A Dynamic Pricing Based Scheduling Scheme for Electric Vehicles as Mobile Energy Storages
Bibliographic record
Abstract
The rechargeable battery of a plug-in electric vehicle (PEV) endows the PEV with dual roles in the power grid as power load and mobile energy storage (MES). Owing to the technical advancement of autonomous driving, private PEVs that are parked most of the day can be used as private MESs (PMESs) to autonomously deliver energy for overloaded charging stations (CSs). In this paper, we investigate an energy compensation problem where PMESs are scheduled to deliver energy to overloaded CSs so that the energy balance can be achieved while the energy delivery time can be minimized. Based on the time-variant CS operation status and traffic conditions, we propose a pricing-based scheduling scheme that considers both PMES navigation and incentive price design. First, to navigate PMESs in the energy-capacitated transportation system, a minimum-cost flow problem is formulated to minimize the energy delivery time. Then, the incentive price is determined to encourage PMESs to follow the optimal navigation results for energy delivery. Simulations are conducted based on the traffic data of California highway to validate the effectiveness of the proposed scheduling scheme.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".