On-Line Event-Driven Scheduling for Electric Vehicle Charging via Park-and-Charge
Bibliographic record
Abstract
Large-scale charging stations become indispensable infrastructure to support the rapid proliferation of electric vehicles. Their operation modes have drawn great attention from both academia and industry. One promising mode called park-and-charge has been recently introduced. This new mode allows customers to park their electric vehicles at a parking lot, where the vehicles are charged during the parking time. Several small-scale experiments, such as the V-Charge project and General Motors' E-Motor plant, have demonstrated its potential. A key enabler for deploying this mode to large-scale stations is effective and efficient charging load scheduling methods. Most existing works confine to the time-driven scheduling policy due to their sole focus on the charging service. Applying their solutions to the park-and-charge mode would jeopardize the unitization of charging resource or cause frequent charging mode switching. This inapplicability motivates us to explore the feasibility and benefits of exploiting the event-driven scheduling policy in park-and-charge systems. Further, to better characterize charging load in this mode, we propose to adopt a metered model, by which a system gains value in proportion to the served charging demand. To be specific, the objective of this paper is to carry out both theoretical and experimental analysis for event-driven algorithms adapted to this metered model. We leverage both the competitive analysis and resource augmentation to demonstrate the non-constant and constant performance bounds for the earliest-deadline-first and highest-value-first algorithms respectively. Moreover, we provide a stronger theoretical result, i.e., the performance bound for the whole class of work-conserving scheduling algorithms. Through extensive simulations, we validate the proposed theoretical results and further provide interesting findings from the in-depth analysis of the simulation results.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".