A Model-in-the-Loop application of a Predictive Controller to a District Heating system

The high weather variability due to climate change and the need to reduce carbon emissions require innovative solutions for energy systems and grids. In particular, improvements in control strategies allow to increase efficiency without changing the system configuration. Adaptive controllers, as currently proposed, base the management of the system on its past behavior. The main drawback of these methods is the lack of flexibility required to face the mentioned scenario. This paper presents a Model Predictive Control approach which, instead, is based on the prediction of the future evolution of the controlled system. Since it allows to consider the external conditions variability, a more resilient way to manage District Heating and Cooling networks can be achieved. The novel control strategy is developed and tested through a Model-in-the-Loop application to a thermal energy network. This latter is composed by combining physics-based dynamic models from a dedicated library of energy systems components developed by the authors in the Matlab®/Simulink® environment. The network model is controlled by the MPC controller model, which shows to be flexible and reliable in the optimization and management of energy systems.