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Record W2292058067 · doi:10.14288/1.0108525

LCA : batteries and fuel cells for commercial buildings in British Columbia

2014· article· en· W2292058067 on OpenAlexaboutno aff
Jacob Fountain, Estella Peng, Neysa Angeles

Bibliographic record

VenuecIRcle (University of British Columbia) · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsFuel cellsArchitectural engineeringBusinessEnvironmental scienceForensic engineeringEngineering

Abstract

fetched live from OpenAlex

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.”

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.004
GPT teacher head0.163
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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