Electric vehicle charging : impact review for multi-user residential buildings in British Columbia
Bibliographic record
Abstract
Early steps in shifting the energy delivery means for urban automotive transportation from fossil fuels to electricity have encountered somewhat unexpected obstacles in the case of charging infrastructure installations in multiple unit residential buildings (MURBs). In these buildings, unfamiliarity on the part of the general public with electricity and electric vehicle technology combined with numerous strata governance and installation cost issues have combined to slow the rate of electric vehicle charging installations despite available incentives. The amount of power required for electric vehicle charging can create significant effects within building electrical distribution systems depending on the level of implementation planned. Present regulations mandate that each electric vehicle (EV) charging circuit must be considered as a full, continuous electrical load for the purposes of designing electrical wiring and equipment. For a level 2 charging circuit (6.6 kW), this translates into an electrical load larger than a standard residential clothes dryer which must be treated as if it is always in use for each circuit of this type installed. Making provisions for EV charging in new MURB designs can be achieved technically by the building design community. This is now in progress and is motivated largely due to changes to City of Vancouver building regulations that came into force in 2011. However, if the planned number and type of vehicles to use EV circuits does not materialize as new MURBs become occupied, then these provisions will result in unused building electrical distribution system infrastructure and attendant sunk costs. Adding EV charging in existing MURBs is much more challenging and expensive than for new construction projects. Retrofitting for significant levels of new EV loads will result in a lack of electrical capacity in the lower portions of building distribution systems first and create the need for electrical equipment upgrades. In smaller MURBs, provision for significant amounts of EV charging will have relatively more impact and the effects may be felt higher up the distribution system towards the service entrance. Regulations governing how EV loads must be accounted for in B.C. building electrical designs will likely be modified with more widespread EV adoption as demand control features are integrated into building control systems. In the near term, basic demand control systems can control individual EV chargers in an on/off manner but eventually, smart grid technology will allow building control systems or outside agencies to control the chargers for all connected EVs to most efficiently use the available building electrical capacity while still providing satisfactory recharging performance for EV owners. 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".