Development of an Operational Adequacy Evaluation Framework for Operational Planning of Bulk Electric Power Systems
Bibliographic record
Abstract
Proper long-term planning for investment in resources, timely operational planning to prepare resources and to decide on operational strategies, and proper operating decisions to respond to disturbances during real-time system operation are important to supply reliable power to customers as economically as possible. However, existing utility procedures are insufficient to comprehend uncertainties of modern renewable-integrated power systems and to provide suitable quatitative indicators to assist in operational planning. Independent system operators and utilities around the globe are developing new and unique approaches to operational planning to manage rising uncertainties in power generation from renewable sources like wind and PV. It is desirable to establish uniformity in operational adequacy evaluation methods and quantitative metrics applicable to all power systems in the operational planning horizon of days, weeks, or even months. This will help standardize the operational planning methodology and metrics, and simplify implementation of operational strategies. To address this need, this paper presents a probabilistic analytical methodology for operational adequacy evaluation of a bulk power system integrating the concepts of state enumeration and a novel Dynamic System State Probability Evaluation (DSSPE) approach in time series analysis to accommodate the operational as well as network characteristics. The proposed methodology is implemented on a test system to demonstrate operational adequacy-based operational planning, and to analyze the impact of factors such as unit commitment decisions, locational distribution of load, and generation on the operational adequacy of the 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".