Incorporating reliability index probability distributions in performance based regulation
Bibliographic record
Abstract
The move towards deregulation in the electric power industry has created a new climate of competition and customer choice. There are also concerns that customer reliability could suffer under the new regime. Performance based regulation (PBR) is being introduced in a number of jurisdictions in an attempt to maintain an acceptable balance between appropriate customer service qualities and customer service costs. A reward/penalty structure integrated in a PBR plan works like a contract that penalizes and/or rewards a utility based on its performance and in doing so introduces an element of financial risk to an electric power utility due to the uncertainty associated with maintaining a specific level of system reliability. This paper illustrates the utilization of time sequential Monte Carlo simulation to develop reliability index probability distributions for a feeder, a bus and a system, and assesses such risks by incorporating reliability index probability distributions into the reward/penalty structure. The paper applies this approach using some real system reliability data from Canadian service continuity reports. These concepts should prove useful for regulatory agencies responsible for setting initial PBR procedures in place, and for electric power utilities to reduce their risk and increase their rewards in the new environment.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".