Impact of Plug-in Hybrid Electric Vehicles and their optimal deployment in Smart Grids
Bibliographic record
Abstract
This paper develops mathematical model of Plug-in Hybrid Electric Vehicles (PHEVs) combined with distribution system components model in an optimization framework, which can be used to study the impacts of PHEVs in distribution systems and also to optimally schedule numerous PHEVs connected to a distribution system for the benefits of distribution system operators (DSOs) and/or the PHEV owners. The developed mathematical model is based on the information exchange among individual PHEV and various entities, and on the communication and control capabilities which will eventually evolve in the Smart Grid. The developed model is first used to study the impacts of uncoordinated and coordinated charging of PHEVs in distribution system operations considering a 15-node distribution feeder with 10%, 25%, and 50% PHEV penetrations in residential loads. The results showed that the coordinated charging of PHEVs could be beneficial to the DSOs to reduce distribution losses, and to improve voltage profiles and load factor, while on the other hand, the uncoordinated charging leads to more losses and increased peak load despite yielding optimized energy costs for the PHEV owners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".