Practical experience in evaluating adequacy of generating capacity in the Western interconnection
Bibliographic record
Abstract
Paper presents practical experience in evaluating adequacy of generating capacity by different regions in the Western interconnection. This includes analysis of uncertainties associated with load, intermittent energy sources and forced and maintenance outages on generating units and transmission facilities. The paper is a joint effort by the utility industry and Western Electricity Coordinating Council (WECC). Adequacy assessment of systems with ongoing integration of variable resources such as wind and solar has added new requirements to enhance the present methodologies and tools for computing the known Loss-of-Load-Expectation (LOLE) index. The LOLE index is influenced by load, generation, export/import, generation and transmission forced and maintenance outages, transmission operating constraints and various uncertainties related to load, generation and system operating conditions. Application of probabilistic approaches has already been used by industry and is accepted by a number of WECC member utilities. Adequacy indexes such as LOLE and Expected Unserved Energy (EUE) allow the meaningful assessment of the effective generating capacity margin and help utilities to determine if new resources are needed to meet reliability standard. This paper reviews the probabilistic approaches presently used by utilities in Western Interconnection in regard to planning and operating generating resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".