Resilience Investment Against Extreme Weather Events Considering Critical Load Points in an Active Microgrid
Bibliographic record
Abstract
The increasing frequency and severity of extreme weather events pose significant threats to power systems, particularly at the distribution level. The most detrimental consequence of such events is observed in critical loads due to high outage costs. As a result, there is a pressing need for utilities to invest in enhancing system resilience, which requires a comprehensive resilience investment framework and metrics to evaluate system performance. This paper proposes a distribution system resilience assessment framework to guide strategic investment decisions. The framework incorporates a mathematical model that estimates system restoration time after an extreme event, considering the criticality of loads, the interdependence of component failures and repair sequences, and the availability of repair crews. In addition, two new resilience metrics—disconnected load point hours (DLH) and normalized DLH (NDLH)—are introduced, which provide a more comprehensive view of system resilience by reflecting both vulnerability and the ability to withstand and recover from extreme events. Case studies are performed on a modified IEEE 69-bus test system utilizing the developed framework. The results evaluate the effectiveness of different resilience investment strategies, including infrastructure hardening, distributed energy resources management, and repair process coordination, in improving the system resilience for maintaining the critical loads and the overall distribution 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".