An approximate dynamic programming approach for coordinated charging control at vehicle-to-grid aggregator
Bibliographic record
Abstract
A vehicle-to-grid (V2G) aggregator is an agent between the power grid and plug-in hybrid electrical vehicles (PHEVs). This paper studies the coordinated charging control of a V2G aggregator, which aims at minimizing the charging cost and reducing the power losses incurred by the fluctuating load. On one hand, a lower cost of charging gives the owners of PHEVs an incentive to cooperate. On the other hand, with an increasing popularity of PHEVs, the impact on the power grid such as power losses should be of concern to the aggregator. As an inherent property of a V2G aggregator, we enable bidirectional electric power flows between PHEVs and the power grid. Given the planned schedules of all the vehicles that are managed by an aggregator, we formulate the coordinated charging control as a dynamic programming problem. Due to the curse of dimensionality, we apply an approximate dynamic programming approach, which reduces the dimensionality of both state space and control space, to obtain the control sequences. We conduct simulations given the 24-hour schedules of 100 vehicles. Simulation results show that coordinated charging control can reduce both the total cost of charging and power losses significantly, compared with the scheme where each vehicle starts charging as soon as it is connected to the grid.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".