Optimization of Integrated Hybrid Renewable Energy Systems for Hydrogen Refueling Stations
Bibliographic record
Abstract
Abstract This study investigates hydrogen production using renewable energy, with an emphasis on system reliability and hydrogen storage methods for periods of insufficient electric power. The study was conducted at a refueling station in Dumat Al-Jandal, Saudi Arabia. It utilized HOMER software to simulate different arrangements incorporating solar (PV) arrays, wind turbines (WT), converters, batteries, electrolyzers, and hydrogen tanks. The purpose is to optimize the system to fulfill a daily hydrogen need of 25 kg. The results indicate that the most efficient setup consists of a 208 kW photovoltaic (PV) array, 7 wind turbine (WT) units, a 71 kW converter, a 100 kW electrolyzer, a 100 kg hydrogen storage tank, and a 342-battery array. The arrangement attains a levelized energy cost of $0.452 per kilowatt-hour, a hydrogen production cost of $6.22 per kilogram, and a net present cost of $645,000, allowing a significant reduction of CO2 emissions by preventing the release of 372 metric tons of CO2 per year. The findings provide stakeholders with crucial information on optimizing hybrid renewable energy for hydrogen production at refueling stations. The study underscores the significant potential for integrating such systems into refueling infrastructures as a pathway toward environmentally sustainable and economically viable hydrogen production.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".