Optimal Coordinated Control of Multi-Renewable-to-Hydrogen Production System for Hydrogen Fueling Stations
Bibliographic record
Abstract
Under the pressure of climate change, the demands for alternative green hydrogen (H2) production methods have been on the rise to conform to the global trend of transition to a H2society. This article proposes a multirenewable-to-hydrogen production method to enhance the green H2production efficiency for renewable-dominated hydrogen fueling stations (HFSs). In this method, the aqueous electrolysis of native biomass can be powered by wind and solar generations based on electrochemical effects, and both electrolysis current and temperature are taken into account for facilitating on-site H2production and reducing the electricity consumption. Moreover, a capsule network based H2demand forecasting model is formulated to estimate the gas load for HFS by extracting the underlying spatial features and temporal dependencies of traffic flows in the transportation network. Furthermore, a hierarchical coordinated control strategy is developed to suppress high fluctuations in electrolysis current caused by volatility of wind and solar outputs based on model predictive control framework. Comparative studies validate the superior performance of the proposed methodology over the power-to-gas scheme on electrolysis efficiency and economic benefits.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".