Supervisory Scheduling of Storage-Based Hydrogen Fueling Stations for Transportation Sector and Distributed Operating Reserve in Electricity Markets
Bibliographic record
Abstract
The proliferation of hydrogen fueling stations as a critical infrastructure is necessary for the successful materialization of hydrogen-powered vehicles. Such fueling stations can, in part, utilize the renewable/inexpensive electricity, which would otherwise be curtailed, to generate and store hydrogen. The stored hydrogen can later be used to serve the transportation sector and straightforwardly yield profit for the operator of the stations. The available energy in the storage stations, however, would not be utilized effectively during offpeak hydrogen demand by the transportation sector. While hydrogen fueling stations are primarily contemplated as the suppliers to hydrogen vehicles, this paper shows how the storage capacity in each station can be exploited to provide operating reserve (OR) to an electricity market. To that end, this paper proposes a new supervisory-based model for the optimal scheduling of distributed hydrogen storage stations for 1) energy supply to hydrogen-powered vehicles; and 2) OR provision to an electricity market. As such, the economic feasibility of the investment in such stations would be further intensified due to extra financial settlements for the stations via joint applications. This paper, then, unveils a model that brings about more opportunities for the deployment of hydrogen fueling stations, thereby further inspiring the private investment in such an area by private sectors. The efficacy and feasibility of the proposed model are validated using numerical illustration conducted on a test 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".