Optimizing Wind-to-Hydrogen Production in Newfoundland for Export: A Techno-Economic Perspective
Bibliographic record
Abstract
This study explores the feasibility of generating green hydrogen using wind energy in Newfoundland and Labrador (NL) for potential export to Germany, aiming to reduce their heavy reliance on grey hydrogen. NL features abundant wind resources, deep-water export harbours, and proximity to Europe, making it an ideal location to contribute to Europe’s energy security. Utilizing the Hybrid Optimization of Multiple Energy Resources (HOMER Pro) microgrid software, we conducted a techno-economic analysis of a wind-to-hydrogen case study at the Port au Port location aimed at offsetting 1% of Germany’s grey hydrogen consumption. The optimal system comprises 49 wind turbines, each with 4.2 MW capacity, a 130 MW PEM electrolyzer, a liquid hydrogen storage facility, and a grid as a backup. We evaluated various financial metrics, including Net Present Cost (NPC), Levelized Cost of Energy (LCoE), and Levelized Cost of Hydrogen (LCoH) for short-term, mid-term, and long-term storage scenarios. The financial metrics were compared with similar case studies around the globe to highlight the economic competitiveness of clean hydrogen production in Newfoundland and Labrador.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".