Open Master's Thesis Positions

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**Background and Topic Description:** Traditional building energy simulations often suffer from inaccuracies because they rely on static, rural weather data (EPW files) that ignore the localized physics of the urban environment [1]. To achieve a more accurate building energy model (BEM), it is necessary to integrate Urban Microclimate Modeling (UMM). This research focuses on the specific role of urban trees as a dynamic variable within the microclimate-building feedback loop [2]. Recent computational advancements underscore the necessity of dynamic, two-way coupling frameworks to accurately capture how buildings and their surrounding microclimates mutually influence each other in real-time [3,4]. To address this, the thesis will utilize an advanced, coupled modeling workflow. By integrating established building energy simulation tools (e.g., EnergyPlus, Honeybee) with microclimate simulation tools (e.g., ENVI-met, Butterfly, SimScale), this study will comprehensively evaluate how selected urban tree configurations can impact building energy demands. Crucially, the built environment will remain as fixed morphological baselines, allowing the research to strictly isolate and quantify the specific impact of the urban trees. **Thesis Objective:** The core objective of this thesis is to develop and implement a robust, parametrical, coupled simulation workflow to precisely quantify impacts-of selected archetypical urban greenery configurations on building energy performance. **Case study:** Following a consultation with the supervisors regarding data availability, a specific urban context will be selected as the primary case study. Using typical meteorological data, the current built environment, and existing greenery configurations, a performance baseline will be established as a foundational activity in the thesis timeline. **Scenario Development:** To thoroughly test the impact of NBS during the analysis stage, multiple urban greenery scenarios will be formulated. These scenarios will parametrically explore a wide spectrum of configurations, including tree typology, quantity, and spatial placement **Research Questions:** To achieve the stated goals, this thesis will systematically address the following research questions, focusing specifically on the parametric variation of greenery within fixed morphological contexts: - Quantifiable Impact: What is the specific, quantifiable impact of selected urban tree configurations on building energy demands? - Methodological Accuracy: To what extent does a dynamic, coupled simulation workflow improve the accuracy of building energy performance in urban settings compared to traditional uncoupled or static methodologies? - Optimal Configuration: How can the spatial arrangement, density, and typology of urban greenery be optimized to maximize outdoor thermal comfort and achieve the best year-round building energy performance within a fixed urban archetype? **Potential Outcome and Expected Contribution:** This thesis will deliver a highly resolved, quantitative assessment of how urban trees can impact building energy consumption. By utilizing a coupled-simulation approach, the research will establish a superior methodological framework over traditional static modeling techniques. **Potential Implementation Steps:** Step 1: Literature Review and Software Familiarization: - Conduct a comprehensive literature review focusing on urban trees and recent advancements in coupled Urban Microclimate Models (UMM) and Building Energy Models (BEM). - Familiarize with the selected simulation software stack (e.g., EnergyPlus, Honeybee, ENVI-met, or Butterfly) and the specific scripting environments (Python or R) required for the coupling workflow. Step 2: Case Study Selection and Data Collection: - Select a specific urban context for the case study in consultation with the UESL supervisory team, ensuring adequate data availability. - Gather necessary input data, including typical meteorological data (EPW files), urban morphology, building geometries, and existing construction materials. Step 3: Baseline Model Creation and Validation: - Develop the standalone building energy model and the microclimate model for the selected case study. - Establish the baseline performance metrics for building energy demands. - Validate the baseline models against available empirical data or established benchmarks to ensure accuracy before introducing parametric variables. Step 4: Parametric Scenario Development: - Define a set of urban tree configuration scenarios to be tested