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

• URL: http://arxiv.org/abs/2404.03222v1
• Date: Thu, 4 Apr 2024 06:10:57 GMT
• Title: Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage
• Authors: Alvaro Carbonero, Shaowen Mao, Mohamed Mehana,
• Abstract summary: Underground Hydrogen Storage (UHS) emerges as a promising long-term storage solution. This paper introduces its widespread implementation from a data-driven perspective. It outlines a roadmap for integrating machine learning into simulations, thereby facilitating the large-scale deployment of simulations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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