Peer-to-Peer Energy Trading Enabled Optimal Decentralized Operation of Smart Distribution Grids
Bibliographic record
Abstract
Currently, the distribution systems are moving towards decentralized operation due to the high penetration of distributed energy resources (DERs). Peer-to-peer (P2P) energy trading has been an emerging concept that promotes autonomous DER participation in energy markets while preserving their privacy concerns. In this work, a novel P2P energy trading enabled decentralized market framework is proposed for the optimal operation of distribution grids. Nodal agents and P2P agents are established as market participants, and market equilibrium is iteratively achieved via alternating direction method of multipliers based algorithms. The proposed market framework guarantees grid constraint satisfaction, market equilibrium, and global optimality for all market participants without violating their privacy concerns. The agent coordination and local optimization are designed such that fairness of the market clearing mechanism, prosumer autonomy, and prosumer anonymity is preserved without compromising the market efficiency. Further, costs/rewards of ancillary services associated with the P2P energy transactions are considered as trade-offs within the market mechanism, and those are accurately allocated to the respective trading pairs. The case studies illustrate the effectiveness and scalability of the proposed market framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".