against the fixed morphological baseline. Step 5: Coupled Simulation Execution: - Implement the dynamic, coupled simulation workflow, integrating the microclimate outputs with the building energy inputs. - Run the established urban tree scenarios through the coupled workflow, ensuring data feeds back between the models accurately to capture real-time localized microclimate shifts. Step 6: Synthesis, Guidelines, and Thesis Writing: - Synthesize the findings to directly answer the defined research questions. - Compile the methodology, data, and conclusions into the final academic thesis document and prepare the final presentation for the UESL team. **Key Related References:** This research will be grounded in the latest academic developments in urban energy modeling and microclimate coupling [4]. It will draw upon various established approaches, including: - Coupling EnergyPlus and ENVI-met utilizing the Urban Weather Generator (UWG) [3, 7, 11] - Coupling the City Energy Analyst (CEA) with ENVI-met [8] - Utilizations of TRNSYS [9,10] - Coupling ANSYS and IDA ICE [11] - Coupling SALIB, Dragonfly, and EnergyPlus [13] - Utilizations of CitySim [6] - Targeted studies focusing on urban greenery configurations [5] **Reference:** [1] Wang, W., Li, S., Guo, S., Ma, M., Feng, S., & Bao, L. (2021). Benchmarking urban local weather with long-term monitoring compared with weather datasets from climate station and EnergyPlus weather (EPW) data. Energy Reports, 7, 6501–6514. https://doi.org/10.1016/j.egyr.2021.09.108 [2] Li, Q., Xu, G., & Gu, Z. (2024). A novel framework for multi-city building energy simulation: Coupling urban microclimate and energy dynamics at high spatiotemporal resolutions. Sustainable Cities and Society, 113, 105718. https://doi.org/10.1016/j.scs.2024.105718 [3] Pasandi, L., Qian, Z., & Woo, W. L. (2025). Quantifying urban microclimate feedback on building energy use using a coupled simulation workflow. Building and Environment, 285, 113657. https://doi.org/10.1016/j.buildenv.2025.113657 [4] Sezer, N., Yoonus, H., Zhan, D., Wang, L., Hassan, I. G., & Rahman, M. A. (2023). Urban microclimate and building energy models: A review of the latest progress in coupling strategies. Renewable and Sustainable Energy Reviews, 184, 113577. https://doi.org/10.1016/j.rser.2023.113577 [5] Liu, A., Wang, C., Shang, G., & Hong, B. (2025). Assessing tree canopy cooling efficiency in different local climate zones: A cost-benefit analysis. Urban Forestry & Urban Greening, 105, 128694. https://doi.org/10.1016/j.ufug.2025.128694 [6] Mohammed, A., Khan, A., Khan, H. S., & Santamouris, M. (2023). Cooling energy benefits of increased green infrastructure in subtropical urban building environments. Buildings, 13(9), 2257. https://doi.org/10.3390/buildings13092257 [7] Toren, B. I., & Sharmin, T. (2023). Comparison of building energy performance in three urban sites using field measurements and modelling in Kayseri, Turkiye. Journal of Physics Conference Series, 2600(3), 032007. https://doi.org/10.1088/1742-6596/2600/3/032007 [8] Mosteiro-Romero, M., Maiullari, D., Esch, M. P., & Schlueter, A. (2020). An integrated Microclimate-Energy demand simulation method for the assessment of urban districts. Frontiers in Built Environment, 6. https://doi.org/10.3389/fbuil.2020.553946 [9] Allegrini, J., Dorer, V., & Carmeliet, J. (2012). Influence of the urban microclimate in street canyons on the energy demand for space cooling and heating of buildings. Energy and Buildings, 55, 823–832. https://doi.org/10.1016/j.enbuild.2012.10.013 [10] Palme, M., Privitera, R., & La Rosa, D. (2020). The shading effects of Green Infrastructure in private residential areas: Building Performance Simulation to support urban planning. Energy and Buildings, 229. https://doi.org/10.1016/j.enbuild.2020.110531 [11] Brozovsky, J., Radivojevic, J., & Simonsen, A. (2022). Assessing the impact of urban microclimate on building energy demand by coupling CFD and building performance simulation. Journal of Building Engineering, 55, 104681. https://doi.org/10.1016/j.jobe.2022.104681 [12] Ramesh, S., & Lam, K. P. (2015). Urban Energy Information Modeling – a framework for coupling Macro-Micro factors affecting building energy consumption. Building Simulation Conference Proceedings. https://doi.org/10.26868/25222708.2015.3050 [13] Bian, C., Cheung, K. L., Chen, X., & Lee, C. C. (2024). Integrating microclimate modelling with building energy simulation and solar photovoltaic potential estimation: The parametric analysis and optimization of urban design. Applied Energy, 380, 125062. https://doi.org/10.1016/j.apenergy.2024.125062

