A Blockchain-Enabled Decentralized Energy Trading Mechanism for Islanded Networked Microgrids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interconnected microgrids are becoming a building block in smart systems. Initiating secure and efficient energy trading mechanisms among networked microgrids for reliability and economic mutual benefits have become a crucial task. Recently, integrating blockchain technologies into the energy sector have gained significant amount of interest, e.g. transactive grid. This paper proposes a two-layer secured smart contract-based energy trading mechanism to allow microgrids to establish coalitions, adjust the electricity-trading price, and achieve transparent and decentralized secure transactions without intervention of a third trusted party. Since reliability benefits are main drivers of microgrids operation in islanded mode, a new decentralized smart contract based-energy trading model for islanded networked microgrids is proposed in the first layer with an objective to achieve demand generation balance. In the second layer, and to achieve a higher security, all executed contracts are verified and saved in a blockchain based on a new developed two-phase consensus method that utilizes practical Byzantine Fault Tolerance (pBFT), and a modified Proof of Stake (PoS). Simulations are conducted in Python environment to validate the proposed energy trading model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it