Speaker
Description
EnergyProtect aims to identify present and future risk hotspots, defined as sites of renewable energy infrastructure with elevated exposure to potentially disruptive meteorological conditions. We i) use physics-informed ML to detect patterns of adverse weather, ii) dynamically downscale ensemble time slices to convection permitting resolutions, and iii) estimate uncertainties, return periods and changes in intensity. These datasets are then overlaid with renewable energy infrastructure to identify current and future risk hotspots. We present preliminary results for different severity levels of wind speed ramping and high wind events, that have potential to cause turbine cut-outs, reduced efficiency, or grid balance destabilization. We map the average annual occurrence of such risk events using multiple meteorological datasets (hourly, 1–30 km resolution), thereby identifying key hotspots in Austria and quantifying related uncertainties.
