OPTIMAL PLACEMENT AND SIZING OF ELECTRIC VEHICLE CHARGING INFRASTRUCTURE USING DC POWER FLOW MODEL
Bibliographic record
Abstract
With the burgeoning interest in electric vehicles (EVs) due to their sustainable attributes, concerns arise regarding the electrical grid’s capacity to handle the consequent rise in electricity demand from charging stations. Ontario’s aspiration to ensure that 5% of all vehicle sales are electric by 2020, driven by the province’s Climate Change Action Plan, accentuates these concerns, particularly with the potential rise in fossil fuel power generation. This study delves into the optimization of generator outputs and the strategic placement and sizing of EV charging stations in Ontario. The goal is to curtail overall generation costs, adhering to the demand, generation, and transmission constraints. Through the utilization of a representative system, modeled after the IEEE 34-node test feeder due to data unavailability, the research explores Ontario’s power dynamics over a 24-hour period in 2020. The findings provide insights into ideal locations and dimensions for charging stations, while also quantifying the environmental ramifications of the increased electrical grid load. This paper offers a comprehensive strategy to mitigate grid stress while bolstering EV infrastructure efficiently.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".