Generator contribution coefficients for pricing transmission services
Bibliographic record
Abstract
The Federal Energy Regulatory Commission (FERC) in the United States has acknowledged the shortcomings of the traditional contract path method as a mechanism for implementing transmission tariffs and has encouraged the development of alternate tariff schemes. This paper presents an alternate method for determining transmission facility usage by a generator. It outlines and discusses mathematical formulae for decomposing the total real power flow that is estimated to exist in a transmission facility. The decomposition produces numerical results that express a transmission line's total real power flow as the summation of the contributions made by each generator, thereby estimating each supplier's use of the system. The technique generalizes a linear expression that is derived from assumptions similar to those used in DC load flow analysis. Each expression is unique to the dispatch on the system at the time. The technique is applied to the AC power flow solution for a modified IEEE standard 14-bus system to evaluate its accuracy on loop feeds, radial feeds and for counter flows. Each generator's contribution to the line flow is totaled and compared to the original AC load flow solution to evaluate accuracy. The results are then applied to certain flow mile topics.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".