Microgrid Hybrid Solar/Wind/Diesel and Battery Energy Storage Power Generation System: Application to Koh Samui, Southern Thailand
Bibliographic record
Abstract
This paper presents the optimization of a 10 MW solar/wind/diesel power generation system with a battery energy storage system (BESS) for one feeder of the distribution system in Koh Samui, an island in southern Thailand. The main objectives are to maximize the deployment of renewable energy-based power generation and to minimize the levelized cost of energy (LCOE). A hybrid renewable energy-based power generation system, consisting of solar PV, wind turbine generators, diesel generator (DiG), bi-directional grid-tied charging inverter (CONV) and BESS, was simulated using HOMER Pro®. This study accessed the database of the National Aeronautics and Space Administration (NASA) for the Surface meteorology and Solar Energy (SSE) for the global solar radiation and temperature, along with the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) wind database. The simulations show that Scenario 1 (PV/Wind/DiG/BESS/CONV) and Scenario 3 (PV/DiG/BESS/CONV) are the optimal configurations regarding the economic indicators (i.e. minimum net present costs (NPC) of 438 M$ and LCOE of 0.20 $/kWh) and the environmental indicators (i.e. lowest greenhouse gases (GHG) emission avoidances of 6,339 tonnes/year and highest renewable fraction (RF) of 89.4%). Furthermore, the sensitivity analysis illustrates that Scenario 3 offers the optimal system type with the largest annual energy production (AEP). Besides contributing to the body of knowledge of optimization methodologies for microgrid hybrid power systems, the outcome of this work will assist the regional energy practitioners and policy makers regarding optimal configurations of microgrid hybrid systems in the development of a Green Island concept for Koh Samui.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".