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: Kirill Kuroptev
Earliest start: immediately
Type: Bachelor Thesis

This thesis deals with the critical area of frequency reserve mechanisms, which are essential for maintaining the stability of electricity grids. The objective is to present and partially simulate the activation of frequency restoration reserve providers in the European control reserve market. The activation function optimizes reserve providers' use to restore the electric grid's frequency in case of deviations.

The study includes the investigation of the frequency restoration reserve, where the technical requirements, the activation processes, and the participation of the market participants are elaborated.

Furthermore, the paper describes the formulation and constraints of the activation optimization function derived from the preceding technical and economic considerations. The resulting mathematical optimization problem is then implemented for a simplified example using programming languages such as Python, Julia, or Matlab.

To illustrate the identified activation optimization function, the thesis includes a numerical case study with different scenarios, such as limited cross-border capacity and increased volatility of control power demand. The results of this case study are analyzed to draw meaningful conclusions.

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

Electric transmission grids are critical energy infrastructures in every country. Intelligent attackers may attempt to damage specific components of the grid to cause maximum load shedding, and grid operators respond by solving the power flow problem to minimize load shedding using the remaining intact components. This raises the question: How vulnerable is the grid to adversarial attacks?

This project focuses on implementing and understanding an iterative optimization algorithm proposed by Javier Salmeron et al. [1] for the Electricity Network Interdiction problem. The algorithm formulates the problem as a bilevel programming problem, where the attacker aims to maximize load shedding, and the grid operator aims to minimize load shedding through optimal power flow in the attacked network.
Project Tasks:
1. Implementation: Implement the optimization algorithm in Python, ensuring clean and well-structured code.
2. Documentation: Provide clear and concise documentation for the implemented code, explaining key functions and algorithms.
3. Testing: Develop and execute test cases to validate the functionality of the implemented code.
4. Version Control: Utilize Git for effective code management and version control.
5. Presentation: Prepare a concise presentation that explains the project's objectives, methodology, and findings.

Prerequisites:
• Proficiency in Python programming.
• Basic understanding of mathematical optimization concepts.

Reference:
[1] Salmeron, Javier, Kevin Wood, and Ross Baldick. "Worst-case interdiction analysis of large-scale electric power grids." IEEE Transactions on Power Systems 24.1 (2009): 96-104.

Supervisor: Andreas Bott
Earliest start: immediately
Type: Bachelor Thesis

In order to reduce CO2 emissions, district heating grids will have to utilise decentralised heat sources such as large-scale heat pumps and industrial waste heat. Operating these so called 4th generation heating grids requires the development of new algorithms. If a grid has multiple feed ins, at one point the water from different supplies has to mix. Identifying this point is of special interest, as it is most likely to violate grid constrains, e.g. for the water to be to cold or the pressure to be to low. This thesis analyses a special measurement arrangement in order to identify this mixing point.

We assume to a grid segment with multiple uncertain demands. The position of the mixing point depends on the realisation of the demand. We assume, that the pressures and the mass flows at both inlets to the grid section are measured. This setup is especially interesting, as it allows to formulate the power estimation problem as a quadratic optimisation. The thesis should formulate and evaluate the results of this optimisation problem to evaluate the measurement setup.

Supervisor: Kirill Kuroptev
Earliest start: immediately
Type: Bachelor Thesis

Disruptive Technologien wie das "Internet of Everything" und das "Metaverse" führen zu einer erhöhten Auslastung der Telekommunikationsknotenpunkte, die sich im Energieverbrauch niederschlägt. Ein relevanter Anteil der eingesetzten Energie wird dabei für die Kühlung der Server verwendet. Da Server häufig über eine interne Kühlung, z.B. durch Lüfter, verfügen, diese jedoch nicht ausreichend und im Detail schwer zu bestimmen ist, werden verschiedene externe Kühltechnologien eingesetzt, um eine Überhitzung der Server zu vermeiden. Es stellt sich daher die Frage, ob der Gesamtenergiebedarf von Central Offices durch eine intelligente Wahl der externen Kühltechnologien reduziert werden kann, da die interne Kühlung der Server weniger Energie benötigt und somit Energieeinsparungen erzielt werden können.

