A Blockchain-Enabled Decentralized Energy Trading Mechanism for Islanded Networked Microgrids
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".