M. Sc. Armin Gießler
Karlsruher Institut für Technologie (KIT) Campus Süd
Institut für Regelungs- und Steuerungssysteme
Geb. 11.20 (Engler-Villa)
Studies of electrical engineering and information technology at Karlsruhe Institute of Technology (KIT) with a semester abroad at Linköping University in Sweden. Internship at Pepperl+Fuchs GmbH in Mannheim in the department of identification systems for factory automation (2017). Bachelor thesis at Vector Informatik GmbH in Stuttgart on the subject of "Optimization of parallelized flash processes within a vehicle" (2019).
Subsequent master studies at KIT with a specialization in control engineering and a semester abroad at the Instituto Superior Técnico in Portugal. Master thesis at the Institute of Control Systems (IRS) on the subject of "Distributed Optimization for Distributed Model Predictive Control" (2021).
Since January 2022 research and teaching assistant at IRS.
Techno-economic energy management
In the fight against climate change and for reasons of sustainability, the share of renewable energy sources in the electrical energy supply continues to increase. The increasing number of volatile energy sources (e.g. solar energy and wind power) and the limited storage options for electrical energy pose a major challenge for power grids.
An important role in the energy industry is played by the balancing group manager, who predicts the feed-ins and withdrawals in his balancing group or rather distribution grid (e.g. microgrid) and forwards this forecast to the transmission system operator (TSO). Based on these forecasts, the TSO creates its schedule management, which ensures stability in the power grid. In case of forecast deviations, balancing energy costs arise, which are partly passed to the end customer via network fees. In the course of my research, I develop methods for smart energy management of cooperating balancing groups, which improves the forecast fidelity of balancing groups and optimally utilizes renewable energies. The goal is to improve the economics, ecology, and energy efficiency of the energy grid while maintaining stability and reliability.
Since the energy industry is highly regulated, the considerations take place in a techno-economic framework that considers the interplay of politics, economics, science and sustainability. Furthermore, I ask myself how the grid operation (grid architecture and regulations) of the future electricity industry must look like in order to ensure a safe, cheap, efficient and environmentally friendly supply of electricity to the general public.
|Reinforcement Learning für die zentrale Regelung von DC Microgrids||Master Thesis|
|Reinforcement Learning für die primäre Regelung von DC Microgrids||Master Thesis|
|Title||Type||Person in Charge|
|Reinforcement Learning für die Primärregelung von DC Microgrids||Bachelor Thesis|
|Passivity-based Reinforcement Learning||Master Thesis|