Cost-Aware Smart Microgrid Network design for a sustainable smart grid
Bibliographic record
Abstract
Smart MicroGrids (SMGs) are expected to be a significant ingredient of the future smart grid as they simplify the integration of distributed renewable energy generation units, offer reliable service, provide faster restoration capabilities and support sustainable grid operation. Furthermore, by the help of the Information and Communication Technologies (ICT), energy management systems of the SMGs can communicate with each other and provide a platform where microgrids can export or import power, provided that certain power quality constraints and standards are met. These virtually connected microgrids form the Smart Microgrid Network (SMGN). In this paper, we propose the Cost-Aware Smart Microgrid Network (CoS-MoNet) design scheme that enables economic power transactions within the SMGN. CoSMoNet is based on an Integer Linear Programming (ILP) formulation that matches the excess energy in the storage banks of a group of SMGs to the demands of other SMGs whose load cannot be accommodated by their local supply. Our results show that CoSMoNet enables cost-efficient power transactions among microgrid communities, increases the utilization of renewable energy, reduces the dependency of the microgrids to the utility grid, and consequently reduces the load on the grid.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".