Optimal Resource Allocation to Enhance Power Grid Resilience Against Hurricanes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Optimal resource allocation is critical when maximizing the resilience of the electrical power distribution network against natural disasters. This paper presents a two-step optimization strategy that integrates a pre-disaster preparedness plan and a post-disaster resource re-allocation procedure to optimize the resilience of the power distribution network against hurricanes. Emergency resources are operationally interdependent, and it is these interdependencies that determine how the resources should be distributed to the critical loads in the network. This work uses the concept of the Human Readable Table (HRT) to relate the interdependencies among these resources. The resource allocation optimization is then formulated into a Mixed-Integer Nonlinear Programming (MINP) problem. The proposed method is tested on the IEEE 70-node system. The results show that this two-step procedure decreases the probability of failure for the critical nodes during the pre-hurricane stage and increases the system's ability to recover during the post-hurricane stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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