Joint Route Selection and Charging Discharging Scheduling of EVs in V2G Energy Network
Bibliographic record
Abstract
Thanks to the advantages of zero carbon dioxide emissions and low operation cost, the number of on-road electric vehicles (EVs) is expected to keep increasing. They usually get charged through charging stations powered by either the grid or renewable plants. Due to the potential difference in electricity price between the grid and the renewable plants, an EV may purchase electricity at charging stations powered by renewable plants, and then discharge the surplus energy in the battery to the grid, to gain profits and enhance the overall renewable energy utilization. In this work, we aim to optimize the route selection and charging/discharging scheduling to improve the overall economic profits of EVs, taking into account the constraints, including the time-varying energy supply caused by the intermittent generation of renewable energy, the limited number of charging piles in a charging station, and the traveling delay tolerance of EVs. Firstly, a time-expanded vehicle-to-grid graph is designed to model the objective and related constraints. Then, we apply an AI-based A* algorithm to find K-shortest paths for each EV. Finally, a joint routing selection and charging/discharging algorithm, namely, K-Shortest-Paths-Joint-Routing-Scheduling (KSP-JRS), is proposed to minimize the total cost of EVs by maximizing their revenue from energy discharging under time constraints. The proposed approach is evaluated using the real traffic map around Santa Clara, California. The simulation, with different numbers of testing EVs, shows the feasibility and superiority of the proposed algorithm.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".