4 présentations

• 
  15h30 - 15h55

  Competitive EV charging station location with queues

  • The Minh Nguyen, prés., UdeM, CIRRELT
  • Nagisa Sugishita, UdeM, CIRRELT
  • Margarida Carvalho, UdeM, CIRRELT
  • Amira Dems, Hydro-Quebec

  Electric vehicle (EV) charging infrastructure planning faces significant challenges in competitive markets, where multiple service providers influence congestion and user behavior. While existing literature often assumes monopolistic settings or simplifies queueing models for electric vehicle charging stations, this research extends these frameworks by incorporating the presence of competitors' stations and more realistic queueing systems.

  First, we evaluate four finite queueing systems with different numbers of servers (representing charging outlets) and service time distributions: single-server exponential ($M/M/1/K$), multi-server exponential ($M/M/s/K$), single-server Erlang-$r$ ($M/E_r/1/K$), and multi-server Erlang-$r$ ($M/E_r/s/K$). Specifically, we derive analytic expressions for metrics modeling users' behavior, such as the average waiting time and the probability of balking, validated through simulations. Second, we integrate the user behavior model into a bilevel program, where the upper level models the location of new charging stations and the lower level models the selection of stations by users. Third, we apply a reformulation methodology from competitive congested user-choice facility location models to solve the bilevel optimization problem. Fourth, we apply our model to a real-world instance, showing not only the effectiveness of our approach but also managerial insights on the impact of competition. Our work improves charging service quality and supports sustainable EV adoption by optimizing infrastructure expansion, contributing to the broader goal of promoting sustainable transportation systems.

• 
  15h55 - 16h20

  Centralized scheduling of home charging operations for electric vehicles: A gamification approach

  • Alireza Ghahtarani, prés., University of Montreal
  • Jorge Mendoza, HEC Montréal
  • Martin Cousineau, HEC

  We study the centralized scheduling of home charging operations for electric vehicles (EVs) using a gamification-based approach. Inspired by Tetris, we model the charging schedule as a dynamic block-stacking game, where scheduling decisions impact grid efficiency. We propose heuristic methods and apply DAGGER (Dataset Aggregation) to train an agent that learns to optimize EV charging schedules through imitation learning. The primary objective is peak shaving, ensuring a balanced distribution of charging demand to reduce grid stress. Our approach is compared against reoptimization method and full information optimization, evaluating trade-offs between computational efficiency and performance. Simulation results demonstrate that gamification offers a practical and scalable alternative, effectively mitigating peak loads while maintaining scheduling efficiency.

• 
  16h20 - 16h45

  Fair network design problem: an application to EV charging station capacity expansion

  • Nagisa Sugishita, prés., Université de Montréal
  • Ismail Sevim, Universite de Montreal
  • Margarida Carvalho, Université de Montréal
  • Amira Dems, Institut de recherche d’Hydro-Québec
  • Ribal Atallah, Hydro-Québec

  This study addresses the bilevel network design problem (NDP) with congestion. The upper-level decision-maker (a network designer) selects a set of arcs to add to an existing transportation network, while the lower-level decision-makers (drivers) respond by choosing routes that minimize their individual travel times, resulting in user equilibrium. In this work, we propose two novel single-level reformulations: one based on strong duality and the other based on the value function of the lower-level problem. Unlike existing approaches in the literature, which are specialized for optimizing the total travel time of all drivers, our approach is flexible and can optimize other metrics related to individual travel times or fairness. We discuss the differences between the two reformulations, as well as their computational performance on academic test instances of the NDP. We then apply our methods to the EV charging station capacity expansion problem. We define a metric, the cost of sustainability, to measure the service quality experienced by individual EV drivers, and optimize the charging station locations to improve this metric. We present the results of experiments using the road network in Quebec, including public fast EV charging stations.

• 
  16h45 - 17h10

  Optimizing EV Charging Station Placement Using a Rank-Based Choice Modelling Approach

  • Shabnam Mahmoudzadeh Vaziri,
  • David Pinzon Ulloa, prés.,
  • Okan Arslan,
  • Yossiri Adulyasak,
  • Margarida Carvalho,
  • Amira Dems,

  Electric vehicle (EV) adoption is rapidly increasing, which requires an efficient charging infrastructure to support user demand. In this study, we propose an optimization model to determine optimal placements of new charging stations while maximizing EV user adoptions through charging availability. Our modeling framework makes use of rank-based choice models to represent user preferences, which allows us to incorporate practical features, including budget constraints, demand seasonality, congestion effects modeled through a queuing system, and competitive interactions between charging providers. Computational experiments conducted using Hydro-Québec's real-world data demonstrate the effectiveness of our approach in optimizing charging infrastructure deployment while improving service accessibility for EV users. The results highlight the impact of user behavior, seasonal variations, and competitive factors on network performance, providing valuable insights for decision-makers in transportation planning.