AI-enhanced national-scale assessment of meteorological risk hotspots for wind and hydropower

Austria’s expansion of renewable energy generation, together with projected increases in climate variability under climate change, is expected to substantially increase the vulnerability of the energy system to weather-driven disruptions. This challenge is particularly acute in the Alpine region, where complex topography and land–atmosphere interactions drive highly heterogeneous and rapidly evolving meteorological conditions. These Alpine-specific processes give rise to localized extreme events that are difficult to forecast and pose significant challenges for energy system operation, infrastructure planning, and grid stability.

The project EnergyProtect aims to identify present and future meteorological risk hotspots, defined as locations of renewable energy infrastructure with elevated exposure to weather conditions that can impair energy production or destabilize the electricity grid. We focus on hazardous meteorological phenomena relevant to wind and hydropower systems, including wind speed ramping, high wind and gust events, and high-precipitation episodes. Rapid wind speed changes can induce mechanical stress on wind turbines and other energy-related infrastructure, reduce operational efficiency, and trigger sudden power fluctuations that challenge grid balancing. Sustained high winds and gusts may lead to turbine cut-outs, structural damage, and pronounced power ramping events. In hydropower systems, extreme precipitation can increase tailwater levels, thereby reducing generation efficiency, while also elevating the risk of electrical faults and infrastructure damage in flood-prone areas.

The meteorological hazard assessment combines several advanced modelling approaches. Key components include (i) physics-informed machine learning techniques to detect and classify patterns of adverse weather, (ii) an ensemble of dynamically downscaled climate simulations at convection-permitting resolutions to capture Alpine-scale processes, and (iii) probabilistic estimates of event frequency, return periods, and future changes in intensity. This framework enables a consistent characterization of both present-day and future extreme weather hazards, while explicitly accounting for model and scenario uncertainty.

These meteorological datasets are subsequently integrated into a spatio-temporal exposure analysis of renewable energy assets to identify current and projected risk hotspots. We present preliminary results for multiple severity levels of wind speed and storm/gust ramping and high wind events with the potential to cause turbine cut-outs, efficiency losses, or grid destabilization. Using hourly meteorological datasets at spatial resolutions ranging from 1 to 30 km, we map the average annual occurrence of these risk events across Austria and quantify associated uncertainties. The results provide a robust basis for climate-resilient planning and adaptation strategies for Austria’s current and future energy system.