Wake downstream of the Lillgrund wind farm - A Comparison between LES using the actuator disc method and a Wind farm Parametrization in WRF

Eriksson, Olle; Lindvall, Johannes; Breton, Simon-Philippe; Ivanell, Stefan

doi:10.1088/1742-6596/625/1/012028

Cited by 37 publications

(51 citation statements)

References 11 publications

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“…Porté-Agel et al [12], and Wu and Porté-Agel [14] performed LESs of the Horns Rev wind farm and compared the turbine power output data from the Horns Rev simulations to the field measurements reported by Barthelmie et al [22,25], showing good agreement between the data sets. Churchfield et al [17], Creech et al [23], Eriksson et al [18], and Nilsson et al [19] showed that the simulated power production in the Lillgrund farm agrees well with field measurements and the overall wind farm efficiency is well predicted. The agreement between the results of the previously mentioned wind farm LESs and the field measurements demonstrated the robustness of the LES technique for the study of ABL flows and their interaction with wind farms.…”

Section: Introductionmentioning

confidence: 81%

Flow Adjustment Inside and Around Large Finite-Size Wind Farms

Porté‐Agel

2017

Energies

117

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Abstract:In this study, large-eddy simulations are performed to investigate the flow inside and around large finite-size wind farms in conventionally-neutral atmospheric boundary layers. Special emphasis is placed on characterizing the different farm-induced flow regions, including the induction, entrance and development, fully-developed, exit and farm wake regions. The wind farms extend 20 km in the streamwise direction and comprise 36 wind turbine rows arranged in aligned and staggered configurations. Results show that, under weak free-atmosphere stratification (Γ = 1 K/km), the flow inside and above both wind farms, and thus the turbine power, do not reach the fully-developed regime even though the farm length is two orders of magnitude larger than the boundary layer height. In that case, the wind farm induction region, affected by flow blockage, extends upwind about 0.8 km and leads to a power reduction of 1.3% and 3% at the first row of turbines for the aligned and staggered layouts, respectively. The wind farm wake leads to velocity deficits at hub height of around 3.5% at a downwind distance of 10 km for both farm layouts. Under stronger stratification (Γ = 5 K/km), the vertical deflection of the subcritical flow induced by the wind farm at its entrance and exit regions triggers standing gravity waves whose effects propagate upwind. They, in turn, induce a large decelerating induction region upwind of the farm leading edge, and an accelerating exit region upwind of the trailing edge, both extending about 7 km. As a result, the turbine power output in the entrance region decreases more than 35% with respect to the weakly stratified case. It increases downwind as the flow adjusts, reaching the fully-developed regime only for the staggered layout at a distance of about 8.5 km from the farm edge. The flow acceleration in the exit region leads to an increase of the turbine power with downwind distance in that region, and a relatively fast (compared with the weakly stratified case) recovery of the farm wake, which attains its inflow hub height speed at a downwind distance of 5 km.

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Section: Introductionmentioning

confidence: 81%

Flow Adjustment Inside and Around Large Finite-Size Wind Farms

Porté‐Agel

2017

Energies

117

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“…The model is integrated for four days from 0000 UTC August 25, 2017 to 0000 UTC August 29, 2017 with a 6 hour spin-up. The 1.5-order, 2.5 level MYNN PBL scheme (Nakanishi and Niino 2009) is selected since the wind farm parameterization is dependent on this scheme and it is widely used in the literature , Volker et al 2015, Eriksson et al 2015. The Kain-Fritsch convective parameterization scheme (Kain andFritsch 1992, Kain 2004) is used to predict the convective component of precipitation and the landsurface model is Noah (Ek et al 2003).…”

Section: Setupmentioning

confidence: 99%

Precipitation reduction during Hurricane Harvey with simulated offshore wind farms

Yang

Yan

Archer

2018

Environ. Res. Lett.

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Hurricane Harvey brought to the Texas coast possibly the heaviest rain ever recorded in US history, which then caused flooding at unprecedented levels. Previous studies have shown that large arrays of hypothetical offshore wind farms can extract kinetic energy from a hurricane and thus reduce the wind and storm surge. This study quantitatively tests whether the hypothetical offshore turbines may also affect precipitation patterns. The Weather Research Forecast model is employed to model Harvey and the offshore wind farms are parameterized as elevated drag and turbulent kinetic energy sources.Model results indicate that the offshore wind farms have a strong impact on the distribution of accumulated precipitation, with an obvious decrease onshore downstream of the wind farms, and an increase in offshore areas, upstream of or within the wind farms. Compared with the control case with no wind turbines, increased horizontal wind divergence and lower vertical velocity are found where precipitation is reduced onshore, whereas increased horizontal wind convergence and higher vertical velocity occur upstream of or within the offshore wind farms. The sensitivity to the size of the offshore array, inter-turbine spacing, and the details of the wind farm parameterization are assessed. The results suggest that large arrays of offshore wind turbines can effectively protect the coast from heavy rain during hurricanes and that smart layouts with fewer turbines over smaller areas can be almost as effective as those with more turbines over larger areas.

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“…The vertical levels are further stretched beyond the boundary layer. In past research involving the WRF WFP scheme, the selections of vertical resolution within the rotor layer include 9-18 m in , about 10-16 m in Volker et al (2015), about 15 m in Fitch et al (2012Fitch et al ( , 2013a and , about 20 m in Miller et al (2015) and Vautard et al (2014), about 22 m in Lee and Lundquist (2017), and about 40 m in Eriksson et al (2015) and Jiménez et al (2015). Moreover, the Mellor-Yamada-Nakanishi-Niino (MYNN) level 2.5 planetary boundary layer (PBL) scheme is required for the WFP in the WRF model version 3.8.1 .…”

Section: Modellingmentioning

confidence: 99%

“…The WRF WFP has been widely used to assess the impacts of onshore and offshore wind farms at different spatial scales and in different stability regimes (Eriksson et al, 2015;Fitch et al, 2013a, b;Jiménez et al, 2015;Lee and Lundquist, 2017;Miller et al, 2015;Vautard et al, 2014). Whereas WFP predictions have been compared to power production of offshore wind farms for a limited set of WSs (Jiménez et al, 2015), here we explore a range of WSs, wind direction (WD), turbulence, and atmospheric stability conditions.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

Lee

Lundquist

2017

Geosci. Model Dev.

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Abstract. Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of windturbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

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Wake downstream of the Lillgrund wind farm - A Comparison between LES using the actuator disc method and a Wind farm Parametrization in WRF

Cited by 37 publications

References 11 publications

Flow Adjustment Inside and Around Large Finite-Size Wind Farms

Flow Adjustment Inside and Around Large Finite-Size Wind Farms

Precipitation reduction during Hurricane Harvey with simulated offshore wind farms

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

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