Design of a hybrid renewable energy system for a northern ontario community
Bibliographic record
Abstract
Renewable energy alternatives for off-grid northern communities are investigated via a specific case study of Fort Hope, Ontario. The current energy systems in most off-grid communities consist of diesel generators, which can be both financially and environmentally unsustainable. In lieu of this, a hybrid system comprised of a 636 kW run-of-the-river hydropower, existing 650 and 375 kW diesel generators, and 150 kg hydrogen storage and 100 kW fuel cell system is proposed. The design achieves a renewable energy penetration of 54%, a levelised cost of energy 41% less than the baseline model, and a reduction of 2,000 tonnes in annual diesel generator emissions. The HOMER model developed for the design is compared to RETScreen results; total power output deviated by 2%, however, renewable energy penetration varied largely due to monthly versus hourly modelling techniques and different controls on the dispatch strategy in each modeling software. Total capital costs and annual operating costs of the proposed design are $4.6 million and $990,000, respectively. Renewable energy systems can be considered a solution to humanitarian concerns due to increasing utility costs, emissions and load constraints. Renewable energy systems in remote communities require further research and development to lower equipment costs and enhance policy to achieve financial viability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".