Optimizing of hybrid renewable photovoltaic/wind turbine/super capacitor for improving self-sustainability
Bibliographic record
Abstract
Abstract The study evaluate the utilization of an ultra supercapacitor as an energy storage unit effectively increase energy self-consumption in applications using microgrid renewable energy systems. Two scenarios were evaluated in this study: (scenario A) a photovoltaic and energy storage system; and (scenario B) a photovoltaic, energy storage, and wind turbine system. The systems analysis was conducted using experimental data for weather and load with a temporal precision of 1 min. The daily average of the electrical load profile was 5.0 kWh/day, with a maximum peak of 4.5 kW, and the annual energy consumption utilized to calculate the electrical load profile was 1859 kWh/year. The research indicates that charging the ultra supercapacitor only with renewable energy sources can greatly enhance self-consumption of energy. Using only six ultra supercapacitors (300 F–2.7 V/unit), the annual percentage of self-consumption increased from 37.01 to 46.65% and the percentage of self-sufficiency increased from 27.54 to 41.69% for scenario (A), and from 38.52 to 48.75% and the percentage of energy self-sufficiency increased from 33.50 to 49.87% for scenario (B). The research shows that by including tiny, rapid-response energy storage, the yearly averaged energy self-consumption for the investigated load rises in comparison to the system without energy storage, making it an attractive candidate for batteries.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".