Optimal Sizing of a Stand-alone Renewable-Powered Hydrogen Fueling Station
Bibliographic record
Abstract
In this paper, we propose a model for optimal sizing of the key components of a renewable-powered hydrogen production and fueling station. Renewable energy generated from on-site wind and solar resources are used to generate hydrogen using electrolysis. The generated hydrogen is stored in an on-site hydrogen storage tank and used to fuel a total hydrogen demand of two ton per day. The model is based on a stochastic mixed-integer linear programming formulation that solves for optimal sizing of the wind turbines, the photo-voltaic arrays, the hydrogen production capacity of the electrolyzers, and the storage capacity of the hydrogen tank. Numerical results are provided using available cost parameters in the context of Canadian market. The simulation results show that an (almost) green hydrogen fueling station powered by only wind and solar energy could produce hydrogen at under 6.8 $/kg when financing costs are considered or under 5 $/kg when financing costs are neglected. A hybrid fueling station that supplies hydrogen using a mix of on-site generated green hydrogen and imported blue hydrogen (<18 %), could produce hydrogen at under 5 $/kg considering financing cost or under 3 $/kg when such costs can be neglected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".