Wind power opportunities for remote mine sites in the Canadian North
Bibliographic record
Abstract
High energy cost and high wind resource are creating an interest for wind-diesel hybrid power production for remote grids in the Canadian North. With the first industrial wind turbine in Nunavik, the Raglan Mine has demonstrated the benefits and viability of wind power as an economical way to produce fuel savings and environment benefits. Results for the first year of wind power production are presented. An after tax economic rate of return of 22,4% is calculated for the project. Financial analyses of adding multiple wind turbines to the remote grid are carried out, using Homer Pro and Retscreen 4 software. Lower economical returns are calculated for every wind turbine added to the grid. A fuel cost superior to 1 CAD/L is needed to achieved a minimal rate of return of 15%, as the integration rate decreases for every additional wind turbine. Three energy storage options are evaluated in order to better utilise excess wind power. In presence of 2 or 3 wind turbines of 3MW nominal capacity each, a lithium-ion battery and a fly-wheel are financially the best energy storage technologies to increase the wind penetration. A hydrogen loop is calculated to be too expensive. An extreme high wind penetration rate, defined as 4 to 6 of the same wind turbine, is deemed not economically viable for the Raglan Mine, regardless of the energy storage technology considered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".