A reliability based model for electricity pricing
Bibliographic record
Abstract
In an attempt to re-regulate the distribution segment of an electric power system, public utility commissions (PUCs) areincreasingly adopting a reward/penalty framework in order to guarantee acceptable electric supply reliability. A distribution utility’s historical reliability performance records provide the basis for creating practical performance based ratemaking (PBR) mechanisms at the corporate level and identifying substandard areas within a utility’s distribution system. The paper presents an application using a graphical methodology for the stratification of a utility’s historical reliability indices data base into various categories, i.e., corporate level, regional level and crew level to quickly identify substandard areas for achieving optimum reliability and to develop PBR frameworks for use in a reregulatedenvironment. A brief analysis of cause contributions to the stratified reliability indices also is presented in this paper. This paper presents actual reliability performance history of a large utility over a period of three years to illustrate how this data can used to develop PBR frameworks for use in a reregulated environment. The historic reliability based PBR framework developed in this paper will find practical applications in the emerging deregulated electricity market.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".