Open Master's Thesis Positions

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The primary objective of this research is to develop a mechanism that effectively distributes flexibility requests generated by a central system among its associated buildings. This allocation mechanism should ensure that all buildings contribute to flexibility provision while optimizing both social and economic welfare. It achieves this by determining the specific adjustments in energy consumption and the corresponding price for each building.

The project will involve the following tasks: 1.Literature Review: Explore different types of flexibility provisions from the grid and how buildings can effectively fulfill these requirements. \item Problem Formulation: Model the flexibility allocation problem as an optimization problem that considers operational constraints and social and economic welfare maximization. 2.Methodology Development: Investigate potential methods for solving the allocation problem, including game-theoretic frameworks and multi-agent optimization techniques, and develop an optimization-based allocation mechanism that distributes flexibility provisions among buildings to maximize social welfare while ensuring compliance with grid requirements. 3.Evaluation and Validation: Validate the proposed mechanism using real-world data from the NEST building in Dübendorf, Switzerland, assessing its effectiveness in meeting flexibility goals.

benita.nortmann@empa.ch mina.montazeri@empa.ch federica.bellizio@empa.ch

Online feedback optimization (OFO) is a controller design paradigm for optimizing the steady- state of a dynamical system. OFO uses an optimization algorithm as a feedback controller, and exploits real-time measurements to cope with the uncertainty on the system dynamics and disturbances. On the downside, existing stability guarantees for OFO require the controller to be much slower than the dynamical system; namely, the control gain has to be very small. This “timescale separation” possibly affects transient performance, settling time, and responsiveness to disturbances —especially in problems with high temporal variability. This project investigates conditions under which stability of the closed-loop can be ensured without requiring any timescale separation. To this end, we use the theory of “monotone system”, which applies for instance to freeway traffic models. The student will have the opportunity to learn about OFO and monotone systems, before applying these concepts to a ramp metering problem, both via analytical analysis and numerical evaluation.

1. Literature review on topics in online feedback optimization and monotone systems 2. Become familiar with OFO algorithms and their implementation (MATLAB/Python/Julia) 3. Analysis and numerical evaluation of OFO algorithms for congestion control in a traffic network model 4. Final report and presentation.

We are looking for a talented and highly motivated student with a background in Automatic Control/Applied Mathematics or related fields, optimization and MATLAB/Python/Julia programming. The student must be enrolled in a master program. If you have further questions on this project, please do not hesitate to get in contact with us. If you would like to apply for this project, please email your CV (including lists of projects and optionally any document helpful to evaluate your background) and current grade transcript in PDF to: schandraseka@ethz.ch, mbianch@ethz.ch.

This thesis will develop a validated setup for measuring the angle-dependent solar, optical and thermal exchanges through facade systems. The main objectives are: Validated extension of the test chamber for measuring U-value, solar gains and visible light transmission through facades, employing the solar simulator (Figure 1). Demonstration of the setup by measuring the solar-optical and thermal properties of a double-glazed window with known properties. Comparing the corresponding measurements and nominal properties of the window, and identifying deviations, sources and impact of errors. Summarizing and discussing the results in terms of U-value and angle-dependent solar heat gain coefficients and visible light transmission.

