P2P Trade With Prosumers’ Actual Approximate Utility Functions Within Near-Potential Games Framework
Bibliographic record
Abstract
There is a potential conflict between P2P trade and distribution network operator (DSO) decisions, which slows down the expansion of P2P trade. Expanding the P2P trade requires reducing this conflict and increasing DSO awareness of the actual behavior of prosumers. In other words, mitigating this conflict requires that DSO approximates prosumers’ utility functions (PUFs) based on their actual behavior. On the other hand, PUFs approximated based on the actual behavior of prosumers have various parameters such as freedom in decision-making, collective influence, privacy, and marginal cost/utility. This is a mathematical challenge for DSO because this class of PUFs may not be convex or continuously differentiable. Hence, in this paper, a near-potential game (NPG) framework is proposed to support the design of P2P trade with PUFs belonging to this class. Also, to develop a realistic model of P2P trade, we classify prosumers into residential and non-residential classes and assume that prosumers have limited information about each other’s decisions. Then within an NPG framework, we introduce a learning model, whereby each prosumer obtains an estimate of the prosumers’ decisions in P2P trade.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".