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Published July 2016 | Submitted
Book Section - Chapter Open

Robust model predictive control for an uncertain smart thermal grid

Abstract

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.

Additional Information

© 2016 EUCA. Research partially funded by the Dutch utility company Eneco, and by the Netherlands Organization for Scientific Research (NWO) under the project Aquifer Thermal Energy Storage Smart Grids (ATES-SG), grant number 408-13-030. Moreover, the authors would like to thank Dr. Mathijs de Weerdt and Dr. Matthijs Spaan from Delft University of Technology and the collaborators from AgroEnergy.

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