Operational Adequacy Planning: Practices and Challenges
Bibliographic record
Abstract
Power system planning, operation, and operational planning methods have evolved over the years to economically supply reliable power to customers. Emerging environmental compliance requirements have dictated needs to develop methods to manage increased uncertainties associated with renewable resources. These new challenges are increasing problems in operational planning, as existing deterministic procedures cannot comprehend these uncertainties to provide suitable indicators in decision making. Uniformity in operational adequacy evaluation methods and quantitative metrics applicable to all power systems is desirable. However, various utilities and independent system operators (ISOs) have their own practices and methodologies to address their unique needs. This paper presents a review of operational planning practices in different ISOs and utilities based on planning-horizon length, granularity, and reliability metrics. The challenges and deficiencies in existing operational planning practices and regulations are identified. Finally, features necessary to maintain the uniformity in operational planning while fulfilling operational adequacy requirements are recommended.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".