Transmission Loss Recovery In A Deregulated Power System Network - A Practical Example
Bibliographic record
Abstract
The re-structuring in electric power system involves many technically complicated issues ranging from system planning and operation to commercial and market areas. In a deregulated open market electric system, an additional challenge is the allocation of transmission losses. Alberta Electric System Operator (AESO), the Independent System Operator (ISO) in the Province of Alberta, Canada has introduced a new methodology based on provincial law for the allocation of transmission losses among the stakeholders effective January 1, 2006. The new model for the recovery of the transmission losses is known as "50% Area Load Methodology Using Corrected Loss Matrix" [1]. The loss factor for each generator is determined directly from the coefficients of a loss matrix derived from the system topology. The methodology is based on load flow solutions and provides fair and transparent allocation of losses. The paper describes the step by step approach of the methodology along with practical results.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".