.

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Large-scale benchmarking of wake models for offshore wind farms

Cited by 16 publications

References 9 publications

Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data

Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data

Large-eddy simulation of a 15 GW wind farm: Flow effects, energy budgets and comparison with wake models

A shear stress parametrization for arbitrary wind farms in conventionally neutral boundary layers

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