A case study of wind farm effects using two wake parameterizations in the Weather Research and Forecasting (WRF) model (V3.7.1) in the presence of low-level jets

Larsén, Xiaoli Guo; Fischereit, Jana

doi:10.5194/gmd-14-3141-2021

Cited by 21 publications

(17 citation statements)

References 41 publications

(58 reference statements)

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“…Projected cumulative power output for these 5 days is a 6.94u10 12 W based on output from the EWP parameterization and a 5.55u10 12 W based on output from Fitch (a 20% difference, Table 1). These values equate to capacity factors (CF) of 33.4% and 26.7%, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Wakes in and between very large offshore arrays

Pryor

Barthelmie

Shepherd

et al. 2022

J. Phys.: Conf. Ser.

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Projected power output and wake extents are presented from new simulations with the Weather Research and Forecasting (WRF) model v4.2.2 for the large offshore wind energy lease areas along the U.S. east coast. These simulations assume nearly 2000 IEA 15 MW reference turbines are deployed with a spacing equal to the mean of smaller European offshore wind farms (7.7 rotor diameters). Results show marked differences across two wind farm parameterizations. Generally, the modified Fitch parameterization (wherein TKE generation by the rotor has been decreased) generates lower power production estimates, and more spatially extensive and deeper wind farm wakes than are manifest in output from the Explicit Wake Parameterization (EWP). For example, under conditions of moderate freestream wind speeds (∼ 4-10 ms−1 at hub-height) and turbulent kinetic energy (TKE ∼ 0.2 to 1 m2s−2), cumulative power output (summed over all 15 lease areas) is substantially greater (∼ 25% higher) in output from EWP than Fitch. These differences have real implications for power production and thus both expected revenues and grid integration. The cumulative power production and mean normalized wake extent also exhibit sensitivity to the order in which the overlapping inner domains are computed and the number of inner domains. This effect is smaller than differences from two wind farm parameterizations. Analyses focusing on the seven adjoining lease areas south of Massachusetts indicate differences in the two schemes are magnified over the largest offshore wind clusters (with expected installed capacity of > 10 GW and spatial extent of 3675 km2).

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Section: Resultsmentioning

confidence: 99%

“…x The second analysis compared EWP and Fitch also with aircraft observations [12] and found: "The WRF model with both the Fitch and EWP schemes can capture the wind speed field rather well and consistently. ….…”

Section: Wind Farm Parameterizations In Wrfmentioning

confidence: 99%

Wakes in and between very large offshore arrays

Pryor

Barthelmie

Shepherd

et al. 2022

J. Phys.: Conf. Ser.

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“…It is also noticeable that the reduction in wind speed shows a negative trend with increasing height. While the maximum height of the lidar measurements may be 200 m and most of the wind turbines in the surrounding area have a total height of between 140 m and 180 m, some interaction effects can also be detected above the wind farm due to vertical wake expansion (Siedersleben et al, 2018a;Larsén and Fischereit, 2021). However, as measurements at a height of 200 m are only partially influenced by the wind turbines, as they are no longer completely behind the rotor surface, this probably explains the lower wind speed difference u dif f (WRF) at 200 m. The wind speed differences can be further emphasized for different atmospheric stability conditions within a region.…”

Section: Directional and Stability Dependence Of Cluster Wakesmentioning

confidence: 99%

Offshore wind farm cluster wakes as observed by a long-range scanning wind lidar

Cañadillas¹,

Beckenbauer²,

Trujillo³

et al. 2022

Preprint

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Abstract. To establish long-term flow measurements for the validation of wake models, a scanning Doppler wind lidar system was installed at the western edge of the wind farm Gode Wind 1 in the German Bight for a period of five months. The main goal was to detect the wakes from clusters for different wind directions and atmospheric stabilities. The lidar data are categorized into five sectors based on the different upstream conditions. The influence of wakes and atmospheric stability are initially investigated with respect to airborne measurements collected within the lidar measurement period. Mesoscale simulations are used as a reference for the free wind flow. The percent wind speed difference downstream of the wind farm clusters and at the location of the scanning lidar measurements (1.5 km downstream the closest wind farm) can reach a maximum of about 30 % for a mean wind speed of 10 m s−1 depending on the wind direction and under stable atmospheric conditions. A good agreement between mesoscale simulations (without any wind farm parameterization) and lidar measurements is found for undisturbed wind sectors and unstable and near-neutral atmospheric conditions. By taking into account the surrounding wind farms through a parameterization in the mesoscale simulations, the agreement of the model with the measurements is relatively good for unstable and near-neutral conditions, including sectors influenced by wind farm wakes. For stable conditions, however, the highest discrepancies between simulations and observations occur. Overall, the scanning lidar dataset can be used as a validation tool for wake model validations.

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“…Wind farms can affect local circulation and regional weather; if sufficiently large, they may even alter the structure of weather systems like low-level jets (Larsén and Fischereit 2 ). Using remote sensing, Christiansen and Hasager 3 reported that as wind flow passed through large arrays of wind turbines, the mean wind speed was reduced by 8–9% and wake length could exceed 20 km.…”

Section: Introductionmentioning

confidence: 99%

“…They also claimed that the previous studies with the bug need to be revised to evaluate the impacts of wind farms. Larsén and Fischereit 2 used the bug-fixed version of WRF to study the wind farm effects in the presence of low-level jets. They found that the value of the correction factor has a significant impact on the results.…”

Section: Introductionmentioning

confidence: 99%

Impacts of offshore wind farms on the atmospheric environment over Taiwan Strait during an extreme weather typhoon event

Lee

Kueh

et al. 2022

Sci Rep

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Wind is one of the cleanest renewable energy resources. Through the “Thousand Wind Turbines Project”, Taiwan is planning to increase the proportion of power generation from renewable energy and has set a target of 5.7 GW for offshore wind by 2025. The effects of future offshore wind farms (OWFs) over the Taiwan Strait on the atmospheric environment have not been evaluated. This study examined the potential effects of proposed OWFs on the atmospheric environment if the OWFs had existed during Tropical Storm Haitang (2017) by using Weather Research and Forecasting (WRF) model. A small set of ensemble simulations was conducted for studying the sensitivity of the ambient conditions in the region to the wind farm locations, the number and density of the turbines, and the initial time of simulations. Following the landfall and northward movement of Tropical Storm Haitang, a series of complex interactions between the typhoon circulation and the wind farm emerged, including small time slots of wake effect and mountain blocking effect. The combination of these rapidly changing OWFs-related effects contributed to a weak reduction in precipitation (− 1.08 mm) and hub-height wind speed (− 0.25 m s−1), as well as minimal warming near the surface (+ 0.13 °C) over southern Taiwan.

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A case study of wind farm effects using two wake parameterizations in the Weather Research and Forecasting (WRF) model (V3.7.1) in the presence of low-level jets

Cited by 21 publications

References 41 publications

Wakes in and between very large offshore arrays

Wakes in and between very large offshore arrays

Offshore wind farm cluster wakes as observed by a long-range scanning wind lidar

Impacts of offshore wind farms on the atmospheric environment over Taiwan Strait during an extreme weather typhoon event

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