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WIREs Energy Environ.

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Mesoscale to microscale wind farm flow modeling and evaluation

Advanced Review

Javier Sanz Rodrigo, Roberto Aurelio Chávez Arroyo, Patrick Moriarty, Matthew Churchfield, Branko Kosović, Pierre‐Elouan Réthoré, Kurt Schaldemose Hansen, Andrea Hahmann, Jeffrey D. Mirocha, Daran Rife

Published Online: Aug 31 2016

DOI: 10.1002/wene.214

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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. WIREs Energy Environ 2017, 6:e214. doi: 10.1002/wene.214 This article is categorized under: Wind Power > Climate and Environment Energy and Climate > Climate and Environment Energy Policy and Planning > Climate and Environment

Diagram of the model development and evaluation framework.

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Example of transition from smooth WRF mesoscale inflow nested onto a microscale domain, where three‐dimensional turbulence is triggered using potential temperature perturbations at a buffer zone in the inflow.

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Example of using the analog ensemble statistical downscaling method to reconstruct a long‐term time series of wind speed based on training short‐duration high‐resolution WRF simulation (could also be a measurement campaign) and a long‐term coarse WRF (Reprinted with permission from Ref . Copyright 2015).

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Wake efficiency down the lines of wind turbines in Horns Rev at 270° ± 2.5° (a, narrow sector) and 270° ± 15° (b, wide sector). Models are categorized as LIN (linearized), ENG (engineering), RANS, and LES, and ensemble‐averaged observations are denoted as SCADA.

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Simulation of wake interaction in the Lillgrund wind farm under neutral conditions with SOWFA. The flow is accelerated across an internal gap corresponding to two missing turbines in a regular layout based on tight spacing of 3.3D × 4.3D, where D is the rotor diameter.

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Visualization of vorticity isosurfaces of a turbine wake immersed in a very‐stable boundary layer. Due to vertical stratification the initial axisymmetric wake can more easily expand horizontally than vertically resulting in an elliptic wake shape, steered by important vertical wind direction changes. The simulation has been done with SOWFA using an actuator‐line rotor model immersed in a LES turbulent field.

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IEA‐Wakebench benchmark results of Askervein 210° neutral flow case from several RANS models: normalized velocity along the AA transect at 10 m above ground level (a, flow is from the left) and stacked normalized mean absolute errors from various horizontal and vertical profiles (b).

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Simulation results of the GABLS1 (a) and GABLS2 (b) ABL cases with several RANS models in CFDWind compared with results from the literature both in terms of single‐column (SCM) and large‐eddy simulation (LES) models. Notation: u_*0 is the friction velocity at the surface, wθ₀ is the kinematic heat flux at the surface, L₀ is the Obukhov length at the surface, and h_τ is the boundary‐layer height based on the shear stress.

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Mean wind speed map at 78 m for a year‐long simulation around the Alaiz mountain range in the North of Spain, using Skiron 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 . Copyright 2013)

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Mean wind power density (WPD) at 100 m for a coastal and offshore area in Denmark from MERRA reanalysis, downscaled to 6 km resolution with WRF and then introducing microscale corrections with WAsP (following the methodology of Hahmann et al.) The mean WPD over all the grid points within the studied domain is compared with the mean WPD over the 50% windiest grid points. This shows how increasing resolution typically results in not only higher wind resource but also a larger extension of the area with high wind energy potential.

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Mesoscale‐to‐microscale model‐chain physics overview and model categories. ABL, atmospheric boundary layer; CCA, canonical correlation analysis; LES, large‐eddy simulation; MCP, measure–correlate–predict; MOS, model output statistics; N‐S, Navier–Stokes; PCA, principal component analysis; PBL, planetary boundary layer; RANS, Reynolds‐averaged Navier–Stokes; SCM, single‐column model; SGS, subgrid scale.

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Wind assessment modeling framework indicating typical model scale ranges, relevant outputs for different applications, and high‐level fidelity levels (the shading indicates the computational cost). All the models share a common model evaluation framework although each model category has different quantities of interest (QoI) and performance metrics depending on the intended use (application).

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Further Reading

Landberg, L. Meteorology for Wind Energy: An Introduction. West Sussex, United Kingdom: Wiley; 2015, 224. ISBN: 978-1-118-91344-4.
Emeris, S. Wind Energy Meteorology: Atmospheric Physics for Wind Power Generation. Springer-Verlag Berlin Heidelberg; 2013, 212, ISBN-10: 3642305229.

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