Enabling Peer-to-Peer Transactions in Measurement-Based Distribution System Market
Bibliographic record
Abstract
This paper presents a measurement-based electricity market structure to establish peer-to-peer (P2P) transactions along with imports from or exports to the upstream network. A key benefit of the proposed P2P market is that participants therein can fully express their proclivities by setting their individual preferences for buying and selling partners independently. Moreover, resulting P2P transactions satisfy power flow constraints of the underlying distribution system without needing an offline network model. Instead, we estimate a linear sensitivity model mapping bus voltages to injections using only online measurements collected from P2P market participants, which is then embedded as an equality constraint in an optimal power flow (OPF) problem. The OPF problem minimizes total cost of P2P transactions incurred to market participants capturing network usage fees, buying/selling preferences, net import/export cost, and operation cost. The optimal solution of the OPF problem comprises the P2P transactions (specifying partners, quantity, and price for each trade), the optimal dispatch, as well as locational marginal prices at buses where measurements are collected. Via numerical simulations involving a 22-bus test system, we demonstrate the effectiveness of the proposed method to establish P2P transactions that respect individual preferences; we also validate notable properties pertaining to the trade prices.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".