A Self-Optimizing Scheduling Model for Large-Scale EV Fleets in Microgrids
Bibliographic record
Abstract
The increasing number of electric vehicles (EVs) demands management solutions to deal with the impacts of EV charging on the efficiency of distribution grids. Many suggested methods are derived from analysis on laboratory-scale systems with declared data, which cannot be implemented for real networks. In this article, a two-step scheduling model is developed that effectively guides a large-scale EV fleet in microgrids without demanding a dynamic monetary scheme. The first step corresponds to prediction-based day-ahead optimal scheduling for large scale EVs, which minimizes the costs of electricity supply and EVs' battery degradation. To avoid dimensional problems in calculations, an improved K-means clustering algorithm is presented to divide vehicles into different clusters. In the second step, online coordination is deployed based on an effective scoring system to encourage drivers to follow the first-step provided model. The proposed model is analyzed on a grid-connected microgrid with photovoltaic system integration. The problem (real) data are derived based on an estimate of the development process on the Ontario energy network over the next ten years. Results show that the introduced model can guarantee the accurate deployment of optimal charging/discharging schedules in large-scale systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".