Coordination of Multi-Agent Orderly Charging Via An Incentive-Compatible Mechanism
Bibliographic record
Abstract
As the cyber-physical system is developed into cyberphysical-social system, the importance of social factors is growing in the interaction between electric vehicle (EV) and power system. This paper aims to develop an incentive-compatible mechanism to coordinate multi-agent orderly charging. Firstly, the travel behaviors of EV cluster are simulated based on Monte Carlo sampling, and the load transfer model considering various social factors is constructed. Then, an orderly charging mechanism involving multiple agents based on Nash bargaining theory is proposed. In the first stage, the total profit of electric vehicle user, power grid company (PGC), and charge station operator is maximized. In the second stage, the revenue of each agent after participating in the cooperation is improved by transfer payment. Next, the carbon trading mechanism is applied in the incentive compatibility model, and the revenue of PGC participating in the carbon market under different scenarios and constraints are calculated. Finally, by comparing with the existing model, the simulation results show that the proposed multi-agent coordinated orderly charging model can reduce the pressure on the power grid caused by the randomness of EV travel, and through a fair profit distribution mechanism, it can maximize the social benefits of the coalition while increasing the revenue of each agent.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".