Salt Cavern Exergy Storage Capacity Potential of UK Massively Bedded Halites, Using Compressed Air Energy Storage (CAES)
Bibliographic record
Abstract
The increasing integration of large-scale electricity generation from renewable energy sources in the grid requires support through cheap, reliable, and accessible bulk energy storage technologies, delivering large amounts of electricity both quickly and over extended periods. Compressed air energy storage (CAES) represents such a storage option, with three commercial facilities using salt caverns for storage operational in Germany, the US, and Canada, with CAES now being actively considered in many countries. Massively bedded halite deposits exist in the UK and already host, or are considered for, solution-mined underground gas storage (UGS) caverns. We have assessed those with proven UGS potential for CAES purposes, using a tool developed during the EPSRC-funded IMAGES project, equations for which were validated using operational data from the Huntorf CAES plant. From a calculated total theoretical ‘static’ (one-fill) storage capacity exceeding that of UK electricity demand of ≈300 TWh in 2018, filtering of results suggests a minimum of several tens of TWh exergy storage in salt caverns, which when co-located with renewable energy sources, or connected to the grid for off-peak electricity, offers significant storage contributions to support the UK electricity grid and decarbonisation efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 source (direct Gemma or distilled Codex), 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".