Utilizing Bulk Electric System Reliability Performance Index Probability Distributions in a Performance Based Regulation Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Parameter distribution analysis and its potential utilization are relatively new concepts in bulk electric system (BES) reliability assessment and decision making. Sequential Monte Carlo simulation is used in this paper to assess the annual variability of BES reliability performance indices. The potential utilization of BES reliability performance index probability distributions is demonstrated by application to the performance based regulation (PBR) concept, proposed by policymakers involved in deregulating the electric power industry. Reliability performance measures such as SAIFI and SAIDI can be used as integral elements in a PBR mechanism to provide power utilities with economic incentives to maintain and improve service reliability, and at the same time to discourage them from sacrificing service reliability in the pursuit of economic objectives. The basic concepts of BES reliability performance index probability distributions associated with a PBR protocol are illustrated in this paper by application to the IEEE-RTS and RBTS using simulation results, and by application using actual historical reliability data
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it