Feasibility of satisfying electrical energy needs with hybrid stand alone systems for application in Tehran, Iran
Bibliographic record
Abstract
The utilization of hybrid energy systems comprised of wind, photovoltaic, biomass, and geothermal technologies is growing, mainly as a result of increasing concerns about the environmental impacts caused by fossil fuels. The economic feasibility is examined here of using hybrid systems to supply the energy needs for a household in Tehran, Iran. Wind-photovoltaic-diesel hybrid systems are considered in this investigation. Hydrogen is employed by the diesel generator so as to ensure a clean fuel is used, leading to little environmental impact. In addition, battery storage is utilized in the system reliability assessment. HOMER software is utilized for the economic assessment and optimization. The key factors considered in the investigation are as follows: incident solar radiation, wind speed, electrical demand profile for the household, equipment characteristics, and hydrogen fuel costs. The average daily electricity consumption, averaged over an annual cycle, is 17 kWh/d, and the peak electrical demand is 1.5 kW, for the household considered. The electrical supply system is required to satisfy these requirements. The analysis results from the case study show that, among five hybrid systems for supplying electrical requirements, the most economical is the wind-hydrogen-battery hybrid system, which has a total net present cost of $63,190 and a cost of energy of $0.783/kWh (based on 2014 US$).
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".