Balanced Graph Partition for Resilient Load Restoration Against Natural Disasters
Why this work is in the frame
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Bibliographic record
Abstract
Microgrids (MGs) constitute a promising solution to enhance power distribution system (PDS) resilience against natural disasters. Yet, in the existing research works, the scale of MGs, which can be quantified by the number of nodes covered by MGs, has not been optimized. An MG containing too many nodes can certainly prolong the restoration process, because loads are typically picked up step by step for transient stability consideration. To this end, this paper proposes a load restoration strategy based on balanced graph partition. The problem is formulated as a multi-objective optimization problem. One objective is to minimize the total weighted load shed, while the other one is to consider the scale of MGs by minimizing the variance of the scale of MGs. To speed up the computation, a decomposition-based objective approximation is presented. The optimality gap is also derived, and proven to be bounded. Lastly, the proposed strategy is performed on the IEEE 37-Node and 123-Node Test Feeders. The simulation results demonstrate that by minimizing the variance of the scale of MGs, a more resilient load restoration can be achieved.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it