LCA : batteries and fuel cells for commercial buildings in British Columbia
Bibliographic record
Abstract
A typical office building might consume 250 kWh at its peak hour. The existence of the ebb and flow of electricity demand is amplified when considering large areas such as cities, or even a 50,000 student university such as the University of British Columbia (UBC). Infrastructure and energy costs, in terms of emissions and dollars, are required to manage “peak”, resulting in excess capacity that still must be paid for during “off peak” hours. Based on these complications, large scale stationary storage has recently become more attractive to better optimize existing infrastructure. This report reviewed this scenario in the context of a “model building” in British Columbia. This building was modeled after the UBC University Services Building, which is similar in electricity demand or load profile as that of commercial office buildings. A life cycle analysis for zinc bromide batteries and hydrogen fuel cells specified to meet this office building demand were completed. The results were scaled to determine what emissions, costs, and peakdemand “load shaving” could result from using these technologies in a fraction of the commercial buildings identified in the Vancouver Metropolitan Area, and for a fraction of the buildings at UBC. Results were disappointing from a costs perspective, in that figures showed over $1.1B would need to be spent to reduce peak demand by 9%, or 795MW, for four hours, for Vancouver. For UBC, costs would be between $23MM (fuel cells) - $26MM (batteries) to reduce peak demand by 17.5 MW, or 37% of current load. This is in comparison to $260k to upgrade the existing 42 MVA transmission lines or ~$10MM to replace the lines with 62MVA lines, which at a 98% power factor would allow load to increase to 60MW, an increase of 13 MW from current 47 MW peak. Emissions results were encouraging, but should be noted that emissions of either technology are strongly dependent on energy consumption and source during the “use” phase. For further analysis, it is recommended that focus on the raw materials emissions and disposal energy/ emissions processes be completed. It is recommended that vendor-specific data be obtained regarding materials in terms of emissions, energy, Net Present Value (NPV) and costs. The analysis does not recommend implementation of either technology unless time-of-use electricity pricing is put in place. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".