Two-Stage Charging Strategy for Plug-In Electric Vehicles at the Residential Transformer Level
Bibliographic record
Abstract
In this paper, the coordinated charging control problem for plug-in electric vehicles (PEVs) with vehicle-to-grid functionality is formulated and investigated at the residential transformer level. A two-stage charging control (TSCC) strategy is proposed to shift the transformer load while achieving good charging performance for all PEVs connected to the grid. The proposed TSCC consists of an aggregator optimizer and a power distributor designed in two stages with different control functions. During the first stage, based on the dynamic aggregator concept, the optimal charging power for all PEVs in the aggregator is derived using the Pontryagin's minimum principle. During the second stage, a power distribution law is developed to allocate the aggregated power from the first stage using the fuzzy logic control approach. The TSCC framework considers the stochastic characteristics and nonlinear battery dynamics of practical vehicle charging scenarios, and therefore, is feasible for practical implementation. Finally, simulation results are presented to validate the control performance of the TSCC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".