A Simplified Probabilistic Framework for Evaluating the Contribution of Solar Photovoltaic Generation to Power Systems
Bibliographic record
Abstract
This paper presents a probabilistic simulation framework for quantitatively evaluating the contribution of solar cell generators (SCGs) to modern power systems. Unlike conventional generators using storable fuels modeled by simple two states, solar generation is non-storable and inherently uncertain, requiring a more realistic approach. A multi-state probabilistic model is therefore developed—not as a fixed structure, but as a direct reflection of solar resource variability. By integrating the PV output curve with the empirical probability density function (pdf) of solar irradiance, the model captures weather-dependent generation behavior. Case studies on a scaled Jeju Island system verify its effectiveness in evaluating reliability (LOLE, EENS), production cost, and CO₂ emissions, and demonstrate its value for scenario-based planning under increased solar penetration. The proposed framework highlights the transition from deterministic assumptions to probabilistic realism, offering a robust foundation for sustainable and data-driven energy planning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".