electric vehicles effects on the power grid considering smart charging/discharging: montréal case study
Bibliographic record
Abstract
Electric vehicles (EVs) are increasingly recognized for their potential to save energy, reduce pollution, and protect the environment. This makes the promotion and adoption of EVs crucial for decreasing our reliance on oil, enhancing energy security at national and regional levels, and supporting sustainable economic and social development. Acknowledging these benefits has led to a strategic focus on encouraging the widespread use of EVs. In this regard, countries around the world have begun to implement policies aimed at accelerating the adoption of EVs. These policies range from incentives for EV purchases to investments in charging infrastructure, reflecting a commitment to transition to cleaner forms of transportation. As a part of these efforts, the Government of Canada has introduced new regulations that establish mandatory Zero-emission vehicle (ZEV) sales targets for manufacturers and importers of new passenger cars, SUVs, and pickup trucks. These regulations require that a minimum of 20 percent of new vehicles sold in Canada must be zero-emission by 2026, escalating to at least 60 percent by 2030 and reaching 100 percent by 2035. In accordance with these new laws and policies, the province of Québec has set its own ambitious target of having two million EVs on the road of Québec by 2030. This goal has led to the need for this study to measure and analyze the impact of Plug-in Hybrid Electric Vehicle (PHEV) charging demand on both the current and future power network of Montréal, the largest city in the province of Québec. In this regard, this study considers the integration of Québec's ZEV policy on the city's grid and will evaluate how the expected growth in the number of PHEVs will affect the network's stability and efficiency. Therefore, a multi-objective problem has been presented in this research study to simultaneously maximize the benefits for PHEV owners while minimizing the power loss in the system for the current and future network of the city of Montréal. The proposed multi-objective problem is also developed using the Epsilon-Constraint technique, which facilitates solving the complex multi-objective function problem. In this regard, the load profiles of three different parts of the city of Montréal have been considered for specific reasons. The downtown area of Montréal has been chosen as it serves both commercial and residential purposes. To analyze the impact of PHEV charging in residential areas, Cote Saint Luc and Notre-Dame-de-Grâce have been included in the study, where both are considered primarily residential neighborhoods. Additionally, Montréal is well-known for its festivals and events, which led individuals to spend considerable time in the city for leisure. As a result, Quartier des Spectacles and the Old Port have been selected as essential areas where people gather during their leisure time. To address the above-mentioned issue and analyze the effect of PHEVs on the network of Montréal, two different phases and approaches were considered in this study: Immediate Charging, which only uses the Grid-to-Vehicle (G2V) charging strategy, and Smart Charging, which uses both G2V and Vehicle-to-Grid (V2G) strategies with the assistance of an EV aggregator. Additionally, to validate the effectiveness of the Smart Charging results, an alternative approach known as Basic V2G was implemented. This Basic V2G approach serves as a basic V2G concept to evaluate and verify the advantages of using the Smart Charging scenario.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".