Local-Scale Framework for Techno-Economic Analysis of Subsurface Hydrogen Storage
Bibliographic record
Abstract
The energy sector is evolving toward increased reliance on renewable energy technologies to meet state, national, and organization decarbonization goals. This trend is creating challenges and opportunities with meeting current and future energy demand under the variable supply conditions that most renewables provide. Hydrogen (H2) is a promising energy carrier that may meet the need for both on-demand and long-duration storage to maintain energy security and resilience. Underground hydrogen storage (UHS) is a method of storing H2 in subsurface geological systems, such as depleted hydrocarbon reservoirs, salt caverns, saline aquifers, hard rock, and other engineered systems. UHS has the potential to store large quantities of H2 over time, providing a reliable source of energy while minimizing surface footprints at a lower investment cost compared to surface storage. Earlier work estimated that, if converted and retrofitted, existing underground natural gas storage (UGS) facilities in the U.S. can store approximately 327 TWh, or 9.8 million metric tons, of pure H2. However, a shift to pure H2 would decrease the collective working-gas energy of the UGS facilities by approximately 75% due to physical and chemical differences between natural gas and hydrogen. The same work also suggests that almost 75% of the existing UGS facilities in the U.S. could maintain current energy demand buffering using a blend of only 20% H2 to 80% natural gas, by volume at surface conditions. If we can take advantage of the suite of mature technologies of existing UGS facilities and natural gas utility systems to accelerate the transition to a hydrogen economy in the U.S., a 20% H2 blend could lead to a 6-7% reduction of greenhouse gas emissions for energy delivered through natural gas utility systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".