Resilience assessment of mobile emergency generator-assisted distribution networks: A stochastic geometry approach
Bibliographic record
Abstract
The escalation of extreme weather events threatens power system infrastructures significantly. Mobile emergency generators (MEGs) render flexible restoration strategies against destructive events. However, with the continuous expansion of distribution networks, quantifying the impacts of MEGs becomes increasingly challenging due to extreme weather event-induced uncertainties. In this paper, we propose a stochastic geometry-based method for assessing the impact of MEG deployment on distribution networks under extreme weather events by investigating structural features. Firstly, we propose a distance measure to depict the electrical connection between power grid components. Subsequently, we adopt the point process and Voronoi tessellation to describe the spatial distribution of power grid components and the service coverage provided by MEGs in different scenarios. Then, we propose a set of assessment metrics to evaluate the survivability of power grid components and the resilience of distribution networks under extreme weather events. Finally, we derive accurate analytical expressions for the distance distribution and resilience metrics, such as coverage probability and load shedding, enabling us to explore the relationship between MEG deployment decisions, structure features, and power grid resilience. The proposed method empowers us to analytically assess the impact of MEG deployment on the resilience of distribution networks and provides beneficial insights to formulate efficient measures for enhancing resilience. Case studies demonstrate that the proposed method is accurate and efficient in dealing with network analysis and assessment problems for distribution networks under massive 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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".