The primary goal of this thesis is to develop a coupled microclimate and energy simulation workflow to precisely quantify how urban tree configurations impact building energy performance. By doing so, the research aims to resolve the inherent inaccuracies of traditional building energy models that currently ignore the dynamic, localized effects of urban greenery.

Dr. Hassan Bazazzadeh (hassan.bazazzadeh@empa.ch) Dr. Robin Mutschler (Robin.Mutschler@empa.ch)

Self-commissioning controllers for simple control devices regulate a single output variable using one actuator. When first connected, the controller learns the system’s dynamic behaviour and automatically designs suitable proportional–integral (PI) gains to ensure smooth and stable performance. For this learning-based approach to work effectively, key hyperparameters of the underlying learning and identification algorithms must be correctly selected during commissioning. In practice, these hyperparameters strongly influence convergence speed, robustness, and closed-loop performance. Misconfigured parameters, overly aggressive or slow responses, or changing operating conditions can therefore require systematic analysis and re-tuning. The goal of this project is to develop a program that can analyse a controller’s behaviour, identify the causes of undesired performance, and automatically adjust its hyperparameters to improve it. In particular, learning-based and system identification methods will be used to support structured hyperparameter identification and performance assessment. The system should reliably diagnose performance issues, tune hyperparameters of the learning algorithm, and ensure robust adaptation in real-world applications. Ultimately, the aim is to move closer to true plug-and-play functionality by enabling the controller to autonomously monitor its behaviour and take corrective actions. This project will be co-supervised with Belimo Automation AG. In addition to simulation-based development, the work will include hands-on experimentation in an industrial setting using real HVAC devices and embedded controllers. The student will gain practical experience with real hardware, data acquisition, controller implementation, and performance evaluation under realistic operating conditions. This provides direct exposure to industrial product development and validation processes. We are looking for a talented, motivated student interested in self-learning control systems and smart building technologies and HVAC systems with • Background/interest in control systems, automation, or mechatronics engineering. • Familiarity with PI control, system identification, and dynamic modelling. • Basic knowledge in data-based and machine learning methods. • Experience with MATLAB/Simulink or a similar simulation environment. • Strong analytical and problem-solving skills.

The tasks that will be carried out in this thesis are: 1. Identify the relevant hyperparameters that are preselected based on the application and analyse how different applications influence these parameters. 2. Define suitable performance metrics and develop a method to analyse the controller’s behaviour online to detect undesired responses 3. Design a robust and systematic program capable of debugging and automatically adjusting the parameters and performing other corrective actions to resolve potential issues 4. Numerical simulations to test the proposed strategy across different application scenarios. 5. Documentation and final presentation of the work

- Dr. Efe Balta efe.balta@inspire.ch - Dr. Varsha N. Behrunani varsha.behrunani@belimo.ch

