Monte Carlo Simulation for Electric Vehicle Charging: The Case Study of Milan - Italy
Bibliographic record
Abstract
The European Union has set ambitious$\text{C O}_{2}$reduction targets, aiming for a 90 % reduction in transport emissions by 2050. The Fit for 55 package reinforces these efforts, especially in road transport, with the goal of zero$\text{CO}_{2}$emissions for new vehicles by 2035. Italy aims to increase the number of Electric Vehicles (EVs) to 6.6 million by 2030, raising potential challenges to the existing charging infrastructure. In this work, a simulation tool for Electric Vehicle Charging in an urban environment was created and implemented, using Monte Carlo methodology useful for estimating results under uncertainty; in particular, the following uncertain factors are considered: i) user behavior, ii) charging infrastructure availability, iii) battery capacity, and iv) driving patterns. The simulation analyzes hourly variations in charging probability to estimate the charging demand of EVs over a 24 -hour period, projecting the occupancy of charging stations and the resulting energy demand for different areas of the city of Milan (Italy). The obtained results, which can also be replicated in other contexts, show important insights into EVs charging dynamics, serving as a basis for optimization and efficient infrastructure management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".