Open Semester & Master Thesis Project Topics

**Key Responsibilities:** - Assist in the assembly of the LED Solar Simulator, including: - Assembly of solar simulator parts (including isolators and reflectors). - Adjusting and securing components on the LED chips - Installing and positioning various parts, including magnets and reflectors, to complete the reconstruction of the simulator. - Work closely with our team to ensure the successful reassembly of the simulator. **Working Hours:** 48 hours in total, spread over two weeks (3 days per week, 8 hours per day). **Required Skills:** - Interest in mechanical assembly and hands-on tasks. - Willingness to learn and work independently after initial training. - Preferably an ETH student with vocational training. - Interest in physics or lighting technology is a plus. - Handy with tools and basic assembly work. **Required Documents:** - CV (emphasizing any relevant job experience) - A motivation letter, a maximum of one page, explaining your background and why you think you are suitable for this role. (optional)

As part of a short-term maintenance project (2 weeks), we are seeking a motivated student assistant to help reconstruct our state-of-the-art LED Solar Simulator (artificial sun).

Dr. Sarah Crosby (crosby@arch.ethz.ch)

This master thesis aims to develop a Model Predictive Controller (MPC) of a grid-interactive Thermally Activated Building System (TABS). The MPC will then be implemented in the HiLo living lab unit at the Nest building at Empa, Dübendorf. The master thesis will build up on an existing MPC and RC-model formulation of the HiLo TABS system.

The research question of this project is to evaluate the potential of a hydronic TABS for grid-reserve provision (primary, secondary, and tertiary reserves) for grid stabilisation & variable renewable energy (VRE) integration. Control variables are defined by the implemented TABS system in the HiLo Living Lab at the Nest building in Dübendorf. The thesis will consist of three work packages (WP): - **WP 1, MPC Formulation**: The student will first familiarize her/himself with the existing MPC of the HiLo unit developed at the chair. Subsequently, the MPC shall be modified, especially in the cost function formulation, to account for incentives of the TABS to respond to primary, secondary and tertiary reserves requests. For that, reserves request scenarios need to be implemented into the MPC. These scenarios will be defined in collaboration with SwissGrid and EWZ. - **WP 2, Simulation**: This WP will also build up on an existing RC model of the HiLo unit developed by the A/S group. After getting familiar with the existing model, the student will adapt it to represent a typical building type representative of the Swiss building stock. Special care in the model adaptation will need to be taken with respect to thermal storage capacity, heat propagation, and temperature phase shifting. Finally, the MPC from WP 1 will be executed on the RC model for both the typical building model and the HiLo building model, considering prior formulated electricity reserves scenarios. - **WP 3, Experiments (optional)**: In case of fast progress in the previous two work packages, the final optional work package will deal with the implementation of the grid-response MPC into the HiLo Living Lab at Nest, Dübendorf. As a first step, the student will need to get familiar with the HiLo controls interface, which will be introduced to the student by researchers of the group and of Nest. Subsequently, the student will calibrate the RC model with newly collected data based on short experiments. Finally, after successful implementation and calibration, a long-term experiment (around 2 full weeks) will be conducted, data collected, and the potential of the HiLo TABS to respond to reserves requests evaluated. **Candidate Description** We are looking for a highly motivated and self-organized candidate with a background in mechanical or electrical engineering, or architectural/civil engineering with strong interest in building controls. Proficient knowledge in at least one programming language (preferably Python or Matlab ) is essential for this project, as well as knowledge in mathematical optimization and building thermal dynamics. Prior experience in Model Predictive Control is desirable. Furthermore, you should enjoy numerical and experimental work. It is beneficial if you have prior experience in implementing building controllers. For this project, you will collaborate with researchers in our group. **References** - Silvestri, A., Coraci, D., Brandi, S., Capozzoli, A., Borkowski, E., Köhler, J., Wu, D., Zeilinger, M.N. and Schlueter, A., 2024. Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control. Applied Energy [Online], 368, p.123447. Available from: https://doi.org/10.1016/j.apenergy.2024.123447 - NEST HiLo website: https://www.empa.ch/web/nest/hilo

Alberto Silvestri (silvestri@arch.ethz.ch), Christoph Waibel (waibel@arch.ethz.ch)