**Objectives** This thesis will develop a validated setup for measuring the angle-dependent solar, optical, and thermal exchanges through facade systems. The main objectives are: - Validated extension of the test chamber for measuring U-value, solar gains and visible light transmission through facades, employing the solar simulator (Figure 1). - Demonstration of the setup by measuring the solar-optical and thermal properties of a double-glazed window with known properties. - Comparing the corresponding measurements and nominal properties of the window and identifying deviations, sources, and impact of errors. - Summarizing and discussing the results in terms of U-value and angle-dependent solar heat gain coefficients and visible light transmission. **Methodology and Tools** The thesis research heavily relies on experimental work, e.g., the design and implementation of an experimental setup for U-value, visible light transmission, and solar gain measurements [2], and the systematic processing and analysis of acquired experimental data. The research will be guided and supported by A/S researchers and technicians who already have experience with the testing setup. Nevertheless, it requires a well-structured and independent approach to experimental work and the development of new skills in the field of solar-optical properties of fenestration. An insulated compartment of the test chamber will be equipped with a cooling system and different types of sensors that allow monitoring of the heat fluxes through the facade component depending on different environmental and solar conditions created in the artificial testing chamber. The operation of the Solar Simulator will be supported by the lab technicians. The setup will then be employed to characterize a simple double-glazed window, and the experimental results will be compared to the properties of the fenestration, as reported by the manufacturer. The found deviations shall be analyzed and, where possible, attributed to the implementation of the Solar Simulator and experimental procedure. If the validation step is successful, a more complex system consisting of a shading device applied to the fenestration can be tested to demonstrate the experimental setup's capabilities. **Expected Outcomes** - By the end of this thesis, the following outcomes are expected: - A documented and validated setup to measure u-value, solar gains and visible light transmission, guided by international testing standards. - The heat loss, solar gains, and visible light transmission of the double glazing, measured for varying incident directions. - A comparison of the experimental solar heat gain coefficient, visible light transmission, and U-value with the facade’s nominal properties with a discussion of sources of error. - A summary of the testing setup capabilities and a discussion of its potential for the future study of complex facade systems (optional: through testing of a more complex facade system) - A final report documenting the research process and findings (thesis). **We are looking for** - A highly motivated and self-organized candidate with a background in civil/environmental/mechanical engineering or in architecture with strong technical skills. - Proficiency in at least one programming language for data processing and analysis (e.g., Python, R, or MATLAB). - Interest and previous experience with experimental setups and the processing and analysis of experimental data are an advantage. - Basic knowledge of solar, optical and thermal properties of facades and building performance metrics is an advantage. **References** [1] Kuhn, T. E. (2017). State of the art of advanced solar control devices for buildings. Solar Energy, 154. https://doi.org/10.1016/j.solener.2016.12.044 [2] Abolghasemi Moghaddam, S., Simões, N., & Gameiro da Silva, M. (2023). Review of the experimental methods for evaluation of windows’ solar heat gain coefficient: From standardized tests to new possibilities. Building and Environment, 242. https://doi.org/10.1016/j.buildenv.2023.110527 [3] Zero Carbon Building Systems Lab. Online. https://systems.arch.ethz.ch/zero-carbon-building-systems-lab (last accessed 2025-01-15)

The master thesis will be supervised by Dr. Lars Grobe and Dr. Valeria Piccioni. Please send your statement of interest including a CV and current transcript of records to Dr. Valeria Piccioni (piccioni@arch.ethz.ch) or Dr. Lars Grobe (grobe@arch.ethz.ch). Please direct questions regarding the position to the same e-mail.

Integrating wind energy into Switzerland’s future energy system remains a complex and widely debated challenge. While previous assessments have primarily focused on determining wind energy potential, a key question remains: How do we optimally use the available potential in an integrated energy system? Although some studies have evaluated wind energy potential using high-resolution wind data, the optimal distri-bution of wind turbines and their impact on the Swiss energy system has not been fully explored. This project aims to improve wind energy assessments by integrating spatial-temporal wind potential into energy system modeling and analyzing its role in Switzerland’s energy transition, also considering social aspects.

In this thesis, you will: • Learn to use ehubX, a Python-based energy system optimization tool developed by Empa’s Urban Energy Sys-tems Lab (USEL) using Mixed Integer Linear Programming (MILP). • Review and improve the wind energy calculation module within ehubX based on a literature study and turbine models. • Abstract and integrate high-resolution spatial-temporal wind data into an existing national energy system model of Switzerland. • Define and implement different scenarios (e.g., optimal locations and capacities for wind turbines in Swiss re-gions) and evaluate the impact of wind energy on the future Swiss energy system in terms of system cost and security of supply. This project offers a great opportunity to develop expertise in energy system modeling, optimization, and renewa-ble energy integration, contributing to Switzerland’s energy transition.

