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

Published as a workshop paper at "Tackling Climate Change with Machine Learning", ICLR 2024 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. Renewable energy, a key player in combating climate change, is gaining increasing global adoption Energy 2020 (2010); United nations (2015). In 2022, renewables contributed 13.1% to the US's primary energy consumption U.S. Energy Information Administration (EIA) (2022) and 21.5% to its utility-scale electricity generation.