Distributed Energy Resources Parameter Monitoring in Microgrids Using Blockchain and Edge Computing
Bibliographic record
Abstract
An increased share of distributed renewable energy sources requires flexible tools for providing reliable and cheap electricity. Smart meters provide information at the consumer level, which could be used as the main source for real-time energy micro-transactions, however, one of the main concerns about direct transactions is information security. Conventional electricity markets rely on centralized information exchange, nevertheless, when intra-day, distributed, electricity consumption and production exchanges are required between customers, this approach might not be enough. This paper presents a proof-of-concept for using Blockchain as a tool for managing the operational transactions in a DC microgrid. The distributed nature of this technology provides an inherently safer approach, by providing an immutable database for transaction history. One of the challenges of using this technology, however, is the required computing power at the nodes and the limited capacity available in the smart meter. To overcome these issues, the authors used a distributed computing technology, -edge computing-, where computation and storage are located closer to the customer, to improve response times by handling the required computational tasks of the Blockchain tool. This approach proved not only to be practically viable but also, offers important insights about the scalability and capabilities of the technology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".