2026-05-14 カナダ・コンコルディア大学
＜関連情報＞
- https://www.concordia.ca/cunews/encs/2026/05/14/Concordia-developed-citycharge-model-could-help-cities-manage-electric-vehicle-charging-demands.html
- https://www.sciencedirect.com/science/article/abs/pii/S0360544226000228
CityCharge：都市部における電気自動車充電需要パターンの高度なモデリング CityCharge: Advanced modeling of urban electric vehicle charging demand patterns
Mohamed Osman, Mohamed Ouf
Energy Available online: 3 January 2026
DOI:https://doi.org/10.1016/j.energy.2026.139920
Highlights
- CityCharge an agent-based tool for simulating urban EV charging demand.
- Capture individual EV behavior using state of charge, location, and infrastructure data.
- Compare survey-based modeling approaches for EV charging demand in Montreal.
- Quantify charging load patterns and identifies potential stress periods and locations.
- Provide spatial-temporal insights for data-driven electric mobility planning.
Abstract
The increasing adoption of electric vehicles (EV) presents substantial complexities for electricity distribution networks, particularly grid strain from uncoordinated charging during peak demand hours. This paper introduces CityCharge, a simulation tool that models spatial and temporal patterns of urban electric vehicle charging demand. The framework employs a hybrid methodology integrating empirical travel schedules with agent-based modeling to simulate individual charging decisions based on vehicle characteristics, state-of-charge thresholds, location preferences, and behavioral types, alongside infrastructure availability and electricity pricing. CityCharge offers both a generic model using Time Use Survey data for broad applicability and a custom model using detailed travel survey data for localized accuracy, demonstrated through a Montreal case study. Comparative simulations reveal the custom model exhibits 24 % higher afternoon peaks. Scenario analyses of Montreal CMA’s EV charging demand show shifting from home-dominant to workplace-dominant charging increases morning peaks by 72 % while reducing evening peaks by 18 %. Moreover, increasing deployment of Level 2 chargers in homes results in 30 % higher peak demand than Level 1 infrastructure. Regional analysis reveals substantial spatial variation, with central areas experiencing peaks that are 100 % higher than those in suburban regions. CityCharge provides valuable insights for utilities, policymakers, and planners to quantify charging load patterns and identifies potential stress periods/locations, optimize infrastructure deployment, and design demand response strategies supporting sustainable transportation.
