Reliable overlay topology design for the smart microgrid network
Bibliographic record
Abstract
Integration of the advances in information and communication technologies to the power grid technologies is changing the architecture and operation of the traditional grid, leading the grid to gradually evolve into a smart grid. Generation in the smart grid will be different than it is in today's grid, mainly due to the high penetration level of distributed renewable power generators. The distributed generator (DG) is favorable for its low transmission losses and low emissions. On the other hand, control of a high number of DGs is challenging. Almost a decade ago, microgrids were proposed to tackle the challenge of handling the growing number of DGs and fault isolation. A microgrid is a medium voltage or low voltage electrical power system that contains DGs, storage units, controllable loads, small-scale combined heat and power units, and an energy management system. In this article, we revisit the microgrid concept in light of survivability approaches borrowed from high-speed networks. We provide an analogy between survivability in metroaccess networks and reliability of the overlay topology for the smart microgrid network (SMGN). We develop the idea of resource sharing among smart microgrids for the sake of increased reliability. In our case, reliability corresponds to supplying power to the loads using the energy generated in the SMGN, without importing power from the utility grid. We provide a reliable overlay topology design scheme that maximizes the usage of renewable energy in the SMGN.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".