Dynamic simulation of a compressed CO2 energy storage system
Bibliographic record
Abstract
Energy storage systems are known to play a key role in the increasing electricity production of intermittency renewable energies. In the past few years, Compressed Carbon Dioxide Energy Storage (CCES) have been proposed in the scientific literature as a new Compressed Gas Energy Storage (CGES), which work usually with air as working fluid (CAES). The main drawbacks of CAES are the geographical restriction with the need of an underground reservoir. CO2 has the advantage of a critical temperature near the ambient temperature with a larger density. Therefore, liquid aboveground storage under non-extreme temperature condition are conceivable. Until now, researchers have been focused on layout’s propositions under steady-state assumption. To improve the understanding of CCES and their behavior under realistic conditions, is crucial to perform dynamic studies with successive cycles. In this paper, a dynamic model of a CCES is presented. It considers the temperature and pression variation in the CO2 reservoirs using real data from a photovoltaic installation. Compared to steady-state CCES systems, the results from this study show a drop in performance. This is especially due to the non-isobaric tanks and Joule effect to stored more energy. This study highlights the need to develop dynamic models of CCES to assess supplemental constraints. In fact, they can result in major changes of performances for aboveground CCES compared to analyses under the steadystate assumption.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".