Capacity expansion strategies for electric vehicle charging networks: Model, algorithms, and case study
Bibliographic record
Abstract
Abstract Governments in many jurisdictions are taking measures to promote the use of electric vehicles. As part of this goal, it is crucial to provide a sufficient number of charging stations to alleviate drivers' anxieties associated with the range of the vehicle. The goal of this research is to help governments develop vehicle charging networks for public use via the application of multistage stochastic integer programming model that determines both the locations and capacities of charging facilities over finite planning horizons. The logit choice model is used to estimate drivers' choices of nearby charging stations. Moreover, we characterize the charging demand as a function of the charging station quantity to reflect the range anxiety of consumers. The objective of the model is to minimize the expected total cost of installing and operating the charging facilities. An approximation algorithm, a heuristic algorithm, and a branch‐and‐price algorithm are designed to solve the model. We conduct numerical experiments to test the efficiency of these algorithms. Importantly, each algorithm has advantages over the CPLEX MIP solver. Finally, the City of Oakville in Ontario, Canada, is used to demonstrate the effectiveness of this model.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".