a model in the loop application of a predictive controller to a district heating system

Abstract 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

Development and Analysis of a Multi-Node Dynamic Model for the Simulation of Stratified Thermal Energy Storage

To overcome non-programmability issues that limit the market penetration of renewable energies, the use of thermal energy storage has become more and more significant in several applications where there is a need for decoupling between energy supply and demand. The aim of this paper is to present a multi-node physics-based model for the simulation of stratified thermal energy storage, which allows the required level of detail in temperature vertical distribution to be varied simply by choosing the number of nodes and their relative dimensions. Thanks to the chosen causality structure, this model can be implemented into a library of components for the dynamic simulation of smart energy systems. Hence, unlike most of the solutions proposed in the literature, thermal energy storage can be considered not only as a stand-alone component, but also as an important part of a more complex system. Moreover, the model behavior has been analyzed with reference to the experimental results from the literature. The results make it possible to conclude that the model is able to accurately predict the temperature distribution within a stratified storage tank typically used in a district heating network with limitations when dealing with small storage volumes and high flow rates