Meta Reinforcement Learning (meta RL) studies machine learning algorithms that learn to reinforcement learn [1]. Rather than learning from scratch on a given task through a large number of repeated episodes, agents in meta RL first perform meta-learning on meta-learning data (outer loop) which is a collection of tasks/environmental realisations sampled from a given distribution. Then, during test-time or online play time, the algorithms fine-tune or adapt to the particular task at hand (inner loop) and ideally do so fast through a small number of episodes (few-shot learning), as visualized in the Figure [1]. Such a setting is especially relevant for robotics applications such as legged robotics [2] or quadrotors[3], where the meta-learning happens in simulation due to safety and cost considerations, and the adaptation to the specific instantiation of the environment happens during test-time or online. Moreover, for many such applications there is no availability to do more than a single episode for fine-tuning, and the agent needs to adapt to control online, during a single trajectory, aka in a zero-shot setting. RL2 [4] and VariBAD [5] are among the algorithms that operate in this zero-shot meta RL setting. In addition, there is significant literature on online learning methods for control that study the zero-shot RL setting often assuming no offline training availability[6, 7]. In contrast to meta-RL, these methods concentrate on linear dynamical systems and provide rigorous theoretical guarantees on stability and online reward performance in the form of regret. This thesis concentrates on the zero-shot meta RL setting and aims to bridge the gap between data-driven meta RL methods and theoretically grounded online/adaptive control. It leverages the theoretical foundations in online learning [8] and adaptive control theory [9], as well as the advancements in meta RL [10, 1, 4] to introduce no-regret (sublinear) algorithms for control-affine dynamical systems. Such systems arise frequently in practice and capture mild nonlinearities through a control-affine structure, where the system dynamics depend linearly on the control input and affinely on the state through a known functional form. A gray-box modeling approach is adopted: the structural form of the system dynamics is assumed known, while key parameters—including the system matrices and additive non-stochastic disturbances—remain unknown. This modeling choice balances expressiveness and tractability, enabling both practical applicability and rigorous theoretical analysis. In addition to reinforcement learning and online learning methods [11, 12], the thesis will draw on optimal control results for control-affine systems [13, 14]. The thesis will explore two fundamental directions of synthesizing RL algorithms: • Model-free policy gradient methods, where the control policy is updated directly online without explicitly identifying a system model. An example of this approach is given in [7], where an affine policy is adapted online via gradient- based updates. • Model-based indirect methods, where the system dynamics and uncertainty are first learned online, and the control policy is subsequently derived using a certainty-equivalence principle. A representative example is the PLOT algorithm [6], which employs a receding horizon controller based on recursively updated model estimates. **By unifying ideas from meta RL, online optimization, and adaptive control, this thesis aims to develop policies that adapt online in a zero-shot setting while providing strong theoretical guarantees on stability and regret.** [1] J. Beck, R. Vuorio, E. Zheran Liu, Z. Xiong, L. Zintgraf, C. Finn, and S. Whiteson, “A survey of meta-reinforcement learning,” arXiv e-prints, pp. arXiv–2301, 2023. [2] A. Belmonte-Baeza, J. Lee, G. Valsecchi, and M. Hutter, “Meta reinforcement learning for optimal design of legged robots,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 12134–12141, 2022. [3] S. Belkhale, R. Li, G. Kahn, R. McAllister, R. Calandra, and S. Levine, “Model-based meta-reinforcement learning for flight with suspended payloads,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1471–1478, 2021. [4] Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel, “Rl2: Fast reinforcement learning via slow reinforcement learning,” arXiv preprint arXiv:1611.02779, 2016. [5] L. Zintgraf, K. Shiarlis, M. Igl, S. Schulze, Y. Gal, K. Hofmann, and S. Whiteson, “Varibad: A very good method for bayes-adaptive deep rl via meta-learning,” arXiv preprint arXiv:1910.08348, 2019. [6] A. Tsiamis, A. Karapetyan, Y. Li, E. C. Balta, and J. Lygeros, “Predictive linear online tracking for unknown targets,” in International Conference on Machine Learning, pp. 48657–48694, PMLR, 2024. [7] A. Karapetyan, D. Bolliger, A. Tsiamis, E. C. Balta, and J. Lygeros, “Online linear quadratic tracking with regret guarantees,” IEEE Control Systems Letters, vol. 7, pp. 3950–3955, 2023. [8] N. Cesa-Bianchi and G. Lugosi, Prediction, learning, and games. Cambridge university press, 2006. [9] A. M. Annaswamy, “Adaptive control and intersections with reinforcement learning,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 6, no. 1, pp. 65–93, 2023. [10] C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Inter- national conference on machine learning, pp. 1126–1135, PMLR, 2017. [11] M. Simchowitz and D. Foster, “Naive exploration is optimal for online lqr,” in International Conference on Machine Learning, pp. 8937–8948, PMLR, 2020. [12] A. Wagenmaker and K. Jamieson, “Active learning for identification of linear dynamical systems,” in Conference on Learning Theory, pp. 3487–3582, PMLR, 2020. [13] Z. Aganovic and Z. Gajic, “The successive approximation procedure for finite-time optimal control of bilinear systems,” IEEE Transactions on Automatic Control, vol. 39, no. 9, pp. 1932–1935, 2002. [14] Z. Yuan and J. Cort´es, “Data-driven optimal control of bilinear systems,” IEEE Control Systems Letters, vol. 6, pp. 2479–2484, 2022