Do not hesitate to apply! Please contact us with a short motivation letter, your CV and your up-to-date transcripts of records (bachelor and master). If available, an overview of completed projects can be an advantage. Dr. Barton Yi-Chung Chen (yi-chung.chen@empa.ch), Dr. Robin Mutschler (robin.mutschler@empa.ch), at Urban Energy Systems Lab at Empa

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Nonlinear, data-driven control methods have gained popularity, yet (often) lack stability guarantees. Lyapunov/energy function methods provide a promising tool for addressing this challenge. One method for designing a stable controller is to use standard, LTI System Identification to learn the state space model, and then a neural network for the Lyapunov function and the controller [1]. This project will take a similar approach, but remove the intermediate System Identification step. That is, the project proposes to learn the neural network-defined energy function and controller directly from data, such that the controlled system is stable and optimal. Specifically, the “QSR” neural network architecture will be used, generalizing [2], as it allows for energy function-based stability guarantees. This will build off a recent submitted work which used the QSR function and a system model. The application will be frequency control of power systems. Specifically, the IEEE 37 Bus Feeder, for which a model and code are available from a recent publication in Neurips [3]. [1] Zhou, Ruikun, et al. "Neural lyapunov control of unknown nonlinear systems with stability guarantees." Advances in Neural Information Processing Systems 35 (2022): 29113-29125. [2] Koch, Anne, Julian Berberich, and Frank Allgöwer. "Provably robust verification of dissipativity properties from data." IEEE Transactions on Automatic Control 67.8 (2021): 4248-4255. [3] Cui, Wenqi, et al. "Structured neural-pi control with end-to-end stability and output tracking guarantees." Advances in Neural Information Processing Systems 36 (2024).

The student will: 1. Study dissipativity theory and data-driven control. 2. Review relevant literature on neural network control design and dissipativity. 3. Conduct simulation on power systems. 4. Final report and presentation. If the final results are adequate, they can potentially be written in a paper.

Please send your CV and transcript of records via email to Dr. Han Wang {hanwang@control.ee.ethz.ch} and Dr. Keith Moffat {kmoffat@ethz.ch}.

Model predictive control (MPC) is a powerful model-based control technique that received significant attention both in industry and academia thanks to its ability to efficiently obtain control inputs that are explicitly optimized for a predefined objective and fulfill process constraints. Designing the cost function and the constraints of an MPC controller is a question that has been thoroughly researched in the literature. Recently, several algorithms have been proposed to automatically obtain MPC controllers that are tailored for a specific task. Despite the great performance benefits that this automated tuning can bring, the MPC solutions one obtains do not work well if the task changes over time. In this case, to retain satisfactory performance, the controller needs to be re-tuned, resulting in significant computational effort. In this project, we wish to alleviate the computational effort required for re-tuning by utilizing meta-learning, a novel strategy for effectively adapting controllers to various environments and reducing the amount of data needed for the training.

This project will focus on the implementation of meta-learning to design an MPC controller for an experimental motion system. To achieve this, the study will first focus on constructing a gradient-based algorithm to automatically design the parameters of an MPC controller for a given task. Subsequently, a meta-learning algorithm will be applied to dynamically fine-tune these parameters in response to environmental changes, such as tracking references with different shapes. The student will implement the devised algorithms on the physical ball-on-a-plate system (see Figure 1), a mechanically actuated plate upon which a ball is balanced. The experimental system can be effectively represented as as LTI system with a nonlinear friction. It is located in the Automatic Control Laboratory, and serves as an ideal test-bed for evaluating learning-based control strategies. In the process, the student will be faced with a variety of topics of arguable importance in modern engineering, including control and machine learning. The goals of the project are as follows: - familiarize with the existing code base and with the interface of the ball-on-a-plate system; - construct and deploy a tuning algorithm to improve the performance of the MPC controller; - apply meta-learning algorithms to adaptively tune the control parameters to accommodate environmental variations or changes in the control task. _Publications_: Some of the results from the project are expected to be utilized in research publications from the lab. If the final results are promising they can be turned into a standalone publication as well.

