Dynamic Pricing for Differentiated PEV Charging Services Using Deep Reinforcement Learning
Bibliographic record
Abstract
With the increasing popularity of plug-in electric vehicles (PEV), charging infrastructure becomes widely available and offers multiple services to PEV users. Each charging service has a distinct quality of service (QoS) level that matches user expectations. The charging service demand is interdependent, i.e., the demand for one service is often affected by the prices of others. Dynamic pricing of charging services is a coordination mechanism for QoS satisfaction of service classes. In this article, we propose a differentiated pricing mechanism for a multiservice PEV charging infrastructure (EVCI). The proposed framework motivates PEV users to avoid over-utilization of particular service classes. Currently, most of dynamic pricing schemes require full knowledge of the customer-side information; however, such information is stochastic, non-stationary, and expensive to collect at scale. Our proposed pricing mechanism utilizes model-free deep reinforcement learning (RL) to learn and improve automatically without an explicit model of the environment. We formulate our framework to adopt the twin delayed deep deterministic policy gradient (TD3) algorithm. The simulation results demonstrate that the proposed RL-based differentiated pricing scheme can adaptively adjust service pricing for a multiservice EVCI to maximize charging facility utilization while ensuring service quality satisfaction.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".