Impact of Wind-Based Distributed Generation on Electric Energy in Distribution Systems Embedded With Electric Vehicles
Bibliographic record
Abstract
In this paper, the synergy between wind-based distributed generation (DG) and plug-in electric vehicles (PEVs) is studied. MonteCarlo is used to address the uncertainties associated with wind speed variations and charging of PEVs hence simulating their impact at the distribution system (DS) level considering different DG penetration (up to 35%) and different PEV penetration (up to 50%). The excess in active/reactive power, energy exceeding normal (EEN), unserved energy (UE), and energy losses are investigated in this study. Forty-eight penetration scenarios involving DGs and PEVs are studied in this work and simulated in the IEEE 123-bus radial power distribution test system after modeling its secondary circuit in OpenDSS. The results of the simulation show that 30% wind-based DG penetration may be adequate to supply the active energy needed to charge PEVs. However, this might result in a reverse reactive power flow back to the substation.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".