SCRO / Journées de l'optimisation

HEC Montréal, 29-31 mai 2023


HEC Montréal, 29 — 31 mai 2023

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OEVCI Optimizing Electric Vehicle Charging Infrastructure I

29 mai 2023 10h30 – 12h10

Salle: TAL Gestion globale d'actifs inc. (vert)

Présidée par Miguel F. Anjos

4 présentations

  • 10h30 - 10h55

    Accelerated Bender's Decomposition for Electric Vehicle Charging Station Placement

    • Steven Lamontagne, prés., Université de Montréal
    • Margarida Carvalho, Université de Montréal
    • Ribal Atallah, Institut de Recherche d'Hydro-Québec

    In areas with an abundance of cheap, renewable electricity (such as Québec), an increase to the number of electric vehicles (EVs) can help reduce greenhouse gas emissions. The importance of public EV charging infrastructure for encouraging widespread adoption has been highlighted in studies and, as such, the location of charging stations is crucial. We propose an optimisation model for EV charging station placement designed to maximise adoption of EVs. This model allows for flexible user preferences and, under certain assumptions, can be reformulated as an efficient maximum covering model. However, this reformulation cannot be solved directly for instances of realistic size. To address this limitation, we integrate a Bender's decomposition approach that is specifically designed for large-scale maximum covering models. Additionally, we propose and discuss several acceleration techniques tailored to our application.

    Key words: Electric Vehicles, Bender's Decomposition, Maximum Covering Model

  • 10h55 - 11h20

    Universal Maximum Flow for Electric Vehicle Charging Station Placement

    • Pierre-Luc Parent, prés.,
    • Miguel F. Anjos, University of Edinburgh
    • Margarida Carvalho, Université de Montréal
    • Ribal Atallah, Hydro-Québec

    With the increasing effects of climate change, the need to step away from fossil fuels is more important than ever. Electric vehicles are one way to reduce them, but their adoption is often limited by the availability of charging stations. As such, the goal is to assign each electric vehicle to an available, or soon to be, charging station. Within cities, we propose to model the assignment of electric vehicles as a universal maximum flow problem. In this way, we can evaluate the quality of service provided by existing and future charging infrastructure. Such models can be solved using a linear program. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle realistic instances.

    Key words: Electric Vehicles, Maximum Flow

  • 11h20 - 11h45

    Optimal pricing for electric vehicle charging via bilevel optimization

    • Gaël Guillot, INRIA Lille
    • Luce Brotcorne, INRIA Lille
    • Miguel F. Anjos, prés., University of Edinburgh
    • Clémence Alasseur, EDF R&D
    • Riadh Zorgati, EDF R&D

    The ongoing increase in the number of electric vehicles raises various challenges in terms of charging infrastructure management. We propose a bilevel optimization model based on dynamic pricing of charging to distribute users in time and space around the pool of charging stations. The followers (users) make decisions independently and the leader ensures that there is no conflict at the charging stations. We present a reformulation of the model as a single-level integer linear optimization problem and we analyze the impact of the various user parameters.

  • 11h45 - 12h10

    Charging Station Location and Routing Problem for Long-haul Electric Heavy Duty Trucks with Drivers' Working Hours

    • Conrado Vidotte Plaza, prés., HEC Montréal and Universidade Federal do Rio de Janeiro
    • Okan Arslan, GERAD, HEC Montréal
    • Laura Bahiense, Universidade Federal do Rio de Janeiro
    • Glaydston Mattos Ribeiro, Universidade Federal do Rio de Janeiro

    Due to the limited infrastructure to enable an integrated transport system, inland freight
    logistic operations are heavily reliant on trucks, leading to negative economic and
    environmental impacts. Heavy duty trucks with diesel engines produce increasing volumes of
    emissions and pollution, which not only cause severe health problems but also have a negative
    impact on global warming. A potential solution is to gradually replace diesel engines with their
    technological alternatives such as electric heavy-duty trucks (eHDT). The charging station
    network for eHDT is still very limited, which may be a hindrance for this alternative due to the
    short operating range. Furthermore, the hours of service (HOS) regulations of commercial
    drivers are regulated by law, and the drivers must be scheduled in such a way that they are able
    to comply with the regulatory framework. The consideration of the driving periods, breaks, and
    rest periods in vehicle scheduling and routing is crucial to increase punctuality and safety in
    road freight transport. In this talk, we present a comprehensive mathematical model for
    location optimization of charging stations by considering the long-haul electric vehicles, their
    routing, the legislative requirements on drivers’ HOS, different charger levels and partial
    recharge strategies.