However, the fluctuating nature of renewable generation subjects these systems to dynamic operating conditions, creating significant challenges for control, efficiency, and long-term durability. While such models provide valuable insight, advanced model-based control methods such as Model Predictive Control (MPC) are often limited by discrepancies between the nominal and the actual system behavior. The thesis offers the opportunity to work on state-of-the-art research at the interface of control engineering, system identification, machine learning, and sustainable energy systems while contributing to the advancement of hydrogen technologies for the energy transition. * Strong interest in control engineering, system dynamics and mathematical modeling - As a member of the Helmholtz Association with some 7,600 employees, we conduct interdisciplinary research into a digitalized society, a ...
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