Coordinated charging control of plug-in electric vehicles at a distribution transformer level using the vTOU-DP approach
Bibliographic record
Abstract
With the increasing public awareness of environmental issues, there is growing interest in plug-in electric vehicles (PEVs) which can be charged from the power grid. Consequently, the large numbers of PEVs could lead to considerable power demand from the power system, which poses a great threat to the power grid security (especially at the distribution level) if the PEV charging strategy is not properly regulated. In this paper, taking advantage of the vehicle to grid (V2G) service, the coordinated PEV charging control problem is studied at a distribution transformer level using the proposed virtual time of use rate dynamic programming (vTOU-DP) approach. For each PEV, the coordinated control problem is formulated as a constrained optimal control problem based on a self-defined concept of virtual time of use rate (vTOU), which reflects the distribution transformer load level. Then, the optimal charging rate is solved using the dynamic programming (DP) technique. The vTOU-DP, which is a plug-and-play control, can be implemented in real time. Simulation results show that the vTOU-DP provides better control performance than two commonly used baseline controllers in terms of transformer peak reduction and transformer load profile smoothness. Compared with the non-PEV base load, by charging PEVs at the transformer using the vTOU-DP, the transformer peak load is reduced by over 20%, and the variance of the transformer load profile is reduced from about 9 kW <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to less than 0.01 kW <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".