A Scalable Digital Infrastructure for Sustainable Energy Grid Enabled by Distributed Ledger Technology
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
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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