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

The efficient design of low-voltage networks is a difficult task. Over-dimensioning of electrical equipment is economically costly, especially in times of high transformer prices. However, under-dimensioning can lead to overloads, equipment damage, and local blackouts. Current practices for grid planning and capacity design have two drawbacks. First, they usually consider a fixed value as limit for the aggregated load, whereas electric loads need to be seen as probabilistic in nature. Second, the type of connected consumers, their individual consumption patterns (e.g. daily/seasonal), and load correlation with other consumers are often neglected, but have a significant impact on the maximum load.
In this thesis, a new methodology for optimal grid planning and configuration is developed. This concerns for example the capacity design, the geographical transformer placement, and switch placement/configuration. The approach is based on analytical, multivariate statistical modeling. To test and evaluate the method, a data analysis of electricity usage patterns of diverse consumers (e.g. residential, commercial, retail, etc) is carried out. Different datasets can be used for timeseries and correlation analysis, e.g. [1]. The developed algorithm shall yield an optimal solution based on a suitable chosen metric.
Requirements:

• Basic Knowledge in (multivariate) statistics
• Interest in statistical modeling and data analysis
• Attendance in lecture “Data-driven Modeling / Machine Learning & Energy” helpful
• Attendance in lecture “Energy management & Optimization” helpful

[1] Schlemminger et. al.: Dataset on electrical single-family house and heat pump load profiles in Germany Link

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

Energy planning models are mathematical optimization models, typically formulated as linear programming problems. These models are commonly used for large-scale, real-world applications such as national-level energy planning. Solving such large-scale problems can take several hours even on powerful computers. Additionally, uncertainties in input data—such as fluctuating prices or the variable availability of renewable resources like wind and photovoltaic (PV) plants—require modelers to design multiple scenarios and solve the model for each. However, the heavy computational burden often limits the number of scenarios that can realistically be run.
One way to address this challenge is by approximating the input-output mapping of these optimization models using computationally lighter alternatives, such as neural networks. These approximations are known as surrogate models[1]. While surrogate models have been applied in areas like optimal power flow, their use in energy planning models is relatively new and not yet thoroughly explored [2].

[1] Van Hentenryck, Pascal. "Optimization Learning." arXiv preprint arXiv:2501.03443 (2025).
[2] Prina, Matteo Giacomo, et al. "Machine learning as a surrogate model for EnergyPLAN: Speeding up energy system optimization at the country level." Energy 307 (2024): 132735.
Prerequisites:

• Knowledge of Machine Learning
• Understanding of Linear Optimization
• Proficiency in Python or Julia

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.