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Record W4281716105 · doi:10.1071/aj21305

Concurrent 2. Presentation for: Feasibility study of adiabatic compressed air energy storage in porous reservoirs

2022· article· en· W4281716105 on OpenAlexaff
Jason Czapla

Bibliographic record

VenueThe APPEA Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsKensington Health
Fundersnot available
KeywordsCompressed air energy storageEnergy storageEnvironmental scienceRenewable energyWind hybrid power systemsCompressed airPumped-storage hydroelectricityPetroleum engineeringProcess engineeringWaste managementEngineeringDistributed generationElectrical engineeringMechanical engineeringPower (physics)

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.255
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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