UserGRIDs

This research project has been completed. Access the UserGRIDs 2.0 Fact Sheet.

Usage-centered planning and regulation of complex, sustainable district energy systems

More and more innovation districts are gradually being established around the world in order to realise energy-efficient and sustainable goals in research and industrial parks, as well as in office and residential districts. The use of so-called ‘digital energy services’ in the planning, generation, distribution and consumption of energy shows great potential. Based on the new possibilities of data processing, methods relating to simulation, IoT platforms, machine learning, artificial intelligence and user integration are leading to completely new approaches to designing smart energy systems.

The UserGRIDs should utilise these for the expansion and transformation of TU Graz Campus Inffeldgasse, which is set to develop into the ‘INNOVATION DISTRICT INFFELD’ by 2030.

Objective of the project UserGRIDs

The aim of the project was to make this research and teaching campus of Graz University of Technology operable with minimal greenhouse gas emissions. This was to be supported by digital planning methods and optimised management of the thermal and electrical storage facilities, as well as the provision of energy from renewable, volatile sources (photovoltaics, geothermal energy and waste heat).

Smart energy systems for numerous players thanks to energy-relevant real-time data

The project provided added value, particularly for operators of complex energy systems and facilities, by developing, testing and evaluating the use of different energy services to optimise energy systems in neighbourhoods. The complexity of these systems results from the large number of interacting technical subsystems and the large number of actors and users who act on the basis of a wide variety of needs. The processing of energy-relevant real-time data and user feedback supported the adaptation of the systems to optimise comfort, efficiency and emissions.

Approach and methodology of the project

The demonstration took place in the innovation district “INNOVATION DISTRICT INFFELD” of Graz University of Technology, in which 30 buildings, with approx. 125,000 m² gross floor area, with extremely different requirements and usage behavior, as well as strongly fluctuating consumption and generation characteristics, are connected to a complex energy system.

Scientific support from several institutes at Graz University of Technology and the BEST competence center ensured scientific documentation and evidence, on the basis of which the exploitation and scaling to other applications could be promoted. As the largest public landowner in Austria, BIG will integrate the results into its sustainability standards and into marketing.

The following points were successfully implemented within the project:

• The design of eight digital energy services for the largest campus of Graz University of Technology has been completed. The campus is to be developed into the Innovation District Inffeld by 2030. The basic development approach of the digital energy services follows the goal of utilising the constantly growing possibilities of digitalisation. Energy efficiency and the use of renewable energy sources in operations have been strengthened. The results will be incorporated into the future development of the site in order to reduce the induced greenhouse gas emissions.
• The functionality of the services is based on an urban structure model in which all relevant and available structural information on buildings and the thermal and electrical urban infrastructure has been implemented. This “single source of truth” serves as a consistent and common database for all other services.
• The developed and implemented IoT platform acts as an intermediary between the energy control systems running in the district. About 400 sensors are periodically queried and the measured values are stored in the database system developed for this purpose. Data series from the measurements can be automatically transferred to other services and external users via a developed interface.
• Based on the structural information and the sensor values, prediction models for the consumption and photovoltaic generation of electrical energy were developed, implemented and tested using machine learning and physical modelling. These models continuously compare current measured values with results from the models and can automatically detect and report operating errors on this basis.
• The sensor values are automated and continuously combined to create a picture of the energy flows in the district and used to analyse the performance of the energy system. Key performance indicators, which provide information on the current and past consumption of energy sources and the associated greenhouse gas emissions, have been implemented for some buildings.
• The energy management of an office building has been running via the CONTROL service of the IoT platform, since December 2022. Measurements and simulations have shown that the predictive control models used, which operate with the direct integration of user feedback, deliver excellent results. Thermal comfort was significantly increased and the energy requirements for heating and cooling were reduced considerably. In an accompanying study, the chosen approach to optimisation was compared with an optimisation approach from the field of machine learning or AI.
• For the analysis of future transformation options, new approaches have been developed for the semi-automated construction of energy-related district models. In addition, two master theses have successfully begun to create semi-automated transfers of building components (walls, ceilings, etc.) and energy technology components (radiators, etc.) from the structural model into simulation programmes.
• The district’s energy model (building, heating network, cooling network) was used to analyse the scenario of implementing a large heat pump using waste heat produced in the district. The energy, environmental and financial benefits of such a solution were analysed. The model was also coupled with predictive control models via CO-simulation in order to design and virtually test the optimised operation of the heat pump system components involved (building, heat pump, deep probe field, PV systems).