Multi‐phase microgrid resiliency assessment framework against extreme weather events
Bibliographic record
Abstract
Abstract The impact of climate change is leading to a phenomenal increase in the frequency and intensity of high‐impact, low‐probability (HILP) weather events, which cause widespread power outages. Consequently, there is a pressing need to develop resilient power distribution systems against such extreme events. Presently, the methods and metrics to assess grid resilience against HILP events are at an early stage of development and need further work to make them widely implementable in grid resilience investment planning. To address this issue, this study proposes a Monte Carlo‐based framework to evaluate the resilience of distribution systems in the presence of distributed energy resources under two distinct phases: (1) during the event as the system succumbs to the extreme forces, and (2) in its aftermath as the restoration proceeds. This allows power system utilities to analyse the effectiveness of various resilience enhancement strategies for different phases of extreme weather events. The framework also establishes a mathematical relationship to determine the post‐event restoration time based on the hierarchical sequence of component repairs, which depends on the inter‐dependence of component failures and repair crew availability. The framework's effectiveness is demonstrated through case studies on the modified IEEE 69‐bus system.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".