1. Perform literature review of state-of-the-art meta RL and online control methods, 2. Get familiar with the SIMULINK/Python simulation of a test environment to test and validate derived algorithms, 3. Implement existing methods, such as [4, 5, 7, 6] on the simulator and judge their performance, 4. Propose an improved algorithm that is suited for control-affine/bilinear systems, 5. Provide theoretical guarantees in the form of regret, under suitable assumptions, 6. Verify the proposed method in simulation, 7. Prepare the final report and presentation **Qualifications** • Good programming skills (Python, Matlab) • Good understanding of Control and RL foundations **Arrangements** The project will be co-supervised with Belimo Automation AG.

Please send an email to: aren.karapetyan@belimo.ch, and efe.balta@inspire.ch

Object manipulation with contact-rich interations involves switching between contact and no-contact modes. Open-loop execution of stiff position control fails under these hybrid dynamics by ignoring dynamic coupling, by being too slow to solve the combinatorial planning of optimal motion sequences, and by being inaccurate under occlusion and physical uncertainties. Thus, the motivation for robust, fast feedback controllers, ideally which address the impracticality of modeling changing environments. Several classes of feedback-based control strategies have been proposed. Compliance-based strategies, such as impedance control, avoid crashing by adjusting control magnitude based on resistance. Extending the idea, variable impedance control adapts online based on safety and dexterity (precision) requirements. Recent work [1] learns a general representation of variable impedance expert demonstrations through inverse reinforcement learning (IRL) to generate new policies with policy gradient reinforcement learning (RL), while [2] uses probabilistic ensembles as a variable impedance model within model predictive control (MPC). Beyond compliance based strategies, hybrid force-motion control explicitly jointly satisfies constraints needed to maintain contact during manipulation. [3] plans over a predefined skill set to specify force and velocity targets, which are then tracked using a low-level torque controller inconveniently requiring careful parameter tuning. [4] instead learn low-level force control parameters with RL, integrating RL into both hybrid force position control and admittance controllers. Optimization-based formulations such as MPC-based approaches offer the advantage of incorporating look-ahead planning over contact modes. [5] trains with supervised learning a classifier to select contact mode sequences offline, then solves a fast reactive controller online. [6] devises a mode-invariant, contact-implicit MPC formulation using linear complementarity contact formulation and bilevel trajectory planning. [1] X. Zhang, L. Sun, Z. Kuang, and M. Tomizuka, “Learning variable impedance control via inverse reinforcement learning for force-related tasks,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2225–2232, 2021. [2] A. S. Anand, J. T. Gravdahl, and F. J. Abu-Dakka, “Model-based variable impedance learning control for robotic manipulation,” Robotics and Autonomous Systems, vol. 170, p. 104531, 2023. [3] J. Liang, X. Cheng, and O. Kroemer, “Learning preconditions of hybrid force-velocity controllers for contact-rich manipulation,” in Proceedings of The 6th Conference on Robot Learning (K. Liu, D. Kulic, and J. Ichnowski, eds.), vol. 205 of Proceedings of Machine Learning Research, pp. 679–689, PMLR, 14–18 Dec 2023. [4] C. C. Beltran-Hernandez, D. Petit, I. G. Ramirez-Alpizar, T. Nishi, S. Kikuchi, T. Matsubara, and K. Harada, “Learning force control for contact-rich manipulation tasks with rigid position- controlled robots,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5709–5716, 2020. [5] F. R. Hogan, E. R. Grau, and A. Rodriguez, “Reactive planar manipulation with convex hybrid mpc,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), p. 247–253, IEEE Press, 2018. [6] S. Le Cleac’h, T. A. Howell, S. Yang, C.-Y. Lee, J. Zhang, A. Bishop, M. Schwager, and Z. Manch- ester, “Fast contact-implicit model predictive control,” IEEE Transactions on Robotics, vol. 40, pp. 1617–1629, 2024.

