Lithium-Ion-Capacitor-Based Distributed UPS Architecture for Reactive Power Mitigation and Phase Balancing in Datacenters
Bibliographic record
Abstract
With the rapid proliferation of cloud computing, the energy cost of datacenters and the impact of reactive power on the grid have become major concerns. This paper presents two energy management control schemes for distributed uninterruptible power supply (UPS) architectures utilizing lithium-ion ultracapacitors (LIC) for improved system efficiency and reactive power mitigation. One scheme is proposed for low-power servers with an internal dc bus, while another is proposed for medium-to-high-power server racks with an in-rack dc bus. The LIC and the dc bus interface with a bidirectional dc-dc converter, which allows the power supply unit to run in the optimal operating region for the improved system efficiency and power factor. The server-level UPS architecture is experimentally tested using a 200-kHz, bidirectional, multiphase dc-dc converter with hysteretic current-mode control, while achieving 33% reactive power mitigation. Based on the SciNet datacenter in Toronto, ON, Canada, a rack-level distributed UPS architecture is built in MATLAB, and system-level simulations indicate a 37% reduction in the reactive power.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.003 | 0.001 |
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".