DISTRHEAT - Digital Intelligent and Scalable Control for Renewables in Heating Networks
The results of the study will establish a reference for the energy stakeholders and will re-form the way energy systems are controlled.
District heating and cooling has demonstrated promising potential towards reducing carbon dioxide emissions, enhancing energy efficiency and integrating renewable energy resources. However, load allocation, control and management of these systems is challenging, especially in presence of the highly variable climate conditions anticipated in the short future. Therefore, innovative control methods are required for the operation and optimization of thermal networks towards reducing the adverse environmental impact of heat and power industry.
Unlike traditional control methods, model predictive control allows control and optimization of the system accounting for the prediction of its behavior in a future time horizon. It is a well-established technique in the process and chemical industry but hardly deployed for controlling energy systems. DISTRHEAT pursues the demonstration of a new controller that will reduce the global energy consumption of multi-source energy systems and thermal networks.
The project explores both technology and socio-economic impacts of the proposed technology aiming at identifying potential barriers and opportunities associated with its diffusion. The potential of model predictive control will be demonstrated on two different test sites: (i) a medium-size district heating network for the fulfillment of thermal demand in the service sector in Italy and (ii) a large-size district heating network for the fulfillment of thermal demand in the residential sector in Sweden.
The results of the study will establish a reference for the energy stakeholders and will re-form the way energy systems are controlled. Moreover, the dissemination of the promising smart technology at industrial level will pave the way for further developments across Europe.