Blockchain-based sequential market-clearing platform for enabling energy trading in Interconnected Microgrids
Bibliographic record
Abstract
Interconnected Microgrids (IMGs) are considered a futuristic paradigm of power grids that offer modularity, resilience, and independence with energy exchangeability. In this context, each MG is accountable to its own citizens (i.e., generators or loads) and can participate in a market with its neighbours, if this enhances the benefits of its citizens. Thus, greedy behaviour is assumed to be rational for MGs participating in such a market. In this paper, a novel decentralized platform to facilitate energy trading between IMGs is developed. The platform would allow interested MGs to participate and gain benefits assuming self-benefit-driven (SBD) actions from participating MGs. The proposed platform provides a market-clearing approach based on sequential rounds. In each round, the MG with the cheapest energy price is privileged to export its surplus energy and maximize its own benefits. In order to identify the round champ, a decentralized ranking algorithm is developed to determine the MG with the cheapest energy price. The effectiveness of the proposed platform is validated using various case studies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".