Hybrid Energy System Design for Enhanced Sustainability in Rigolet, Newfoundland and Labrador
Bibliographic record
Abstract
Amid growing concerns over global climate change and the need for sustainable infrastructure development, remote communities such as Rigolet in Newfoundland and Labrador (NL), which primarily rely on diesel generators, face unique challenges and opportunities.This study proposes a transition to a hybrid energy system (HES) that integrates wind and solar energy with battery storage and diesel generator backups.The feasibility and implications of this transformation in Rigolet were assessed using HOMER Pro software, contrasting it with the current diesel-centric model.The feasibility, environmental impact, and economic implications of implementing a HES in Rigolet were thoroughly examined.The methodology employed includes a detailed simulation and optimization of the HES configuration suitable for 125 households with a population of 327.The findings reveal that integrating wind and solar electricity with the existing diesel infrastructure, coupled with battery storage, reduced diesel consumption by 352 tons per year and Carbon Dioxide (CO2) emissions by 929 tons per year.Additionally, other pollutants such as Carbon Monoxide (CO), Particulate Matter (PM), Sulfur Dioxide (SO2), and Nitrogen Oxide (NO) were significantly reduced.The proposed system demonstrates a reasonable Net Present Cost (NPC) of $5.17 million with a Levelized Cost of Energy (LCoE) of $0.22/kWh.This shift towards a HES not only illustrates significant environmental advantages and an increase in the percentage of renewable energy but also provides economic benefits through cost reductions over the long term compared to the existing diesel-dependent configuration.The proposed system provides a reliable and sustainable energy solution for Rigolet, presenting a replicable and innovative model for other similar remote locations aiming for a greener future.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".