Dynamic Pricing, Scheduling, and Energy Management for Profit Maximization in PHEV Charging Stations
Bibliographic record
Abstract
Recently, as plug-in hybrid electric vehicles (PHEVs) take center stage for the eco-friendly and cost-effective transportation, commercial PHEV charging stations will be widely prevalent in the future. However, previous studies in the fields of the management of PHEV charging stations have not synthetically taken practical charging systems into account. In this paper, we study the profit-optimal management of a PHEV charging station under the realistic environment addressing not only various types of vehicles but waiting time guarantee for PHEV customers as well. This paper is first to jointly take into account pricing for charging services, scheduling of reserved vehicles to PHEV chargers, dropping of reserved vehicles, and management of the energy storage in a unified framework that contains key features of a practical PHEV charging station. Based on this framework, we develop an algorithm to find the parameters required for charging management by invoking the “Lyapunov drift-plus-penalty” technique. Through theoretical analysis, we prove that the proposed algorithm achieves close-to-optimal performance under particular conditions by exploiting opportunism of time-varying arrival of charging vehicles, price of electricity, and renewable energy generation, but it requires no probabilistic future information. Finally, we find several significant messages via trace-driven simulation 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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".