Dunkelflaute Events and Grid Impact

• Dunkelflaute events are defined as extended periods of low renewable output, identified using statistical thresholds like a 48-hour capacity factor below 0.06.
• They exhibit non-Gaussian, fat-tailed fluctuations and temporal clustering that challenge grid stability during high-demand winter periods.
• Mitigating these events requires long-duration backup solutions, advanced forecasting, and enhanced cross-border interconnection to ensure resource adequacy.

Dunkelflaute events are extended periods characterized by substantially reduced output from variable renewable energy sources (wind and solar), often occurring simultaneously over large geographic areas and coinciding with high electrical demand, especially in winter. These events present distinct challenges for grid stability and resource adequacy in systems striving for decarbonization and relying heavily on weather-dependent generation.

1. Physical and Statistical Characterization of Dunkelflaute Events

Dunkelflaute is strictly defined by multi-hour to multi-week periods where renewable availability falls below a system-critical threshold. Empirically, many studies use a moving average of the capacity factor—e.g., for Germany, a Dunkelflaute is identified when the 48-hour running mean capacity factor of combined wind and solar falls below 0.06 (Mockert et al., 2022, &&&1&&&). Similar thresholding applies at the European portfolio level, using multi-threshold frameworks that set the event-defining limit relative to the long-run mean (Kittel et al., 30 Sep 2024).

Wind and solar power outputs do not exhibit simple Gaussian statistics; instead, they show intermittent, non-Gaussian behaviors manifesting as fat-tailed ramp events on short time scales (Anvari et al., 2016). Turbulence-induced variations result in power spectra scaling as $S(f) \sim f^{-5/3}$, typical of Kolmogorov turbulence, while increments obey $q$-exponential distributions:

$$P(X_\tau) = A \cdot [1 - \beta(1-q)|X_\tau|]^{1/(1-q)}$$

This statistical structure implies high probabilities of abrupt drops, meaning aggregation at larger spatial scales does not fully mitigate Dunkelflaute risk.

Wind extremes also exhibit temporal clustering—substantiated by high coefficients of variation and Allan factor scaling, especially in complex terrain and at higher altitudes (Telesca et al., 2018). This clustering causes extended gaps in generation, reinforcing Dunkelflaute episode persistence.

Recent deep learning–based downscaling of GCM outputs confirms the historical event definition remains robust under future climate scenarios, with national statistics for Germany indicating stable frequency and duration of Dunkelflaute events under both SSP2-4.5 and SSP5-8.5 (Strnad et al., 29 Sep 2025).

2. Meteorological Drivers and Weather Regime Dependency

Dunkelflaute events are meteorologically driven by persistent large-scale anticyclonic (high-pressure) regimes that suppress wind and solar output. In Central Europe, the European Blocking regime is responsible for most observed Dunkelflauten, characterized by minimal pressure gradients and uniform calm conditions (Mockert et al., 2022). Scandinavian and Greenland Blocking regimes also play a major role, the latter being associated with pronounced cold anomalies, thus compounding stress on energy systems via increased demand for heating.

Regime lifetimes during Dunkelflaute episodes are significantly extended (41% longer for Greenland Blocking), facilitating predictability at the subseasonal to seasonal (S2S) scale. Cold and weak-wind events show positive co-occurrence correlations, especially over land, and are best modeled using Gaussian copula frameworks conditioned on both month and regime (Tedesco et al., 2022). Maximum compound event frequencies (three days per month in winter) coincide with negative NAO phases and blocking (Tedesco et al., 2022, Mockert et al., 2022).

3. Quantification Methodologies and Event Detection

Dunkelflaute event identification methods are varied, with threshold-based run-length schemes, moving average–based algorithms (FMBT/VMBT), and Sequent Peak Algorithms (SPA) prevalent (Kittel et al., 9 Feb 2024, Kittel et al., 30 Sep 2024, Biewald et al., 18 Dec 2024). The choice of threshold—absolute or relative—critically affects frequency, duration, and severity statistics. Relative thresholds ($thres = frac \times \overline{avail}$) enable system-scale comparability.

Event detection is further refined by incorporating demand via the residual load metric (demand minus renewable generation). The Otero’22 method has emerged as a preferred approach for operational adequacy screening, demonstrating the highest skill in reproducing resource adequacy simulation results when applied to daily resolution residual load data (Biewald et al., 18 Dec 2024). Severity and duration metrics are constructed as normalized sums over threshold violations.

For power system planning, multi-threshold, variable-duration methods are advocated, as these provide unique, non-overlapping event sets and better represent the compound nature of extreme shortages (Kittel et al., 30 Sep 2024, Kittel et al., 9 Feb 2024).

4. System Impact and Flexibility Requirements

Dunkelflaute events critically constrain grid operation and reliability. Conventional flexibility options—such as demand response and battery storage—are insufficient for events lasting more than a few days to weeks (Cozian et al., 2023). Absolute shortfalls (~60 GW across Europe, 10–15 GW for France during a 20-year return event) establish required backup or storage capacity.

