An autonomous demand response program in smart grid with foresighted users
Bibliographic record
Abstract
In smart grid, demand response is a viable approach to motivate users towards shifting the demand during the peak load periods. Each user can also benefit by reducing its total cost. In most of the existing studies, the demand response program is modeled as a one-shot game among myopic users, who aim to minimize their cost in one period of time. In this paper, we show that the Nash equilibrium (NE) in the one-shot game can be inefficient in reducing the peak load demand and the users' cost. We address the inefficiency of the NE by modeling the demand response program as a repeated game. A grim-trigger strategy is proposed to determine the subgame perfect equilibrium. To address the issue of fairness, we partition the set of users into groups. In each time period, only one group of users are required to participate in the demand response program. Simulation results show that the proposed demand response repeated game can benefit both the users, by reducing their long-term cost, and the utility company, by reducing the peak-to-average ratio in the aggregate load demand.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".