Open Theses

Bachelor Thesis

Supervisor: Sara Mollaeivaneghi
Earliest start: immediately
Type: Bachelor Thesis


The aim of this bachelor thesis is to assess the potential for recovering waste heat generated by Information and Communication Technology (ICT) equipment in local low-temperature district heating systems. This project will focus on investigating the technical feasibility and economic viability of implementing ICT waste heat recovery, considering both environmental and economic benefits. The work is conducted based on data from a large German telco company.

Supervisor: reLI
Earliest start: immediately
Type: Bachelor Thesis


The increasing penetration of renewable energy sources has led to the need for more flexible and responsive grid management. Battery energy storage systems (BESSs) have been identified as a promising technology to provide frequency reserve services to support the stability and reliability of the grid. In this thesis, we develop a BESS model to evaluate its performance and optimize its operation for providing frequency reserve services. The proposed model considers the electrochemical dynamics of the BESS, as well as its power and energy constraints. The optimization problem is formulated to maximize the revenue from frequency reserve services while ensuring the battery's integrity. Numerical simulations are performed to demonstrate the effectiveness of the proposed model and optimization approach.

More information and the contact e-mail adress can be found here

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: Kirill Kuroptev
Earliest start: immediately
Type: Bachelor Thesis


This bachelor thesis applies knowledge engineering from computer science to electrical engineering with the goal of improving understanding and management in the power system domain. The knowledge engineering task should be based on the development of an ontology, which involves the creation of structured representations of knowledge that facilitate effective information organization, sharing, and reasoning.

Applying ontology engineering fundamentals to the power system context, the study reviews existing ontologies, summarizes their contributions and scope, and identifies avenues for further exploration. In particular, an ontology for a virtual power plant (VPP) is developed. This VPP ontology represents the tasks and components of a distributed power plant and is based on the EPEX [1] and Open-energy [2] ontologies.

The thesis concludes with a reflection on the research approach, acknowledging limitations and challenges. It also envisages future enhancements for the ontology, suggesting avenues for ongoing refinement and extension.

Through the use of ontology-based knowledge representation, this research enhances the ability to understand and manage complexity within the energy system, fostering improved decision making, collaboration, and innovation in the energy sector.

Overall, the work provides deep insights into the various actors and functionalities of the electric power system, especially in the highly relevant context of virtual power plants.

[1]: https://ieeexplore.ieee.org/document/8071411
[2]: https://www.sciencedirect.com/science/article/pii/S2666546821000288

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.

Master Thesis

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


Energy Planning Models (EPMs) are essential 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 could manipulate the input data of EPMs to align with their interests.
This thesis aims to address this problem by formulating it as a bilevel mathematical programming model. The upper-level problem represents the adversary who attempts to alter the input data, while the lower-level problem corresponds to the EPM. While some algorithms can effectively solve the linear upper and lower-level form of this problem, more advanced and detailed EPMs require the use of binary variables, resulting in a much more challenging Bilevel Mixed Integer Linear Programming.
The student will review available methods for solving bilevel programming problems with binary variables and tailor these methods to the aforementioned adversarial attack, taking into account the size, special form, and other specifications of the EPM. The designed algorithms will be implemented, and the results will be interpreted. This study will contribute to developing robust energy planning models that are resistant to adversarial attacks, ensuring that decision-making processes are fair and transparent.
• Familiarity with energy planning models
• Proficiency in python programming
• Experience with mathematical programming languages

Supervisor: reLI
Earliest start: immediately
Type: Master Thesis


The increasing penetration of renewable energy sources has led to the need for more flexible and responsive grid management. Battery energy storage systems (BESSs) have been identified as a promising technology to provide frequency reserve services to support the stability and reliability of the grid. In this thesis, we develop a BESS model to evaluate its performance and optimize its operation for providing frequency reserve services. The proposed model considers the electrochemical dynamics of the BESS, as well as its power and energy constraints. The optimization problem is formulated to maximize the revenue from frequency reserve services while ensuring the battery's integrity. Numerical simulations are performed to demonstrate the effectiveness of the proposed model and optimization approach.

More information and the contact e-mail adress can be found here

Supervisor: Andreas Bott
Earliest start: immediately
Type: Master 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. Classical approaches are computational costly, due to the complex non-linear behaviour of district heating grids. AI based algorithms on the other hand are promising to deliver reliable and fast results.

This Thesis should investigate the optimal dispatch problem, i.e. the question which plant should produce heat at which temperature, such that all grid constrains are satisfied. This problem resembles a mixed integer, nonlinear optimisation problem, which is difficult to solve. The goal of this thesis is therefore, to learn the solution of the optimal dispatch problem directly based on the current demand situation.

The true cost function and boundary conditions are (practically) not differentiable. This leads to different approaches, which can be analysed within the scope of the Thesis:

-       Use Reinforcement algorithms to train the networks on the true loss function. As evaluating the cost function is still costly, this would require pre-identifying critical points to reduce computational costs.

