Posters

Abstract

Stable conditions within the atmospheric boundary layer represent a major, and often under-acknowledged, source of bias and uncertainty in wind resource assessment and Energy Yield Assessment (EYA). Under stable stratification—most commonly at night or during periods of strong surface cooling—vertical wind shear and directional veer typically increase, while near-surface turbulence intensity and vertical mixing are reduced. Consequently, hub‑height and rotor‑layer winds can differ strongly from measurements taken at conventional mast heights. Stable conditions strongly influence wind characteristics, turbulence levels, and the formation and evolution of wind turbine wakes. The influence of these conditions is not yet represented in most industry-standard engineering wake models, particularly on a time series basis. Atmospheric stability may be classified using various methods, such as the Monin–Obukhov Length (MOL) and the Bulk Richardson number. These metrics can be derived from site-specific measurements when available, or from reanalysis data sources such as ERA5. Standard EYA approaches typically rely on long-term averages of wind speed and direction, assuming neutral atmospheric conditions and thus failing to capture stability-driven variations in wind shear, turbulence intensity, and wake behaviour. These simplifications can introduce systematic biases in predicted energy production, turbine loading, and associated uncertainties, especially in complex terrain, coastal regions, and sites with pronounced diurnal or seasonal stratification patterns. By integrating atmospheric stability—derived from on-site measurements, remote sensing, reanalysis products, or mesoscale models—into flow modelling, wake-loss estimation, and long-term corrections, EYA can better represent real operating conditions. This real-case study work at the Western Green Energy Hub (WGEH) outlines a practical, measurement-based framework to quantify and embed atmospheric stability effects into wind resource and EYA processes. The methodology combines multi-level on-site measurements from tall meteorological masts and/or remote sensing (LiDAR or SoDAR) with mesoscale modelling or reanalysis data. By explicitly accounting for stability, the framework aims to reduce structural model bias, yield more reasonable estimates of wake and other operational losses, and introduce an explicit uncertainty component associated with the stability representation. The results indicate that accounting for atmospheric stability is critical for robust project design, bankable assessments, and risk-informed decision-making in modern wind farm development. This is particularly important for large-scale, atmospherically stable sites, such as WGEH, where staged project planning needs to take long wakes and operational interactions into account throughout the project life.