Observing and Simulating Wind-Turbine Wakes During the Evening Transition

Lee, Joseph C. Y.; Lundquist, Julie K.

doi:10.1007/s10546-017-0257-y

Cited by 46 publications

(42 citation statements)

References 67 publications

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“…CWEX-13 campaign took place between late June and early September 2013 in a wind farm in central Iowa, the same wind farm studied in previous CWEX campaigns; however, CWEX-13 focused on a part of the wind farm that is different from what is discussed in Rajewski et al (2013), Rhodes and Lundquist (2013), Mirocha et al (2015), and Lee and Lundquist (2017). The region exhibits strong diurnal cycles of atmospheric stability and frequent nocturnal low-level jets (Vanderwende et al, 2015).…”

Section: Cwex-13 Observational Datasetmentioning

confidence: 99%

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

2017

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Abstract. The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

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Section: Cwex-13 Observational Datasetmentioning

confidence: 99%

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

2017

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“…This campaign was a component of the larger CWEX project, which explored the interactions of wind turbines with crops, surface fluxes, and near-surface flows in different atmospheric stability regimes in flat terrain (Rajewski et al, 2013). Research facilitated by the CWEX projects include diurnal changes in observed turbine wakes (Rhodes and Lundquist, 2013), turbine interactions with moisture and carbon dioxide fluxes , LES modelling of turbine wakes in changing stability regimes , nocturnal low-level jet (LLJ) occurrences (Vanderwende et al, 2015), diurnal changes of the microclimate near wind turbines (Rajewski et al, 2016), multiple-wake interactions (Bodini et al, 2017), the evolution of turbine wakes during the evening transition (Lee and Lundquist, 2017), and coupled mesoscalemicroscale modelling (Muñoz-Esparza et al, 2017).…”

Section: Observationsmentioning

confidence: 99%

“…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%

“…8d). Moreover, strongly stable conditions tend to have stronger and more distinct wakes (Abkar and Porté-Agel, 2015b; Lee and Lundquist, 2017;Magnusson and Smedman, 1994;Rhodes and Lundquist, 2013).…”

Section: Power Simulationsmentioning

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%

See 2 more Smart Citations

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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One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

et al. 2020

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One-way nested mesoscale to microscale simulations of an onshore wind farm have been performed nesting the Weather Research and Forecasting (WRF) model and our in-house high-resolution large-eddy simulation code (UTD-WF). Each simulation contains five nested WRF domains, with the largest domain spanning the North Texas Panhandle region with a 4 km resolution, while the highest resolution (50 m) nest simulates microscale wind fluctuations and turbine wakes within a single wind farm. The finest WRF domain in turn drives the UTD-WF LES higher-resolution domain for a subset of six turbines at a resolution of ∼ 5 m. The wind speed, direction, and boundary layer profiles from WRF are compared against measurements obtained with a met-tower and a scanning Doppler wind LiDAR located within the wind farm. Additionally, power production obtained from WRF and UTD-WF are assessed against supervisory control and data acquisition (SCADA) system data. Numerical results agree well with the experimental measurements of the wind speed, direction, and power production of the turbines. UTD-WF high-resolution domain improves significantly the agreement of the turbulence intensity at the turbines location compared with that of WRF. Velocity spectra have been computed to assess how the nesting allows resolving a wide range of scales at a reasonable computational cost. A domain sensitivity analysis has been performed. Velocity spectra indicate that placing the inlet too close to the first row of turbines results in an unrealistic peak of energy at the rotational frequency of the turbines. Spectra of the power production of a single turbine and of the cumulative power of the array have been compared with analytical models.

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Observing and Simulating Wind-Turbine Wakes During the Evening Transition

Cited by 46 publications

References 67 publications

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

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

One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

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