Sensitivity Analysis of Subsurface Hydrogen and Thermal Energy Storage with Insights from a Review of Techno-Economic and Life-Cycle Assessments

Abstract Renewable energy is gaining momentum as the answer for the world greenhouse gas (GHG) emission issues. However, the unpredictable nature of wind and solar energy puts immense pressure on the electric grid, leading to substantial energy losses, as seen in Texas. While storage technologies hold promises in addressing this challenge, their integration remains limited due to inefficiencies and an incomplete understanding of their impact, highlighting the need for further study and development. This paper reviews the results and approaches of existing techno-economic analysis (TEA) and life-cycle assessments (LCA) of two subsurface energy storage technologies: subsurface hydrogen (H2) storage and geothermal energy storage, while addressing the gap in TEA and LCA. In addition, this study integrates TEA and LCA frameworks to perform a sensitivity analysis, evaluating the influence of various factors across multiple phases—including production, processing withdrawal, and power generation—on simulation outcomes, overall efficiency, cost, and environmental impacts. The review revealed that while TEA remains the dominant approach, LCA is increasingly recognized as a critical complement—offering a more comprehensive understanding of environmental impacts throughout a system's life span. Most hydrogen-related studies continue to focus on the production phase, particularly via electrolysis, with limited attention to subsurface storage in porous formations such as aquifers. Similarly, geothermal research is primarily centered on electricity generation, while aquifer thermal energy storage (ATES) is often studied only for heating and cooling applications, not power production. As a result, there is a noticeable gap in integrated TEA–LCA assessments specifically targeting long-duration subsurface energy storage technologies in porous media. In addition, the conducted probabilistic and one-at-a-time (OAT) sensitivity analyses revealed how variations in curtailed energy availability, electrolyzer efficiency, and reservoir performance parameters influence system efficiency, levelized cost, and greenhouse gas emissions. These findings emphasize the need for more site-specific modeling and suggest that subsurface hydrogen storage may offer improved efficiency when key parameters are optimized. The sensitivity analysis adds further insight by quantifying how critical variables influence system performance. Together with the literature review, this study contributes to a more informed basis for evaluating long-duration subsurface storage technologies.