Application of the value-based approach to the planning of customer delivery systems
Bibliographic record
Abstract
Customer delivery systems have been planned to deterministic criteria for a long time. With these criteria, new capacity is determined when the system load is just equal to the system limited time rating. Even, some electrical utilities in North America allow for some positive reserve margin in order to cover for load forecast uncertainty. These deterministic criteria do not quantify the reliability of the system and in many situations, can result in over-designed systems and therefore, the electricity users can end up paying higher prices for electricity. In a competitive electricity market, transmission system owners or providers should try to keep the cost of upgrading, operating and maintaining their systems as low as possible while meeting the expectations of their customers and regulatory rules. This paper describes the application of a value-based approach to the planning of customer delivery systems. In this application, the reliability of the system is expressed as a function of the reserve margin and the optimal reserve level is obtained when the total system cost is minimal. Sensitivity studies are carried out to determine the impact of changes in some key parameters on the optimal reserve margin. An example is presented 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".