SMART – Smart control of district heating networks integrating next generation energy-efficient buildings
SMART will look at the integration of physics-based models of district heating network, with real-time AI-based load prediction and model-predictive control. The project will include models of next generation attached and detached single- and multi-family solar buildings to be connected to the regional heat and power distribution system.
The energy sector is facing a transition towards decarbonization. This can be achieved by increasing system efficiency, flexibility and renewable resources integration. When dealing with space heating and cooling, this is particularly challenging as the production of thermal energy has to take place near its use.
This well-known limit of renewable energies could be overcome by integrating them in heating and cooling networks. However, their non-programmability and production discontinuity introduced new problems of management and control of the entire networks. In the same way, the possibility to recover waste heat from different resources as well as implementation of prosumers through heating and cooling networks would increase the efficiency of the system but would pose additional management and control issues.
Hence, the SMART project aims to solve these issues by dealing with the development of an energy system that makes it possible to efficiently provide, host and utilize high shares of renewables. This would be achieved by pursuing
- the introduction of innovative technologies for prosumers
- the digitalization of the energy system with high flexibility on the demand side.
Indeed, SMART consists of an optimized hybrid energy system with regional heat and power distribution system as the basis and including solar blind system (a series of adjustable PV modules that supply energy while provide shading effect), heat pump and biomass boiler.
SMART will develop a new controller that will provide fundamental transformation of urban energy systems towards a sustainable, low carbon and climate friendly economy by allowing
- an efficient integration of low-temperature heat sources providing sustainability
- a reduction in primary energy consumption, with a consequent reduction of carbon emissions
- a multi-agent optimization strategy to face highly variable climate conditions as well as demand side.
This project addresses these demands and propose a multidimensional integration framework including the development of a smart energy system, enhance the local and regional development and promote the cross sectoral integration.
The SMART project will combine the promising results of machine learning with industrial IoT and Cloud technologies in order to enable fully automated optimization of energy flows in DHN and corresponding consumers.