Energy Solutions
The energy-efficient operation of technical systems is of increasing importance in view of global warming and the transition to renewable energies. Control systems engineering has a high potential to contribute to the efficient use of energy, for example, regarding energy consumption of buildings, energy conversion and storage, as well as power grids. Our research in terms of optimization and learning-based control methods forms the ideal basis to develop sustainable solutions in energy-related applications.
The efficient and sustainable use of energy is one of the grand challenges today. Control engineering is a key technology to accomplish this goal by providing tailored automation solutions. For instance, heating, ventilation and air conditioning (HVAC) systems in residential and non-residential buildings belong to the largest energy consumers. Advanced control schemes can help to efficiently control HVAC systems while learning the building characteristics like thermal dynamics or room occupancy patterns and considering stochastic weather forecasts to reduce the energy consumption in a predictive manner.
Another example are electrical grids that connect energy sources (power plants, wind turbines, etc.) and energy sinks (e.g. factories, households). On the lower level, each involved component, e.g. drives and inverters, can be designed to reduce undesired power dissipation. On the higher level, the energy distribution itself can be optimized using, for instance, distributed model predictive control for smart grids.
Despite the current prevalence of batteries as mobile energy storages, hydrogen-based fuel cells are a promising alternative for applications with high energy demand as, for example, trains or ships. One option to solve the problem of save hydrogen storage is the use of liquid organic hydrogen carriers (LOHC), either as storage/transport medium or for direct fuel cell usage. The de-/hydrogenation of LOHC is a complex process that offers high potential for improvement by modern control methods.
Related projects
AGENT-2: Predictive and learning control methods
To achieve climate targets, CO2 emissions in the building sector have to be significantly reduced. However, the integration of renewable energy sources increases the complexity of building energy systems and thus the requirements for the operation strategy. Model-based and predictive controllers are necessary for efficient operation. However, due to the high complexity of the energy systems, the development, implementation, and commissioning are very complex leading to high costs, which is why…
Robust control of modular multi-level converters
AGENT: Agent-based systems for intelligent and robust control of complex energy systems in non-residential buildings as part of a superordinate energy system
The consortium of the project AGENT consists of the partners RWTH Aachen, Friedrich-Alexander- Universität Erlangen-Nürnberg and Robert Bosch GmbH, who already conduct research in the field of building energy system technology and building automation technology. The practitioners’ perspective shows that increasing complexity of energy systems within non-residential buildings leads to further challenges within their operation. These are caused by complex interaction and the attempt to control such systems by means of a central instance using a supervisory control system. The goal in this project is the development of a future building automation system based on agents. A building with its energy system will be enabled to optimize not only its own operation but to serve as a part of a superordinate energy system and to behave as a grid-friendly building. The agent-based system will be self-configuring and, for instance, optimize the energy consumption of the building. Therewith, it reduces the effort of construction, commissioning and operation of energy and building energy systems. For the task of controlling energy systems, generic tasks of single components and groups of components will be defined and included into a practitioner’s guide. Practical usability will be ensured to allow for dissemination in the building energy sector.
Robust energy-based control of MMC/HVDV systems
Thermische Umrichtermodellierung für elektrische Antriebssysteme
Related publications
Since 2021
- Schumann, M., Ebersberger, S., & Graichen, K. (2023). Online learning and adaptation of nonlinear thermal networks for power inverters. In Proceedings of the 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023). Marina Bay Sands (Singapore).
- Schumann, M., Ebersberger, S., & Graichen, K. (2023). Online learning and adaptation of nonlinear thermal networks for power inverters. In Proceedings of the 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023). Marina Bay Sands (Singapore).