Analysis and optimization of hybrid renewable energy systems for remote community applications
Bibliographic record
Abstract
Hybrid renewable energy systems (HRESs) can provide an effective approach to replacing diesel power in remote communities in Canada where people live off-grid. This paper deals with analyzing and optimizing HERSs that consist of solar photovoltaic (PV) panels, wind turbines, a biomass power generator, and batteries with different combinations for remote community applications. A model is developed to design, simulate, and optimize the HRESs, aiming at minimizing the net present cost (NPC) and levelized cost of electricity (LCOE) of the systems for Canada’s remote and northern communities. Economic assessment of the HRES with different configurations is conducted, and the amount of electricity produced by each subsystem is calculated. The NPC ranges from $4.17 M to $8.68 M and the LCOE ranges from $0.33.9/kWh to $0.693/kWh for the five optimized HRES configurations in a selected remote community. It is shown that in Configuration A, the HRES generates 824,152 kWh/year of which the biomass electricity accounts for 484,632 kWh/year, the solar electricity accounts for 72,939 kWh/year, and the electricity generated by wind turbines accounts for 266,581 kWh/year. Through the present research, HRES is shown to be an effective option to supply green electricity 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".