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.
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.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".