Resilience Assessment of Distribution Systems Integrated With Distributed Energy Resources
Bibliographic record
Abstract
The resilience of electric systems is receiving growing attention due to their increased vulnerability to infrastructure damages and widespread outages from frequent extreme climactic conditions attributed to global warming effects. Resilience evaluation methods should recognize the uncertainties and correlations in the performance variations of different types of energy resources, load characteristics, extreme events and their impacts on the grid elements. However, there is a lack of established methods and resilience metrics that are widely accepted. In this context, this paper presents the development of probabilistic extreme event model, impact assessment model, and optimal restoration model for active distribution systems, and integrates the models using a non-sequential Monte Carlo Simulation framework. The inter-dependencies of time-varying demand, renewable energy output, and energy storage characteristics are incorporated in the framework. A set of metrics is proposed to quantify the resilience of the system against extreme events and their outage impacts at the load points. The metrics and their probability distribution thus obtained can be utilized in probabilistic value-based investment planning to select appropriate measures to enhance the system resilience. Selected case studies are conducted on the IEEE 69-bus test system to show the efficacy of the proposed framework.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".