PV, Wind and Space Heating Electrification Utilization Analysis for a Small Canadian Arctic Hybrid Microgrid
Bibliographic record
Abstract
Canadian Arctic remote communities mostly rely on diesel gensets (DGSs) to produce electricity, which is expensive and emits greenhouse gases (GHG) that pollute the environment and affect the air quality. These communities can utilize their renewable energy (RE) potential to reduce both their fuel consumption and the associated GHG emissions. \nIn the first part of the project, the potential of PV and wind turbines (WTs) is evaluated. The PV utilization analysis results in a contribution of nearly 22% of the yearly community energy requirement and diesel savings of up to 18% with a rated PV power of 200 kW or 125% of the community load peak power. As for the analysis of the wind system, the renewable energy contribution reaches close to 36% alongside fuel savings of 29% for three 25 kW WTs. When combining PV and WTs, the portion of energy supplied by the renewable energy (RE) system reaches 44% along with 36% fuel savings. However, when including RE to the microgrid, its penetration, or percentage of total energy provided by PV, and its associated fuel savings are limited by curtailment, to prevent the DGSs from operating with low loading. \nThe second part of the project evaluates the addition of electric thermal storage (ETS) to the microgrid, which allows for recycling excess (curtailed) RE production to electrify a portion of the heating requirements of the community which is currently oil-based. When pairing ETS units with WTs and PV, the RE curtailment is significantly reduced and can be lowered by up to 90% when ETS units are installed in all the houses of the considered community. Lastly, ETS units can increase the total fuel savings and associated GHG emissions by 46%, when compared to the first part of the study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".