Hybrid Renewable Energy Systems and Their Optimization for Remote Community Applications
Bibliographic record
Abstract
There is a growing interest in implementing hybrid renewable energy systems (HRES) in remote communities where people are using diesel power. In the HRES, two or more renewable energy sources are combined, allowing the communities to counteract the weaknesses of one renewable energy source with the strengths of another. This study aims to design, simulate and optimize the HRES consisting of photovoltaic panels, wind turbines, and a bio-based generator for applications in remote and northern communities in Canada. A methodology is developed to optimize HRESs so that net present cost (NPC) and levelized cost of electricity (LCOE) of the energy systems can be minimized. The optimization is performed using a genetic algorithm-based model. A sample remote northern community in Canada is selected for a case study. The HRES being investigated targets to supply an average load demand of 2205 kWh/day and the peak load of 236.06kW for the sample community. Results show that the LCOE for the top optimal HRES configuration is $0.308/kWh, and its NPC is $4.29M. Through the present study, the HRES is shown to be an effective approach to replacing diesel power in remote communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".