A Multi-Period Regulation Methodology for Reliability as Service Quality Considering Reward-Penalty Scheme
Bibliographic record
Abstract
In distribution systems, reliability insurance and financial performance are often hard to reconcile due to a natural monopoly. While many studies have proposed regulatory design of reward-penalty scheme (RPS) as an effective performance-based regulation framework to compensate for this natural monopoly, little attention is devoted to consideration of RPS in reliability as service quality. In this paper a novel methodology is proposed for considering an RPS in regard to reliability assessment problems to ensure a reasonable balance between reliability improvement and financial performance. In the proposed methodology, the impact of the utilities’ financial and reliability performance in one regulation period is considered as to how it influences the next periods, i.e., multi-period modeling. The implementation results in an IEEE test system are utilized to reveal possible improvements in both reliability and financial performance, which lead to the delivery of a satisfactory level of service quality to customers in both the short- and long-term. The proposed methodology can be regarded as a performance-based standard for reliability improvement and efficient investment in distribution systems.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".