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SUMMARY:Vortrag von Dr. Mathias Hudoba de Badyn
DESCRIPTION:Dr. Mathias Hudoba de Badyn\nDept. of Information Technology and Electrical Engineering \nETH Zürich\nZurich, Switzerland\n  \nFriday 2023-05-12 2:00 p.m.\nIST Seminar Room 2.255 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen  \nAbstract\nThe climate crisis is likely to be the largest factor in inequality growth over the next\ncentury. Mitigating these effects require novel techniques in both decarbonizing energy production,\nand minimizing current energy consumption. In this talk, I discuss how large-scale infrastructure\nsystem control can address both problems simultaneously. One challenge with increasing the\npenetration of renewable energy in the power grid is that this results in higher levels of\nuncertainty and variability of supply and demand in the grid. I argue that electricity demand, such\nas from large residential buildings, can be used to help balance these fluctuations in supply in\nreal-time by varying the building demand (known as demand response). This necessitates the control\nof large numbers of individual apartment units, as well as heating/cooling energy systems, in a\ncoordinated fashion in order to produce a desired aggregate electricity demand during real-time\noperation. Via novel distributed control of the energy consumption of buildings, one can provide a\nservice to the electrical grid by enabling real-time demand response. This allows online balancing\nof the supply and demand of the electrical grid, all while minimizing the electrical bill of the\nbuilding occupants.\n\nThe theoretical core behind distributed control algorithms lies in fundamental network\nscience, and the interplay of machine learning techniques with robust control systems. The presence\nof networks in distributed control systems leads to interesting theoretical questions regarding\nscalability and modularity of such methods: how large of a network can you control, and how does\nyour system behave when new systems are added to the network? I also discuss how fundamental\nnetwork science can inform us about how we can modify pre-existing infrastructure networks for\nimproved control, or how to design them a priori. When using machine learning tools in the loop\nwith a control system, it is difficult to design the controller using traditional design criteria.\nI discuss how one can achieve such desirable properties of control systems, such as model\nconvexity, with state-of-the-art machine learning tools. I will address areas of infrastructure\ncontrol that highlight the importance of each of these theoretical questions, and demonstrate the\nviability of our distributed control methods with experimental results from the NEST Smart Building\nDemonstrator facility.  \nBiographical Information\nMathias Hudoba de Badyn is a postdoctoral scholar in the Automatic Control Laboratory (ifA) at\nthe Swiss Federal Institute of Technology (ETH), Zürich. He received both his Ph.D. degree in the\nDepartment of Aeronautics and Astronautics, and an M.Sc. degree in the Department of Mathematics in\n2019 at the University of Washington. In 2014, he graduated from the University of British Columbia\nwith a BSc in Combined Honours in Physics and Mathematics. His research interests include the\nanalysis and control of networked dynamical systems.\n   
DTSTART;TZID=Europe/Berlin;VALUE=DATE:20230512
URL;VALUE=URI:https://www.ist.uni-stuttgart.de/de/veranstaltungen/Vortrag-von-Dr.-Mathias-Hudoba-de-Badyn/
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