A Scalable Digital Infrastructure for Sustainable Energy Grid Enabled by Distributed Ledger Technology
Bibliographic record
Abstract
The future of renewable energy transportation and distribution is dynamic and complex, with distributed renewable resources in required distributed control.It is suggested that Distributed Ledger Technology (DLT) is a timely innovation with the potential to facilitate this future.The transition to full renewable energy requires an infrastructure capable of handling intermittent production that has a low marginal cost.This requires a distributed control logic where devices with embedded intelligence coordinate local production, a decentralized energy market where prices are not primarily based on production, and an underlying digital infrastructure to enable both.Simulations and experiments have demonstrated great potential in such a digital infrastructure, but real-life tests have identified scalability as a remaining challenge.In this paper, we propose a DLT-based architecture for the energy grid with the development of existing solution concepts by implementing scalability solutions.To this end, we derive energy market components as a framework for building efficient microgrid.Then, we discuss the microgrid as a case study of such a market according to the required components within energy production, transmission, and distribution; distributed ledger platform operations, IoT device manufacturing,; software development; and research in IoT, edge and cloud computing, and energy systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".