On Effects of PEVs in Islanded Microgrids Resilience
Bibliographic record
Abstract
The continuous developments toward active distribution systems have been providing the necessary conditions for islanded microgrids operation. In this mode, the islanded regions are expected to sustain the system operation within adequate limits, locally performing several control actions previously assumed by the main grid. Among these controls, the guarantee of generation/demand balance is one of the most critical aspects, which due to the limited amount of generation capacity can lead to massive load shedding. In this perspective, this paper seeks to evaluate the effects of plug-in electric vehicles (PEVs) in the improvement of islanded microgrids resilience. For this, a holistic methodology is proposed to determine whether the implementation of sophisticated controls for the use of PEVs as flexible resources render actual benefit for the islanded network. Simulations are held in the IEEE 34-bus test system considering modifications to represent a microgrid environment. The results indicate that the application of PEVs as flexible resources can significantly enhance microgrids resilience in the mitigation of load shedding.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".