Multi-objective Optimization Charging Strategy for Plug-in Electric Vehicles Based on Dynamic Time-of-use Price
Bibliographic record
Abstract
With the increase of plug-in electric vehicles (PEV), the uncontrolled charging of them may pose a wide pressure on the operation of regional distribution network. In order to reduce adverse impacts of PEVs, an intelligent charging strategy for a cluster of PEVs is proposed. Considering several constraints such as the charger’s maximum charging power, a multi-objective optimization scheduling model is proposed with the objectives of minimizing the total charging cost and minimizing load variance basing on dynamic time-of-use (TOU) price. The Non-dominated Sorting Genetic Algorithm II (NSGA-Ⅱ) is adopted to solve the optimization problem, and the MATLAB calculation results prove the feasibility and effectiveness of the proposed strategy. Factors such as the number of PEVs, the TOU price and the length of time-window are also analyzed to further study PEV charging load’s characteristics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".