Presentations

Abstract

Europe’s wind fleet is increasingly exposed to volatile meteorological regimes, where rapidly evolving atmospheric dynamics can trigger rapid ramps, turbine icing events, and high-wind cutouts, leading to significant deviations between forecasted and actual production. As grid operators and asset owners face tighter balancing requirements, understanding how these extreme‑event dynamics unfold and how to quantify their uncertainty becomes mission-critical. In this talk, we present operational case studies from North American wind deployments that experienced these challenges. We then translate the case studies into European applications, focusing on how to express forecast uncertainty in ways that materially improve decision-making for traders, control centers, and asset managers. We evaluate multiple forecasting approaches designed specifically for extreme‑event risk and communicated meaningfully to forecast users: 1. Probability of Exceedance (POE) forecasts which combine numerical weather prediction model (NWP) output with machine learning (ML) models to quantify risk, not just predict the mean. 2. Scenario-based forecasts that predict a range of possible discrete outcomes for events of interest from NWP and ML models, providing users with structured information upon which decisions can be made during high-impact weather events. 3. Probabilistic ramp‑rate models that directly target changes in wind power output, as opposed to power output itself, providing early warning of user-defined ramps that traditional power‑centric models tend to miss. 4. Event-based alerts that translate key details of a possible extreme event into operationally relevant data points for decision-makers at single or multiple operating assets, such as timing windows, ramping amplitudes, spatial footprint, and severity of discrete meteorological variables.  5. At-risk Megawatt (MW) forecasting, a hybrid approach that quantifies potential production loss if an extreme event manifests, paired with a counterfactual “no‑impact” forecast to bound operational risk. For each method, we outline strengths and limitations of the output, including accuracy evaluation metrics and use cases in operational decision-making. Attendees will walk away with a practical understanding of probabilistic and extreme-weather forecast capabilities that are meteorologically robust and operationally actionable. The goal is to enhance the framework of forecast users decision-making strategy grounded on extreme-event forecasting capability regardless of their system complexity, mix of generating assets, or bidding strategies. Because each decision-maker’s application and risk tolerance are unique, multiple forecasting approaches often provide the best input for determining the optimal response to extreme weather events.