Uncertainty Reduction for Data Centers in Energy Internet by a Compact AC-DC Energy Router and Coordinated Energy Management Strategy
Bibliographic record
Abstract
The concept of energy internet (EI) is a promising way to increase the utilization of small-scale renewable distributed generation and to reduce the effect of their intermittency on an autonomous power system. In such systems, resources are shared among the various participants in the grid, and the reliability of the overall system will increase. In the control level, an energy management strategy is required to meet the demand with respect to the available renewable distributed generation, energy storage, and flexible loads. In order to actuate the energy management strategies in an EI, controllable multiport converters are required to control energy flow. These converters are called energy routers (ERs). ERs are keystones in an EI as they control power flow and assure system power balance at local and global levels. ERs that interface AC and DC sub-grids are typically built with a combination of several converters that increase the cost and size of such converters. The higher number of ERs increases the flexibility of energy management in an EI, but the cost and size of such converter are a limiting factor at system-level design. This paper proposes a new, less expensive structure with a tailor-made control strategy to overcome these issues. The performance of the ER structure and the control strategy is confirmed with simulation and experimental results.
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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) | 0.001 | 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 | 0.000 | 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".