A Game-Theoretic Approach for Charging Demand Management of Electric Vehicles During System Overload
Bibliographic record
Abstract
To manage the charging demand of electric vehicles (EVs) under maximum power limit constraints, a single-leader-multi-follower Stackelberg game theory-based solution approach is proposed in this study. A utility function is formulated for EVs considering the sensitivity of the EV owners to the battery degradation and the current energy level. A pricing mechanism for charging station operators is also devised to incentivize EVs for managing their charging demands locally, without violating the maximum power limit set by the distribution system operator. To this end, a decentralized welfare maximization model is formulated, where EVs do not need to share their private information with the charging station operator. The developed model is solved in a distributed way using the primal-dual subgradient method. The performance of the proposed method is analyzed for different power limits along with different sensitivity and energy levels. Results have shown that the proposed method can manage the charging demand of EVs considering individual sensitivities and maximum power limits of the charging station.
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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".