The project will investigate low-level control approaches for achieving desired carton configurations based on estimated state feedback. Cartons will be modeled as articulated structures with com- pliant joints, and control objectives will be defined in terms of target fold angles, panel poses and contact constraints. The student will explore model-based and optimization-based control formula- tions, such as hybrid force–position control, impedance control, or constrained optimal control (e.g. constrained MPC), to regulate interaction forces and motion under uncertainty. The developed con- trollers will be validated on a robotic packaging setup, with experiments conducted on-site at the Swiss Cobotics Competence Center (S3C) in Biel.

Please send your resume/CV (including lists of relevant publications/projects) and transcript of records in PDF format via email to Paula Stocco (pstocco@control.ee.ethz.ch), Dr. Efe Balta (efe.balta@inspire.ch), and Dr. Hakan Girgin (hakan.girgin@s3c.swiss)

**Background** Energy communities are primarily designed to increase local renewable utilization and collective self-consumption. However, in communities with high photovoltaic (PV) penetration, internal demand is often insufficient to absorb peak generation, particularly during midday hours. As a result, surplus electricity is exported to the grid at relatively low feed-in remuneration, limiting the economic value captured within the community. Recent research has proposed extending community charging infrastructure to external electric vehicles (EVs) during peak PV production as a means to improve surplus utilization. While such approaches demonstrate potential gains in local renewable consumption, they typically focus on specific asset types and tariff adjustments. More broadly, electrified mobility introduces a range of mobile electricity consumers, such as shared EV fleets or mobile battery-based services, that operate across spatial and institutional boundaries. These assets possess flexible demand or storage capabilities that could, in principle, absorb surplus generation. However, integrating such mobile consumption into energy community frameworks raises questions regarding boundary definition, economic viability, and regulatory compatibility. A systematic assessment of how mobile energy assets can contribute to community-level renewable utilization is therefore needed. **Project description** This project investigates how energy communities can expand their effective operational boundaries by coordinating with mobile electricity consumption to absorb PV surplus generation. The central research question is: How can energy communities integrate suitable forms of mobile electricity consumption to enhance renewable utilization while ensuring economic feasibility and regulatory compatibility?

(1)Develop an evaluation framework to classify mobile electricity consumers based on flexibility, mobility constraints, controllability, infrastructure needs, economic incentives, and regulatory compatibility. (2) Analyze alternative coordination mechanisms (e.g., open-access charging, contractual partnerships, aggregator models) within the Swiss LEC institutional and regulatory context. (3) Build a techno-economic modelling framework to quantify impacts on grid interaction, infrastructure use, and stakeholder cost-benefit distribution under different mobility-integration scenarios. (4) Synthesize findings to identify implementation barriers, regulatory constraints, and business opportunities for expanding energy community boundaries through mobile energy assets.

Dr. Na Li (na.li@empa.ch); Manon Ratinaud (Manon.Ratinaud@empa.ch); Dr. Binod Koirala (Binod.Koirala@empa.ch)

Switzerland provides a safe haven for the safeguarding of cultural property threatened by armed conflicts, disasters, or emergencies (Safe Haven for Cultural Property). This safeguarding has traditionally been done visually on microfilm stored in bunkers. The safeguarding will now be transitioned to a digital form, utilizing a cloud solution. It is expected to reach the same level of protection and will be considered as critical infrastructure within Switzerland. The master’s thesis will conceptually design how these decentralised data centre can be integrated and utilised within different types of buildings [1]. There is a strong emphasis on achieving a technical solution that is both redundant and sustainable. Different types of buildings with different energy demands will be modelled to outline the integration of data centres.

