Capacity Expansion of Electric Vehicle Charging Network: Model, Algorithms and A Case Study
Bibliographic record
Abstract
Governments in many counties are taking measures to promote electric vehicles. An important strategy is to build enough charging infrastructures so as to alleviate drivers’ range anxieties. To help the governments make plans about the public charging network, we propose a multi-stage stochastic integer programming model to determine the locations and capacities of charging facilities over finite planning horizons. We use the logit choice model to estimate drivers’ random choices towards different charging stations nearby. The objective of the model is to minimize the expected total cost of installing and operating the charging facilities. Two simple algorithms are designed to solve this model, an approximation algorithm and a heuristic algorithm. A branch-and-price algorithm is also designed for this model, and some implementation details and improvement methods are explained. We do some numerical experiments to test the efficiency of these algorithms. Each algorithm has advantages over the CPLEX MIP solver in terms of solution time or solution quality. A case study of Oakville is presented to demonstrate the process of designing an electric vehicle public charging network using this model in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".