Analytical Framework Predicts Wind Farm Power Spectra Using Turbulence and Layout Parameters Alone

The framework integrates a random-sweeping spatio-temporal spectrum, a fully developed turbine array boundary layer model, and an extended vertical filtering kernel. It requires only atmospheric conditions, turbine characteristics, and row spacing. When tested on an extensive LES database, the model reproduces advection-induced spectral peaks between rows, the inertial-range roll-off from rotor averaging, and spectra from staggered or random turbine subsets without empirical tuning.

This approach treats the entire farm as a distributed sensor array for boundary-layer structures. It directly addresses grid-integration challenges by forecasting fluctuation statistics that determine reserve requirements and storage sizing. Prior observational studies captured only point measurements or short records; the new model supplies a physics-based extrapolation across layouts and scales.

Next steps include coupling the spectrum predictor to mesoscale weather models and testing against SCADA data from operating farms. Validation thresholds should target spectral energy errors below 10 percent across 0.001–0.1 Hz bands within two years to influence real-time dispatch algorithms.