Efficient Trading of Aggregate Bidirectional EV Charging Flexibility with Reinforcement Learning
Bibliographic record
Abstract
We study a virtual power plant (VPP) that trades the bidirectional charging flexibility of privately owned plug-in electric vehicles (EVs) in a real-time electricity market to maximize its profit. To incentivize EVs to allow bidirectional charging, we design incentive-compatible, variable-term contracts between the VPP and EVs. Through deliberate aggregation of the energy storage capacity of individual EVs, we learn a reinforcement learning (RL) policy to efficiently trade the flexibility, independent of the number of accepted contracts and connected EVs. The proposed aggregation method ensures the satisfaction of individual EV charging requirements by constraining the optimal action returned by the RL policy within certain bounds. We then develop a disaggregation scheme to allocate power to bidirectional chargers in a proportionally fair manner, given the total amount of energy traded in the market. Evaluation on a real-world dataset demonstrates robust performance of the proposed method despite uncertainties in electricity prices and shifts in the distribution of EV mobility.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".