Student Projects

This semester/master thesis project aims to investigate the costs and benefits of local multi-energy systems in enhancing resilience and thereby providing a secure electricity and heat supply to end-users. To this aim, the in-house developed multi-energy system design optimization model needs to be enhanced such that component failures from multiple failure modes (such as random failures, from hazards such as snowstorms/windstorms) and variable repair time are characterized and incorporated as a stochastic input to the design problem. The final tasks of the project vary depending on the type of project (semester or master thesis), and they are highlighted in the task description.

Researchers use energy system models, such as Nexuse, to study future scenarios for the power system. With this model, we can study the impact of policies or new technologies on the Swiss electricity system in terms of system costs. When analyzing system costs, it is important to properly compute the costs and revenues associated with electricity imports and exports. That’s what this semester project is about.

This master's thesis or semester project investigates the communication challenges within Virtual Power Plants (VPPs) and their impact on Fast Frequency Response (FFR) in the context of transitioning energy landscapes. With a focus on Distributed Energy Resources (DERs) and the emergence of Fast Frequency Response (FFR) as a critical ancillary service, the study delves into the vulnerabilities posed by the current Border Gateway Protocol (BGP) based internet architecture. The proposed solution involves evaluating the effectiveness of the SCION architecture in mitigating these challenges. Through simulation-based analysis, the project aims to formulate a cyber-physical model for FFR services within VPPs and assess the communication requirements under various scenarios. Ultimately, the goal is to optimize FFR performance while considering future developments in power grid and communication networks, thus ensuring grid stability in a rapidly evolving energy landscape.

Electric vehicles (EVs) are vital to net-zero emissions transportation, with batteries as the core technologies. Batteries typically account for 30% to 40% of the value of an EV , and the race to net zero will focus attention on the security of battery supply chain. The battery supply chain consists of several segments, from resource production to material, component, and technology manufacturing, which involves global economies interacting closely. To simulate and optimize the global supply chain of batteries to 2030, this master’s thesis aims to develop an agent-based model for global regions and analyze the impact of supply chain transition on economic, social, and environmental factors.

Health assessment of power transformers is important for ensuring timely maintenance and situational awareness of power system operators. Traditional methods that are popular in the literature can provide conflicting predictions for the same measurement, and exhibit varying accuracies for different operating conditions. Machine learning and deep learning-based approaches, on the other hand, offer black-box solutions and face skepticism by the industry. This master's thesis aims to develop an approach that offers the interpretability of features and the ease of an automated algorithm for feature crafting. The student will get the opportunity to work on the development of the algorithm made up of evolutionary algorithm and symbolic methods, and its evaluation on real-life data collected from devices in Switzerland.

This master's thesis aims to understand the socio-economic impact on the recovery of blackout events. The student will investigate the relationship between the power outage frequency, magnitude, and recovery and vulnerable communities in Italy. This project will build upon ongoing RRE research at the intersection of reliable electricity systems and energy justice.

This master's thesis aims to improve power grid operation by considering the risk of cascading failures. The goal is formulating a risk-​informed Optimal Power Flow (OPF) model, using a metric called a risk curve. A previous study developed a faster way to evaluate risk. The goal is to integrate this approach into the OPF for more efficient and secure grid operations.

The simulation of water distribution systems is a powerful tool for understanding how these systems behave under different disruption scenarios, which would otherwise be too costly to reproduce in the real world. Although Python packages are readily available to model water systems, these software require accurate water network data in input, which are often unavailable to infrastructure analysts. In order to facilitate the usage of simulation software by minimizing data inputs, the aim of this master's thesis is to build a model for the generation of water networks by combining open-source and geo-referenced data (e.g., from OpenStreetMap).

This Master thesis project will identify and develop frameworks to characterize the reliability of autonomous vehicles. Demonstrating a sufficient level of reliability and compliance with risk targets will require advancing methods to cope with the complexity of such driverless systems.