Design and Implementation of an Electricity System Optimization Model for Remote Communities in Canada
Bibliographic record
Abstract
This study presents an energy system optimization model based on linear programming techniques to predict least-cost electricity generating systems for five remote communities in Quebec, Canada. The model integrates hourly electricity demand data, hourly wind speed data, and hourly solar power generation data, and considers relevant costs, to identify the optimal combination of generating technologies capable of meeting the communities' electricity demand throughout the year. To account for environmental considerations, the model was subject to two separate constraints. First, a carbon tax on carbon emissions from the system was incrementally increased. Second, carbon emissions were gradually constrained, ultimately reducing to zero allowed emissions. The results suggest that even in the absence of either aforementioned constraint, the least-cost system already incorporates wind power in conjunction with existing diesel generation, and a system with zero carbon emissions is less expensive still than a system fully reliant on diesel. Further, the results suggest that a carbon emissions constraint is a more impactful policy option to incent carbon emissions reductions than a carbon tax for the five communities studied, as the carbon tax increased system price while providing insignificant carbon emissions reductions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".