Effects of correlated photovoltaic power and load uncertainties on grid‐connected microgrid day‐ahead scheduling
Bibliographic record
Abstract
Due to the increasing integration of photovoltaic‐based distributed generators (PV‐DGs), uncertainties resulted from both PV‐DG power and loads have posed a serious challenge in microgrid day‐ahead scheduling and operation. In this study, the effect of uncertainties in both PV‐DG power and loads on the microgrid day‐ahead scheduling is assessed. Specifically, the correlation between the PV‐DG power and load uncertainties is taken into account as this is closer to the reality. The probabilistic optimal power flow (P‐OPF) model is formulated to analyse the impact of the correlated PV‐DG power and load uncertainties. A modified Harr's two‐point estimation method (MH‐2PEM) is introduced to provide computation‐efficient estimation of the P‐OPF solution. Results obtained by using the MH‐2PEM and Monte Carlo simulation are compared in an equivalent 44 kV distribution feeder system and the accuracy and efficiency of the MH‐2PEM are verified. The variation ranges of the microgrid day‐ahead scheduling solution resulted from uncertainties in PV power and load are obtained with various confidence levels.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".