The role of biofuels for sustainable MicrogridsF: A path towards carbon neutrality and the green economy
Bibliographic record
Abstract
Today, with the progress of technology, the world is facing an increasing growth in power consumption. Since the fuel of most power plants is supplied from fossil fuels, it has caused an increase in global fossil fuel consumption and environmental degradation. ّFurthermore, the volatility of fossil fuel prices and unstable energy security have prompted international organizations and governments to apply policies to restrict fossil fuel use and examine alternatives to fossil fuels. Since biofuels come from renewable sources and are clean fuels, they can be an appropriate alternative to fossil fuels and play a more expansive role in supplying energy for transportation industries, power plants, and heat production systems. Although there is some research about the drawbacks of using fossil fuels and the commendation of using biofuels in various industries such as transportation, the literature lacks a comprehensive study on the evaluation and analysis of the potential of using biofuels instead of conventional fuels in power generation systems. The primary purpose of this study is to evaluate the impact of utilizing biofuels instead of fossil fuels in microgrids to achieve carbon neutrality objectives. Furthermore, this paper reviews previous research studies that have operated biofuels in three categories: solid, liquid, and gas, to generate electricity and analyzes the potential of different biofuels to produce heat and electricity for microgrid power systems. In addition to outlining the present knowledge gaps in this area, this study explores the prospects and threats associated with expanding the use of biofuels in the power production industry and the development of sustainable microgrids. This study indicated that if the technical and economic problems of employing biofuels are overcome, these clean fuels have a great potential to obtain the maximum share of the global power generation market and move toward Net Zero Emissions by 2050 Scenario (NZE) goals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".