Supervisor: Carolin Ayasse
Earliest start: immediately
Type: Bachelor Thesis

Spatial aggregation, and the resulting copper plate assumption, is a common simplification in energy systems modeling. This thesis should examine under which conditions using the copper plate assumption might lead to significantly different outcomes compared to spatially resolved models. The research begins with a comprehensive literature review to identify key factors in the urban heat sector that could markedly influence optimization results depending on the chosen spatial resolution.
Using the CESM optimization framework ( https://github.com/EINS-TUDa/CESM/tree/main), two distinct models of the urban heat sector will be developed. One model will adopt the copper plate assumption by aggregating heat sources and demands over the entire area, while the other will incorporate spatial resolution to account for local variations. The comparative analysis will focus on parameters such as heat demand distribution, network losses, and localized renewable resource potentials to determine how these factors contribute to discrepancies between the two modeling approaches.
The expected outcome of this study is to establish a methodology for comparing aggregated and spatially resolved models, and to identify specific scenarios where the simplified aggregation is either appropriate or insufficient.

Supervisor: Sina Hajikazemi
Earliest start: 01.05.2025
Type: Bachelor Thesis

CESM (Compact Energy System Model[1] is a minimalistic and extensible framework used for large-scale energy system research. However, its usability is limited by the lack of a graphical user interface (GUI), making it less accessible to non-programming users. This thesis aims to design and implement a minimal GUI that simplifies key workflows such as model configuration, data input management, and result visualization.

The GUI will be developed using Python or Julia, focusing on simplicity and modularity to align with CESM’s existing design philosophy.

[1] Hajikazemi, S., & Barbosa, J. Compact Energy System Modeling Tool (CESM) (Version 0.0.9) [Computer software]. https://github.com/EINS-TUDa/CESM Prerequisites:

• Proficiency in either Python or Julia
• Familiarity with mathematical optimization techniques is beneficial but not mandatory.

Supervisor: Sina Hajikazemi
Earliest start: immediately
Type: Bachelor 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 (see github.com/EINS-TUDa/CESM) and exploring the results and insights it can provide to decision makers.
Project Tasks:

1. Understand the Modeling to Generate Alternatives (MGA) methodology.
2. Apply the MGA methodology to the German energy transition model using the Compact Energy System Modelling Tool (CESM).
3. Investigate the outcomes of the MGA implementation and identify the insights it offers for policymakers.
4. Evaluate the strengths, weaknesses, and obstacles associated with the MGA methodology.
Prerequisites: Proficiency in Python programming.

Learning Objectives: Through completion of this thesis, you will:

1. Gain basic knowledge of mathematical programming techniques necessary for basic optimization tasks relevant to energy planning models.
2. Develop a fundamental understanding of energy planning models, including their components, basic methodologies, and applications in energy policy analysis.
3. Learn basic skills in reporting and justifying the outcomes of energy planning models, including simple interpretation of findings and basic assessment of model validity.

These learning objectives are tailored to provide essential skills and knowledge suitable for bachelor-level students to engage meaningfully in energy policy analysis and decision-making processes.
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.

Supervisor: Sina Hajikazemi
Earliest start: immediately
Type: Bachelor Thesis

The increasing integration of renewable energy sources into energy systems requires the development of larger models with finer time resolutions, leading to very large optimization problems.
Scaling is an important preprocessing step that significantly affects solution speed and numerical stability.
Although solvers like Gurobi use general scaling algorithms for this purpose, they do not consider the specific structure of the model. Considering this, it is possible to develop tailored scaling algorithms for specific modeling systems. Bröchin et al. [1] formulate the problem of finding the optimal scales as a small MILP problem.
The student will review the general-purpose scaling algorithms currently used in mathematical optimization solvers. Theoretical Comparison: The student will theoretically compare these general-purpose algorithms with the approach presented in [1]. Implementation and Evaluation: The student will implement the method developed in [1] within CESM.jl [2], an educational compact energy system model developed at EINS.
Finally, the student will evaluate the effectiveness of this method compared to manual scaling performed by experts.

