Collective and nonlinear structure of wind power correlations

TL;DR

This paper analyzes the complex correlation structure of wind power fluctuations in a large farm, revealing universal, collective, and nonlinear correlations that impact power variability and grid integration strategies.

Contribution

It uncovers the presence of nonlinear and long-range correlations in wind farm output, providing new insights into wind power variability and its underlying dynamics.

Findings

Identification of a dynamical scaling transition in turbine correlations

Detection of long-range non-Gaussian feature correlations

Highlighting the role of nonlinear correlations in power fluctuations

Abstract

We describe the correlation structure of wind power fluctuations in a farm of 80 turbines, sampled over 5 years. We report the presence of universal, collective, and nonlinear correlations, responsible for the excess persistency and intermittency of farm-aggregated power output. A first cross-correlation analysis of turbine production reveals a dynamical scaling transition (\`a la Family-Vicszek) from local decoherence to large-scale turbulence-driven scaling, and responsible for the geographical smoothing effect, previously reported beyond farm scale [M. M. Bandi, Phys. Rev. Lett. 118, 028301 (2017)]. A second bivariate analysis shows the long-range correlation of non-Gaussian features, responsible for their amplification in total farm output. These findings provide a new perspective on wind power variability, highlighting the importance of nonlinear correlations in power production dynamics. By better characterising these fluctuations, our results can inform strategies for grid management, storage optimization, and wind farm design, ultimately improving the integration of wind energy into modern power systems.