Energy Technologies

Innovative technologies for intelligent decentralized energy systems

Intelligent decentralized energy systems are of outstanding importance for the transformation of the energy supply. This is particularly true in the industrial area, but also for larger residential units, quarters and for commercial enterprises. Decentralized energy systems offer ideal conditions for local energy coupling of different sectors like electricity, heating, cooling, gases and mobility. Fossil fuels can be replaced by integrating renewable energies with a high percentage of self-consumption. Therefore, these systems offer great potential for improving the carbon footprint.

The challenge lies in developing a flexible and intelligent operating strategy for combining the components and plant subsystems into an overall system that is operated in an optimized manner and adapted to local conditions. An intelligent decentralized energy system of this type has been set up at Fraunhofer IISB in Erlangen, linking a wide range of components and thus creating a unique real-world laboratory for energy research. It can be used as a blueprint for industry, small and medium-sized enterprises and quarters.

Energy System Simulation

Simulations are carried out at Fraunhofer IISB for non-invasive (i.e. without intervention in the real system) investigations on the influence of optimization measures on the energy system.

In the course of optimizing energy systems, sub-systems are often changed, which in turn influences the rest of the energy system. In order to implement concrete objectives, such as peak shaving with energy storage, efficiency increase of generation plants, CO2 reduction through optimized use of resources and self-consumption optimization of electricity from renewable energy sources in your company, we carry out a simulation in advance to analyze the consequences.

At Fraunhofer IISB, a comprehensive model library for components from the entire energy sector is being developed and used for system simulations. The models are trained using historical measurement data and are partially self-optimizing. In addition, Fraunhofer IISB has an extensive data-base, which is necessary for the development of component models, system simulations and intelligent operating strategies.

Energy Data Analysis and Artificial Intelligence

Modern methods from the field of artificial intelligence and machine learning enable automated and predictive optimization of local decentralized energy systems.



To identify optimization measures, a qualitative and quantitative analysis of the load and energy generation profiles is carried out and load forecasts can be created. Based on the data of your energy monitoring system, we offer you a comprehensive data analysis of your energy system as well as the development of forecast algorithms for your energy load profiles.

The research activities at Fraunhofer IISB aim at automated, self-learning forecasting algorithms and also focus on the feasibility of the algorithms in real applications. The forecasts are used to identify potential for efficiency increase and savings. Algorithms for automated data-based model generation are also being developed. These are important components for an automated, self-learning energy management system.

Hydrogen Systems

The optimized system integration of hydrogen technologies in mobile and stationary applications is a crucial prerequisite for the large-scale use of hydrogen as a potential energy carrier of the future.

Fraunhofer IISB's research activities cover the entire chain from electrolytic hydrogen production to storage and conversion to electricity in PEM fuel cells. This results in a wide range of research services, such as optimized system integration of fuel cells and electrolysers for mobile and stationary applications or simulation-based design and development of intelligent operating strategies for hybrid hydrogen systems.

In-house test benches and research platforms enable testing and characterization of fuel cell systems as well as operational optimization of energy storage systems based on liquid organic hydrogen carriers (LOHC). Fraunhofer IISB's services also include modeling of hydrogen systems (digital twins) and developing and evaluating concepts for stationary (for example, quarters or industrial plants) and mobile hydrogen applications in the scope of studies.

Peak Shaving

The reduction of electrical load peaks enables a reduction in network charges and thus in electricity costs. In addition, intelligent load management can avoid further grid expansion and reduce the load on the power grid.

Production plants could be shut down to reduce peak loads. However, this represents a significant intervention in the sensitive production processes of industrial companies and therefore has to be avoided. To reduce peak loads in your company, we offer algorithms and intelligent operating strategies that optimize the control of your energy supply instead. These are used for peak shaving together with energetic plants and storage systems, such as battery storage and combined heat and power plants with heat storage.

The storage units are discharged when there is a risk of peak loads and charged at times of lower demand. The actual function of the respective energy systems, for example the supply with heat, must not be negatively influenced in the process. With our simulations and based on your load profile, we can calculate and evaluate various scenarios in advance, estimate potentials and optimize parameters (for example, battery capacity or heat storage volume).

Thermal Systems and Sector Coupling

Thermal storages decouple the current demand for energy from the need for continuous generation, allowing the runtime of plants be variably shifted over time.

We support you in the design, investigation and implementation of optimization measures in your thermal systems. One type of application, for example, is the use of cold thermal energy storages and battery storages for peak load shaving. We use simulations to investigate different scenarios.

Due to the large number of system-internal and -external influential factors in the system efficiency, predictive, forecast-based operating strategies are developed. They react dynamically to the requirements and boundary conditions of the system under consideration (for example, weather conditions, plant utilization). The approach pursues active and predictive control of storage charging and discharging, for example by operating chillers primarily during nighttime hours or using waste heat efficiently with the help of a groundwater heat pump.


Innovative Technologien für intelligente dezentrale Energiesysteme

Hrsg.: M. März, R. Öchsner, IISB




Intelligent Energy Systems

Smart Energy from Milliwatts to Gigawatts