A Multi-Purpose Method for Sizing and Placement of Electric Vehicle Charging Stations in Urban Areas
Bibliographic record
Abstract
To support the trend of replacing gasoline-powered vehicles with electric vehicles (EVs), strategic insight into the sizing and placement of EV charging stations is an inevitable part of urban planning. Inadequate consideration of cost factors in urban areas, including the cost of the area, upgrades to protection devices, and voltage support services, can lead to uneconomical outcomes. Meanwhile, paying attention to customer satisfaction by providing easier access to stations and charging ports, as long as the cost does not exceed the budget, is another aspect addressed in our work. Our multi-purpose framework highlights the trade-offs among these factors, applicable to different cities worldwide. A simple meta-heuristic optimization algorithm, Particle Swarm Optimization (PSO), is employed in this paper to determine the minimum value of the single aggregated cost function. Numerical results illustrate the trade-offs and interrelationships among key factors, as well as the effectiveness of the proposed strategy in determining the optimal sizing and placement of EV charging stations. The results indicate an 8.5% cost reduction while maintaining a customer dissatisfaction index (CDSI) below the critical value.
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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 | 0.000 |
| 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".