Hybrid Solar-Biogas System for Efficient Energy Management in Electric Vehicle Charging Station
Bibliographic record
Abstract
One way to reduce the rate of global warming is the electrification of the transportation industry, which is one of the main sources of greenhouse gas emissions. The development of electric vehicles requires the provision of energy from clean sources for charging and discharging. If this energy is sourced from the national grid, which is primarily dependent on fossil fuels, the carbon emissions will increase. Therefore, establishing charging and discharging stations for electric vehicles using renewable energy can help achieve the primary goal of electrifying the transportation industry and reducing emissions. This study analyzes a hybrid energy system consisting of a solar power plant and a biogas-based gas power plant. The solar power plant has a peak capacity of 560 kW, and the biogas plant generates 1 MW, both located at Kermanshah University of Technology. This research is the first to simulate an electric vehicle charging and discharging station at the university and to examine the performance of these systems. Real operational data were used for the simulation, and the solar-biogas hybrid system and its performance were compared both independently and as a hybrid system. The main objective of this study is to analyze the optimization of the hybrid system operation to supply power to the electric vehicle charging and discharging station and to propose solutions for improving energy efficiency and sustainability in this domain.
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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 | 0.001 | 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) | 0.004 | 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".