Optimal Planning of Electric Vehicles Energy Exchange in Parking Lots Considering Uncertainties
Bibliographic record
Abstract
With the growing concern for energy conservation and environmental protection around the world, the use of Electric Vehicles (EVs) is being encouraged. In this way, the optimum allocation of EVs’ parked in a distribution network is a challenging issue. In this paper, the optimal energy exchange between EVs and the distribution network is analyzed in two types of parking lots, which will be controlled by a charging provider or aggregator, and where the randomness related to vehicles and vehicle-to-grid (V2G) capability at the charging stations will be considered. In this way the stochastic behavior of an EV during the day was formulated as a mixed integer linear programming (MILP) problem, and Monte Carlo simulations were used to estimate the optimal loading and unloading patterns of EVs inside the parking lots based on the time-of-use (ToU) tariffs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".