A comprehensive Thermoeconomic assessment of liquid air and compressed air energy storage with solid/liquid/hybrid thermal energy storage (TES): Addressing air and TES material storage cost impacts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Present study undertakes a comprehensive thermoeconomic evaluation of Liquid Air Energy Storage (LAES) and Compressed Air Energy Storage (CAES), with a focus on cost implications concerning exergy and energy storage, material containment, and TES units. By addressing previous uncertainties, we aim to enable informed decision-making in the energy sector. The investigation unveils a significant spatial disparity between CAES and LAES systems. CAES allocates considerable space to Compressed Air Storage (CAS), while LAES dedicates a similar volume to TES unit containment as Liquid Air Tanks (LAT). When considering all spatial factors affecting energy density, LAES demonstrates an impressive energy density advantage, surpassing CAES by a factor of 6.9. In the assessment of air storage, we emphasized the importance of cushion gas analysis for underground storage (UG), highlighting the cost-effectiveness of salt caverns, especially for short-term power generation in CAES. In contrast, our analysis of LAES shows that costs increase significantly in systems with lower power capacities, particularly those below 400 MW. Regarding the cost impact of various TES materials, our findings reveal distinct patterns. For CAES, solid TES materials are cost-effective for pressures below 110 bar, while liquid TES materials are more suitable beyond this threshold. In LAES, the dynamics differ. Solid TES materials exhibit considerably higher costs, making hybrid TES, especially at charging and discharging pressures of 150/90 bar, an attractive option, offering a 14 % cost reduction. Our comprehensive evaluation highlights substantially higher storage costs for LAES due to extensive TES material and air storage requirements. Economic analysis indicates that non-storage equipment costs are similar for both technologies, but LAES faces a 3.1 times higher cost for material containment, resulting in a 65 % higher total cost than CAES. The choice between CAES and LAES depends on project-specific needs and budget constraints, with LAES showing exceptional potential, particularly in areas where geological limitations affect CAES feasibility. • Comprehensive cryogenic tank size calculations reduce errors at low power. • Hybrid TES advancements: CAES, solid TES below 110 bar, liquid TES above; LAES, 14 % cost reduction with hybrid TES. • Air storage is economical in LAES, yet drives higher total plant costs for efficient alternatives. • LAES holds potential but grapples with elevated storage expenses, including TES materials and air storage. • CAES plant demonstrates significantly better cost compared to LAES.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it