Techno-economic analysis of a renewable-based hybrid energy system for utility and transportation facilities in a remote community of Northern Alberta
Bibliographic record
Abstract
Many off-grid communities in Canada are dependent on diesel generators to fulfill their utility and transportation needs, causing destructive environmental impact. This study aims to optimize and investigate the techno-economic feasibility of a hybrid renewable energy system to satisfy the 1.6 MWh/day electricity, 184.2 kWh/day thermal, and 428.38 kg/year hydrogen demand simultaneously, Trout Lake, a remote community of Northern Alberta. A novel hybrid energy system consisting of solar PV, wind turbine, electrolyzer, hydrogen tank, battery, fuel cell, hydrogen boiler, and thermal load controller has been proposed to generate electricity, heat, and hydrogen by renewables which reduce carbon emission utilizing the excess energy (EE). Five different scenarios were developed in HOMER Pro software, and the results were compared to identify the best combination of hybrid renewable energy systems. The results indicate that the fifth scenario is the optimal renewable energy system that provides a lower cost of energy (COE) at $0.675/kWh and can reduce 99.99% carbon emission compared to the diesel-based system. Additionally, the utilization of thermal load controller, battery, and fuel cell improved the system's reliability, increasing renewable fraction (RF) (93.5%) and reducing EE (58.3%) significantly. In comparison to the diesel-based systems, it is also discovered that battery energy storage is the most affordable option, while fuel cells are the more expensive choice for remote community. Sensitivity analyses are performed to measure the impact of different dominating factors on COE, EE, and RF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".