Optimal V2G Commitment in Multi-unit Residential Buildings
Bibliographic record
Abstract
This work studies the cost-effectiveness of vehicle-to-grid (V2G) in residential applications and presents a self-maintained and scalable solution for optimizing the scheduling of a cluster of V2G units distributed across multi-unit residential buildings (MURBs). The mathematical model takes into account both the revenue and costs associated with a self-committed V2G technique, serving electric markets for ancillary services and regulation. The proposed mixed-integer non-linear programming (MINLP) model establishes cost-effectiveness boundaries for V2G techniques, to maximize availability at minimal cost and minimize degradation of electric vehicle (EV) batteries. The outcomes demonstrate that utilizing V2G for the sole purpose of selling electricity to the grid may not be an optimal choice for households equipped with small-rated charging units. However, the prospect of V2G adoption becomes more promising in MURBs, where the deployment of larger V2G units with higher power outputs is well-suited for efficient utilization during peak hours. The study provides valuable insights into the potential benefits and limitations of V2G integration in diverse residential settings.
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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".