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: Tobias Gebhard
Earliest start: 01.03.2025
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