Long-duration storage, notably hydrogen-based systems, is indispensable for bridging such gaps. In modeling exercises, the most extreme pan-European drought (winter 1996/97) requires 159–351 TWh storage, a significant fraction (3–7%) of annual consumption even under perfect interconnection (Kittel et al., 26 Nov 2024, Kittel et al., 30 Sep 2024). The drought mass metric, aggregating severity and duration over a range of thresholds, serves as an operational driver for storage discharge decisions.

Portfolio effects from combining wind and solar PV, as well as geographical balancing across interconnected grids, significantly alleviate both the frequency and duration of Dunkelflaute events—reducing maximum event durations by up to 65% when moving from national to fully interconnected (“copperplate”) European systems (Kittel et al., 30 Sep 2024, Kittel et al., 26 Nov 2024). Nonetheless, simultaneous multi-region Dunkelflaute events represent a cost driver in robust optimization models, increasing system costs non-linearly up to 71% compared with base cases (Bernecker et al., 15 Jul 2025). Only extensive roll-out of long-duration flexibility and coordinated cross-border infrastructure investment can systematically mitigate such risks.

5. Forecastability, Climate Dependencies, and Future Projections

Dunkelflaute episodes embedded in persistent weather regimes enable S2S forecast opportunities, providing grid operators with actionable lead times (often >10 days) (Mockert et al., 2022, Tedesco et al., 2022). Gaussian copula–based joint modeling of cold and weak-wind events conditioned on weather regimes informs probabilistic forecasting and operational risk assessment (Tedesco et al., 2022).

High-resolution downscaled GCM projections indicate that, under current emission scenarios, the statistical properties of Dunkelflaute events (frequency, duration, spatial distribution) for Germany are expected to remain stable throughout the 21st century (Strnad et al., 29 Sep 2025). This suggests that, from a meteorological perspective, no substantial increase in grid stress should be anticipated purely due to climate change, although localized pattern shifts may occur.

However, system impacts depend also on shifts in demand, electrification trends (e.g., heating), and storage or interconnection expansion. Model sensitivity analyses show only minor reductions in long-duration storage needs when moderate amounts of firm zero-emission backup are introduced; thus, system resiliency will remain highly dependent on superior flexibility and balancing capacity (Kittel et al., 26 Nov 2024, Bernecker et al., 15 Jul 2025).

6. Comparative Evaluation and Policy Implications

Methodological harmonization across technologies and regions requires thresholds to be scaled relative to intrinsic system availability (full-load hours or mean capacity factor) and time series to be normalized and aggregated according to installed shares (Kittel et al., 9 Feb 2024, Kittel et al., 30 Sep 2024).

For practical system screening and adequacy planning, residual load–based event definitions—such as those implemented in the Otero’22 method—offer optimal trade-offs between simplicity, data requirements, and predictive skill (Biewald et al., 18 Dec 2024). These are easily integrated into stress-testing protocols for grid operators, facilitating preselection of critical historical and projected climate years for full economic dispatch simulation.

Policy recommendations derived from cross-paper findings include:

• Prioritizing cross-border interconnection and diversification of the renewable portfolio to exploit portfolio and balancing effects.
• Scaling up long-duration storage, especially for systemic bottlenecks in Central European regions.
• Modelling investment and system adequacy using multi-year weather records to capture extreme variability, with robust optimization frameworks (ARO) to identify and plan for worst-case compound events (Bernecker et al., 15 Jul 2025).
• Designing market mechanisms or policy incentives for rarely dispatched backup technologies, particularly hydrogen-based storage and zero-emission firm generation.
• Forecasting regime-dependent risk for proactive grid operations and reserve scheduling (Tedesco et al., 2022, Mockert et al., 2022).

7. Current Controversies and Open Research Directions

The definition of Dunkelflaute events remains sensitive to threshold selection, averaging intervals, and the physical variables used (availability, residual load, resource adequacy outcomes) (Kittel et al., 9 Feb 2024, Kittel et al., 30 Sep 2024). A systematized consensus is emerging that multi-threshold, duration-flexible, and residual load–based algorithms are preferable for detection and risk quantification (Biewald et al., 18 Dec 2024).

Open research questions include:

• Translating regional atmospheric patterns into pan-European risk maps under evolving climate and technological mixes.
• Incorporation of multi-energy system couplings (heating, hydrogen, storage) into Dunkelflaute risk models (Cozian et al., 2023).
• Investigation of tail distributions for return times of the most extreme events, especially given limited observational records (Cozian et al., 2023).
• Integration of generative deep learning frameworks for downscaling climate data to support stochastic risk simulations in operational planning (Strnad et al., 29 Sep 2025).

In summary, Dunkelflaute events are meteorologically forced extended periods of extreme low renewable output that challenge decarbonized electricity systems. Their robust detection, characterization, and the associated flexibility requirements are central to planning and resiliency of future power systems, demanding harmonized methodologies, system-wide balancing mechanisms, and strategic investment in long-duration storage and cross-border infrastructure.