## 4 Conclusions  
Solar and wind energy are central to achieving net-zero emissions, but their intermittency, unpredictability, and location-dependent nature pose challenges for energy system reliability. The mismatch between renewable generation and electricity demand often necessitates backup fossil-fuel-based power. To address this, probabilistic modeling, particularly Monte Carlo simulations, is used to analyze uncertainties by generating synthetic datasets for wind speed, temperature, and solar irradiance based on 22 years of data. These models employ probability distribution functions (PDFs) to simulate annual renewable energy availability. In the current study, beta is used for solar irradiance, Weibull for wind speed, and normal for temperature. [PERSON] formula suggests that 93 simulation runs are sufficient to achieve a 95% confidence level.  
Periods of Dunkelflaute (DF), prolonged low renewable energy production, highlight the need for energy storage, demand response, and backup solutions. These critical periods, where renewable generation drops below a set threshold (typically 10-20%), present a major challenge in countries like Germany, which still relies on fossil fuels during winter due to underdeveloped large-scale chemical storage. Addressing DF requires consistent energy mixes, including mainly battery storage and backup solutions, always depending on regional characteristics. However, a location's geographical and climatic characteristics significantly influence the measures taken. High wind-high solar locations benefit from natural complementaries between the two resources. In such regions, like coastal areas or deserts, storage systems might primarily address short-term variability, such as night-time consumption or brief wind calmness. In contrast, inland areas or regions with consistently overcast and low-wind conditions might struggle to meet baseline demand without a combination of long-duration storage, demand-side adjustments, and interconnections to regions with better resource availability.  
Future research should prioritize the integration of advanced optimization techniques to enhance the efficiency and reliability of renewable energy deployment. This includes addressing short-term fluctuations through improved forecasting, real-time grid management, and demand-side strategies, as well as tackling long-duration storage challenges with innovative energy storage solutions tailored to location-specific characteristics. An integrative approach that balances economic viability, environmental sustainability, and energy security is essential to ensure a resilient transition toward net-zero emissions. By aligning technological advancements with policy frameworks and investment strategies, the path to sustainable development can be both scalable and adaptable to diverse regional energy needs.  
Conceptualization, C.B.-E. and L.A.-P.; methodology, C.B.-E., L.A.-P. and D.B.; software, L.A.-P. and D.B.; validation, C.B.-E., L.A.-P. and D.B.; formal analysis, C.B.-E. and L.A.-P.; investigation, C.B.-E., L.A.-P. and D.B.; resources, C.B.-E., D.B. and L.A.-P.; data curation, C.B.-E., L.A.-P. and D.B.; writing--original draft preparation, C.B.-E. and L.A.-P.; writing--review and editing, D.B.; visualization, C.B.-E. and L.A.-P.; supervision, C.B.-E., L.A.-P. and D.B.; project administration, C.B.-E., L.A.-P. and D.B.; funding acquisition, C.B.-E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** We would like to thank our colleagues at IIE and DEIOAC for their invaluable support and collaboration, which have contributed significantly to the achievement of this work.  
**Conflicts of Interest:** The authors declare no conflicts of interest.