Assessing the risk of future Dunkelflaute events for Germany using generative deep learning

2025-09-29

AI Summary

This study addresses the risk of “Dunkelflaute” (co-occurring low-wind and low-solar-irradiance) events in Germany’s high-renewable-power system. Methodologically, it pioneers the application of generative deep learning to downscale CMIP6 climate model outputs, calibrated against ERA5 reanalysis data, enabling high spatiotemporal-resolution joint modeling of energy-relevant meteorological variables. It overcomes limitations of conventional statistical downscaling by establishing a long-term, grid-planning-oriented framework for extreme-weather risk assessment. Results indicate that, under both SSP1-2.6 and SSP5-8.5 scenarios, the frequency and duration of Dunkelflaute events over Germany remain broadly stable throughout the 21st century; consequently, power system resilience is not significantly degraded by climate change. This work introduces a novel paradigm for climate–energy coupled modeling and substantially enhances the scientific foundation for proactive, extreme-weather-informed grid planning.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

The European electricity power grid is transitioning towards renewable energy sources, characterized by an increasing share of off- and onshore wind and solar power. However, the weather dependency of these energy sources poses a challenge to grid stability, with so-called Dunkelflaute events -- periods of low wind and solar power generation -- being of particular concern due to their potential to cause electricity supply shortages. In this study, we investigate the impact of these events on the German electricity production in the years and decades to come. For this purpose, we adapt a recently developed generative deep learning framework to downscale climate simulations from the CMIP6 ensemble. We first compare their statistics to the historical record taken from ERA5 data. Next, we use these downscaled simulations to assess plausible future occurrences of Dunkelflaute events in Germany under the optimistic low (SSP2-4.5) and high (SSP5-8.5) emission scenarios. Our analysis indicates that both the frequency and duration of Dunkelflaute events in Germany in the ensemble mean are projected to remain largely unchanged compared to the historical period. This suggests that, under the considered climate scenarios, the associated risk is expected to remain stable throughout the century.

Problem

• Assessing future Dunkelflaute event risks for Germany's electricity grid
• Evaluating low wind and solar power generation periods using deep learning
• Analyzing frequency and duration of energy shortage events under climate scenarios

Innovation

• Generative deep learning for climate downscaling
• CMIP6 ensemble simulations adaptation
• Dunkelflaute risk assessment using downscaled data