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Open Opportunities

Data-Driven Demand-Side Flexibility Quantification Empa Urban Energy Systems The integration of distributed renewable energy sources into electric power grids is essential for transitioning to low-carbon energy systems. However, the intermittent nature of distributed renewable energy poses challenges to grid stability. Demand-side flexibility has emerged as a key solution, allowing consumers to adjust their electricity usage to help balance supply and demand. Buildings, as major energy consumers, offer substantial demand-side flexibility potential by shifting or reducing their energy use without compromising occupant comfort. To harness this potential, predictive energy management systems have been developed to optimize energy usage and quantify flexibility, typically represented as flexibility envelopes. These envelopes are used by distribution system operators (DSOs) for effective grid coordination. However, existing methods for flexibility quantification are largely optimization-based, requiring significant computational resources—especially problematic for real-time or rolling updates, which involve repeatedly solving complex models under varying conditions. This limits their scalability and responsiveness in practice. This research aims to develop a machine learning-based approach to predict flexibility envelopes using historical data. The goal is to provide real-time flexibility estimates with significantly reduced computational cost, making this method more practical for integration into smart energy systems. Engineering and Technology Master Thesis

Advanced Volume Control for Pipetting ETH Zurich Automatic Control Laboratory Improving volume control precision and robustness in automated pipetting remains a challenge, often limited by traditional indirect methods. This project explores direct volume control by leveraging internal air pressure measurements and the ideal gas law. Key obstacles include friction, pressure oscillations, varying liquid viscosities, evaporation, and liquid retention. Collaborating with Hamilton Robotics, the goal is to develop a robust control architecture for their precision pipette (MagPip) suitable for diverse liquids. The approach involves mathematical modeling based on sensor data, designing robust control strategies to handle nonlinearities and disturbances, and validating through simulation and real-world experiments. Control Engineering, Systems Theory and Control, Systems Theory and Control Semester Project

Demand-Side Flexibility Allocation for Buildings: An Optimization-Based Approach Empa Urban Energy Systems The increasing integration of distributed renewable energy sources into electric power grids has highlighted the critical need for demand-side energy flexibility to balance intermittent power generation and ensure grid stability. Buildings, as major energy consumers, present a promising source of flexibility by adjusting their energy consumption to support grid requirements while maintaining occupants' thermal comfort. To achieve this, each building must manage its room temperatures to minimize cost while adhering to technical and operational constraints, as well as fulfilling flexibility provisions. Our preliminary studies indicate that reinforcement learning (RL) is a promising control strategy that can effectively meet these objectives \cite{svetozarevic2022data}. However, in practice, aggregators often prefer to provide flexibility targets to groups of buildings rather than to individual units, making direct implementation of RL-based control challenging. This project aims to develop a mechanism for distributing flexibility provisions from a central system to individual buildings within a designated group. The goal is to allocate flexibility efficiently while maximizing social and economic welfare across all buildings in the category. Engineering and Technology Master Thesis

Feedback Optimization for Freeway Ramp Metering ETH Zurich Automatic Control Laboratory Online Feedback optimization (OFO) is a beautiful control method to drive a dynamical system to an optimal steady-state. By directly interconnecting optimization algorithms with real-time system measurements, OFO guarantees robustness and efficient operation, yet without requiring exact knowledge of the system model. The goal of this project is to develop faster OFO schemes for congestion control on freeways, in particular by leveraging the monotonicity properties of traffic networks. Engineering and Technology Master Thesis

Experimental Testing of Facade Components with Solar Simulator ETH Zurich Chair of Architecture and Building Systems As the main interface between interior and exterior environments, facades hold great potential to support the decarbonization of the building sector, while providing high visual and thermal comfort levels. The Solar Simulator and climatic chamber at the Zero Carbon Building Systems Lab [3] offer the opportunity to test and analyze the performance of envelope components, supporting the development of new facade system. After the completion of a first calibration phase, the lab infrastructure shall now be extended to a validated experimental setup for measuring solar gains, heat and light transmission through 1:1 scale facade components. The establishment of testing protocols and their validation is essential to enable the use of the laboratory infrastructure for future research projects. This thesis will develop a validated setup for measuring the angle-dependent solar, optical and thermal exchanges through facade systems. Building Science and Techniques ETH Zurich (ETHZ), Master Thesis

System Integration and optimization of Wind Energy Potentials in Switzerland Empa Urban Energy Systems Integrating wind energy into Switzerland’s future energy system remains a complex challenge, with optimal use of available potential in an integrated system still underexplored. This project aims to enhance wind energy assessments by incorporating spatial-temporal wind potential into energy system modeling and analyzing its role in Switzerland’s energy transition, including social aspects. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis

Data-driven Safe Control Design: A Certificate Function Approach ETH Zurich Automatic Control Laboratory Safety is a fundamental requirement for critical systems such as power converter protection, robotics, and autonomous vehicles. Ensuring long-term safety in these systems requires both characterizing safe behaviour and designing feedback controllers that enforce safety constraints. Control Barrier Functions (CBFs) have recently emerged as a powerful tool for addressing these challenges by defining safe regions in the state space and formulating control strategies that maintain safety. When the dynamical system is precisely modeled, a CBF can be designed by solving a convex optimization problem, where the state-space model is incorporated into the constraints. However, designing valid CBFs remains difficult when system models are uncertain or time-varying. More importantly, CBFs and control laws derived from inaccurate models may lead to unsafe behaviour in real-world systems. To overcome these difficulties, this project aims to develop a data-driven approach for constructing CBFs without relying on explicit system models. Instead, we will leverage behavioural systems theory to replace model information in the design program by persistently exciting data. The proposed method will be applied to output current protection in power converters or robotics collision avoidance. Engineering and Technology Master Thesis, Semester Project