Demand allocation policies for the charging station location and sizing problem
Bibliographic record
Abstract
We investigate the problem of optimizing the expansion of an existing public charging network for electric vehicles, over multiple years and multiple daily demand periods. Considering node-based demand, we optimize decisions related to setting up new charging stations (CSs), and the number of charging points to install at each CS. The objective of the problem is minimizing the installation costs subject to a minimum coverage requirement. One important assumption of the problem pertains to how demand is allocated to CSs. To this end, we propose seven compact models, considering four different demand allocation policies, two of which are new, considering fractional and binary assignment for three such policies. We investigate the implications of different allocation policies on the solutions to the problem, which we quantify using an adaptation of the Wasserstein distance. We conducted a set of computational experiments considering instances adapted from the literature and instances based on data from Bologna and Genova, Italy. Through our experiments, we demonstrate the impact of the different allocation policies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".