4 Presentations

• 
  10:30 AM - 10:55 AM

  Optimal and approximate solutions to linear quadratic regulation of a class of graphon dynamical systems

  • Shuang Gao, presenter, McGill

  In this paper, we study the linear quadratic regulation (LQR) problem for dynamical systems coupled over large- scale networks and obtain locally computable low-complexity solutions. The underlying large or even infinite networks are represented by graphons and the couplings appear in both the dynamics and the quadratic cost. The optimal solution is obtained for graphon dynamical systems where the graphons are exactly characterized by finite spectral summations. Based on this, we provide a suboptimal solution for problems with general graphon couplings via spectral approximations. The complexity of generating these control solutions involves solving d + 1 scalar Riccati equations where d is the number of non- zero eigenvalues in the graphon spectral representation. A numerical example is given to illustrate the explicit solution and demonstrate the simplicity of the solution.
• 
  10:55 AM - 11:20 AM

  Optimal Control of Large-Scale Networks of Stochastic Linear Systems

  • Shu-Jun Liu, McGill Visiting Professor
  • Peter Caines, GERAD - McGill University
  • Shuang Gao, presenter,

  Owing to the extremely large number of nodes and dynamic elements in large-scale complex networks, it is effectively impossible to achieve desired control objectives .
  In this work, we develop an approximate control theory for large-scale network stochastic linear systems by the use of stochastic control theory of limiting (infinite dimensional) stochastic systems. First, in order to represent arbitrary-size networks of linear systems with additive noise, dynamical stochastic system models are formulated in an appropriate infinite dimensional space. Second, control of the infinite dimensional system is analysed. Finally, from the stochastic control for the limit infinite dimensional system we obtain feedback control laws for the network system, with guaranteed approximation errors. In particular, this is carried out within the framework of the linear quadratic regulator problem for large-scale network stochastic systems.
• 
  11:20 AM - 11:45 AM

  An affine decision rule approximation for modeling uncertainty of demand response in smart grids

  • Sajad Aliakbari, presenter, HEC Montréal
  • Erick Delage, GERAD, HEC Montréal
  • Olivier Bahn, HEC Montréal

  In this research, we design a linear program to model expansion of smart energy systems. In this modeling, consumers are able to contribute to the network by means of flexible loads (demand response). We have considered this demand response uncertain. An affine adjustable robust optimization technique is used to cope with uncertainty in this problem.
  
  Keywords: Demand response management, Robust optimization, affine decision rule
• 
  11:45 AM - 12:10 PM

  A quantilized mean field game approach to energy pricing with application to fleets of plug-in electric vehicles.

  • Rinel Foguen Tchuendom, presenter, Ecole Polytechnique à Montréal

  We consider the problem of designing the price of electricity by an energy provider to a pool of homogeneous loads.
  The energy provider is risk sensitive and considers that its energy production cost at any particular time is related to the instantaneous maximum excursion of the random aggregate demand of the loads. A statistical measure of this excursion is the $\alpha$-quantile of the distribution of the individual electricity demands of the loads, or equivalently the value da at risk $\alpha$, of the electricity demand per vehicle. The price is assumed to be a known and possibly time varying function of $d_\alpha$. The loads are associated with individual price sensitive costs. For a very large number of loads, in particular a large fleet of electric vehicles, this results in a mean field game (MFG). The existence of an MFG equilibrium associated with a price trajectory, and the epsilon- Nash property of the resulting limiting control
  laws, are established in this work.