Wind farms as sensor arrays of turbulent boundary layer spatio-temporal flow structure

Abstract

Temporal fluctuations of wind farm-generated power arise from the interaction between atmospheric turbulence, turbine properties, and wind-farm layout. However, accurately characterizing these fluctuations remains an open challenge. We here present and extend an analytical framework to predict the temporal spectrum of wind farm power-fluctuations, and compare its predictions with detailed large-eddy-simulations (LES) of a wind farm operating within a conventionally neutral boundary layer. The modeling framework assembles several established concepts from turbulent boundary layer physics: a spatio-temporal turbulence spectral model accounting for mean advection and assuming random sweeping by large eddies, a top-down wind farm model of a fully developed wind turbine array boundary layer flow providing the required mean-flow and turbulence scales, and a spatial sampling kernel representing turbine positions and finite rotor size. The latter is extended to three dimensions to represent filtering of spatial fluctuations of turbulence along the vertical direction. Using only atmospheric, turbine, and layout parameters, the model predictions are evaluated against an extensive LES database of a large wind farm on flat terrain. The model accurately predicts the aggregate power frequency spectrum, including peaks associated with advection between turbine rows, the decay of inertial-range turbulence fluctuations due to rotor averaging, and spectra of aggregate power signals from various arrangements of groups of turbines within the array (e.g. staggered or random subsets). The ability to predict wind power fluctuation spectra from fundamental fluid dynamics and existing boundary layer turbulence models could help improve wind farm grid integration.