A New Method for Peer Matching and Negotiation of Prosumers in Peer-to-Peer Energy Markets
Bibliographic record
Abstract
This article presents a scalable mechanism for peer-to-peer (P2P) energy trading among prosumers in a smart grid. In the proposed mechanism, prosumers engage in a non-mediated negotiation with their peers to reach an agreement on the price and quantity of energy to be exchanged. Instead of concurrent bilateral negotiation between all peers with high overheads, an iterative peer matching process is employed to match peers for bilateral negotiation. The proposed negotiation algorithm enables prosumers to come to an agreement, given that they have no prior knowledge about the preference structure of their trading partners. A greediness factor is introduced to model the selfish behavior of prosumers in the negotiation process and to investigate its impact on the negotiation outcome. In order to recover the costs related to power losses, a transaction fee is applied to each transaction that enables the grid operator to recover incurred losses due to P2P trades. The case studies demonstrate that the proposed mechanism discourages greedy behavior of prosumers in the negotiation process as it does not increase their economic surplus. Also, it has an appropriate performance from the computation overheads and scalability perspectives.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".