Optimal Charging Scheduling of Electric Vehicles based on Principal Component Analysis and Convex Optimization
Bibliographic record
Abstract
The widespread presence of plug-in Electric Vehicles (PEVs) in distribution power grids brings significant concerns regarding their energy demand and peak power requirements. In order to reduce the PEV load impact on power distribution networks, different strategies have been proposed based on the electricity price. This paper proposes a PEV charging approach that takes advantage of a disciplined convex optimization to reduce charging station costs. The proposed method can provide an optimal charging scheduling according to Time-of-Use (TOU) rates and maximum power penalty. Furthermore, Principal Component Analysis (PCA) is utilized to rank critical users in PEV charging stations based on their contribution to on-peak periods. Simulations are carried out for nine PEV during 24hour by using real-world data from a charging station, located on-campus. The results show the capabilities of the proposed approach to effectively reduce the electricity charging costs and identify the critical users.
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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 | 0.001 |
| 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".