Using spatial-temporal flexibility of data center building in energy management of distribution grid coupled with multi-energy hubs and energy storage
Bibliographic record
Abstract
Due to the progress made and the huge production of data, the need to create data center buildings is increasing. Therefore, data center buildings are one of the most important consumption loads of the distribution network in the coming years. Data center buildings can transfer some of the loads on them to other data center buildings or other operating times, which is the spatial-temporal flexibility of data center buildings. Considering that data center buildings have many processors that generate heat by processing data, the heat produced by these centers can be used in hubs. In this article, a formulation for energy management of the distribution network with multi-energy hubs in the presence of data center buildings and energy storages is presented. First, the formulation of the distribution network with the existence of hubs and the modeling of the equipment inside the hubs including energy storages are presented. Then, by modeling the data center building and defining the spatial-temporal flexibility of this unit, its aggregation has been done in the problem of energy management of the distribution network. Finally, due to the existence of uncertainties in the problem, the final formulation of the problem has been done using the robust optimization method. Considering the distribution network of 33 buses on which 4 hubs are placed, the simulation has been done in two modes of equal distribution of load between data center buildings and distribution based on spatial-temporal flexibility. The amount of losses has increased from 4488 to 5039 kW, and the amount of gas purchased has increased from 40,800 to 41,377 kWh. The objective function value has decreased from 48266 to 43184, and the peak power received from the sub-distribution substation has decreased from 4022 to 3524 kVA. Although in the second case, the amount of losses and purchased gas have increased by about 12 % and 1 %, respectively, but the amount of costs and the peak power received from the sub-distribution substation have decreased by about 10 % and 12 %, respectively. Considering the importance of economic issues and the need to reduce the peak load of the network, it is possible to ignore the increase in losses in contrast to the reduction of these indicators and show that the spatial-temporal flexibility method of data center buildings has improved the scheduling conditions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".