|12. Mai 2023
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Dr. Mathias Hudoba de Badyn
Dept. of Information Technology and Electrical Engineering
Friday 2023-05-12 2:00 p.m.
IST Seminar Room 2.255 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen
The climate crisis is likely to be the largest factor in inequality growth over the next
century. Mitigating these effects require novel techniques in both decarbonizing energy production,
and minimizing current energy consumption. In this talk, I discuss how large-scale infrastructure
system control can address both problems simultaneously. One challenge with increasing the
penetration of renewable energy in the power grid is that this results in higher levels of
uncertainty and variability of supply and demand in the grid. I argue that electricity demand, such
as from large residential buildings, can be used to help balance these fluctuations in supply in
real-time by varying the building demand (known as demand response). This necessitates the control
of large numbers of individual apartment units, as well as heating/cooling energy systems, in a
coordinated fashion in order to produce a desired aggregate electricity demand during real-time
operation. Via novel distributed control of the energy consumption of buildings, one can provide a
service to the electrical grid by enabling real-time demand response. This allows online balancing
of the supply and demand of the electrical grid, all while minimizing the electrical bill of the
The theoretical core behind distributed control algorithms lies in fundamental network science, and the interplay of machine learning techniques with robust control systems. The presence of networks in distributed control systems leads to interesting theoretical questions regarding scalability and modularity of such methods: how large of a network can you control, and how does your system behave when new systems are added to the network? I also discuss how fundamental network science can inform us about how we can modify pre-existing infrastructure networks for improved control, or how to design them a priori. When using machine learning tools in the loop with a control system, it is difficult to design the controller using traditional design criteria. I discuss how one can achieve such desirable properties of control systems, such as model convexity, with state-of-the-art machine learning tools. I will address areas of infrastructure control that highlight the importance of each of these theoretical questions, and demonstrate the viability of our distributed control methods with experimental results from the NEST Smart Building Demonstrator facility.
Mathias Hudoba de Badyn is a postdoctoral scholar in the Automatic Control Laboratory (ifA) at the Swiss Federal Institute of Technology (ETH), Zürich. He received both his Ph.D. degree in the Department of Aeronautics and Astronautics, and an M.Sc. degree in the Department of Mathematics in 2019 at the University of Washington. In 2014, he graduated from the University of British Columbia with a BSc in Combined Honours in Physics and Mathematics. His research interests include the analysis and control of networked dynamical systems.