Optimal Sizing and Scheduling of LOHC-Based Generation and Storage Plants for Concurrent Services to Transportation Sector and Ancillary Services Market
Bibliographic record
Abstract
Hydrogen-powered vehicles have recently attracted significant attention from both the private sector and the governmental organizations as an alternative to the conventional fossil-fueled vehicles. In addition, the liquid organic hydrogen carrier (LOHC) technology now offers a promising solution for the reliable and safe storage of hydrogen. The proliferation of hydrogen-based vehicles depends heavily on the economic viability of the LOHC-based hydrogen generation and storage plants. This paper demonstrates how such plants should be sized and operated for joint applications, in order to enhance the system rate of return. To that end, a new model is proposed for optimal sizing and scheduling of the LOHC-based generation and storage plants for concurrent services to both the transportation sector and ancillary services market. The ancillary service signals are incorporated into the optimal scheduling model, in order to prepare the LOHC-based plant for the successful contribution to the market. The efficacy of the model is numerically evaluated using historical operating data, and the results are discussed. It is demonstrated that the proposed model can alleviate the gap between the present and the expected rate of return of the LOHC-based plants via joint scheduling for multiple services.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".