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

Research output: Research - peer-reviewJournal article – Annual report year: 2017

View graph of relations

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
Volume25
Issue number2
Pages (from-to)590-9
ISSN1063-6536
DOIs
StatePublished - 2016
CitationsWeb of Science® Times Cited: 3

    Research areas

  • Hybrid power systems, Mixed-integer linear programming (MILP), Model predictive control (MPC), Production scheduling
Download as:
Download as PDF
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
Word

ID: 130664774