Avaliação crítica de inserção de eletropostos em redes de distribuição considerando mitigação por agendamento de recargas
Bibliographic record
Abstract
This study seeks to assess and mitigate the installation impact of electric vehicle charging stations in distributed systems in terms of voltage level. The methodology applies the Monte Carlo simulation to obtain the possible critical cases of the system, given the installation of the recharging station, during a year of operation. A meta heuristic method, called Evolutionary Particle Swarm Optimization (EPSO), is used to schedule the electric vehicles recharges at the recharging station. The EPSO method uses as the objective function the ratio between the peak power and the mean power of the system (PAPR) in order to reduce the peak load seen by the grid. The case study was developed in a 33-bus test system. The load curves of the system are related to a distributed system from Canada and the electric vehicles recharge data used came from an UK project. The results showed that the insertion of the recharge station can lead to the appearance of critical cases in the system. However, with the use of the scheduling process it is possible to mitigate them, thus emphasizing the importance of this study.
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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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".