Thermoelectrochemical model for RFB with an application at a grid level for peak shaving to reduce cost of the total electricity
Bibliographic record
Abstract
Reliable, low-cost energy storage solutions are needed to manage variability, pro-vide reliability, and reduce grid-infrastructure costs. Redox flow batteries (RFB) area grid-scale storage technology that has the potential to provide a range of services.Desirable characteristics are long cycle life, high efficiency, and high energy density.A key challenge for aqueous redox flow battery systems is thermal sensitivity. Oper-ating temperature impacts electrolyte viscosity, species solubility, reaction kinetics,and efficiency. Systems that avoid the need for active thermal management whileoperating over a wide temperature range are needed. A promising RFB chemistry isiron-vanadium because of the use of low-cost iron. This is an analysis of the thermalresponse of on Iron-Vanadium (Fe/V) RFB using a zero-dimensional electrothermalmodel. The model accounts for the reversible entropic heat of the electrochemicalreactions, irreversible heat due to overpotentials, and the heat transfer between thestack and environment. Performance is simulated using institutional load data forenvironmental conditions typical of Canadian jurisdictions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".