Techno-economics of interior-point optimization for grid-integrated wind-hydrogen systems
Bibliographic record
Abstract
Clean hydrogen can be produced by linking wind energy to water electrolyzers. However, the main challenge is the fluctuation in wind power due to varying wind speeds. This can be overcome by connecting the system to the grid. The integration should consider different factors while being implemented. This paper investigates scheduling methods for hydrogen production with alkaline electrolyzers powered by wind energy and grid connectivity. Interior-Point optimization (IP) is used for a scheduling strategy that maximizes revenue and is compared with a rule base scheduling strategy. Operation in several Canadian regions are examined, with a particular focus on Newfoundland and Labrador. The aim is to assess the performance, efficiency, and economic feasibility of diverse energy management strategies. By analyzing the predicted LCOH, hydrogen output, and grid power exchange, the research provides new insights into scheduling approaches for large-scale hydrogen generation from large quantities of intermittent wind resources. Key findings predict Placentia, NL, to have the lowest LCOH of CAD 3.3/kg and Nicolet, QC, to have the highest LCOH (CAD 14.8 to 16.1/kg). Both scheduling strategies produced a similar LCOH for locations with high average wind speeds. However, the IP optimization method resulted in a significantly lower LCOH for locations with low average wind speeds. These insights highlight the importance of tailored scheduling strategies to optimize hydrogen production and promote sustainable energy solutions in various geographic contexts.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.005 | 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".