Generalized coupled wake boundary layer model: applications and comparisons with field and LES data for two wind farms

Stevens, Richard J. A. M.; Gayme, Dennice F.; Meneveau, Charles

doi:10.1002/we.1966

Cited by 49 publications

(53 citation statements)

References 39 publications

(149 reference statements)

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“…In distributed roughness models, turbines act as distributed roughness elements in which the ambient atmospheric flow is modified. Furthermore, there are some models that combine kinematic models with distributed roughness models (e.g., [10,11]). In the present study, we propose a new wind farm analytical model, which is a type of kinematic model, to predict the performance of wind farms of arbitrary size and layout.…”

Section: Introductionmentioning

confidence: 99%

Analytical Modeling of Wind Farms: A New Approach for Power Prediction

2016

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Wind farm power production is known to be strongly affected by turbine wake effects. The purpose of this study is to develop and test a new analytical model for the prediction of wind turbine wakes and the associated power losses in wind farms. The new model is an extension of the one recently proposed by Bastankhah and Porté-Agel for the wake of stand-alone wind turbines. It satisfies the conservation of mass and momentum and assumes a self-similar Gaussian shape of the velocity deficit. The local wake growth rate is estimated based on the local streamwise turbulence intensity. Superposition of velocity deficits is used to model the interaction of the multiple wakes. Furthermore, the power production from the wind turbines is calculated using the power curve. The performance of the new analytical wind farm model is validated against power measurements and large-eddy simulation (LES) data from the Horns Rev wind farm for a wide range of wind directions, corresponding to a variety of full-wake and partial-wake conditions. A reasonable agreement is found between the proposed analytical model, LES data, and power measurements. Compared with a commonly used wind farm wake model, the new model shows a significant improvement in the prediction of wind farm power.

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Section: Introductionmentioning

confidence: 99%

Analytical Modeling of Wind Farms: A New Approach for Power Prediction

2016

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“…As we focus on the optimal spacing for very large wind farms, we assume that the average power output of the wind turbine is the same as the turbine power output in the fully developed region of the wind farms. The power ratio P 1 =P 1 is given by the ratio of cubed mean velocity at hub-height with wind turbines compared with the reference case without wind farms: In earlier work, we showed 13,25 that the CWBL model gives improved predictions for the power output in the fully developed region compared with the top-down model introduced by Calaf et al 21 or stand-alone wake models. 3 In addition, the CWBL model is able to predict the difference between the performance of different wind farms geometries.…”

Section: Input Predictions From the Cwbl Modelmentioning

confidence: 87%

“…25 Because of the two-way coupling between the wake and the top-down model, w f depends on parameters such as the streamwise distance between the turbines s x , the relative positioning of the turbines and the wake coefficient in the fully developed region of the wind farm k w,1 . As we focus on the optimal spacing for very large wind farms, we assume that the average power output of the wind turbine is the same as the turbine power output in the fully developed region of the wind farms.…”

Section: Input Predictions From the Cwbl Modelmentioning

confidence: 99%

“…In this paper, we use the coupled wake boundary layer (CWBL) model of Stevens et al 13,14,25 to account for wake effects in the capital/O&M cost curve, and then combine it with an improved methodology for estimating the cost curve for very large farms. Because the CWBL model is an analytical model, it allows a very fast evaluation of the wind farms' performance.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 3, we introduce the CWBL model 13,25 that will be used to estimate the power and turbulence intensity for different wind farm designs. In Section 4, the modeling approach introduced by Meyers and Meneveau 24 is extended to include linear costs (e.g., the costs of cables, roads and resistance losses that scale linearly with the distance between turbines) while in Section 5 the optimal turbine spacing is determined by optimizing the profit per unit area of the wind farms instead of the normalized power per unit cost.…”

Section: Introductionmentioning

confidence: 99%

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Combining economic and fluid dynamic models to determine the optimal spacing in very large wind farms

et al. 2016

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Wind turbine spacing is an important design parameter for wind farms. Placing turbines too close together reduces their power extraction because of wake effects and increases maintenance costs because of unsteady loading. Conversely, placing them further apart increases land and cabling costs, as well as electrical resistance losses. The asymptotic limit of very large wind farms in which the flow conditions can be considered 'fully developed' provides a useful framework for studying general trends in optimal layouts as a function of dimensionless cost parameters. Earlier analytical work by Meyers and Meneveau (Wind Energy 15, 305-317 (2012)) revealed that in the limit of very large wind farms, the optimal turbine spacing accounting for the turbine and land costs is significantly larger than the value found in typical existing wind farms. Here, we generalize the analysis to include effects of cable and maintenance costs upon optimal wind turbine spacing in very large wind farms under various economic criteria. For marginally profitable wind farms, minimum cost and maximum profit turbine spacings coincide. Assuming linear-based and area-based costs that are representative of either offshore or onshore sites we obtain for very large wind farms spacings that tend to be appreciably greater than occurring in actual farms confirming earlier results but now including cabling costs. However, we show later that if wind farms are highly profitable then optimization of the profit per unit area leads to tighter optimal spacings than would be implied by cost minimization. In addition, we investigate the influence of the type of wind farm layout.

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Effects of turbine spacing on the power output of extended wind‐farms

2015

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We present results from large eddy simulations (LES) of extended wind-farms for several turbine configurations with a range of different spanwise and streamwise spacing combinations. The results show that for wind-farms arranged in a staggered configuration with spanwise spacings in the range ≈ [3.5, 8]D, where D is the turbine diameter, the power output in the fully developed regime depends primarily on the geometric mean of the spanwise and streamwise turbine spacings. In contrast, for the aligned configuration the power output in the fully developed regime strongly depends on the streamwise turbine spacing and shows weak dependence on the spanwise spacing. Of interest to the rate of wake recovery, we find that the power output is well correlated with the vertical kinetic energy flux, which is a measure of how much kinetic energy is transferred into the wind-turbine region by the mean flow. A comparison between the aligned and staggered configurations reveals that the vertical kinetic energy flux is more localized along turbine rows for aligned windfarms than for staggered ones. This additional mixing leads to a relatively fast wake recovery for aligned wind-farms.

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Generalized coupled wake boundary layer model: applications and comparisons with field and LES data for two wind farms

Cited by 49 publications

References 39 publications

Analytical Modeling of Wind Farms: A New Approach for Power Prediction

Analytical Modeling of Wind Farms: A New Approach for Power Prediction

Combining economic and fluid dynamic models to determine the optimal spacing in very large wind farms

Effects of turbine spacing on the power output of extended wind‐farms

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