Quantifying variability of Large Eddy Simulations of very large wind farms

Andersen, Søren Juhl; Witha, Björn; Breton, Simon-Philippe; Sørensen, Jens Nørkær; Mikkelsen, Robert Flemming; Ivanell, Stefan

doi:10.1088/1742-6596/625/1/012027

Cited by 26 publications

(25 citation statements)

References 14 publications

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“…tried to reproduce the wind conditions around an offshore wind turbine by forcing the simulation with time series from a mesoscale model. They found a generally good agreement when comparing the PALM results with data from a meteorological mast and LiDAR measurements Andersen et al (2015). compared the results of the WTM and other LES models for very large idealised wind farms and explored how to best present and compare the resulting variability.27https://doi.org/10.5194/gmd-2019-103 Preprint.…”

mentioning

confidence: 97%

Overview of the PALM model system 6.0

Maronga¹,

Banzhaf²,

Burmeister³

et al. 2019

Preprint

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Abstract. In this paper we describe the PALM model system 6.0. PALM is a Fortran based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model by components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.

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mentioning

confidence: 97%

Overview of the PALM model system 6.0

Maronga¹,

Banzhaf²,

Burmeister³

et al. 2019

Preprint

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“…Gebraad et al [8] and Fleming et al [9], the turbine(s) are not intentionally yawed here. The turbines are aligned with the main wind direction, but as shown in Andersen et al [27] the instantaneous wind direction varies significantly (more than ±15 • ) even within an aligned row of turbines. So these turbines experience unintentional and "unknown" yaw misalignment like real operating turbines.…”

Section: Wind Turbine and Wake Positionmentioning

confidence: 97%

Instantaneous Response and Mutual Interaction between Wind Turbine and Flow

Andersen¹,

Sørensen²

2018

J. Phys.: Conf. Ser.

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Abstract. The mutual fluid-structure interaction between wind turbine(s) and the highly turbulent flow deep inside a large wind farm is investigated in order to elucidate on how to implement and perform dynamic wind farm control. The study employs a fully coupled LES and aeroelastic framework, which provide time resolved flow and turbine response governed by a controller. The results show a large correlation between incoming flow and turbine response, which extends several radii upstream and could be utilized for turbine control by e.g. installing a lidar on top of the wind turbine. Similarly, the results are valuable for utilizing nacelle mounted lidars for power curve assessments in large wind farms. However, the correlations between turbine and wake flow as well as the dynamic wake position are low, which is potentially discouraging for attempts to do instantaneous yaw steering.

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“…A blending height approach has been used to semi‐analytically link the wind farm canopy shear stress with the ABL shear stress aloft . This is also studied in LES using periodic boundary conditions to study how the wind farm layout affects the asymptotic statistics of the turbulent structure of the infinite wind farm . These idealized conditions can be used to design engineering wake models more suitable for large wind farms and as basis for wind farm parameterizations in mesoscale models.…”

Section: Microscale Modelingmentioning

confidence: 99%

“…For instance, Pinto et al (2009) use PCA to define the leading large‐scale weather components followed by K‐means clustering to define an optimum classification. Frank and Landberg), Frey‐Buness et al, and Badger et al create wind climate classes from GCM outputs by dividing them into bins of wind direction, wind speed, and stability classes to downscale each class afterward with a mesoscale model. Similarly, a simple speed‐up correction depending on the wind direction sector was used, based on a linear flow model, to derive the high‐resolution map of Figures and and reduce systematic under‐prediction of mean wind speed in complex terrain.…”

Section: Mesoscale‐to‐microscale Modelingmentioning

confidence: 99%

Mesoscale to microscale wind farm flow modeling and evaluation

Rodrigo

Arroyo

Moriarty

et al. 2016

WIREs Energy & Environment

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The increasing size of wind turbines, with rotors already spanning more than 150 m diameter and hub heights above 100 m, requires proper modeling of the atmospheric boundary layer (ABL) from the surface to the free atmosphere. Furthermore, large wind farm arrays create their own boundary layer structure with unique physics. This poses significant challenges to traditional wind engineering models that rely on surface-layer theories and engineering wind farm models to simulate the flow in and around wind farms. However, adopting an ABL approach offers the opportunity to better integrate wind farm design tools and meteorological models. The challenge is how to build the bridge between atmospheric and wind engineering model communities and how to establish a comprehensive evaluation process that identifies relevant physical phenomena for wind energy applications with modeling and experimental requirements. A framework for model verification, validation, and uncertainty quantification is established to guide this process by a systematic evaluation of the modeling system at increasing levels of complexity. In terms of atmospheric physics, 'building the bridge' means developing models for the so-called 'terra incognita,' a term used to designate the turbulent scales that transition from mesoscale to microscale. This range of scales within atmospheric research deals with the transition from parameterized to resolved turbulence and the improvement of surface boundary-layer parameterizations. The coupling of meteorological and wind engineering flow models and the definition of a formal model evaluation methodology, is a strong area of research for the next generation of wind conditions assessment and wind farm and wind turbine design tools. Some fundamental challenges are identified in order to guide future research in this area. mesoscale model at~4 km horizontal resolution (a), nested down with WRF to 1.67 and 0.55 km resolution, and introducing microscale speedups with WAsP at 100 m resolution (b). (Reprinted with permission from Ref 35.

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Quantifying variability of Large Eddy Simulations of very large wind farms

Cited by 26 publications

References 14 publications

Overview of the PALM model system 6.0

Overview of the PALM model system 6.0

Instantaneous Response and Mutual Interaction between Wind Turbine and Flow

Mesoscale to microscale wind farm flow modeling and evaluation

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