Techno-Economic Assessment of Utility-Scale Dual-Rotor Wind Power Generation: A Case Study of Siam Eastern Industrial Park, Rayong Province, Thailand
Bibliographic record
Abstract
The energy transition to renewables is considered one of the primary ways to limit the emissions of greenhouse gases (GHG), as the electricity sector is among the major users of fossil fuels. Solar and wind power are leading the way and becoming more cost-effective than coal and other fossil fuels. As renewable energy is becoming more economical, industries worldwide are adopting it to reduce their carbon footprints. This study is aimed at the techno-economic assessment of a 20 MW utility-scale dual-rotor wind power plant installed at Siam Eastern Industrial Park in the Rayong province of Thailand. Using the MERRA-2 wind database, Digital Elevation Model (DEM), and the rough digital data of the study area, computational fluid dynamics (CFD) wind flow modeling was used to create a microscale wind resource map of the study area. The modeling yielded an average windspeed of 5.4 m/s at the hub height of 90 m above ground level (agl). Using four 5 MW dual-rotor wind turbine generators, the wind power plant would have an annual energy production (AEP) of 75.7 GWh/yr with a capacity factor (CF) of 43%. The economic assessment of the power plant was performed using various economic indicators, notably the benefit-cost ratio (BCR), the net present value (NPV), the internal rate of return (IRR), and the payback period (PBP) at different benefit scenarios defined by the Provincial Electricity Authority of Thailand (PEA) and private power purchase agreements. The financial parameters were all positive for each of the PEA’s benefits scenarios, even without the carbon trading benefits, thus making this wind power plant economically viable. Studies like these are essential to build the confidence of investors and developers by providing them with well-informed information on the feasibility of wind power plant projects and their benefits, thus contributing to the development of wind energy in various jurisdictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".