Robust model predictive control for an uncertain smart thermal grid

Samira Safaei Farahani, Zofia Lukszo, Tamas Keviczky, Bart De Schutter, R.M. Murray

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 Citations (Scopus)


The focus of this paper is on modeling and control of Smart Thermal Grids (STGs) in which the uncertainties in the demand and/or supply are included. We solve the corresponding robust model predictive control (MPC) optimization problem using mixed-integer-linear programming techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we compare the robust MPC approach with the robust optimal control approach, in which the day-ahead production plan is obtained by optimizing the objective function for entire day at once. There, we show that the robust MPC approach successfully keeps the supply-demand balance in the STG while satisfying the constraints of the production units in the presence of uncertainties in the heat demand. Moreover, we see that despite the longer computation time, the performance of the robust MPC controller is considerably better than the one of the robust optimal controller.
Original languageEnglish
Title of host publicationProceedings 2016 European Control Conference (ECC)
EditorsAnders Rantzer, John Bagterp Jørgensen, Jakob Stoustrup
Place of PublicationPiscataway, NJ, USA
ISBN (Print)978-1-5090-2591-6
Publication statusPublished - 2016
Event2016 European Control Conference, ECC 2016: 15th annual European Control Conference - Aalborg, Denmark
Duration: 29 Jun 20161 Jul 2016


Conference2016 European Control Conference, ECC 2016
Abbreviated titleECC'16
Internet address


  • Greenhouses
  • Cogeneration
  • Resistance heating
  • Boilers
  • Robustness


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