Optimizing Electric Vehicle Coordination Over a Heterogeneous Mesh Network in a Scaled-Down Smart Grid Testbed
Bibliographic record
Abstract
High penetration of renewable energy sources and electric vehicles (EVs) create power imbalance and congestion in the existing power network, and hence causes significant problems in the control and operation. Despite investing huge efforts from the electric utilities, governments, and researchers, smart grid (SG) is still at the developmental stage to address those issues. In this regard, a smart grid testbed (SGT) is desirable to develop, analyze, and demonstrate various novel SG solutions, namely demand response, real-time pricing, and congestion management. In this paper, a novel SGT is developed in a laboratory by scaling a 250 kVA, 0.4 kV real low-voltage distribution feeder down to 1 kVA, 0.22 kV. Information and communication technology is integrated in the scaled-down network to establish real-time monitoring and control. The novelty of the developed testbed is demonstrated by optimizing EV charging coordination realized through the synchronized exchange of monitoring and control packets via an heterogeneous Ethernet-based mesh network.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".