Optimizing hybrid renewable energy systems: Techno-economic feasibility and CFD study for residential load applications in Stephenville, NL
Bibliographic record
Abstract
Communities in Newfoundland and Labrador continue to rely heavily on grid electricity, which is often expensive and vulnerable to weather-related disruptions. In this context, hybrid renewable energy systems offer a practical way to improve energy security while lowering emissions. The transition to clean energy is crucial for mitigating climate change, particularly in Canada, where fluctuating temperatures and environmental shifts pose significant challenges. This study evaluates the techno-economic feasibility of a hybrid renewable energy system designed for residential use in Stephenville, Newfoundland and Labrador, integrating wind turbine technology (Enercon E-44), solar technology (Canadian Solar Dymond), and grid electricity. Using HOMER Pro, the system was optimized based on NASA wind data (average speeds: 7.2 m/s in winter, 5.32 m/s in summer). Results show a levelized cost of energy (LCOE) of $0.0356/kWh, a net present cost of $1.56 million, and annual CO 2 reductions of 222,514 kg, with 60.1% renewable penetration. Computational fluid dynamics (CFD) analysis in ANSYS Fluent, focusing on the NACA 63-415 airfoil, confirmed the turbine’s aerodynamic efficiency across seasonal winds. This study highlights hybrid renewable systems as cost-effective, sustainable solutions, aligning with Canada’s net-zero goals while ensuring energy security.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".