Development of degradation cost models for electrolyzers and fuel cells considering megawatt-scale applications
Bibliographic record
Abstract
To accelerate the large-scale integration of renewables for transition to a low-carbon energy system, the deployment of megawatt-scale electrolyzers and fuel cells systems is important. For their widespread integration and effective operations, the appropriate consideration of electrolyzer and fuel cell degradation is necessary. To this effect, this study presents two novel comprehensive degradation cost models, which are developed based on their dynamic degradation rates and life-cycle operating hours. The proposed degradation cost models are state transition-based method and ramp rate-based method , designed to quantify degradation as a function of input/output current density and ramp rates, respectively. To ensure their practical application in megawatt-scale power system operations, wherein their dispatch is typically based on electric power rather than electrochemical current density, these models were reformulated in terms of the power consumption/production. To estimate the parameters of the proposed degradation cost models, three regression approaches — linear, quadratic and third-degree polynomial — were carried out. Comparative analysis demonstrates that higher-degree polynomial models offer improved accuracy by capturing the nonlinear degradation behavior during dynamic operations, with state transition-based models consistently showing superior performance. Furthermore, the prediction performance of the estimation models are compared with the actual costs, followed by a sensitivity analysis of the estimated cost parameters.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".