Incorporating Reliability Index Probability Distributions in Financial Risk Assessment with Performance Based Regulation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Regulatory authorities are increasingly adopting performance based regulations (PBR) in the new electric power utility environment. A PBR regime is intended to provide distribution utilities with economic incentives. It could also introduce potential financial risk due to the integration of a reward/penalty structure into the PBR plan. In this new environment, distribution utilities will need to adjust their reliability performance strategy to avoid possible financial risk, and could decide to invest in new capital projects to improve system reliability and be financially rewarded. This article illustrates the utilization of Monte Carlo simulation to develop the relevant reliability indices and their distributions due to reliability improvements in an electric distribution system. Quantitative consideration of the effect of system reliability improvements on the financial risk under imposed reward/penalty structures is presented. This work should be useful for electric power utilities working to reduce their risk and raise their profit in the new regulatory environment.
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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 | 0.000 |
| 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