We are looking for motivated students with some prior knowledge about optimization (e.g. gradient descent) and Python. Please send your resume/CV (including lists of relevant projects/courses) and transcript of records in PDF format via email to nikschmid@ethz.ch and rzuliani@ethz.ch.

**Research Questions** 1. How can existing thermal infrastructure be integrated into district thermal network designs to optimize energy performance and scalability? 2. What phasing strategies can maximize ROI for thermal network implementation under investment constraints? 3. How can thermal network performance feedback inform urban design metrics, such as energy efficiency, carbon footprint, and phasing strategies? **Methodology** 1. Literature Review Survey global best practices for integrating existing thermal infrastructure into new network designs. Explore how thermal networks interact with and influence urban design metrics. 2. Network Model Development Incorporate existing infrastructure into thermal network design (pipes, plants, pumps). Develop phasing strategies for incremental implementation. Integrate metrics to evaluate the impact of network designs on urban parameters: Energy efficiency (e.g., network losses, plant capacity factor). Environmental impact (e.g., emissions). Land use efficiency and spatial constraints. 3. Feedback Loop for Urban Design Use the enhanced network model to evaluate urban design scenarios. Quantify how urban form changes (e.g., building volume, land use type mix, phasing and retrofitting strategy) affect network performance (e.g., return of investment) and vice versa. 4. Case studies Zurich: Heating-dominant network design. Singapore: Cooling-dominant network design. Shanghai: Mixed heating and cooling requirements. **Candidate Description** We are looking for a highly motivated candidate with a background in architecture/urban design (with a strong interest in engineering/computational modelling/data science), or engineering (with a strong interest in architecture/urban design). You should have good verbal and written communication skills in English. Experience in CAD (e.g., Rhino 3D), parametric design programs (e.g., Grasshopper), basic programming using Python, urban building energy modelling (UBEM) and an understanding of district heating/cooling engineering are essential. You should be impact-driven with a product-thinking mindset. The research outcome is expected to be integrated into the open-source City Energy Analyst benefiting the global UBEM community for decarbonising cities through urban and architectural design.

**Deliverables** 1. Technical Report: Framework for integrating existing infrastructure and phasing district thermal networks. Metrics for evaluating the interactions between urban design and network performance. Results from case studies in Zurich, Singapore, and Shanghai. Heuristics on urban design and district thermal network engineering interplays. 2. Computational Model: A robust model for thermal network design and phasing strategies. If feasible, a prototype for integration into tools like CEA, enhancing its thermal network feature.

The master thesis will be supervised by Prof. Dr. Arno Schlüter, Dr. Zhongming Shi and Mathias Niffeler. Please send your statement of interest including a short CV and current transcript of records to Dr. Zhongming Shi(shi@arch.ethz.ch). Please direct questions regarding the position to the same e-mail. Information on the A/S Research Group can be found here: https://systems.arch.ethz.ch/ Information on the City Energy Analyst can be found here: https://www.cityenergyanalyst.com/

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This project is set to limited visibility by its publisher. To see the project description you need to log in at SiROP. Please follow these instructions: - Click link "Open this project..." below. - Log in to SiROP using your university login or create an account to see the details. If your affiliation is not created automatically, please follow these instructions: http://bit.ly/sirop-affiliate

