A Statistical and Machine Learning Framework for Operational Threshold Detection and Deployable Dispatch Controller Development in Hydrogen Multi-Energy Systems
arXiv:2606.14601v1 Announce Type: new Abstract: This study presents a statistical and machine learning framework for characterizing a hydrogen-based multi-energy system (H-MES) using one year of high-resolution operational data. Statistical analysis revealed a binary operation driven by renewable surplus, with solar irradiance explaining 45.7% of rank-based variance in hydrogen production, a large effect by conventional standards. Only high-irradiance periods triggered meaningful electrolyzer engagement, while electricity demand exerted a weaker inverse suppression effect ($\epsilon^2 = 0.126$
The increasing focus on renewable energy integration and the development of hydrogen infrastructure necessitates advanced control systems to optimize operational efficiency and reliability.
This framework provides a data-driven approach to optimize hydrogen multi-energy systems, critically impacting the feasibility and scaling of hydrogen as a clean energy vector, which is vital for industrial decarbonization.
The ability to accurately detect operational thresholds and deploy machine learning-driven dispatch controllers fundamentally alters how hydrogen production and multi-energy systems in general can be managed, moving from reactive to predictive optimization.
- · Renewable energy producers
- · Hydrogen infrastructure developers
- · Analytics and AI solution providers
- · Industrial energy consumers
- · Inefficient conventional energy producers
- · Legacy grid operators without intelligent control integration
Improved efficiency and reliability of hydrogen production from renewable sources.
Accelerated adoption of hydrogen in various industrial and energy sectors due to lower operational costs and enhanced stability.
Reduced reliance on fossil fuels as optimized hydrogen systems become competitive alternatives, contributing to global decarbonization goals.
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