Incentive-based Peer-to-Peer Distributed Energy Trading in Smart Grid Systems
Bibliographic record
Abstract
This paper studies incentive-based peer-to-peer (P2P) energy trading between sellers and buyers in a smart grid distributed system. When designing an energy trading model the main goal is to maximize the social welfare of the involved parties. Peer-to-peer energy trading is a novel mechanism of power system operation which allows users to generate and trade renewable energy. Buyers are considered to be consumers and sellers are considered to be prosumers (producers and consumers of energy) with respect to time. Buyers are more motivated to purchase energy from the seller since the energy was generated from a green source and is typically cheaper than the main grid. Furthermore, by producing clean energy and distributing within small networks, the transmission line stress is mitigated since the overall demand required from the main grid is reduced. In this paper, we model the interaction as single-sided auction taking into consideration the grid infrastructure constraints (e.g capacity) and cost while maximizing the profit of the players. We assess through theoretical analysis and simulations the bidding properties including individually rational, truthful, computationally efficient, and fairness.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".