Linkage of transportation demand model and production cost model to investigate flexibility benefits of electric vehicles for the electricity grid
Bibliographic record
Abstract
Uptake of electric vehicles (EVs) is accelerating as governments around the world aim to decarbonize transportation. While EV adoption is widely promoted in Canada, swift and widespread EV adoption will require some degree of controlled charging to mitigate the challenges that EV charging imposes onto the power system, such as increased cost and emissions from electricity generation. In this analysis, the potential benefits of utility controlled charging (UCC) are evaluated for the city of Regina, Saskatchewan, which aims to be 100% renewable by 2050. The flexibility that UCC can contribute, and its effectiveness for integrating variable renewables is tested in configurations with solar resources, wind resources, and a mix of both. A novel modelling methodology is developed to do so, which links a travel demand model (TASHA) and an electricity system production cost model (SILVER), using a novel intermediate charging model to simulate electric vehicle travel behaviour and utility controlled charging. The use of operational models allows for an accurate representation of both travel demand and electricity system operating costs and emissions at a high spatial and temporal resolution. By linking sectoral models in this way, the interactions between the two sectors - transportation and power – can be investigated simultaneously with detailed insight into the two individual sectors. Results show that uncontrolled charging will increase average emissions from the electricity grid, but controlled charging decreases both greenhouse gas emissions as well as operating costs. By shifting vehicle charging to times when renewable energy production is high, UCC reduces operating costs and emissions by 7% compared to uncontrolled charging, without requiring changes to travel scheduling and behaviour. The temporal characteristics of wind generation is found to be more compatible with controlled charging than solar PV, due to its longer generation periods and higher capacity factor in the winter, when demand is also high.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".