Energy Transformation – Paradigmatic Change and Digitalization

Modern Prediction Methods for Energy Supply and Consumption, and User-Friendly Control Tools

© Map data: Stadt Freiburg i. Br. – Vermessungsamt, „dl-de/by-2-0“, www.govdata.de/dl-de/by-2-0, Editing and Animation: Fraunhofer ISE

Coupling between the electricity, heat and transport sectors will result in more complex inter-sectoral interaction in the future, so that a finely resolved overview of energy supply and demand will become increasingly important. Within its numerous digitalization projects, one of the aspects that Fraunhofer ISE is addressing is therefore the development of modern prediction methods for energy supply and consumption, and user-friendly control tools.

»The Energy Transformation – Intelligent and Digital« was the title of the most recent annual conference of the German Research Association for Renewable Energy FVEE. German non-university research institutions for renewable energy discussed the many facets and potentials of digitalization, which has been identified as both an »enabler« and a »driver« of the energy transformation. It plays a key role in finding solutions
for decentralization and achieving flexibility, as well as in the efficient use of energy and resources.  

Dimensions of Digitalization

Digitalization: Current R&D Activities at Fraunhofer ISE

Digitalization is an essential part of our research work - across all subject areas.

© Fraunhofer ISE

Photovoltaic Modules and Power Plants

© Fraunhofer ISE

Energy Efficient Buildings

© Fraunhofer ISE

Solar Thermal Power Plants and Industrial Processes

Due to the volatility of electricity generated by photovoltaics and wind turbines, implementing the energy transformation will be accompanied by a paradigmatic change in the supply model. The provision of energy as required by large power plants will be replaced by a system in which equilibrium is continually maintained between supply and consumption by complex interaction between time-matched energy generation, sector coupling between electricity, heat and transport, temporary application of flexible generation systems, and storage units. The integration of modern prediction methods for generation and consumption will complement the organization and management of this more complex system. This whole constellation is almost inconceivable without wideranging application of digitalization technology and methods.

Digitalization is the »enabler« when it is a matter of managing the complex system and the complex interactions of numerous technical components in the system and actors in the market. It makes efficient, intelligent usage of infrastructure and hardware feasible. Digitalization is the »driver« when there is a need to transfer new technical potential into practice, to offer new services and to introduce new business models. Methods and application fields for digitalization extend over all aspects of energy supply, from generation, through grids, trading and distribution, to consumption and production. Digitalization is also playing an increasingly important role in energy research. Power electronics is a fundamental technology for digitalization. The smart grid demands interconnected inverter systems. Smart inverters will become key elements of the future electricity systems. They will compensate structural changes in the electricity grid by an integrated management strategy for grid shortfalls. Communications are essential for operating the grid, the market and technical systems. An increasingly distributed grid management system demands new system services for grid operation. The simulation, dimensioning and quality control of decentralized PV-battery stand-alone grids are based on digitalization. Battery systems are controlled by smart inverters and make a higher degree of autonomy feasible. The operation management strategies of hydrogen supply systems are almost inconceivable without digitalization.

In photovoltaic manufacturing, processes based on artificial intelligence can improve the product quality. For instance, autonomously learning image recognition processes can predict solar cell parameters. Yield analyses for cells and modules become feasible by machine-based learning. The parameters for battery cell production can be improved by applying digital twins.

For applications in the »Internet of Things (IoT)«, technologies are being developed which will enable the integration of energy supply and data communications in a single component, with highly efficient solar cells or organic PV as the energy supply.

The schedules for solar thermal power plants can be optimized by machine-based learning that draws on long-term meteorological data.

Digitalization is a prerequisite for sector coupling at the regional level. For example, it is an integral component in the pioneering »EnStadt:Pfaff« project in Kaiserslautern, where a sustainable quarter for technology, health and residence, including a »Living Lab«, is being established on the former manufacturing site of Pfaff sewing machines. Fraunhofer ISE is playing a leading role in this process.