Towards efficient methods for estimating spatio-temporal wind energy yields in mountainous regions

Complex mountain winds provide a largely unknown wind energy potential. Mountainous terrain influences air flow by e.g., wind flow sheltering, ridge acceleration, channelling, deflections, blocking and recirculation. Its impact on the energy production of wind turbines has not yet been thoroughly quantified, but various studies show that it could be significant. To accurately assess the wind energy potential in mountainous terrain, spatio-temporal wind fields capturing local wind-topography interactions are required. Ground measurements can retrieve spatio-temporal wind fields, but even with a dense weather station network, atmospheric models are still needed to capture the full spatial variability. However, it is challenging to generate the necessary fine-scale wind fields over long timescales and large regions computationally efficiently. Wind farm planning in mountainous regions is therefore much more challenging and uncertain than in flat areas.

Here, we present our concept that addresses this challenge by evaluating and enhancing various state-of-the art computationally efficient downscaling methods (statistical and dynamical). These methods generate highly resolved spatio-temporal wind fields, considering dominant local wind-topography interactions. Using these fields, we can derive time-resolved wind energy yield potential. The evaluation involves assessing the methods across fine spatial scales (e.g., dekameter scale), large spatial extents (up to tens of kilometers), high temporal resolution (e.g., hourly scale), and long timescales (several years) in real Swiss mountain settings using wind field and energy production measurements. Our overall goal is to provide wind modelers and energy planners with recommendations for efficient methods for obtaining highly resolved spatio-temporal wind fields, enabling accurate energy yield estimations in mountainous terrain.