Estimating Probabilistic Load Coincidence Factors for Distribution System Operation
Bibliographic record
Abstract
The standard definition of the load coincidence factor relates the maximum coincident demand from a set of customers in a distribution network to the sum of their individual peak demands. As a complement to the standard usage, this paper presents the concept of a minimum load coincidence factor characterizing the lower bound of coincident demand from a set of customers as it relates to the sum of their individual peak demands. Such a characterization can help to avoid unnecessary curtailment of local generation available in distribution networks in order to mitigate potential over-voltage issues possible under low loading conditions. We further examine statistical properties of both the standard maximum and proposed minimum load coincidence factors using publicly available data sets of household demand drawn from realistic distribution networks. The statistical analyses validate probabilistic modelling of both types of load coincidence factors for a large number of customers as a ratio of two Gaussian random variables. Probabilistic models of load coincidence factors are potentially useful in uncertainty-aware operational decision making via the solution of a robust or chance-constrained optimal dispatch.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".