Techno-Economic Assessment of a 100 kWp Solar Rooftop PV System for Five Hospitals in Central Southern Thailand
Bibliographic record
Abstract
This paper presents a techno-economic assessment of a 100 kWp solar rooftop photovoltaic (PV) system at five hospitals in central southern Thailand. The system encompasses 100 kWp PV panels, 100 kW grid-tied inverters and balance of system (BOS) under the grid code of the Provincial Electricity Authority (PEA). The latest PV technology of bifacial mono-crystalline solar panels, inverters and BOS were simulated along with the Meteonorm 7.3 database using the PVsyst simulation toolkit with different tilt angles, orientations, solar radiations and ambient temperature. The technical aspects of solar rooftop PV power generation systems include the annual energy output and the performance ratio (PR) under IEC standard. Further, an economic analysis of the model was examined using a cost benefit analysis (CBA) and various assumptions. Four main financial criteria, i.e., benefit cost ratio (BCR), net present value (NPV), internal rate of return (IRR), and payback period (PBP) were evaluated under three different scenarios: (1) self-consumption scheme, (2) feed-in tariff (FiT) scheme, and (3) private power purchase agreement (PPA) scheme. Finally, the levelized cost of energy (LCOE) was also calculated. The results reveal that the Takua Thung hospital is characterized by the maximum average global horizontal irradiation (GHI) and the maximum annual produced energy of 199 kWh/m2 and 164.8 MWh/year, respectively. The PR calculated for all hospital sites is above 85%. The outcomes of the financial analysis show that the optimum scenarios are PPA and FiT schemes. The LCOE analysed in this study indicates that the Takua Thung hospital site has the lowest LCOE at 2.47 THB/kWh (0.07 USD/kWh). This research confirms the potential for hospitals and stakeholders in central southern Thailand for investments in solar rooftop PV systems
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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 | 0.001 | 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 | 0.001 | 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".