Optimal Scheduling of Electrolytic Hydrogen-Based Steelmaking Facility Participating in Capacity Auction Market
Bibliographic record
Abstract
The adoption of electrolytic hydrogen-based steelmaking technology has recently garnered increasing attention as a promising solution for reducing carbon emissions within the framework of industrial decarbonization. Nonetheless, the high costs associated with hydrogen production via electrolysis stand as a key challenge to the widespread deployment of this technology, necessitating strategies to improve its economic feasibility. To that end, this paper introduces an optimal scheduling model for a hydrogen-based steelmaking facility equipped with an on-site electrolysis-based hydrogen production and storage system. The model mainly aims to: i) exploit lower electricity prices to minimize the overall operating costs of the entire system, ii) contribute to the capacity auction market as a demand response resource for the joint benefit of the steel sector and electricity grid operator, and iii) ensure the safe operation of the hydrogen production system via keeping the hydrogen content in oxygen within safety permissible limits. As such, the economic feasibility of the hydrogen-based steelmaking technology would be further improved due to energy cost savings and extra financial settlements from capacity auction participation. The efficacy of the proposed model is validated using numerical case studies, demonstrating its potential to support the transition to low-carbon steel 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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".