[1] Bröchin, Manuel, et al. "Harder, better, faster, stronger: understanding and improving the tractability of large energy system models." Energy, sustainability and society 14.1 (2024): 27.
[2] https://github.com/SinaHKazemi/CESM.jl

Supervisor: Helena Sax
Earliest start: immediately
Type: Bachelor Thesis

With the increasing number of converter-based renewable energy sources, the security of converter control systems becomes a major concern. Manipulation or misconfiguration of converter parameters (e.g., control gains or droop coefficients) can threaten grid stability. Grey-box converter models could offer an opportunity to identify such parameter changes from measurement data, without knowing the exact control structure of the converter.
Methodology

1. **Model setup:** Reproduce a simplified nonlinear grey-box converter model (see 10.36227/techrxiv.171639117.73358192/v1)
2. **Data generation:** Generate system response data for various operating conditions and include normal and manipulated scenarios
3. **Parameter estimation:** Use the grex-box model to perform parameter estimation based on system response data
4. **Rule-based anomaly detection:** Develop rule-based detection logic to identify if the estimated parameters were manipulated to destabilize the grid
5. **Evaluation:** Evaluate the approach for selected case studies (e.g., normal vs. manipulated converter control parameters).

Supervisor: Julia Barbosa
Earliest start: immediately
Type: Bachelor Thesis

Game-theoretic models provide a valuable framework for analyzing strategic interactions in economics, engineering, and the social sciences. In the energy sector, they are particularly useful for studying competitive behavior in energy trading as well as attack-defense dynamics in resilience analysis. Classical examples include Cournot, Bertrand, and Stackelberg competition, which can often be formulated as part of a broader class of equilibrium problems. These formulations offer a unifying mathematical structure for analyzing existence, uniqueness, and computation of equilibria. This thesis focuses on visualizing equilibrium problems to highlight their underlying mathematical structure and solution properties. For classical competition models, the student will develop small, illustrative examples with relevance to energy systems. Special emphasis will be placed on portraying equilibrium sets, best-response mappings, and geometric interpretations of variational formulations. The goal is to provide intuitive, visual representations that may help researchers in developing new solution algorithms.

Supervisor: Benedikt Grüger
Earliest start: immediately
Type: Master Thesis

Since modern power systems increasingly depend on power electronic converters accurate dynamical stability studies of distribution grids become more necessary. Reduced-order models are expected to contribute to velocity and scalability of simulation frameworks. Order reduction i soften based on time-scale separation arguments: Fast inner control loops and slower outer dynamics uncouple. However, this assumption may break down during transient events caused by large perturbations leading to erroneous conclusions on stability.
This thesis explores the dynamical interaction of controllers in grid-following converters. Using both a full-order Simulink model and reduced-order models, the system’s response to disturbances—such as voltage fluctuations at the point of common coupling—is analyzed. With this, we explore which type of perturbations lead to instability. Particular attention is given to the validity of time-scale separation and how different control layers influence one another during transients. The existing code base, developed in Matlab and Julia, is extended to support this analysis.
The research contributes to a deeper understanding of converter behavior under severe perturbations and informs the development of more accurate and reliable modelling approaches.
The following papers are considered a good starting point:

• Synchronization stability and multi-timescale analysis of renewable-dominated power systems (Ma et al. 2023)
• Voltage Dynamics of Current Control Time-Scale in a VSC-Connected Weak Grid (Zhao et al. 2016)
• Understanding Small-Signal Stability of Low-Inertia Systems (Markovic et al. 2021)

Supervisor: Carolin Ayasse
Earliest start: immediately
Type: Master Thesis

Common energy system models often fail to account for real-world barriers that limit the adaptability of energy systems to changing circumstances. These models tend to overestimate flexibility by assuming that the energy system can quickly and extensively adjust in response to new developments. This is especially evident in multi-stage energy system models, where some uncertainties are only revealed at a later stage. Previous worked showed, that in such models, the calculated regret, i.e., the additional cost of making planning decisions based on incorrect assumptions about the future, is often underestimated. This underestimation occurs because the model assumes an unrealistically high capacity for rapid adjustment once uncertainties are revealed.
In reality, energy systems face significant inertia due to various institutional, technical, and logistical constraints. These may include long planning and permitting timelines, slow approval processes, and extended construction durations. Such factors can substantially delay the implementation of corrective measures, thereby increasing the actual regret associated with incorrect initial assumptions.

