Network science in control: From smart cities to a smart future
Event details
| Date | 03.07.2023 |
| Hour | 11:00 › 12:00 |
| Speaker | Mathias Hudoba de Badyn (ETH Zürich) |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract:
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. The theoretical core behind these distributed control algorithms lies in fundamental network science. 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. I demonstrate the viability of our distributed control methods with experimental results from the NEST Smart Building Demonstrator facility.
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. The theoretical core behind these distributed control algorithms lies in fundamental network science. 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. I demonstrate the viability of our distributed control methods with experimental results from the NEST Smart Building Demonstrator facility.
Bio:
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.
Practical information
- General public
- Free