Towards Harmonious Decentralization of Energy Systems: A Vision of Interoperable Peer-to-Peer Energy Markets
Bibliographic record
Abstract
We present a hierarchical framework aimed at decentralizing the distribution systems market operations using localized peer-to-peer energy markets. A hierarchically designed decision-making algorithm approaches the power systems market operations from a bottom-up perspective. The three layers of the hierarchical framework operate in orchestration to enable prosumers (the grass-root ac- tors) to maximize their revenues - hence, a prosumer-centric framework. The design of the framework incorporates existing smart grid technologies (Virtual Power Plants, Microgrids, Distributed Energy Resources) and redefines their functional objectives to align them with the decentralization paradigm focused on empowering the bottom-up grid operations approach. On one hand, the framework is enabling prosumers with simultaneous access to the buy-sell choices that help them maximize their cost savings while ensuring their consumption patterns and preferences are not being traded off as a result of top-down operational decisions. On the other hand, it is designed to operate in harmony with the existing top-down grid operations mechanisms - thereby reducing the potential friction in its adaptation. This marriage of the top-down and bottom-up operational approaches is facilitated through meticulous orchestration of operational timescales. The framework’s novel design also incorporates scalability and interoperability considerations, thereby tackling the challenge of decentralization holistically.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".