Abstract
Abstract. Wind turbine wake models typically require approximations, such as wake superposition and deflection models, to accurately describe wake physics. However, capturing the phenomena of interest, such as the curled wake and interaction of multiple wakes, in wind power plant flows comes with an increased computational cost. To address this, we propose a new hybrid method that uses analytical solutions with an approximate form of the Reynolds-averaged Navier–Stokes equations to solve the time-averaged flow over a wind plant. We compare results from the solver to supervisory control and data acquisition data from the Lillgrund wind plant obtaining wake model predictions which are generally within 1 standard deviation of the mean power data. We perform simulations of flow over the Columbia River Gorge to demonstrate the capabilities of the model in complex terrain. We also apply the solver to a case with wake steering, which agreed well with large-eddy simulations. This new solver reduces the time – and therefore the related cost – it takes to simulate a steady-state wind plant flow (on the order of seconds using one core). Because the model is computationally efficient, it can also be used for different applications including wake steering for wind power plants and layout optimization.
Highlights
We present an improved formulation of the curled wake model (Martínez-Tossas et al, 2019) that can be used in the context of a wind power plant without the need to use a wake superposition method
This is a similar equation used in the original formulation of the curled wake model, but we focus on a new approach to derive the equations and some generalizations used for a wind plant approach as opposed to a single wind turbine wake
We presented a simplified and fast solver for wind turbine wakes based on the curled wake model presented in MartínezTossas et al (2019)
Summary
We present an improved formulation of the curled wake model (Martínez-Tossas et al, 2019) that can be used in the context of a wind power plant without the need to use a wake superposition method. The curled wake model uses a simplified version of the RANS equations to predict the wake of a wind turbine in yaw (Martínez-Tossas et al, 2019). Most wake models are used in the same manner by first computing the wake of the individual turbines and using a superposition method afterward to obtain the flow over the entire domain (Annoni et al, 2018) This new curled wake solver overcomes the use of a superposition method by solving the flow over the entire wind plant. This is done by solving only the streamwise component of the linearized RANS equations and parametrizing the effects of the spanwise and wall-normal components using semianalytical solutions
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