Optimal Scheduling of Electrolysis Hydrogen Production and Storage for Decarbonized Steelmaking with Capacity Auction Participation
Bibliographic record
Abstract
Hydrogen has been recently considered as a potential clean alternative to decarbonize the iron and steel industry. However, it remains less competitive to natural gas or coke due to high costs of production and delivery. Therefore, there is a pressing need to find solutions to reduce or offset these costs. In this regard, this paper introduces an optimal scheduling model for on-site electrolysis hydrogen production and storage in a hydrogen-powered steelmaking facility. The model aims to minimize the total operating costs of the hydrogen system via: i) participating in the capacity auction market as a form of grid service provision, with the electrolyzer acting as an hourly demand response resource; ii) determining the optimal setpoints for the electrolyzer to exploit lower electricity prices; and iii) achieving cost savings by utilizing the electrolysis by-product, oxygen, which is important for the steelmaking process in electric arc furnaces, instead of purchasing it. Numerical results show that revenues from the capacity auction market and utilization of the oxygen by-product could achieve savings of 4.79% and 2.59% on the total levelized cost of hydrogen, respectively.
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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 | 0.002 |
| 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) | 0.004 | 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".