Assessment of customer supply reliability in performance-based contracts
Bibliographic record
Abstract
The number of performance-based contracts between customers (end-users of electricity) and transmission system providers is expected to grow when the electricity market opens up to competition and for customer choice. In these contracts, specified levels of supply reliability with regard to service continuity and power quality will be specified and rewards for honoring these performance levels may be awarded and penalties may be imposed for failing to honor them. These rewards and penalties will be clearly spelled out in the contracts. Transmission system providers may have to assess in advance the level of supply reliability to customers before entering into any of these agreements in order to minimize the financial risk associated with these contracts. This paper describes a probabilistic method for evaluating the level of supply reliability to a customer entering into a performance-based contract with a transmission provider. The evaluation process includes performance measures that reflect both reliability and quality of power supply to the customer. The proposed method can be used not only to assess various utility solutions for improving the reliability and power quality to the customer but also to link the level of supply reliability to the cost of service. An example is given to illustrate the concepts involved.
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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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".