Optimizing industrial compressed air energy storage performance: A novel exergoeconomic framework via pressure-temperature dependent cost analysis
Bibliographic record
Abstract
Over the past two decades, the assessment of Compressed Air Energy Storage (CAES) systems has gained significant attention for global sustainability. While research on material selection based on site conditions exists, a comprehensive framework for comparative analysis and guidance is lacking. This study explores the interplay of multi-stage compression and expansion in CAES plants, focusing on how industrial temperature classifications influence production costs. Three advanced adiabatic CAES (AA-CAES) systems — Low, Medium, and High-Temperature CAES (LTA-CAES, MTA-CAES, HTA-CAES) — are scrutinized via exergoeconomic. The outcomes highlighted the exergetic cost for HTA-CAES at 0.081 $/kWh, while MTA-CAES and LTA-CAES demonstrate lower exergetic costs of production at 0.076 $/kWh and 0.075 $/kWh, respectively. This cost disparity is a direct consequence of employing high-temperature materials in TES construction. Further granularity is added through an exploration of TES materials, revealing a critical trade-off between exergetic costs and materials. Solid TES materials demonstrate cost advantages over liquid counterparts at specific charging pressures. Subsequently, a multi-objective optimization identifies the MTA-temperature as the most beneficial range for optimal CAES installation. • The main highlights are as follow: • What is the effect of plant's multi-staging on materials and therefore cost? • Industrial-based Temperature Classes: Material Challenges and Cost Trade-offs. • Liquid and Solid TES Material analysis Exposing a Critical Trade-offs in Exergetic Costs. • Optimizing Plant Installments: Balancing Technical Performance and Cost Viability. • Introducing comprehensive equations, addressing underground storage and its salvage cost.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".