Technoeconomic Models for the Optimal Inclusion of Hydrogen Trains in Electricity Markets
Bibliographic record
Abstract
Hydrogen-based railway (Hydrail) vehicles are rising as a solution that decreases the environmental impact caused by carbon emissions from diesel engines and at the same time avoids the enormous capital costs associated with direct electrification (DE) of rail lines. This article introduces new technoeconomic models for the inclusion of Hydrail in electricity markets. Exploiting the size and flexibility that large Hydrail electricity demand imparts, price-taker and price-maker scenarios are outlined and compared. Furthermore, this article presents a novel optimal scheduling mechanism for the hydrogen electrolysis process chosen for hydrogen production in the models.This mechanism minimizes electricity costs based on a linear programming model which optimizes the energy drawn from the grid for hydrogen generation, incorporating hydrogen reservoir capabilities and hydrogen input and output rates. This article proves the strengths of these new technoeconomic models for the inclusion of Hydrail in electricity markets and the effectiveness of the optimal scheduling mechanism, through a case study for the deployment of a Hydrail system in the Greater Toronto Area (GTA) in Ontario's electricity market. After comparison to a DE option, this article presents Hydrail as a strong option for the evolution of sustainable, integrated, cost-effective, and low-carbon-emission solution for public transportation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".