A firm-union bargaining game approach for PHEV charging access control
Bibliographic record
Abstract
As the penetration level of plug-in hybrid electric vehicles (PHEVs) increases, the charging demand of PHEVs is expected to have a major impact on the loading of distribution systems. Charging access control, which determines the starting time of PHEV charging, is critical to mitigate such impact. In this paper, we investigate how to achieve PHEV charging access control by leveraging the electricity price set by electricity retailer and the speculative prices of PHEV owners. A firm-union bargaining game approach is proposed to study the interactions between the electricity retailer and PHEV owners. In order to eliminate the requirement of a centralized energy management system, we extend the original centralized firm-union bargaining game approach to a semi-distributed approach. In particular, the retailer plays the role of the union for wage control by setting the electricity price for PHEV charging, while balancing the revenue and cost. On the other hand, each PHEV owner uses a responsive strategy to optimize his/her own benefit by choosing an appropriate charging starting time. Through nonlinear programming, a sub-game perfect Nash equilibrium solution can be attained. The PHEV owners in the game can determine their strategies independently to maximize their own benefits. The model is further extended to consider the cost of charging demand fluctuation reflected by the cost of purchasing frequency regulation or spinning reserve services by distribution utilities. Extensive numerical results are presented to evaluate the performance of the proposed approach.
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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".