Student: Moritz Schoch
Supervisor: Benedikt Grüger
Time period: 11/18/2024 - 02/14/2025
Type: Project Seminars Bachelor
coming soon
Student: Mika Wittenberger
Supervisor: Hans Stenglein
Time period: 05/12/2025 - 10/13/2025
Type: Bachelor Thesis
Die steigende Verfügbarkeit von PV-Anlangen insbesondere bei Haushalten, die traditionell als Verbraucher im elektrischen Netzwerk angesehen werden, kann eine Herausforderung für die Kontrolle des elektrischen Netzwerks darstellen. Dabei kommt es häufig zu der Situation, dass die Haushalte nicht mehr nur Energie verbrauchen, sondern netto dezentral produzieren. Dies führt dazu, dass der Strom im elektrischen Netz nicht mehr in eine fixierte Richtung fließt, sondern bidirektional. Im Rahmen dieser Arbeit soll dieses Verhalten basierend auf der erwartbaren Leistungsflussrichtung in Python modelliert werden, um daraufhin lokale topologische Eigenschaften des resultierenden gerichteten Graphenen, wie bspw. den Knotengrad, für verschiedene Situationen in realen Netzwerken zu untersuchen. Die Fragestellung ist dann, wie sich die Verteilung dieser topologischen Eigenschaften qualitativ verändert, je nach der aktuellen Situation der verteilten Energieresourcen.
Student: Kai Harder
Supervisor: Julia Barbosa
Time period: 01/15/2024 - 01/15/2025
Type: Master Thesis
The energy transition will require new approaches to energy contracting. Currently, energy contracting is an option to reduce the supply risk of utilities and the energy cost risk of large energy consumers, and mostly neglects the coupling between the demand for different energy commodities. With the electrification of heat supply through heat pumps, and the increased variability of electricity prices due to the integration of variable renewable energy, new designs of energy contracting and tariff structures are required to ensure fair pricing for consumers and utilities. The proposed Master's thesis will focus on the design and evaluation of energy contracting structures that consider the coupling between heat and electricity generation and demand, using the university energy system as a study case.
Student: Fazeel Muhammad
Supervisor: Sina Hajikazemi
Time period: 03/01/2025 - 08/31/2025
Type: Master Thesis
Energy planning models are essential for analyzing energy and climate policies at national and global scales. However, these models face various uncertainties, categorized into uncertainties in input parameters, such as future fuel prices, and uncertainties in the structure of the model, such as the complexities and constraints inherent in different technologies. While methods such as global sensitivity analysis, stochastic programming, and Monte Carlo simulation address parameter uncertainties, they often overlook uncertainties in the model structure. In addition, policymakers are faced with considerations outside the scope of conventional modeling, such as political feasibility, regulatory challenges, and the timing of actions. As a result, policymakers may choose feasible but suboptimal solutions due to the challenges of quantifying intangibles in energy optimization models.
Modeling to Generate Alternatives (MGA), a technique borrowed from the operations research literature, is a valuable approach to address structural uncertainties inherent in energy planning models as well as uncertainties in input parameters. MGA efficiently explores the feasible region around the optimal solution and generates alternative solutions with maximum diversity. By providing a spectrum of viable options beyond the conventional optimal solution, MGA provides invaluable insights for policy makers. These alternative solutions shed light on trade-offs and considerations often overlooked in conventional energy planning models, enabling policymakers to make more nuanced and informed decisions amid uncertainty and real-world constraints. This thesis focuses on implementing this approach in a German energy transition model (EINS-TUDa/CESM) and exploring the results and insights it can provide to decision makers.
Project Tasks:
- Understand the Modeling to Generate Alternatives (MGA) methodology.
- Apply the MGA methodology to the German energy transition model using the Compact Energy System Modelling Tool (CESM).
- Investigate the outcomes of the MGA implementation and identify the insights it offers for policymakers.
- Evaluate the strengths, weaknesses, and obstacles associated with the MGA methodology.
- Gain basic knowledge of mathematical programming techniques necessary for basic optimization tasks relevant to energy planning models.
- Develop a fundamental understanding of energy planning models, including their components, basic methodologies, and applications in energy policy analysis.
- Learn basic skills in reporting and justifying the outcomes of energy planning models, including simple interpretation of findings and basic assessment of model validity.
References: [1] DeCarolis, Joseph F. "Using modeling to generate alternatives (MGA) to expand our thinking on energy futures." Energy Economics 33.2 (2011): 145-152.
[2] Brill Jr, E. Downey, Shoou-Yuh Chang, and Lewis D. Hopkins. "Modeling to generate alternatives: The HSJ approach and an illustration using a problem in land use planning." Management Science 28.3 (1982): 221-235.
Student: Lennart Herud
Supervisor: Kirill Kuroptev
Time period: 10/15/2024 - 03/15/2025
Type: Project Seminars Bachelor
This thesis deals with the critical area of frequency reserve mechanisms, which are essential for maintaining the stability of electricity grids. The objective is to present and partially simulate the activation of frequency restoration reserve providers in the European control reserve market. The activation function optimizes reserve providers' use to restore the electric grid's frequency in case of deviations.
The study includes the investigation of the frequency restoration reserve, where the technical requirements, the activation processes, and the participation of the market participants are elaborated.