Ziel der Arbeit ist es, eine empirische Studie für die Ermittlung des Zusammenhangs zwischen verschiedenen Kühlstrategien sowie dem Raumtemperaturniveaus und dem Gesamtenergieverbrauch von Telekommunikationsknotenpunkten zu konzipieren und zu erproben. Dazu sollen zunächst die für den Gesamtenergieverbrauch relevanten Parameter mit Fokus auf die klimatechnischen Komponenten recherchiert werden. Basierend auf den Recherchen soll ein holistisches Simulationsmodell der Telekommunikationsknotenpunkten erstellt werden, anhand dessen es möglich sein soll den Effekt verschiedener Kühlungsstrategien auf den Gesamtenergieverbrauchs abzubilden. Des Weiteren soll eine empirische Studie zur Ermittlung der Effektstärken der relevanten Parameter der Komponenten konzipiert werden. Die Methodik der empirischen Studie soll anhand des implementierten Simulationsmodells erprobt werden.

Die Konzeption der Studie soll unter Betrachtung eines Teststandorts eines großen deutschen Telekommunikationsunternehmens durchgeführt werden.

Supervisor: Kirill Kuroptev
Earliest start: immediately
Type: Bachelor Thesis

This thesis deals with exploring the hazard of grid frequency deviation and instability by dynamic load altering attacks. These attacks describe an adversary manipulating load in the power grid such that the frequency deviates substantially and/or even an instability in the frequency arises.
The thesis includes the reimplementation of an existing work[1] and enhancing it by a lag element and a saturation element to explore the system’s response and the system’s stability with realistic conditions. A potential method for this could be the application of the root locus-curve[2] to the linearized power grid system model. Hence, fundamental knowledge in control engineering is beneficial, as well as some first experiences with Matlab/Simulink. If desired, the necessary implementation can also be carried out in Python or Julia.
The student will learn about the frequency control in power grids[3] and gain experience in the threat of dynamic load altering attacks as well as its potential to destabilize the power grid’s frequency.
We support and recommend the possibility to split the thesis in a proseminar and bachelor thesis.

• [1]: S. Amini, H. Mohsenian-Rad and F. Pasqualetti, "Dynamic load altering attacks in smart grid," 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2015, pp. 1-5, doi: 10.1109/ISGT.2015.7131791.
• [2]: Tomislav B. Šekara, Milan R. Rapaić, “A revision of root locus method with applications”, Journal of Process Control, Volume 34, 2015, Pages 26-34, ISSN 0959-1524, https://doi.org/10.1016/j.jprocont.2015.07.007.
• [3]: Frequency containment reserve (FCR)

Supervisor: Andreas Bott
Earliest start: immediately
Type: Master Thesis

Die Wärmewende bedeutet für den Fernwärmesektor eine grundlegende Umstellung. Statt wie bisher die Wärme aus wenigen zentralen Heizkraftwerken zu verteilen gewinne dezentrale, fluktuierende (Ab-)Wärmequellen zunehmend an Bedeutung. Dieser flexible Betrieb erfordert neue Algorithmen um eine sichere Versorgung zu garantieren.

Eine besondere Herausforderung im Kontext von Wärmenetzen ist die relativ langsame Fließgeschwindigkeit des Wassers. Hierdurch kommt es zu einer erheblichen Verzögerung bis eine Betriebsentscheidung am Kraftwerk den gewünschten Effekt im Netz zeigt. Entscheidungen müssen daher vorausschauend getroffen werden und mögliche Entwicklung unsicherer Größen, beispielsweise die Entwicklung des Wärmebedarfs, vorab mitberücksichtigen.

Aus dieser Problematik lassen sich zwei Voraussetzungen für Netzmodelle ableiten.

1.      Die Modelle müssen die Zeitabhängigkeiten und Laufzeiten korrekt abbilden
2.      Die Rechenzeit muss relativ gering sein um in probabilistischen Algorithmen, z.B. in Monte Carlo Simulationen, eingebunden zu werden.

Klassische Simulationssoftware, wie Modelica, erfüllen zwar den 1. Punkt, sind aber zu rechenaufwendig für Echtzeitanwendungen.

In dieser Arbeit soll eine neuartige datengetriebene Modellierungstechnik reduzierter Ordnung vom Typ der neuronalen ODE angewandt werden, um ein genaues und schnelles Ersatzmodell der Simulationsmodelle zu generieren, das zudem einen geringen Rechenaufwand aufweist. Die Arbeit umfasst dabei einerseits die Konstruktion konventioneller Modelle in Modelica als Benchmark sowie zur Erzeugung von Trainingsdaten, andererseits auch Einbinden und Trainieren der datengetriebenen basierten Modelle in Python. Sie schlägt damit eine Brücke zwischen klassischer Modellierung und modernsten KI-basierten Ansätzen.