Demand responce through interactive incorporation of plug-in electric vehicles
Bibliographic record
Abstract
PEV coordination introduces a significant challenge in demand response programs (DR). In one hand, there is a serious challenge due to uncertainty and dynamics associated with PEVs to be devoted to DR. In another hand, with proper charging and communication infrastructure, PEVs may play a dual role in smart grids; they may eventually either turn into Interruptible Loads (IL) when plugged in for charging or act as grid-able storage responding to the pricing commands. This paper aims to provide an approach that realises DR using aggregated PEVs in parking lots. This approach includes a real-time interaction between the aggregator and the PEV owner, where the aggregator suggests different offers and, accordingly, the owner responds based on his/her preference. A multi-stage optimization solution is proposed here to accommodate properly different offers to the PEV owners, while it is benefiting from an ANN-based forecast model to incorporate the effect of the future PEV arrivals in the decision actions. Implementation results on the 38-bus test system indicate how effectively the proposed solution could help future smart parking lots in DR contribution.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".