Enhancing grid flexibility through electric vehicles: The role of driver travel patterns
Bibliographic record
Abstract
Abstract This study examines how different driver travel patterns impact electric vehicle (EV) energy flexibility potential in Montreal, Quebec. Using Canadian Time Use Survey data, we identified three distinct driver travel patterns: Normal Work Hours, Extended Work Hours, and Non-Commuter. We implemented a decentralized reinforcement learning (RL) approach to control EV charging across ten households, aiming to minimize electricity consumption during peak hours. The RL controller was benchmarked against a rule-based controller (RBC) that charges EVs immediately upon connection. Results demonstrate that Non-Commuter patterns provided the greatest flexibility potential, with the RL controller able to provide 2204 kWh discharged back to the grid across all 10 households during peak periods while the RBC consumed 2602 kWh during the same period. These actions translated to 15% cost savings for the RL controller as opposed to 50% increase in cost with the RBC for the Non-Commuter driver pattern. The RL controller reduced electricity consumption during peak periods significantly across all driver patterns while maintaining 97% departure state-of-charge levels, thus highlighting the significant energy flexibility potential. The findings provide valuable insights for grid operators and policymakers on how mobility patterns affect demand response potential and highlight the importance of time-varying electricity rates in incentivizing vehicle-to-grid participation.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".