Peaking Unit Considerations in Generating Capacity Adequacy Assessment
Bibliographic record
Abstract
Generating capacity adequacy assessment is an important task in power system planning and development. The basic model used to incorporate a generating unit in an adequacy assessment is a two state representation in which the unit is available or unavailable for service. This model is a valid representation for base load units but does not adequately represent intermittent operating units used to meet peak load conditions. Peaking units are started when they are needed and normally operate for relatively short periods. The two-state model for a base load unit has been extended to a four-state representation, which is widely used in practice. The indices used to represent the unavailability of a peaking unit are the utilization forced outage probability (UFOP) and the derated adjusted utilization forced outage probability (DAUFOP). The UFOP and DAUFOP values are used to incorporate peaking units in analytical generating system reliability assessments. The UFOP used in an analytical method is normally a fixed value calculated under a certain system condition and is applied to a wide range of situations. The actual UFOP, however, is not a fixed value and varies with changes in the system operating conditions. This paper illustrates the utilization of a sequential Monte Carlo simulation technique for generating system adequacy assessment and examines the variability of peaking unit UFOP as a function of the unit loading order, system load levels and required operating reserve. The concepts presented in this paper are illustrated by application to a practical test system
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".