Adaptive ADMM for Distributed Peer-To-Peer Energy Trading
Bibliographic record
Abstract
This paper proposes an adaptive Alternating Direction Method of Multipliers (ADMM) framework for distributed energy management in Peer-to-Peer (P2P) energy trading within Active Distribution Networks (ADNs). The model enables decentralized coordination of houses equipped with Photovoltaic (PV) generation and Battery Energy Storage System (BESS), while preserving user privacy. Centralized optimization, Dual Decomposition (DD), and ADMM-based approaches are compared against a baseline scenario of individual household optimization without P2P trading. A heuristic scaling-based adaptive ADMM strategy was proposed and compared with the residual balancing strategy, which reduced the convergence iterations compared to the standard ADMM and DD, while achieving comparable solutions. A comprehensive analysis involving four houses with varying energy resources demonstrates the significant benefits of P2P trading, including a reduction in energy costs and a significant decrease in grid exchanges. Both Time-Of-Use (TOU) pricing and Feed-In-Tariffs (FITs) are considered, with results showing that higher FITs lead to increased P2P energy transactions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".