Optimized scheduling of off-grid wind power hydrogen production system considering battery and electrolyzer degradation
Bibliographic record
Abstract
The development of off-grid wind power to hydrogen systems is crucial for promoting renewable energy, reducing dependence on fossil fuels, and achieving sustainable energy development.However, the volatility of wind power can lead to problems such as shortened service life of batteries and electrolyzers.This study proposes an optimized scheduling strategy for off-grid wind power hydrogen generation systems, considering the degradation of batteries and electrolyzers, with a focus on the impact of battery state of charge (SOC) overrun and electrolyzer overload on system operation.A voltage degradation model for electrolyzers was established by analyzing different operating conditions, aiming to improve utilization capacity and reduce degradation costs.Additionally, a degradation model for energy storage batteries was developed, considering factors such as cycle depth, cycle number, and SOC overrun, to optimize charging and discharging operations, extend battery life, and reduce degradation costs.The effectiveness of the proposed scheduling strategy was verified through detailed simulation analysis, demonstrating improved wind power consumption capacity, slowed degradation of batteries and electrolyzers, and ultimately enhanced economic benefits for the system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".