Novel energy management options for charging stations of electric vehicles in buildings without increasing peak demand for sustainable cities
Bibliographic record
Abstract
Electric vehicles are recognised as critical step in making transportation sector more environmentally friendly, especially when powered by renewable energy sources. A major hindrance to their widespread adoption is the scarcity of charging stations and the absence of grid access. This study explores and examines four distinct ways for enhancing the energy grid of buildings. The primary goal of these solutions is to generate more capacity without raising the electricity peak load of the building through the incorporation of energy storage technologies. The target location is Southern Ontario, Canada, where one of populated-intensive region in Canada. Strategy 3 stands out as the most promising alternative, with an impressive 35% increase in charging station capacity compared to other strategies. Using this method, it is feasible to store 353.4 GWh of energy during summer and 480.1 GWh during winter. In summer, the station can release 243.1 GWh, and in winter, 345.6 GWh of energy can be efficiently used to charge electric vehicles. Strategy 4, which includes hydrogen generation and fuel cell systems, offers a competitive alternative with a 28% increase in capacity. This method allows for charging station capabilities of 52 GW during summer and 13 GW during winter. The strategies emphasize the importance of adaptable capacity solutions to address variations in demand on a seasonal and daily basis, while considering both economic and environmental sustainability. As the adoption of electric vehicles continues to rise, the nuanced operational aspects of these strategies are pivotal for the development of sustainable and efficient transportation ecosystems, ensuring that electric vehicle charging infrastructure keeps pace with the growing demand.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".