Techno-economic study of an energy sharing network comprised of a data centre and MURBs for cold climate
Bibliographic record
Abstract
Due to their significant internal heat gain resulting from computer server banks, data centres require cooling year-round, creating an opportunity to transport the waste heat to heat-deficient neighbouring buildings. This thesis evaluates the quantity of multi-unit residential buildings (MURBs) that should be connected to a given data centre in order to maximize the portion of shared energy which provides the MURBs’ heating energy and the data centre’s cooling energy simultaneously. The thesis then evaluates the financial viability and greenhouse gas (GHG) emissions of three different methods with which energy can be shared from a data centre to surrounding MURBs in a community energy network (CEN). The first method, called the Energy Sharing System involves using a heat pump to produce heating and cooling at the same time for the MURBs and the data centre. The second, called the One-Borefield System, has the same energy sharing aspect as the first, with additional heating and cooling coming from geo-exchange. The third method, called the Two-Borefield System, is an innovative approach to geo-exchange, which uses two separate borefields to achieve free cooling, while also incorporating the energy sharing base. The investigation finds that the optimal MURB area that should be connected to a 4 MW cooling load data centre is 110,000 m2 for the Toronto (Canada) climate. The financial analysis shows that the Energy Sharing System was the most profitable, with a 11.9% 30-year after-tax internal rate of return (IRR). This scenario resulted in the most efficient operation, achieving an overall 4.3 COP for heating and free cooling. This scenario would reduce the MURBs’ annual heating related emissions by 2289 tonnes CO2e (57%) and reduce the data centre’s annual space cooling related emissions by 80 tonnes CO2e (53%).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".