Electric vehicle implications of disaster induced power outages
Bibliographic record
Abstract
The increasing electrification of the transport sector will create an increased vulnerability to power outages caused by disasters. This thesis provides two contributions in this area by offering suggestions for increasing earthquake grid resilience and modeling the use of electric vehicles (EVs) providing aid during a disaster induced outage. In British Columbia, Canada, the Lower Mainland and the Greater Victoria area on Vancouver Island have seen the largest adoption of EVs in the province and are located in an area of high seismic hazard, so it is crucial for the region to understand and plan for the impact of a large earthquake on the power system. This thesis compiles lessons learned from past large earthquakes in Chile, Japan, and New Zealand and applies them to increasing the power system resilience of the Lower Mainland and Vancouver Island. These suggestions are also compared with how fuel infrastructure resilience could be increased in the region of study. When used in conjunction with microgrids, EVs can potentially remain functional for the duration of a power outage. This thesis uses an agent-based model to study the behaviour of a fleet of EVs providing disaster relief during a power outage. EVs are tasked with donating energy to a shelter (Task 1), delivering critical supplies (Task 2), and providing transport for personnel or performing inspections (Task 3). Using a six EV fleet with two of each EV type, it was found that the 250, 350, and 450 kWh storage sizes could provide for outages of 0.5 to 1 day, 1 to 1.5 days, and 2 to 4 days, respectively. The rate of energy donated to the shelter was found to be 350 kWh/day, while the Type 1, 2, and 3 EVs, used energy at the microgrid at a rate of about 200 kWh/day, 100 kWh/day, and 50 kWh/day, respectively. Increasing battery storage size reduced the variation in the average daily energy use of the EVs and creating a six EV population with only Type 2 and 3 EVs was found to reduce variation even further and substantially increased the length of outage that the various microgrid storage sizes could provide for with 250 kWh storage now providing for outages of 2 to 4 days, while 350 and 450 kWh storage sizes routinely accommodated the EVs operating for a full two weeks (the time horizon of the model).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".