Electric Vehicles Optimal Scheduling for Peer-to-Peer Energy Trade and Ancillary Services Provision to the Grid
Bibliographic record
Abstract
Electric vehicles (EVs) have already been proliferated across the globe as an alternative to fossil-fueled vehicles to deal with the environmental issues. The extensive deployment of EVs can bring challenges to the grid if not properly integrated. Such challenges, however, can be turned into opportunities if the huge unused capacity of the battery storage in millions of EVs are utilized for ancillary services to the grid and peer-to-peer (PtP) energy trade. To that end, this paper proposes a new charging scheme for EVs considering PtP energy trade and ancillary services provision to the grid via smart contracts. A smart contract process is introduced to govern the PtP transactions between EV users. Further, an optimal EV charging scheduling algorithm is proposed that incorporates PtP energy offers and ancillary service requests into the scheduling process. The efficacy and feasibility of the proposed algorithm are validated through numerical studies.
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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".