Optimization of hybrid renewable energy systems for remote communities in northern Canada
Bibliographic record
Abstract
Electricity is currently provided in remote communities in Nunavut, Canada, using diesel generators. This study aims to identify the most cost-effective hybrid renewable energy systems by integrating photovoltaic panels and wind turbines to reduce carbon emissions and fuel costs associated with diesel generation. Two system configurations are considered: fully renewable energy systems and hybrid systems incorporating diesel generators. A genetic algorithm-based optimization approach is used to determine the optimal wind farm layout while considering wake effects. The hybrid system achieved levelized costs of electricity (LCOEs) of 0.30 $/kWh in Arviat, 0.35 $/kWh in Rankin Inlet, 0.29 $/kWh in Baker Lake, and 0.37 $/kWh in Sanikiluaq, significantly reducing energy costs compared to diesel-only systems. Additionally, the optimized hybrid configurations led to over 55 % reduction in greenhouse gas (GHG) emissions, while improving annual energy production by 20 % in Arviat and up to 15 % in Sanikiluaq. The study demonstrates that integrating wind and solar energy with existing diesel infrastructure provides a financially viable and environmentally sustainable pathway for energy transition in remote off-grid communities. These findings contribute to ongoing research on optimizing hybrid renewable energy systems for cold-climate regions, ensuring affordability, reliability, and emissions reduction.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".