A potential game framework for charging PHEVs in smart grid
Bibliographic record
Abstract
Due to the proliferation of plug-in hybrid electric vehicles (PHEVs), the peak load in the power grid is expected to increase in future. The peak load can be reduced by implementing appropriate load scheduling schemes using advanced metering infrastructure (AMI) and smart chargers. In this paper, we formulate the charging problem of PHEVs as a potential game to jointly optimize the cost of the utility company and payoff of the customers. The potential game approach enables us to study the existence and uniqueness of the pure strategy Nash equilibrium and to design a polynomial time distributed algorithm to achieve that equilibrium. It also enables us to define a Lyapunov function to show that the Nash equilibrium is globally asymptotically stable, i.e., the proposed distributed algorithm converges to the Nash equilibrium from any arbitrary initial conditions. To evaluate the efficiency of our proposed algorithm, we compare its running time with an algorithm based on the customers' best response.
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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".