A large-scale charging pile and microgrid operation optimization strategy based on smart grid interaction technology
Bibliographic record
Abstract
In the current energy-constrained era, promoting electric vehicles (EVs) is a necessary trend. However, the simultaneous and uncoordinated charging of diverse EVs can negatively impact the power grid. This paper proposes a scaled EV orderly scheduling model, comprising charging demand simulation and a scheduling algorithm. Monte Carlo simulation, based on charging probability models, is used to generate EV cluster entry information and preprocess parameters. Two control strategies are proposed for clean energy dispatch and EV-based grid operation, accounting for user behavior-induced load variations. A microgrid optimization model is developed, with economic cost weights calculated. The model is solved using an improved PSO algorithm (APSO). Results show the APSO achieves better performance, with hourly average exchange loads of 2.7092 P/kW (vs. 1.9979 P/kW for PSO). Under 30–80% user responsiveness, microgrid management and environmental costs are reduced to 28,618.439 yuan and 7,864.685 yuan, respectively.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".