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 kW2to less than 0.01 kW2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".