Two-Stage Resilience-Oriented Unit Commitment of Transmission Systems Against Severe Windstorms
Bibliographic record
Abstract
This paper presents a two-stage unit commitment (UC) model to boost the resilient operational planning of transmission systems against upcoming windstorms. UC scheduling, load curtailment, and repair crew dispatch (RCD) are coordinated in two stages to inform all phases of the resilience trapezoid. Transmission line failure probabilities are calculated using the forecasted wind and component fragility curves. In the first stage, a conservativeness-controlled info-gap (CCIG)-based model is proposed to provide cost-robustness tradeoffs for decision-makers by solving the resilient UC (RUC) problem. Given the realized first-stage decisions, a novel UC-integrated RCD (UCRCD) formulation is presented in the second stage, which employs repair crew teams (RCTs) to minimize load curtailment by scheduling the repair of damaged lines during the UC horizon. The model is further developed to incorporate the RCD rescheduling on a rolling-horizon basis as the event progresses through the system and the damage information is updated. The proposed model is tested on the modified IEEE RTS-79 and IEEE RTS-96 test systems, showing its efficacy in providing optimal cost-robustness tradeoffs for mildly, moderately, and seriously conservative decision-makers and updating restoration schemes according to the new damage information. The results are verified using a sequential Monte Carlo (MC) simulation with 2000 scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".