**Objectives** The research aims to: Plan and execute mechanical tests (bending, compression, tensile) to compare the structural performance of various polymers for 3D printing. Compare the mechanical properties of these materials against the performance requirements for lightweight, self-supporting facades. Use testing data, if time allows, to inform computational assessments of free-form facades under realistic load conditions. **Methodology and Tools** Experiment Planning and Specimen Preparation: - Design an experimental plan, including mechanical testing protocols. - Coordinate the fabrication and preparation of test specimens Mechanical Testing: - Perform bending, impact, and tensile strength tests using relevant machines - Analyze stress-strain behavior, failure modes, and performance metrics Computational Assessment (Optional): - Use testing data to develop computational models and simulate facade performance under realistic loading conditions. Tools: - Universal Testing Machines for mechanical tests (ETH DBAUG). - Standardized testing protocols (e.g., ASTM or ISO standards). - FEA software (e.g., Abaqus, ANSYS) if simulation tasks are conducted. **Expected Outcomes** - A detailed experimental plan and prepared specimens for testing. - Datasets on bending, impact, and tensile strength of various 3D printing polymers. - Comparative analysis of material performance relative to facade requirements. - Initial computational models assessing the structural performance of 3DP façade design based on mechanical testing results (optional) **We are looking for** Students with a background and interest in material science, structural engineering, and experimental research. Preferred skills include: - Experience in planning and conducting material testing experiments. - Knowledge of mechanical testing protocols and data analysis. **References:** Leschok*, Matthias, Ina Cheibas*, Valeria Piccioni* et al., “3D Printing Facades: Design, Fabrication, and Assessment Methods.” Automation in Construction 152 (August 2023): 104918. https://doi.org/10.1016/j.autcon.2023.104918. Sepahi, Mohammad Taregh et al., “Mechanical Properties of 3D-Printed Parts Made of Polyethylene Terephthalate Glycol.” Journal of Materials Engineering and Performance 30, no. 9 (September 2021): 6851–61. https://doi.org/10.1007/s11665-021-06032-4. Anderson, Isabelle. “Mechanical Properties of Specimens 3D Printed with Virgin and Recycled Polylactic Acid.” 3D Printing and Additive Manufacturing 4, no. 2 (June 2017): 110–15. https://doi.org/10.1089/3dp.2016.0054

Contacts and supervision team: Valeria Piccioni (piccioni@arch.ethz.ch), Francesco Milano (milano@arch.ethz.ch), Vlad-Alexandru Silvestru (silvestru@ibk.baug.ethz.ch)

**Objectives** The project's main goal is to develop a predictive model for energy consumption and emissions in robotic 3D printing to support data-driven process analysis. Additional Objectives: - Analyze company-provided data to identify key drivers of energy consumption. - Build a machine learning-based model to predict energy usage under various printing conditions. - Integrate emissions estimation into the model using energy source emission factors. - Provide insights into the environmental performance of robotic 3D printing processes. **Methodology and Tools** - Data Analysis: Utilize fabrication data provided by Saeki Robotics. - Predictive Modeling: Use machine learning techniques to develop forecasting models. - Validation and Process Insights: Validate the data-driven model with physics-based/analytical models and extract key findings on process energy and emissions profiles. **Expected Outcomes** - A validated predictive model for energy consumption and emissions. - Insights into the main contributors to energy usage and emissions in robotic 3D printing. - A comprehensive analysis of the environmental performance of printing processes. **We are looking for** A motivated student with a background in engineering, data science, or sustainability. Experience with programming, machine learning, and 3D printing technologies is a plus. **References:** Yan, Z., et al. (2023): Hybrid mechanism-based and data-driven approach to forecast energy consumption of FDM. Journal of Cleaner Production. DOI: 10.1016/j.jclepro.2023.137500 Ghungrad, S., et al. (2024): Kinematics-guided data-driven energy surrogate model for robotic additive manufacturing. Journal of Manufacturing Processes. DOI: 10.1016/j.mfglet.2024.09.017 Yan, Z., et al. (2022): A new method of predicting the energy consumption of additive manufacturing considering the component working state. Sustainability. DOI: 10.3390/su14073757

For more information or to apply, contact piccioni@arch.ethz.ch.

Modern control methods often rely on explicit online computation. That is, for every time step, the controller needs to perform some optimization, numerical algebra or integration in order to determine the inputs. Naturally, such methods give rise a trade-off: For the same implementation more accuracy requires more computation time. Given the extremely high actuation frequency of modern control plants, and the increasing scale of, for instance, online optimization problems, the analysis and acceleration of such implementations is of great interest. Generally, performance analysis of such controllers relies on time scale separation. In simple terms, we assume that ‘enough’ computation can be performed to obtain a control signal that is ‘close’ to optimal. However, this leaves a number of important question unanswered: ˆ Beyond the analysis of closed loop stability, can we derive or minimize bounds on the transient behavior? ˆ Can we characterize precisely which algorithms in a specific class yield closed-loop stability? ˆ Given the previous, can we design algorithms that do not just converge quickly, but yield robustly stable closed loops? In order to understand these question, and more generally the behavior of closed loops between numerical methods and dynamical systems, this project approaches the algorithm as a dynamical system itself. In doing so, the usual language of convergence of algorithms can be viewed as a special case of stability theory. Moreover, beyond the stability analysis, this opens up the use of controltheoretical methods to analyze for instance noise rejection and, more generally, closed-loops between plants and algorithms.

