A Hierarchical Algorithm for Integrated Scheduling and Control With Applications to Power Systems

Leo Emil Sokoler, Peter Juhler Dinesen, John Bagterp Jørgensen

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The contribution of this paper is a hierarchical algorithm for integrated scheduling and control via model predictive control of hybrid systems. The controlled system is a linear system composed of continuous control, state, and output variables. Binary variables occur as scheduling decisions in the optimal control problem (OCP). The scheduling decisions are made on a slow time scale compared with the system dynamics. This gives rise to a temporal separation of the scheduling and control variables in the OCP. Accordingly, the proposed hierarchical algorithm consists of two optimization levels. The upper level (scheduling level) solves a mixed-integer linear program (MILP) with a low frequency. The lower level (control level) solves an LP with a high frequency. The main advantage of the proposed approach is that it requires online solution of an LP rather than an MILP. Simulations based on a power portfolio case study show that the hierarchical algorithm reduces the computation to solve the OCP by several orders of magnitude. The improvement in computation time is achieved without a significant increase in the overall cost of operation.
Original languageEnglish
JournalIEEE Transactions on Control Systems Technology
Issue number2
Pages (from-to)590-599
Publication statusPublished - 2016


  • Hybrid power systems
  • Mixed-integer linear programming (MILP)
  • Model predictive control (MPC)
  • Production scheduling


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