Concurrent 2. Presentation for: Feasibility study of adiabatic compressed air energy storage in porous reservoirs
Bibliographic record
Abstract
Presented on Tuesday 17 May: Session 2 The Australian electricity sector is undergoing a transformation in which variable renewable energy (VRE) is becoming a dominant generator. VRE sources such as wind and solar have intermittent generation profiles influenced by weather and climate with daily and seasonal variations. To support high penetration rates of VRE, energy storage is required to store energy during times of oversupply and discharge energy during times of under supply. Compressed Air Energy Storage (CAES) is a promising, economic technology to compliment battery and Pumped Hydro by providing storage over a medium duration (4–12 h). CSIRO and MAN-ES conducted a feasibility study on Adiabatic-CAES (A-CAES) based on the premise of storing compressed air in a permeable subsurface reservoir (i.e. depleted gas reservoir). The design assumptions regarding the storage reservoir are based on previous work conducted on behalf of Pacific Gas and Electric (PG&E) which consisted of drilling test wells and conducting air injection and withdrawal tests to determine suitability for a 300 MW-10 h facility. The plant design and equipment are based on commercially available components. This work found that A-CAES has the potential to achieve >60% round trip efficiency and provide levelised cost of storage (LCOS) as low as ~A$108/MWh. To access the presentation click the link on the right. To read the full paper click here
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.126 | 0.013 |
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".