The student will: 1. learn and understand concepts related to the topic, including incremental dissipativity and numerical analysis; 2. explore and review the relevant literature; 3. perform simulations on closed loop systems to validate and compare different methods; 4. bring together the theory and analyze the results;

Dr. Jaap Eising, jeising@ethz.ch.

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Please look into the attached document.

1. Study of opinion dynamics 2. Literature review on online feedback optimization and implementation on MATLAB/Python/Julia; 3. Empirical and theoretical analysis of OFO algorithms to identify influential users in a social network 4. Final report and presentation.

We are looking for a talented and highly motivated student with a background in Automatic Control/Applied Mathematics or related fields, optimization and MATLAB/Python/Julia programming. No specific experience in optimization algorithms and/ or social networks is necessary. The student must be enrolled in a master program. If you have any questions regarding the project, please do not hesitate to get in touch. If you would like to apply, please send a brief email with your CV (including lists of projects and optionally any document helpful to evaluate your background) and current grade transcript in PDF to: schandraseka@ethz.ch.

The integration of renewable energy sources and distributed generation in modern power systems has increased significantly. This resulted in an increased use of power-electronics-based converters, changing the dynamical behaviour of the grid and raising the need for accurate grid impedance estimation methods. Knowledge of grid impedance at the point of common coupling is essential for online stability assessment of grid-connected converters [1]. It is also used for optimizing the closed-loop performance via, e.g., adaptive control design. However, due to the grid's dynamic and nonlinear nature, impedance estimation becomes particularly challenging, in particular when conducted in real-time under closed-loop operation (see e.g., [2]). A new non-parametric identification method has shown potential for accurate and rapid grid impedance identification. While initial simulations have demonstrated the potential of the method, experimental validation using hardware is crucial to evaluate the method's performance under real-world conditions. This project aims to use two converter emulators ([3]) available in the Automatic Control Laboratory to experimentally validate the new approach. The main goals are to replicate realistic converter/grid conditions, assess the accuracy and robustness of the method, and to explore the limitations and performance boundaries of the method. References: [1] J. Sun, "Impedance-Based Stability Criterion for Grid-Connected Inverters," in IEEE Transactions on Power Electronics, vol. 26, no. 11, Nov. 2011 [2] V. Haberle, et.al., "MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches," 2023 62nd IEEE CDC [3] https://imperix.com/products/power-electronics-test-bench/ **Prerequisites** We are looking for a motivated student with a background in power systems/power electronics, and a previous exposure to system identification or signal processing. Knowledge of grid-connected converters and impedance estimation methods would be highly beneficial. Familiarity with experimental configurations and lab instrumentation is desirable. Competitive candidates will have prior research or work experience that demonstrate the ability to learn and problem-solve independently.

1. _Literature Review_: Review state-of-the-art literature on non-parameteric and parameteric grid impedance identification techniques, and learn the theoretical basis of the LPM method. 2. _Set up an experimental configuration_: Use the available imperix power electronics test benches to replicate a grid-connected converter scenario, allowing for safe and accurate data collection for impedance estimation. 3. _Data collection and analysis_: Collect experimental data under a variety of grid and excitation conditions. Analyze the accuracy and robustness of the LPM method, and compare to alternative estimation methods. 4. _Results interpretation and conclusions_: Document the findings, noting any discrepancies between simulation-based results and experimental outcomes. Propose potential improvements to the estimation method or additional experiments for further study, with the aim of ensuring reliability and robustness in real-world applications. 5. _Benchmark comparisons_: Examine the performance of alternative methods in a benchmark comparison study, highlighting the differences in terms of accuracy, used devices and experimentation time. 6. _Reporting_: Prepare a final report and a 30 minutes final presentation.

