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

TY - JOUR

T1 - Wind-farm power prediction using a turbulence-optimized Gaussian wake model

AU - Zehtabiyan-Rezaie, Navid

AU - Perto Justsen, Josephine

AU - Abkar, Mahdi

PY - 2024/12

Y1 - 2024/12

N2 - 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 a refined 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.

AB - 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 a refined 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.

KW - Wind-farm wakes

KW - Farm-to-farm interactions

KW - Power prediction

KW - Gaussian wake model

KW - Wake-added turbulence

U2 - 10.1016/j.weer.2024.100007

DO - 10.1016/j.weer.2024.100007

M3 - Journal article

SN - 2950-3604

VL - 2

JO - Wind Energy and Engineering Research

JF - Wind Energy and Engineering Research

M1 - 100007

ER -