Model Predictive Control for an Uncertain Smart Thermal Grid

Smart Thermal Grids (STG) represents a new concept in the energy sector that involves the use of the smart grid concept in heat grids connecting several parties to each other via bidirectional transport of heat. The focus of this paper is on modeling and control of STGs in which the uncertainties in the demand and/or supply are included. To this end, we use Model Predictive Control (MPC), which is one of the most widely used advanced control design methods in the process industry. We solve the worst-case MPC optimization problem using mixed-integer-linear programming (MILP) techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we show that this approach successfully keeps the supply-demand balance in the STG while satisfying the physical constraints of the network in the presence of uncertainties in the heat demand.