Review of available transmission capability (ATC) calculation methods
Bibliographic record
Abstract
In last few years, transfer of bulk electrical power over long distances has increased in order to have a reliable and economical electrical supply. For example, hydroelectric power generated in Canada can be transferred to consumers and industry in United States using the high voltage transmission system. But only limited amount of power can be transferred over the transmission system subject to thermal limits of transmission lines, bus voltage limits, dynamic stability and voltage stability limits. The maximum power that can be transferred over the existing amount is called the available transfer capability. To operate the power system safely and to gain the benefits of the bulk power transfers, the transfer capabilities must be calculated and the power system planned and operated so that the power transfers do not exceed the transfer capability. The purpose of this paper is to explain concepts and calculation methods used to calculate available transfer capability and also discuss advantages and disadvantages of each. The methods reviewed in this paper are based on DC and AC Power Transfer Distribution Factors (PTDF), Continuation Power Flow (CPF) and repeated AC Power Flow. Simulation results are presented for each method on IEEE 6-bus and 39-bus system.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".