The aim of this thesis is to:

1. Identify and analyze real-world factors that contribute to inertia in energy system transformation.
2. Implement inertia-inducing dynamics into a two-stage energy system model, focusing on the most impactful factors.
3. Define a set of future scenarios to represent uncertainty
4. Evaluate the regret based on a previously developed evaluation framework for different scenarios
5. Assess how increased system inertia affects regret

Supervisor: Etalytics
Earliest start: immediately
Type: Master Thesis

Mehr Informationen in der Ausschreibung

Supervisor: Etalytics
Earliest start: immediately
Type: Master Thesis

Mehr Informationen in der Ausschreibung

Supervisor: Helena Sax
Earliest start: immediately
Type: Master Thesis

The increasing integration of renewable energy sources challenges power system operation. Decentralized renewable energy generation lead to an increase in average transmission distances and a higher utilization of the transmission grid to balance geographical mismatches between supply and demand. Maintaining system security requires costly redispatch measures. Corrective redispatch offers a way to increase grid operation efficiency by allowing higher line loadings after contingencies, e.g. a line outage. To comply with pre-contingency line limits again, corrective actions, e.g. the adjustment of controllable assets after a contingency to relieve overloads, are taken. However, conventional centralized corrective redispatch decisions often suffer from communication delays.
Concept and Objectives
This work investigates **local control laws** for corrective (also known as curative) redispatch actions, which can provide **faster system responses** compared to centralized decision-making. Such control schemes can predefine local rules that determine corrective actions directly based on local measurements, ensuring rapid reaction without requiring communication. Building on previous research on corrective redispatch via **power flow redirection using HVDC lines**, this thesis will **expand the work to include Grid Boosters**, which are large-scale battery storage systems capable of relieving transmission lines during overloads. Integrating these assets introduces new degrees of freedom but also new dynamics, particularly regarding their state of charge and response time.
Research Questions

1. How can existing local control laws for HVDC redispatch be extended to include **Grid Boosters**?
2. What is the impact of fast-acting local control on **transient stability** and dynamic system behavior?

Research impact This work will provide insights into:

• Improved **grid utilization** without requiring immediate infrastructure expansion,
• Reduced **redispatch costs**, enabling more **cost-efficient and sustainable grid operation**,
• Enhanced **stability and resilience** through faster local corrective action.

Supervisor: Tobias Gebhard
Earliest start: Immediately
Type: Master Thesis

With the rapid electrification of mobility and heating, as well as the rise of solar PV and flexible demand, distribution grids face unprecedented challenges. The increasing energy demand often requires expansion of infrastructure, which is costly. Existing design practices are based on simple, outdated rules, often leading to over-dimensioning of capacities because reliability margins cannot be quantified sufficiently. First, they usually treat power demand as a fixed maximum value, ignoring the inherently probabilistic nature of electric loads. Second, the type of consumers, their individual consumption patterns (e.g. daily/seasonal), and load correlations with other consumers are neglected, but can have a significant impact on the maximum load. For a successful and cost-efficient energy transition, new data-driven approaches for grid planning and optimization are needed.
This thesis aims to change the way, low-voltage (LV) grids are designed by developing a data-driven, probabilistic, correlation-aware methodology. An optimization problem for capacity design, topological transformer placement, and switch placement/configuration is defined and analyzed. The approach is based on multivariate statistical modeling (e.g. normal distribution). To test and evaluate the method, a data analysis of electricity usage patterns from heterogeneous consumers (e.g. residential, commercial, retail, etc) is carried out.

