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: 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.