Wind-farm power prediction using a turbulence-optimized Gaussian wake model

TL;DR

This paper introduces an optimized Gaussian wake model incorporating a turbulence formulation that improves wind-farm power predictions by reducing overestimation and enhancing accuracy in intra- and inter-farm wake modeling.

Contribution

It presents a novel turbulence-optimized formulation integrated with Gaussian wake models, improving prediction accuracy for wind farm power output.

Findings

Reduces overestimation of power in wind farm models

Enhances accuracy of wake and turbulence predictions

Effective for both standalone and clustered wind farms

Abstract

In this study, we present an improved formulation for the wake-added turbulence to enhance the accuracy of intra-farm and farm-to-farm wake modeling through analytical frameworks. Our goal is to address the tendency of a commonly used formulation to overestimate turbulence intensity within wind farms and to overcome its limitations in predicting the streamwise evolution of turbulence intensity beyond them. To this end, we utilize high-fidelity data and adopt an optimization technique to derive an optimized functional form of the wake-added turbulence. We then integrate the achieved formulation with a widely used Gaussian wake model to study various intra-farm and farm-to-farm scenarios. The outcomes reveal that the new methodology effectively addresses the overestimation of power in both standalone wind farms and those impacted by upstream counterparts. Our new approach meets the need for accurate and lightweight models, ensuring the effective coexistence of wind farms within clusters as the wind-energy capacity rapidly expands.