Double-Sided Auction Mechanism for Peer-to-Peer Energy Trading Markets
Bibliographic record
Abstract
The emerging smart grid uniquely combines two-way communication and energy flow, allowing consumers to become active participants in market-based energy supply and demand strategies. In such a market, Peer-to-Peer (P2P) energy trading paradigm allows local communities and individuals who generate electricity to freely decide how and with whom they are going to trade it. The greatest challenge of P2P energy trading is how to design efficient mechanisms among rational participants that maximize their monetary benefits. Furthermore, since utility companies own the transmission lines, a key question that yet to be addressed in P2P markets is: how to match between different energy buyers and sellers while taking into account the physical constraints of the underlying grid infrastructure, e.g., capacity, congestion, and line transmission costs. This paper proposes a novel double-sided auction mechanism with a matching algorithm that addresses the aforementioned challenges. In this paper, the social welfare of the participants is modeled as an optimization problem with cost constraints incurred due to energy generation, operating and maintenance, capacity, and line transmission costs. The study provides theoretical analysis of the P2P auction model including mechanism design properties such as individual rationality, computational efficiency, and truthfulness. The results of the experiments indicate that the proposed auction model outperform existing systems and yields better economic incentives for participants.
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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".