Development and application of an optimization model to evaluate future charging demand for long-haul electric vehicles in Ontario, Canada
Bibliographic record
Abstract
Establishing a charging network is critical to support vehicle electrification. Determining the ideal locations of charging stations can be challenging due to conflicting stakeholder constraints. This study aims to identify the optimal number of on-route charging locations that can support the projected maximum charging demand of long-haul electric vehicles (LHEVs) in the Canadian province of Ontario in the year 2040. A flow-based path-segment coverage model is proposed, which considers the time when each charging event at each candidate location is likely to occur. The impact of charging LHEVs on the grid is also evaluated. Based on projected 2040 LHEV adoption, results suggest that almost 90 % of the trips will be completed without the need to recharge. However, at least 82 fast charging station locations must be established throughout Ontario to support the remaining LHEV trips within the province. Moreover, almost 75 % of these trips are dependent primarily on 18 of these locations. In general, more than 46 GW of electricity per day is expected to be used when LHEVs have been adopted on a much larger scale in Ontario. Specifically, the Greater Toronto Area (GTA) is likely to experience approximately 4.86 GW of additional energy from LHEV charging activities during peak hours. • Optimization is used to identify the best locations for LHEV charging stations. • 82 on-route charging sites are needed to support the projected LHEV trips in 2040. • More than 8000 charging events occur on these locations per day. • More than 46 GW of electricity per day will be needed to charge LHEVs in Ontario. • The Greater Toronto Area will require about 5 GW per day to support the charging of LHEVs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".