Based on this framing, various research directions can be explored, each with specific research questions. Potential research questions are: i. How does a spatial distribution of data centres affect local electricity and heat networks in urban energy systems, also considering seasonality? ii. How can regulatory and policy constraints influence the optimal integration of decentralised data centres in urban buildings in Switzerland? iii. How could an applied scenario including a decentralised “Culture Cloud” be modeled to represent resilient and sustainable digital infrastructure?

Jenny Hansson, jenny.hansson@empa.ch

**Background:** The transition to renewable energy and the increasing frequency of extreme cli-mate events challenges the resilience of residential energy systems. Assessing how different building types, heating systems, and flexibility assets (such as solar PV panels and electric vehicles) respond to extreme scenarios is crucial for ensuring energy security, minimizing emissions, and maintaining occupant comfort and affordability. A systematic, simulation-based resilience assessment framework integrating building models and Python-based analytics can provide actionable insights for energy system planning and policy. **Project Tasks:** 1) Framework Development: The idea here would be to extend an existing framework consisting of a group of residential houses with heterogeneous archetypes (including heat pumps, boilers, EVs, storages, PV, and others). The extension of the framework would mostly be focused on more accurate reflection of the U-values and building properties based on 9 different building archetype definitions for SFH and MFH. In addition, the modelling of the extreme scenarios (heat waves, cold spells, financial crisis) would be an addition to this framework. The information for the extreme scenario modelling would be obtained from the past semester project that investigated the defining and assessment of heat wave and cold spell scenarios in Switzerland. 2) Scenario Analysis: The goal here would be to run simulations for various combinations of building types, heating systems, flexibility assets, and extreme scenarios. Additionally, to be able to compare and quantify the resilience of different configurations, the framework should provide outputs for energy use, emissions, thermal comfort, energy cost, and flexibility. 3) Performance Assessment: Analyze and visualize the results by comparing the resilience of different configurations under extreme conditions. Potentially it would be interesting also to identify trade-offs and synergies between energy performance, comfort, cost, and flexibility. 4) Reporting: Final part of the project would focus on documenting methods, results, and key findings.

1) Extend the existing framework to integrate building archetypes, heating systems, extreme scenarios, and flexibility assets. 2) Quantify the impacts of these factors on key performance indicators: energy use, emissions, thermal comfort, energy cost, and system flexibility. 3) Demonstrate the framework on representative Swiss residential cases.

Arnab Chatterjee (arnab.chatterjee@empa.ch)

The proposed project will develop a common database of metering and sub-metering data from Swiss households. This data will be used to develop NILM algorithms and customer segmentation models. The project will also evaluate the demand flexibility potential of Swiss households and develop targeted demand flexibility programs. The project will develop new tools and knowledge that can be used to design and implement effective demand flexibility programs in Switzerland. The project will also contribute to the development of a more reliable and sustainable power system. The key steps envisioned for the completion of the project are as follows: 1. Literature review: A review of the literature on NILM, customer segmentation, and demand flexibility in the context of the Swiss energy sector will be conducted. This will provide a foundation for the development of the proposed framework and database. 2. Data collection and preparation: A common database of metering and sub-metering data from Swiss households will be prepared. The data will be collected from open-source datasets, collaboration partners, and Nest LL. The data will be cleaned, preprocessed, and standardized to ensure consistency and quality. 3. NILM algorithm development and validation: Popular NILM models found in the literature will be tested in the context of Swiss households to disaggregate energy consumption data into individual appliance loads. 4. Customer segmentation modeling: A customer segmentation model will be developed using the NILM-disaggregated data. The model will be used to identify different groups of customers based on their energy consumption patterns.

