A coupled mesoscale–microscale framework for wind resource estimation and farm aerodynamics

Gopalan, Harish; Gundling, Christopher; Brown, Kevin; Roget, Beatrice; Sitaraman, Jayanarayanan; Mirocha, Jeffrey D.; Miller, W. Thomas

doi:10.1016/j.jweia.2014.06.001

Cited by 57 publications

(35 citation statements)

References 22 publications

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“…Figure shows the benefit of using finer spatial grid resolutions to resolve spatial structures in the wind flow. LES and CFD can be run with generic inflow conditions, including representative large‐scale conditions, however finer spatial grid resolutions have been found to give better wind results at small scales . LES simulations have also been used to simulate cloud processes, which are important for solar energy applications, although LES cloud modeling results do not show a consistent improvement compared to mesoscale NWP models .…”

Section: Methodsmentioning

confidence: 99%

“…LES and CFD can be run with generic inflow conditions, including representative large-scale conditions, however finer spatial grid resolutions have been found to give better wind results at small scales. 7,8 LES simulations have also been used to simulate cloud processes, 9 which are important for solar energy applications, although LES cloud modeling results do not show a consistent improvement compared to mesoscale NWP models. 10 Currently, LES and CFD are too expensive for long-duration, timeresolved simulations (e.g., 1 or more years with outputs every minute to 10 min) for commercial applications, so research is ongoing into ways to implement work flows that reduce the computational resources that are required.…”

Section: Flow Modelingmentioning

confidence: 99%

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Wind and solar resource data sets

Clifton

Draxl

Badger

et al. 2017

WIREs Energy & Environment

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The range of resource data sets spans from static cartography showing the mean annual wind speed or solar irradiance across a region to high temporal and high spatial resolution products that provide detailed information at a potential wind or solar energy facility. These data sets are used to support continental‐scale, national, or regional renewable energy development; facilitate prospecting by developers; and enable grid integration studies. This review first provides an introduction to the wind and solar resource data sets, then provides an overview of the common methods used for their creation and validation. A brief history of wind and solar resource data sets is then presented, followed by areas for future research. WIREs Energy Environ 2018, 7:e276. doi: 10.1002/wene.276 This article is categorized under: Wind Power > Climate and Environment Solar Heating and Cooling > Climate and Environment Energy Infrastructure > Climate and Environment

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Section: Methodsmentioning

confidence: 99%

Section: Flow Modelingmentioning

confidence: 99%

Wind and solar resource data sets

Clifton

Draxl

Badger

et al. 2017

WIREs Energy & Environment

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“…To evaluate the results in terms of mesoscale modeling context of wind farms, we perform spatial averaging of the governing standard RANS equations that form the basis of a large variety of existing mesoscale codes [17]. The mass and momentum equations can be represented as:…”

Section: Parametrization Of Mesoscale Governing Equations In Wind Farmsmentioning

confidence: 99%

On Interactions Between Wind Turbines and the Marine Boundary Layer

Siddiqui

Rasheed

Tabib

et al. 2017

Volume 10: Ocean Renewable Energy

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Most mesoscale models are developed with grid resolution in the range of kilometers. Therefore, they may require spatial averaging to analyze flow behavior over the domain of interest. In doing so, certain important features of sub-grid scales are lost. Moreover, spatial averaging on the governing equations results in additional terms known as dispersive fluxes. These fluxes are ignored in the analysis. The aim of this paper is to identify the significance of these fluxes for accurate assessment of flow fields related to wind farm applications. The research objectives are hence twofold: 1) to quantify the impact of wind turbines on MBL characteristics. 2) to account for the magnitude of dispersive fluxes arising from spatial averaging and make a comparison against the turbulent flux values. To conduct the numerical study the NREL 5MW reference wind turbine model is employed with a RANS approach using k-ε turbulence model. The results are presented concerning spatially averaged velocity, wake deficit behind the turbine, dispersive and turbulent fluxes.

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“…In the field of wind energy, Katurji [9] simulated the turbulent flow of complex terrain by coupling the WRF model and WindSim model. Gopalan [10] compared several different CFD models by coupling them with the WRF model. The results showed that the CFD model can refine the flow structure and simulate the unsteady variation of the vortex in the wind field.…”

Section: Introductionmentioning

confidence: 99%

Modelling of a CFD Microscale Model and Its Application in Wind Energy Resource Assessment

Yue

2016

MATEC Web Conf.

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Abstract. The prediction of a wind farm near the wind turbines has a significant effect on the safety as well as economy of wind power generation. To assess the wind resource distribution within a complex terrain, a computational fluid dynamics (CFD) based wind farm forecast microscale model is developed. The model uses the Reynolds Averaged Navier-Stokes (RANS) model to characterize the turbulence. By using the results of Weather Research and Forecasting (WRF) mesoscale weather forecast model as the input of the CFD model, a coupled model of CFD-WRF is established. A special method is used for the treatment of the information interchange on the lateral boundary between two models. This established coupled model is applied in predicting the wind farm near a wind turbine in Hong Gang-zi, Jilin, China. The results from this simulation are compared to real measured data. On this basis, the accuracy and efficiency of turbulence characterization schemes are discussed. It indicates that this coupling system is easy to implement and can make these two separate models work in parallel. The CFD model coupled with WRF has the advantage of high accuracy and fast speed, which makes it valid for the wind power generation.

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A coupled mesoscale–microscale framework for wind resource estimation and farm aerodynamics

Cited by 57 publications

References 22 publications

Wind and solar resource data sets

Wind and solar resource data sets

On Interactions Between Wind Turbines and the Marine Boundary Layer

Modelling of a CFD Microscale Model and Its Application in Wind Energy Resource Assessment

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