Customer-responsive bulk transmission system reliability performance standards for use in a competitive electricity market
Bibliographic record
Abstract
The reliability of an electric utility transmission system is a measure of its ability to continuously meet the demands of all its customers (i.e., points of delivery). Enhancing the supply reliability is one of the planning and design targets of any utility within the constraints of capital investments, intense free market competition, variable and higher level of customer preferences and safety and environmental considerations. The utilization of transmission system facilities is changing from its traditional role in this new deregulated era and raising significant concerns about the possible erosion of the reliability of utility transmission facilities. It has been recognized that the movement towards a deregulated and competitive electricity market, decisions to improve reliability will be heavily influenced by customer preferences requiring service level benchmarks to be defined. This paper presents an approach to establish customer-responsive transmission system reliability performance standards that provide a measure against which the different planning and design procedures can be evaluated to assist in achieving the primary objective of providing reliable electricity with competitive rates to utility customers. The applications of the developed standards are illustrated using a simple system example.
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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 | 0.018 | 0.035 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".