Direct Model Predictive Control: A Theoretical and Numerical Analysis

Marie-Liesse Cauwet 1 Jérémie Decock 2 Jialin Liu 3 Olivier Teytaud 4
4 TAU - TAckling the Underspecified
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : This paper focuses on online control policies applied to power systems management. In this study, the power system problem is formulated as a stochastic decision process with large constrained action space, high stochasticity and dozens of state variables. Direct Model Predictive Control has previously been proposed to encompass a large class of stochastic decision making problems. It is a hybrid model which merges the properties of two different dynamic optimization methods, Model Predictive Control and Stochastic Dual Dynamic Programming. In this paper, we prove that Direct Model Predictive Control reaches an optimal policy for a wider class of decision processes than those solved by Model Predictive Control (suboptimal by nature), Stochastic Dynamic Programming (which needs a moderate size of state space) or Stochastic Dual Dynamic Programming (which requires convexity of Bellman values and a moderate complexity of the random value state). The algorithm is tested on a multiple-battery management problem and two hydroelectric problems. Direct Model Predictive Control clearly outperforms Model Predictive Control on the tested problems.
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Marie-Liesse Cauwet, Jérémie Decock, Jialin Liu, Olivier Teytaud. Direct Model Predictive Control: A Theoretical and Numerical Analysis. PSCC 2018 - XX Power Systems Computation Conference, Jun 2018, Dublin, Ireland. 〈hal-01701623〉

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