Compressed air energy storage (CAES): current status, geomechanical aspects and future opportunities
Bibliographic record
Abstract
Abstract A compressed air energy storage (CAES) facility provides value by supporting the reliability of the energy grid through its ability to repeatedly store and dispatch energy on demand. Two main advantages of CAES are its ability to provide grid-scale energy storage and its utilization of compressed air, which yields a low environmental burden, being neither toxic nor flammable. The focus of this review paper is to deliver a general overview of current CAES technology (diabatic, adiabatic and isothermal CAES), storage requirements, site selection and design constraints. We discuss underground storage options suitable for CAES, including submerged bladders, underground mines, salt caverns, porous aquifers, depleted reservoirs, cased wellbores and surface pressure vessels. A geomechanical perspective is provided regarding the pressure limits for these options. The impacts of cyclic injection and withdrawal of compressed air, and the importance of caprock assessments with porous rock CAES, are also discussed. In addition, we provide an overview of the large-scale CAES facilities that are currently active or under development and a cost comparison of the diabatic, adiabatic and isothermal CAES options. Lastly, we outline major challenges and future opportunities for CAES and the top priorities for research, industry and stakeholders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".