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 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 | 0.000 |
| 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".