Please send your CV and transcript of records via email to Mohamed Abdalmoaty mabdalmoaty@ethz.ch and Xiuqiang He xiuqhe@ethz.ch.

The use of renewable energy sources and distributed generation, such as wind turbines and solar power plants, in modern power systems has been increasing significantly. This resulted in a heightened use of power-electronics-based converters, changing the dynamical behaviour of the power grid and raising the need for accurate grid impedance estimation methods. Knowledge of grid impedance at the point of common coupling is essential for online stability assessment of grid-connected converters [1]. However, due to the grid's dynamic and nonlinear nature, impedance estimation is particularly challenging (see e.g., [2]). The grid-connected converter's controller must maintain stable operation while simultaneously allowing the injection of small excitation signals necessary for grid impedance estimation. This creates a fundamental trade-off: while a good controller and converter-grid interface filter improve system stability and disturbance rejection, they can also suppress the grid's response to the excitation, reducing the estimation accuracy. On the other hand, injecting larger excitation signals could improve the quality of estimation but risks triggering the converter protection system and/or exciting nonlinear grid behaviors. Moreover, it is not clear what the best class of excitation signals is nor the best injection point within the converter controller. This project aims to address this trade-off and questions by developing optimal excitation schemes for grid impedance estimation using grid-connected converters. A main goal is to obtain a good signal-to-noise ratio at the frequencies of interest while respecting experimental time and signal magnitude constraints. A class of excitation signals of potential interest is random phase multisines [3]. Recent related results in the literature are [4,5] which are based on modelling the noise and the use of multiple sinusoidal injections. References: [1] J. Sun, "Impedance-Based Stability Criterion for Grid-Connected Inverters," in IEEE Transactions on Power Electronics, vol. 26, no. 11, Nov. 2011 [2] V. Haberle, et.al., "MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches," 2023 62nd IEEE CDC [3] Godfrey, Keith, ed. Perturbation signals for system identification. Prentice Hall International, 1993. [4] Y. Zhu, et.al, "Injection Amplitude Guidance for Impedance Measurement in Power Systems," in IEEE Transactions on Power Electronics, vol. 38, no. 6, June 2023 [5] H. Li, et.al., "A Noise Interference Suppression Method With Optimized Frequency Adjustment for Impedance Measurement," in IEEE Transactions on Industrial Electronics, vol. 71, no. 12, Dec. 2024 **Prerequisits** We are looking for a motivated student with excellent written and verbal communication skills, and the ability to learn and problem-solve independently. Competitive candidate will have a background in systems & control, signal processing, and power systems/electronics. A prior experience or coursework in system identification and/or grid-connected converters would be highly beneficial. Proficiency in MATLAB/Simulink or a similar simulation environment is required for implementing and testing the methods.

1. _Literature review & problem definition_: Conduct a literature review on existing grid-impedance estimation techniques, as well as excitation design schemes. Define the requirements and limitations for accurately estimating grid impedance in a real-time, closed-loop setting, while addressing factors that impact estimation accuracy under practical conditions (e.g., experiment time, amplitude of excitation signals, etc). 2. _Development of optimal excitation framework_: Formulate an "optimal" excitation framework tailored for grid impedance estimation via grid-connected converters. 3. _Excitation signal design_: Generate and test the excitation signal in simulation, highlighting the impact on estimation accuracy. Explore the potential of iteratively refining the signal design to improve accuracy. 4. _Benchmark comparisons in simulation_: Compare the performance of your method(s) to existing ones as well as those using arbitrary excitation on benchmark models. Analyze the results to determine the practical effectiveness of the proposed approach for real-time applications in power systems. 5. _Reporting_: Prepare a final report and a 30 minutes final presentation.

Please send your CV and transcript of records via email to Mohamed Abdalmoaty mabdalmoaty@ethz.ch and Xiuqiang He xiuqhe@ethz.ch.