The chair of Prof. Bajcinca focuses on research of modern methods and advanced applications of control and system theory, involving three main pillars: cyber-physical systems, complex dynamical systems and machine learning. Through networking with a large number of national and international research, academic and industrial partners, funding projects with exotic and highly interesting tasks regarding model-based and data-driven control have been acquired on a regular basis. The research work is supported with an excellent laboratory equipment and high-performance computation in the areas of autonomous systems, robotics and energy systems, which is continuously being further developed.

https://www.mv.uni-kl.de/mec/home.
 

Electrical power systems represent complex and dynamic systems, consisting of a large number of interconnected subsystems for supply, demand, and distribution. These subsystems are being increasingly strained by the rapid growth of electromobility and the increasing demand for high power and fast charging. Moreover, the transition to renewable energy sources and the decommissioning of conventional power plants creates supply-stability problems. Both the increase in demand and instability in the supply pose a major challenge that requires innovative solutions to regain the balance in the supply chain.

Demand Side Management (DSM) enables the adjustment of the loads in the grid to ensure a balanced operation, while simultaneously optimizing the utilization of the resources in the electrical power system. It also provides an opportunity for energy consumers to function as providers, energy storage buffers, and demand pattern regulators. Today’s DSM systems are limited to local energy grids, and the load-balancing solutions within the local grid itself. A larger rollout of the same idea can be achieved by utilizing mathematical planning and machine learning methods.

New protocols supporting bidirectional charging (BC) of electric vehicles (e.g. CCA3.0) are establishing an innovative platform for implementation of DSM and for that also of sector coupling with mobility. Design of intelligent management algorithms for power flow between a mass of vehicles and the power grid on the basis of real-time information flow represents the central task of the newly initiated research project DERIVE www.mv.uni-kl.de/mec/projekte, which is funded by the Federal Ministry BMVI and runs in cooperation with various research and industrial partners.
 

Cyber-physical systems (CPS) are integrations of computation with physical processes: embedded computers control physical processes, which in return affect computations through feedback loops. The design of cyber-physical is a complex task. Design contracts have been applied to specifically address the constraints stemming from the cyber and physical subsystems of a CPS. For instance, a design contract can specify some constraint on the time instances at which some operations are performed such as sampling, control computation, or actuation. The goal is then to verify and design contracts for a set of control systems sharing a given number of resources. In addition to theoretical in-depth work, the research task is defined in the context of an application in the sector coupling related to energy and electromobility domains. 

Models of cyber-physical systems are heterogeneous by nature: discrete dynamic systems for computations and continuous differential equations for physical processes. At the core of the project lie tasks concerning development of new methodologies in optimization and predictive control, as well as their application for demand - side management coupling of the grid and a collection of electrical vehicles.

• The first part of the project is devoted to modelling of the underlying application in sector coupling under the premise of design contracts. This comprises mathematical description as well as implementation in some existing platforms (e.g. Matlab/Simulink, Python).
• The second part of the work will deal with codesigning schedulers and controllers of the resulting models, which consists in formulating a mixed-integer linear optimisation problem to be solved online. This will be done using existing solvers (e.g. Gurobi) or by developing new dedicated numerical algorithms.
• Optionally it is possible to test the designed algorithms in a HIL or/and Power HIL environment which is available in the chair. Moreover, the successful candidate can participate in developing an open-source toolbox in cooperation with our research partners.
   

• University degree in electrical engineering or mathematics with an above-average success 
• Technical skills in control engineering and optimization that go well beyond the basic university courses
• Experienced with formulation and solution of problems in nonlinear optimization
• Knowledge on differential equations and dynamical systems
• Highly motivated and eager to work within a team or independently 
• Familiarity with at least one of the programming language amongst Matlab, Julia or Python
• Organizational and collaborative skills with scientific and industrial partners from different disciplines

• Payment according to TV-L E13 with an initial one-year time limit
• The possibility to do a PhD and to teach is given in case of scientific aptitude
• TUK strongly encourages qualified female academics to apply
• Severely disabled persons will be given preference in the case of appropriate suitability (please enclose proof)
• Electronic application is preferred. Please attach only one coherent PDF.

You can expect an interesting, diversified and responsible task within a young, highly motivated and interdisciplinary team of a growing chair with great personal creativity freedom.

Prof. Dr.-Ing. Naim Bajcinca
Phone: +49 (0)631/205-3230
Mobile: +49 (0)172/614-8209
Fax:  +49 (0)631/205-4201
Email: mec-apps(at)mv.uni-kl.de

Model-predictive control
Mixed-integer optimization
Power systems
Demand-Side Management
 

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31. Oktober 2023

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