Design and Analysis of a Floating Hybrid Solar-Wind Plant in Kaptai Lake
Bibliographic record
Abstract
This study explores the integration of solar photovoltaic (PV) and wind turbine systems to develop an efficient hybrid renewable energy model. This research aims to optimize energy generation by leveraging the complementary nature of solar and wind resources, ensuring stable power output throughout the year. Using HOMER Pro software, various hybrid configurations are simulated and analyzed to determine the most efficient system setup. The optimization process considers factors such as solar radiation, wind speed variations, and system component costs. The economic feasibility of the hybrid system is assessed by calculating the total net present cost and the levelized cost of energy, which are determined to be $464,269.10 USD and $7.94 USD/kWh, respectively, indicating the system’s costeffectiveness over its operational lifespan. To evaluate reliability, MATLAB simulations are conducted to determine mean time to failure and reliability. The results indicate an availability rate of 88% for wind turbines and 70.9% for solar PV, highlighting differences in system durability and maintenance requirements. Additionally, the study highlights the environmental benefits of integrating hybrid renewable energy systems, such as reducing greenhouse gas emissions and minimizing excess electricity loss. By leveraging advanced modeling techniques, this research presents a practical framework for sustainable energy integration, contributing to the development of resilient, cost-effective, and environmentally friendly energy solutions for future applications.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".