Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage

TL;DR

This paper proposes integrating machine learning with underground hydrogen storage to reduce computational costs and enable large-scale deployment, supporting renewable energy resilience and climate change mitigation.

Contribution

It introduces a data-driven approach to underground hydrogen storage and outlines a roadmap for machine learning integration to enhance scalability.

Findings

Machine learning can significantly reduce simulation costs for UHS.

A strategic roadmap for ML integration in UHS is proposed.

Enhanced UHS deployment supports renewable energy stability.

Abstract

To address the urgent challenge of climate change, there is a critical need to transition away from fossil fuels towards sustainable energy systems, with renewable energy sources playing a pivotal role. However, the inherent variability of renewable energy, without effective storage solutions, often leads to imbalances between energy supply and demand. Underground Hydrogen Storage (UHS) emerges as a promising long-term storage solution to bridge this gap, yet its widespread implementation is impeded by the high computational costs associated with high fidelity UHS simulations. This paper introduces UHS from a data-driven perspective and outlines a roadmap for integrating machine learning into UHS, thereby facilitating the large-scale deployment of UHS.