4 présentations

• 
  10h30 - 10h55

  Net power maximization in a beam-down solar concentrator

  • Miguel Diago, prés., GERAD
  • Nicolas Calvet, Khalifa University
  • Peter R. Armstrong, Khalifa University

  The reflectors of a beam-down solar concentrator are adjusted to maximize the net power collected at the receiver. The performance of the solar plant is predicted with a Monte Carlo ray-tracing model as a function of a feasible reflector geometry. Optimization is carried out with NOMAD, an instance of the mesh-adaptive direct search (MADS) blackbox algorithm. Challenges include reducing the number of optimization variables, dealing with the stochastic aspect of the blackbox, and the selection of effective MADS optimization parameters.
• 
  10h55 - 11h20

  Long-term planning of a flexible generation portfolio

  • Navdeep Dhaliwal, prés., McGill University
  • François Bouffard, McGill University
  • Mark O'Malley, University College Dublin

  To bridge the gap between long-term capacity planning and short-term intra-hour flexibility, our approach exploits the linear time-invariant feature of variable generation using historical phase planes of capacity(in MW) and ramp(in MW/min). Compared to other proposals, it is much more computationally tractable while adequately capturing the short-term operational features.
• 
  11h20 - 11h45

  Hydropower control with a reinforcement learning approach

  • Maël Veron, prés., HEC
  • Michel Denault, GERAD - HEC Montréal
  • Pascal Côté, Rio Tinto

  We apply a reinforcement learning technique to optimize
  hydropower at a site in British Columbia. The policies are generated
  by a neural network which is tuned to maximize power while
  satisfying a set of environmental constraints.
• 
  11h45 - 12h10

  A linear mixed-integer formulation of the short-term hydropower problem

  • Maissa Daadaa, prés., Université du Québec à Chicoutimi
  • Sara Séguin, UQAC, GERAD
  • Kenjy Demeester, Rio Tinto
  • Miguel F. Anjos, GERAD, Polytechnique Montréal
  • Pascal Côté, Rio Tinto

  We present a mixed integer model to solve the short-term hydropower problem. It determines the volumes for given pairs of maximum efficiency discharges and power production. The objective function is calculated using energy losses from maximum storage and penalizes unit startups. Constraints on the maximum number of turbine changes are imposed to find a viable solution in practice. Computational results are presented.