1. Development of black-box household model: Black-box models for household appliances will be developed using the NILM-disaggregated data and customer segmentation model. The models can be used to simulate and predict energy consumption under different conditions. 2. Demand flexibility analysis (Optional): The impact of demand flexibility on power system operation will be analyzed using the customer segmentation model, NILM-disaggregated data, and occupant comfort criteria.

Dr. Arnab Chatterjee - arnab.chatterjee@empa.ch

The project aims to categorize the extreme events and analysis their impact on flexibility provision particularly the demand side in the Swiss context. The envisioned steps for the project are as follows: 1) Literature Review: Conduct a thorough literature review to understand current methodologies and findings related to flexibility provision and the impact of extreme events on the power system. This review will help you identify gaps in the research and potential methodologies we can apply to our study. 2) Data Collection: Collect information on extreme events, including their type, duration, severity, and geographical impact and variations. Any additional data on the fluctuations in flexibility provision (specific aspect) during past extreme events would be beneficial. 3) Categorization Framework: Categorize the scenarios based on several criteria, such as the probability of occurrence, potential severity, and expected socio-economic impact. 4) Quantitative Assessment: Use the collected data and chosen methodology to quantitatively assess the impact of extreme events on flexibility provision. It could be achieved through statistical analyses, machine learning models, or simulation studies. 5) Case Studies: Conduct case studies on specific extreme events in Switzerland to provide a detailed analysis of the impact on flexibility provision. 6) Documentation: Document the entire process and findings in a comprehensive report.

arnab.chatterjee@empa.ch

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The increasing integration of power-electronics-based resources is significantly altering power system dynamics. Modern grids are characterized by complex interactions between subsystems, including network dynamics and converter-based devices. Impedance-based analysis provides a powerful framework for both stability assessment and control design in such systems. In practice, the dynamic behavior of the power grid is continuously changing and is generally unknown from the perspective of converter nodes. Online grid impedance identification is therefore essential to enable real-time stability analysis and control design. Traditional single-port dynamic impedance identification methods focus on a single interconnection point of a converter-based resource. These methods assume an isolated identification scenario, where only one agent conducts the identification at a time, and do not consider the effects of simultaneous identification by multiple agents. Furthermore, multi-port scenarios, where grid data from multiple ports is used to model the interconnections between multiple sources, remain largely unexplored. This thesis addresses these gaps by investigating multi-agent grid impedance identification scenarios in three-phase power systems. Both global multi-port and locally simultaneous multi-agent single-port identification frameworks will be developed and systematically compared. The study will also explore downstream applications such as stability assessment and control design based on the identified dynamic grid impedances.

- Introduce the concept of dynamic grid impedance modeling and its relevance in three-phase power systems for stability studies and control design. - Analyze relationships between different grid impedances (global multi-port vs. multiple local single-port) and assess the influence of grid topology on these relationships. - Develop and study system identification frameworks for both global multi-port and multi-agent single-port scenarios. Investigate suitable excitation methods and compare the identification accuracy and reliability of the two approaches. Implementation will be conducted in MATLAB/Simulink. - Apply the identified dynamic grid impedances to a downstream task, such as stability analysis, oscillation damping, or control design. The application will be implemented and evaluated in MATLAB/Simulink.

Dr. Mohamed Abdalmoaty (mabdalmoaty@ethz.ch) Dr. Verena Häberle (verenhae@ethz.ch) _Remark:_ This project is intended as a master thesis and aims to strengthen collaboration between the Automatic Control Laboratory (IFA) and the Power Systems Laboratory (PSL) at ETH Zurich. Depending on the student's interest, we can provide help to publish the results in a conference paper. _Background:_ Competitive candidates will have a background in systems & control, signal processing, and/or power systems/electronics. A prior experience or coursework related to grid-connected converters or system identification would be highly beneficial. Proficiency in MATLAB/Simulink is required for implementing and testing the methods.