A Novel Clustering Method for Extracting Representative Photovoltaic Scenarios Considering Power, Energy, and Variability
Bibliographic record
Abstract
Due to the significant uncertainty in photovoltaic (PV) power generation, grid operation scenarios with a high proportion of PV integration are complex and varied. To accurately extract representative scenarios for PV power generation, this paper proposes a novel clustering model that simultaneously considers PV power, energy, and variability. Compared to traditional clustering models that rely on Euclidean distance, the proposed clustering model not only takes into account the Euclidean distance, but also incorporates the daily PV power generation and the characteristics of PV power curves, enabling a more accurate quantification and analysis of the impact of PV on the electricity networks. To solve the proposed clustering model, an alternating optimization algorithm is proposed, based on linear optimization, Lagrange multipliers, and eigenvalue decomposition. The highlights of this paper are the dual verification of the proposed method through theoretical proof and simulation examples. Theoretically, the computational complexity of the algorithm is illustrated, and the convergence of the algorithm is demonstrated. The proposed method is tested using real PV data from Australia and the IEEE 69-bus system, successfully generating 13 representative PV generation scenarios with a maximum similarity distance of the morphological trend as low as 0.3062, ensuring the most representative PV generation peak times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".