Optimization of distributed communication architectures in advanced metering infrastructure of smart grid
Bibliographic record
Abstract
Advanced metering infrastructure (AMI) is a major part of a smart grid system, and it deals with both data collection from smart meters and processing of those data. The traditional AMI architecture uses a centralized operation center with a centralized meter data management system (MDMS), which makes this system non-scalable. The system needs to be scalable so that with increased demand, it can be expanded at minimal cost. In this paper, we used two types of scalable distributed communication architectures, as initially proposed by Zhou et al. [1], namely, communication architecture with distributed MDMS and fully distributed communication architecture to minimize the deployment cost. We modified the analysis approach and used MATLAB-based code incorporating a Heuristic algorithm to determine close-to-optimal solutions for optimization problems. The unique feature of our work is the process of calculating accumulated bandwidth distance, in which distances between different components of an AMI were calculated according to the practical grid system layout of a city's infrastructure system. Theoretically developed scalability analysis was performed following [1], and the results were compared with the simulated results to indicate the validity of the asymptotic theoretical analysis. In our simulation, we found that the average distance between MDMS and the operation center was significantly different from that of Zhou et al. [1]. Our simulation results also indicated that both of the proposed architectures were scalable with significantly lower total deployment cost compared to the existing centralized communication architecture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".