Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage
This addresses the problem of energy storage variability for renewable energy systems, but it is incremental as it focuses on improving existing UHS methods rather than introducing a new paradigm.
The paper tackles the challenge of high computational costs in Underground Hydrogen Storage (UHS) simulations, which hinder its deployment for renewable energy storage, by proposing a data-driven approach and a roadmap for integrating machine learning to enable large-scale implementation.
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.