4 présentations

• 
  10h30 - 10h55

  A two-stage Optimization Framework for Electric Vehicle Fleet Day-ahead Smart Charging

  • Arian Shah Kamrani, prés., Polytechnique Montreal
  • Hanane Dagdougui, Polytechnique Montreal

  Nowadays electric vehicles (EVs) have become one of the important means of transportation all over the world.
  The importance of EV owners’ privacy, as well as smart EV fleet charging, has always been one of the challenges in smart charging planning and management. Furthermore, in smart charging, the distribution system operator must also coordinate with EV aggregators to ensure that the power system is operated within security limits while reducing charging costs and satisfying EV users’ energy needs. In this work, a semi-private framework for EV owners has been introduced which solves a two-stage optimization problem for the smart control of EV charging. This framework considers charging cost reduction and peak load shaving as well as satisfying power grid constraints. At the higher stage, based on optimal power flow calculations, the proposed control signals are transferred to the lower stage in
  order to facilitate optimal scheduling in accordance with the mentioned goals. The obtained results based on the proposed optimal method implemented on the IEEE 33-bus network show that compared to uncontrolled charging, the cost of charging and the peak of the network are reduced by 5.31% and 4.90%, respectively. Moreover, all the constraints of the power grid are satisfied.
• 
  10h55 - 11h20

  Mitigating Power Network Equipment Overloads due to Electric Vehicle Charging Using Customer Incentives

  • Feng Li, prés., Polytechnique Montreal
  • Ilhan Kocar, Polytechnique Montreal
  • Antoine Lesage-Landry, Polytechnique Montreal

  In this work, we first present a model to perform a time-series impact analysis of charging electric vehicles (EVs) to loading levels of power distribution network equipment. The stochasticity in charging habits of EV owners is characterized by probability distributions of charging hours and durations. From the analysis results, if the equipment is observed to be constantly overloaded during the peak hours, its service lifetime will be shortened. To mitigate the overloads and maintain equipment’s lifetime, a novel incentive-based strategy is then designed to shift the EV charging from the peak hours to the off-peak hours when the equipment is less loaded. The incentive level and corresponding contributions from EV owners to alter their EV charging habits are determined by a search algorithm and a constrained optimization problem. Finally, the mitigation strategy is illustrated on the IEEE-8500 test feeder with a high EV penetration to maintain the lifetime of the substation transformer by reducing the overloads.
• 
  11h20 - 11h45

  Clustering-Based Partitioning Scheme for Simulation-Based Optimization: A Case Study on Electric Vehicle Charger Location for Ride-hailing Services

  • Tommaso Schettini, prés., HEC Montréal, École de technologie supérieure
  • Fausto Errico, École de technologie supérieure
  • Jorge Mendoza, HEC Montréal
  • Carolina Osorio, HEC Montréal

  Discrete Simulation-based Optimization (DSO) problems typically involve highly complex stochastic systems such as urban road networks. For such problems, computing the value of its objective function requires computationally intensive simulations. Thus, the methods employed tend to rely on iteratively sampling and partitioning the solution space so as to progressively concentrate the sampling effort on the most promising portions of the feasible space. To boost the effectiveness of such methods, it is fundamental to be able to quickly identify which portions of the feasible region are relevant to explore through an effective partitioning scheme, where solutions within the same partition behave similarly to one another with respect to the simulated objective function. In this talk, we discuss the use of a clustering approach to partition the search space of a DSO problem and identify high-quality regions of the search space. We will show the effectiveness of the proposed framework on a simulation-based charger location problem for an autonomous fleet of ride-hailing vehicles. Then, we will combine our clustering approach with simplified deterministic MILP models to serve as an approximate evaluation of the objective function informing the clustering procedure to achieve more cohesive partitions with respect to the simulated objective function.
• 
  11h45 - 12h10

  Stochastic Planning of Electric Charging Stations: An Integrated Deep Learning and Queueing Theory Approach

  • Hani Pourvaziri , prés., Dalhousie University
  • Hamid Afshari, Assistant Professor at Dalhousie University
  • Majid Taghavi , Saint Mary's University, Halifax, Nova Scotia, Canada
  • Nader Azad, Assistant Professor of Operations Management, Faculty of Business and IT, Ontario Tech University
  • Hassan Sarhadi, Fred C. Manning School of Business, Acadia University, Canada