EV Charging Network Design with Transportation and Power Grid Constraints
Bibliographic record
Abstract
Connected electric vehicles (EVs) are a key component of future intelligent and green transportation systems, and the penetration of EVs depends on convenient and cost-effective charging services. In addition to being charged at home or on parking lots, a charging network is needed for EVs right off the road. This paper first focuses on the optimal charging network design for charging service providers, considering the time-varying and location-dependent demands from vehicles and constraints of power grids. To optimize the charging station locations and the number of chargers in each station, we first model the coverage area of each possible location to estimate the dynamic charging requirements of EVs. Then, we formulate the problem as profit maximization, which is a mixed-integer program. To make the problem tractable, we investigate the features of the problem and obtain a necessary condition to deploy a charging station and derive the upper and lower bounds of the number of chargers in each station. Given the analysis, we take two steps to transform and relax the problem to convex optimization. A fast-converging search algorithm is further proposed based on the profit of each possible location. Using real vehicle traces, simulation results show that the proposed algorithm can maximize the total profit when fewer charging stations and chargers are initially needed, which is more attractive for charging service providers.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".