Towards cognitive energy management system of microgrid in enabling transportation electrification
Bibliographic record
Abstract
This paper introduces the concept of cognitive energy management system (CogEMS) for microgrid applications. The CogEMS is based on the four building principles of human cognition: the perception-action cycle, memory, attention and intelligence. It would enable an efficient and robust optimization of the energy flow within the microgrid and between the power grid and the microgrid, voltage and frequency control, and protection of the devices. The purpose is to equip the microgrid with an intelligent control system in support of transportation electrification. In future, more and more electric vehicles (EVs) will be connected to the grid via the vehicle-to-grid technology (V2G), posing both challenges and opportunities for microgrids. EVs can be regarded either as the loads of the microgrid during the charging stage, or energy storage devices during the discharging stage. In this context, the energy management system should be able to intelligently coordinate the two stages.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".