On the use of teletraffic theory in power distribution systems
Bibliographic record
Abstract
Loads on the electrical grid are multiplexed at distribution transformers in the same way that traffic from data sources is multiplexed at a router. This motivates the use of teletraffic theory to size power distribution networks just as it is used to size telecommunication access networks. Specifically, we prove the equivalence between a model of a distribution branch comprised of a transformer and storage that we want to size for a given underflow probability ε, and a queuing model that we want to size for a given overflow probability ε. Based on this equivalence, we show how existing teletraffic analysis can be applied to size transformers when there is no storage. We compute such sizings using load models obtained from our measurement testbed and load models derived from an electricity demand simulator. We show not only that teletraffic theory agrees well with numerical simulations but also that it closely matches with the heuristics used in current practice by electric utilities, thus validating the use of teletraffic theory.
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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 | 0.003 | 0.014 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".