Effects of correlated photovoltaic power and load uncertainties on grid‐connected microgrid day‐ahead scheduling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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 it