Research on power fluctuation matching control of large-scale wind power hydrogen production system based on multi-factor cyclic queue method
Bibliographic record
Abstract
Coupling and controlling renewable energy wind power and large-scale electrolytic hydrogen production system to adapt to the fluctuation of wind power can obtain green hydrogen energy from the source and reduce the waste phenomenon of renewable energy, so as to promote the development of a green and low-carbon energy society. In this paper, by constructing a large-scale electrolytic hydrogen production system with an alkaline water electrolyzer as the main body, a multi-factor cyclic queue control strategy developed based on the state parameters of the electrolyzer device is coupled, and the strategy simulation work is carried out in combination with the 241h wind power duration curve of a certain place. The simulation results show that the control strategy not only meets the requirements of the hydrogen production system for fast and accurate response to wind power fluctuation input, but also optimizes and balances the overall running time of the electrolytic hydrogen production system, effectively improving the life consistency of the hydrogen production system. The results of this study can provide a new technical development direction for large-scale consumption and application of renewable energy.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".