Optimal Anti-Icing and De-Icing Coordination Scheme for Resilience Enhancement in Distribution Networks Against Ice Storms
Bibliographic record
Abstract
Ice storms can cause serious damage to power distribution systems, thus the development of effective anti-icing and de-icing strategies is of great significance. In this article, an optimal anti-icing and de-icing coordinated operation scheme is proposed to enhance the resilience of distribution systems against ice storms, which is based on the ice-melting capacity of distribution lines. Firstly, a comprehensive risk analysis for anti-icing and de-icing in ice storms is provided. Then, a novel critical condition for anti-icing initiation is proposed based on the weather forecast deviation and potential load loss in distribution systems. On this basis, the coordinated optimization model for anti-icing and de-icing in distribution networks with intelligent soft open point and energy storage systems is established, aims at minimizing the overall load loss. Considering the double risk of overloading anti-icing and de-icing methods, the power flow limit violation function is used in the optimization model to reasonably allocate the proportion of various risks. Finally, the proposed method is verified in the IEEE 33-node distribution network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".