• Literature review of current practices and methods for LV grid planning
• Research for electricity demand datasets of small public/commercial buildings (e.g. shops, retail, hotel, bakery etc)
• Analyze consumer demand correlations of power demand time series data
• Develop a probabilistic methodology for capacity design, optimal transformer placement, and/or consumer partition by considering the correlations
• Implement and test the algorithm and evaluate the performance by comparing it with traditional, deterministic approaches

Requirements:

• Interest in optimization and statistical modeling
• Basic experience with programming and data analysis (e.g. Python)
• Attendance in lecture “Data-driven Modeling / datengetriebene Modellierung (Machine Learning)” helpful
• Attendance in lecture “Energy Management & Optimization” helpful
• If the thesis is done as B.Sc, very good grades and self-organized acquisition of the prerequisites are expected

Supervisor: Benedikt Grüger
Earliest start: immediately
Type: Master Thesis

With the ongoing large-scale integration of renewable energy sources, distribution grids are increasingly dominated by grid-following converters. Since converter control operates on significantly shorter time scales, evaluating their dynamic stability under varying grid conditions becomes crucial. In the presence of large disturbances, the non-linear behavior of converter dynamics becomes particularly important. In such cases, stability analysis can be based on the concept of the region of attraction—the set of initial states from which the system returns to its desired operating point without external intervention. Determining this region, however, is computationally demanding, especially for higher-order systems. To address this, several numerical methods have been developed to approximate the boundary of the region of attraction, such as the unstable manifold method and the interval stability method. This thesis investigates different numerical techniques for approximating the region of attraction boundary for a grid-following converter. In particular, these methods are compared against the standard Monte Carlo approach. Their practical applicability is demonstrated using two lower-order models of grid-following converters. Overall, this work contributes to ongoing efforts in converter modeling and provides insights into the impact of non-linear dynamics on the stability of future power systems.

• Stability threshold approach for complex dynamical systems (Klinshov, Nekorki, and Kurths 2015)
• Interval stability for complex systems (Klinshov et al. 2018)
• Domain of Attraction’s Estimation for Grid Connected Converters With Phase-Locked Loop (Zhang et al. 2022)
• Dominant Transient Equations of Grid-Following and Grid-Forming Converters by Controlling-Unstable- Equilibrium-Point-Based Participation Factor Analysis (Ma et al. 2024)

Supervisor: Sina Hajikazemi
Earliest start: immediately
Type: Master Thesis

One way to address uncertainty in energy system models is to explore near-optimal solutions, a process known as MGA [1]. There are multiple methods for generating these solutions. One method, the random vector method, randomly selects a new direction for the objective function[2]. This method has two advantages. First, it is parallelizable. Second, it effectively explores the near-optimal solution space.
The main disadvantage is that, since the two consecutive directions are not close enough, the method does not utilize the solver's warm start feature. Another disadvantage is that the near-optimal solution space is not normalized along all axes, so uniform sampling of directions from a unit ball does not lead to uniform exploration of the feasible space.
The student will develop methods for using warm start in optimisation solvers, for better estimation of the explored space, and for non-uniform sampling based on ranges of change along different axes.

[1] Price, James, and Ilkka Keppo. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models." Applied energy 195 (2017): 356-369.
[2] Lau, Michael, Neha Patankar, and Jesse D. Jenkins. "Measuring exploration: evaluation of modelling to generate alternatives methods in capacity expansion models." Environmental Research: Energy 1.4 (2024): 045004.

Supervisor: Sina Hajikazemi
Earliest start: immediately
Type: Master Thesis

Time-series aggregation (TSA) methods play a crucial role in reducing the computational complexity of energy system optimization models while preserving the accuracy of results [1]. Building on existing reviews and research, this thesis aims to explore novel approaches to TSA that address key challenges such as error bounding, handling extreme days, and optimizing aggregation for specific datasets like wind and solar profiles. The focus will be on developing methods that improve accuracy without significantly increasing computational burden.

[1] Teichgraeber, Holger, and Adam R. Brandt. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities." Renewable and Sustainable Energy Reviews 157 (2022): 111984.