Eliciting Multi-Dimensional Flexibilities From Electric Vehicles: A Mechanism Design Approach
Bibliographic record
Abstract
Electric vehicles (EVs) have been well recognized as a deferrable load with the flexibility to shift their energy demands over time. Although this one-dimensional flexibility has been extensively investigated both by research and industrial implementations, the expanding energy demand and the associated uncertainties still make the integration of a large population of EVs into power system reliably and economically greatly challenging. In this paper, we design an auction scheme via mechanism design to elicit two additional flexibilities from EVs, namely energy flexibility and deadline flexibility. An offline mechanism is firstly designed as a benchmark based on the famous Vickrey-Clark-Groves mechanism. Then based on the primal-dual approach, we propose an online auction, in which all bids are truthful, the loss of social welfare is bounded by competitive ratio, and the mechanism can be implemented in polynomial time. By the numerical results, we quantitatively show that both the power system operators and individual EVs can benefit from the integration of the multi-dimensional flexibilities through our proposed mechanisms.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".