Cost-Aware Dynamic Bayesian Coalitional Game for Energy Trading among Microgrids
Bibliographic record
Abstract
The future electricity distribution system will be highly impacted by the emergence of peer-to-peer energy trading within microgrid (MG) communities. The idea of peer-to-peer energy trading is to export the surplus energy of a MG to a nearby MG or a group of MGs whose electrical load exceeds their generation. The variations in demand and generation, and the dynamic nature of these communities result in uncertainty on whether MGs will be able to satisfy their trading commitment or not. In this paper, the problem of energy trading among MGs is addressed with the objective of minimizing the cost under uncertainty. A Bayesian coalitional Game (BCG) based scheme is proposed, which helps the MGs to minimize the overall cost by forming stable coalitions. The results show 15% to 30% improvement in terms of cost minimization compared to an existing Q-learning based scheme and a conventional coalitional game theory (CG)-based approach from the literature.
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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 | 0.000 | 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".