## Running theses and research assistant jobs

## Project seminar

Unfortunately, there is nothing available in the moment.

## Bachelor theses

**Student:** Jan Niklas Witt

**Supervisor:** Sina Hajikazemi

**Time period:** 08/01/2023 - 02/01/2024

**Type:** Bachelor Thesis

Energy Planning Models (EPMs) are critical tools for simulating and guiding decisions about multimodal energy systems in specific regions or countries. These models provide strategies to meet future demands and environmental targets, which are often used by large energy companies to negotiate with the government and other investors. However, there is a concern that these profit-driven companies may manipulate the input data of EPMs to align with their interests.

This problem can be modeled as a bilevel linear programming problem, with linear lower and upper-level problems. Although bilevel programming problems are challenging in general, there are algorithms that can effectively solve them in the linear upper and lower-level form.

In this bachelor thesis, the student will develop interesting and interpretable examples of this problem for real-world EPMs that are relevant to current debates in energy policy. The goal is to show the potential impact of adversarial attacks on EPMs and to highlight the importance of developing robust models that are resistant to such attacks. The developed examples can be used as case studies to illustrate the impact of this problem on energy policy decisions and raise awareness among stakeholders in the energy sector. Requirements

• Proficiency in python programming

• Understanding of the fundamentals of mathematical programming

• Familiarity with energy planning models

• Experience with mathematical programming languages

**Student:** Niyu Tong

**Supervisor:** Andreas Bott

**Time period:** 04/22/2024 - 09/25/2024

**Type:** Bachelor Thesis

Design and implementation of evaluation algorithms for optimizing the Preload Loss Test (PLT)

**Student:** Tan Phat Toni Tran

**Supervisor:** Benedikt Grüger

**Time period:** 05/01/2024 - 11/01/2024

**Type:** Bachelor Thesis

A key ingredient of stable AC transmission grid operation is phase angle stability. Orderly power transport is possible only if the current signals have same frequency. So-called inter-area oscillations, where generators of different regions start to oscillate against each other, pose a major threat to system stability. Without adequate mitigation measures, they can have catastrophic consequences, such as power line failures, desynchronization, and, finally, blackout. This electro-mechanical phenomenon can be analysed with the (non-linear) coupled swing equations, alias Kuramoto model. Interestingly, the corresponding differential equations, which describe the phase angle dynamics, are relevant for a vastly larger range of systems studied in physics, engineering, and biology. Given the relevance for today's energy transition towards renewable energies and the complexity of the system behaviour the study of grid oscillations is a fascinating field of research. This thesis explores the guiding principles of inter-area oscillations with the coupled swing model, a common dynamical power grid model. In particular, we are interested in the role of heterogeneous generator inertia in system control. Further steps may involve the development of design patterns for power system stabilizers. Experience in control theory, complex systems theory, and/or electrical engineering is advantageous. Successful completion requires mathematical maturity and robust programming skills in either Python, Julia, or C++.

**Literature**

- Investigation of impacts on the disturbance propagation in power systems (Semerow et al. 2016)
- Low frequency oscillations in the interconnected system of Continental Europe (Grebe et al. 2010)
- Dynamic Study Model for the interconnected power system of Continental Europe in different simulation tools (Semerow et al. 2015)

**Student:** Helena Sax

**Supervisor:** Andreas Bott

**Time period:** 04/04/2022 - 08/01/2022

**Type:** Bachelor Thesis

District heating grid can help achieving CO2-reduction goals by providing renewable heat in densely populated areas. In combination with power-to-heat applications they can also help to balance the fluctuating power supply by wind and solar feed in, since heat can be stored easily and cheaply in large quantities.

The relative low speeds at which water flows through the network lead to a high inertia of the distribution system and to a significant delay between the time actions taken at the powerplant, e.g. changing the supply temperature, and these changes influencing the consumers. Utilizing the full flexibility of district heating networks therefore requires predictive operation strategies and simulations which are able to incorporate uncertainties such as changes in demand patterns.

Quasi-Steady-State (QSS) power flow calculations simplify the simulations by neglecting time delays for changes in pressures and mass flow rates, which are much lower than the delays implied by temperatures propagating through the networks.

For a given demand time series, these models can be solved by iterative algorithms, which comes at a high computational cost. For sampling-based applications, the simulations are solved repeatedly for randomly sampled demands. We can exploit this randomness for solving the QSS-system by sampling from a proxy-distribution. If the proxy-distribution is close enough to the true distribution, the results can be adjusted afterwards without losing much sampling efficiency.

For the QSS-system the mass flows at the consumers place should be such a close proxy-distribution which provides advantages for the QSS-calculations. Fixing the mass flows allows to separately solve the hydraulic equations, which were assumed to act instantiations and can therefore be solved for each time step independently. The time coupling thermal equations can then be solved in a second step, removing the need to iterate between the thermal and the hydraulic system.

The task of this thesis is to develop a representation of the QSS-equations which is best suited for the two steps solving approach outlined above as well as a corresponding solving algorithm for the QSS-system. The algorithms should be implemented in Python utilizing the Tensorflow package.

This thesis can also be started as Pro-Seminar or Project-Seminar

## Master theses

**Student:** Ruben Chacon

**Supervisor:** Tobias Gebhard

**Time period:** 05/22/2023 - 05/21/2024

**Type:** Master Thesis

The topology of underground electrical distribution infrastructure is often not publicly available because it may contain sensitive information. However, for researchers it is valuable to work with real (or realistic) grid models/data, e.g. to study new optimization algorithms or conduct power flow calculations. Therefore, there is a need for methods that are able to generate synthetic grids in an automated way. For this, publicly available data like GIS (geographic information system) data from OpenStreetMap (containing overhead power lines, street courses, building locations and types…) as well as statistical consumption data can be used.

**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:** Luca Okubo Baudenbacher

**Supervisor:** Benedikt Grüger

**Time period:** 05/13/2024 - 11/10/2024

**Type:** Master Thesis