Furthermore, the paper describes the formulation and constraints of the activation optimization function derived from the preceding technical and economic considerations. The resulting mathematical optimization problem is then implemented for a simplified example using programming languages such as Python, Julia, or Matlab.
To illustrate the identified activation optimization function, the thesis includes a numerical case study with different scenarios, such as limited cross-border capacity and increased volatility of control power demand. The results of this case study are analyzed to draw meaningful conclusions.
Student: Ran Bi
Supervisor: Julia Barbosa
Time period: 06/10/2025 - 11/09/2025
Type: Bachelor Thesis
Modeling the energy system of a country as large and diverse as China presents significant challenges due to its vast geographic scale, regional disparities in energy resources, complex infrastructure, and rapidly evolving energy mix. This bachelor thesis contributes to the development of an open data model of the Chinese energy system, aiming to provide a transparent and adaptable foundation for energy system analysis. By addressing data inconsistencies, integrating multiple regional datasets, and representing the structural complexity of the system, the thesis supports efforts to build a robust model that can serve as a basis for future research and scenario analysis.
Student: Toan Nguyen
Supervisor: Sina Hajikazemi
Time period: 11/05/2024 - 03/20/2025
Type: Bachelor Thesis
Electric transmission grids are critical energy infrastructures in every country. Intelligent attackers may attempt to damage specific components of the grid to cause maximum load shedding, and grid operators respond by solving the power flow problem to minimize load shedding using the remaining intact components. This raises the question: How vulnerable is the grid to adversarial attacks?
This project focuses on implementing and understanding an iterative optimization algorithm proposed by Javier Salmeron et al. [1] for the Electricity Network Interdiction problem. The algorithm formulates the problem as a bilevel programming problem, where the attacker aims to maximize load shedding, and the grid operator aims to minimize load shedding through optimal power flow in the attacked network.
Project Tasks:
1. Implementation: Implement the optimization algorithm in Python, ensuring clean and well-structured code.
2. Documentation: Provide clear and concise documentation for the implemented code, explaining key functions and algorithms.
3. Testing: Develop and execute test cases to validate the functionality of the implemented code.
4. Version Control: Utilize Git for effective code management and version control.
5. Presentation: Prepare a concise presentation that explains the project's objectives, methodology, and findings.
Prerequisites:
• Proficiency in Python programming.
• Basic understanding of mathematical optimization concepts.
Reference:
[1] Salmeron, Javier, Kevin Wood, and Ross Baldick. "Worst-case interdiction analysis of large-scale electric power grids." IEEE Transactions on Power Systems 24.1 (2009): 96-104.
Student: Helena Sax
Supervisor: Benedikt Grüger
Time period: 09/13/2024 - 03/17/2025
Type: Master Thesis
This thesis explores the applicability of physics-informed neural networks to grid-following inverter non-linear stability analysis. In concrete, we train the PINN with trajectories from the full-order inverter model on predicting inverter stability for perturbations in the reduced state space spanned by frequency and phase. Assessing the suitability of the method for this special time series prediction problem, we contribute to the development of new numerical methods for the power grid research community.
Student: David Lange
Supervisor: Kirill Kuroptev
Time period: 10/25/2024 - 03/14/2025
Type: Project Seminars Bachelor
Implementing an online anomaly detection algorithm for the power consumption of a electric vehicle parking lot.
Student: Agnes Engelter
Supervisor: Kirill Kuroptev
Time period: 12/06/2024 - 06/06/2025
Type: Master Thesis
The energy supply system is part of the critical infrastructure and has increasingly become the target of cyber attacks in Europe in recent years. The redispatch process is also a potential target for future cyber-attacks due to the digital transmission of data. An attack on the redispatch could cause major damage. It is therefore necessary to increase the resilience of the energy supply system to cyber-attacks, for example through IT security requirements for data transmission in redispatch.
Student: Eike Schuler
Supervisor: Carolin Ayasse
Time period: 08/12/2024 - 02/10/2025
Type: Master Thesis
Considering uncertainties in long-term energy system design planning is of great importance for a successful energy transition. For instance, the Municipal Heat Planning Law in Germany requires all municipalities in Germany to create a municipal heat plan. The creation of this plan requires the identification of an optimal decarbonization path for the municipality's heating sector by 2045. The character of the optimal path is significantly influenced by assumptions about future developments, e.g. the availability and price development of energy resources. In many cases, perfect foresight is assumed for these uncertain parameters, which may result in a significant increase in costs if the assumed scenario does not occur. Nevertheless, decisions for future developments of energy systems must be made at the present time in order to facilitate a successful energy transition. Two-stage robust optimization allows us to address this issue of uncertainty in energy system planning. It allows the identification of energy system development paths that will perform optimally under a range of potential future scenarios. The first-stage variables should represent variables of a shorter-term time period (e.g., 5-10 years), given that the development is strongly influenced by decisions taken today and that there is less uncertainty. Second-stage variables will represent variables in the far future and depend on different scenarios. This master thesis should develop an optimization model to identify a robust transformation path of the heat sector of a German municipality by taking different uncertain parameters into account. After the identification of relevant uncertainty parameters, an existing single-scenario energy system model should be adapted to identify a robust transformation path for the design of the municipal heat sector. The gained robustness shall be demonstrated with plausible examples.