-       Approximate the true cost function and boundary conditions with a differentiable model (e.g. another neural network) and use this ass a loss function during the training of the dispatcher

-       Learn the optimal dispatch and the description of the district heating grid simulations by employing a physics-aware Loss function

 

Supervisor: Mario Beykirch
Earliest start: immediately
Type: Master Thesis


An established approach to obtain operation schedules for energy systems under uncertainty is to first predict the uncertain residual demands and energy prices with a probabilistic forecast model that minimizes a statistical score such as the logarithmic score or the continuous ranked probability score. Then, in a second, separate step, an optimization problem is solved to determine the optimal schedule given the demand and price forecasts. However, this method is imperfect, particularly for typically finite, small sample sizes, where errors cannot be eliminated, and the statistical score does not necessarily accurately reflect the real task-loss after the optimization step. Consequently, a forecast selected (or even trained) using this task-loss may outperform another forecast with a comparable or worse statistical score in the actual optimization task.

Approaches to use the optimization result’s performance directly as a loss function in the model training exist, e.g., stochastic end-to-end learning, but are computationally expensive because the optimization must be solved in every training iteration. Task-specific score functions, conversely, only approximate the forecast’s performance in optimization tasks. They are simpler to compute since they allow us to assess the forecast quality without solving the actual optimization task. A task-specific score function can be estimated with a machine learning model and then employed as the loss function in the forecast training.

The goal of this thesis shall be to obtain and employ a task-specific score for a simplified case of a market schedule optimization given an uncertain residual demand and a power plant without start-up costs and to quantify the task utility improvement compared to statistical scores.

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


This master's thesis explores the potential of harnessing Large Language Models (LLMs) to transform user interactions with energy system models, with a focus on developing a Textual User Interface (TUI). The study investigates the interconnection between LLMs and Application Programming Interfaces (APIs) in order to facilitate a more intuitive and user-friendly means of engaging with complex energy system models. The research aims to bridge the gap between energy system model developers, decision-makers, and the public by providing a user-friendly TUI that enhances transparency and decision-making processes.

Research Objectives:
1. Examine the emerging role of LLMs in the future of user interfaces and their potential to transition from graphical to textual interfaces.
2. Develop a TUI that interfaces with Energy System Models (ESMs) via well-documented APIs.

Methodology:
1. Investigate and select appropriate tools and frameworks for integrating LLMs with APIs, drawing on existing examples and technologies, such as LangChain's Python Library for Use Cases with APIs (https://python.langchain.com/docs/use_cases/apis) and OpenAI's GPT Function Calling (https://platform.openai.com/docs/guides/gpt/function-calling).
2. Define use cases and questions that the LLM-based TUI can address to enhance understanding and decision-making in the context of energy system models.
3. Create well-documented API specifications using standard formats (e.g., OpenAPI) for a reputable energy planning model, such as "Open and Compact Model for the German Energy Transition," (available on GitHub: https://github.com/OCGModel/OCGModel).
4. Establish a functional connection between the API and LLM to develop a working prototype of the LLM-based TUI.

Significance and Contributions:
This research contributes to the evolving field of human-computer interaction by demonstrating how Large Language Models (LLMs) can be integrated with energy system models, providing an innovative approach to user interfaces in the context of energy planning and governance. The development of a TUI for ESMs enhances transparency and public engagement, while also encouraging model developers to improve precision and openness in their assumptions and methods.
By addressing these objectives, this master's thesis aims to facilitate better-informed decision-making in the energy sector, foster transparency in energy model development, and contribute to the broader discourse on the role of LLMs in future user interfaces and decision support systems.

Keywords: Large Language Models (LLMs), Textual User Interface (TUI), Energy System Models (ESMs), Application Programming Interfaces (APIs), User Experience, Transparency, Decision Support, Energy Planning, Human-Computer Interaction

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


AC Optimal Power Flow is a fundamental building block for several optimization problems of electrical power systems, such as unit commitment and optimal transmission switching. It is a nonlinear and non-convex optimization problem, and solving it to optimality is an NP-hard problem. In practice, this problem is solved using linearized approximations for computational tractability. However, linear approximations can lead to suboptimal solutions or solutions that are infeasible in the original nonlinear problem. With the introduction of stochastic energy sources into the grid, the need to solve the AC-OPF more frequently has increased.

A recent approach to overcome this difficulty is to combine machine learning techniques and optimization algorithms such as Lagrangian dual methods [1]. The learning model exploits the information in the similar states of the network and the optimization algorithm satisfies the constraints of the problem.

In this thesis, the student will conduct a comprehensive review of the existing literature on these innovative techniques. Subsequently, the most promising approach will be implemented and rigorously evaluated. This will be followed by a comparative analysis comparing the performance of the chosen methodology with alternative strategies. The study aims not only to investigate the potential of using these hybrid methods to solve the AC OPF, but also to shed light on the potential of combining deep learning and nonlinear optimization to address complex challenges in modern power system optimization.

[1] Fioretto, Ferdinando, Terrence WK Mak, and Pascal Van Hentenryck. "Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods." [https://arxiv.org/abs/1909.10461]

Supervisor: Tobias Gebhard
Earliest start: immediately
Type: Master Thesis


Residential electricity consumption represents a significant part of the overall electricity demand. In order to forecast the demand, accurate models that describe the consumption of households are needed. While deterministic models are useful for longer periods (e.g. days, years), consumption patterns seem to get stochastic at shorter timescales. If not just one household but the population of a city is considered, the aggregated demand is of interest. It usually stays within a narrow band because consumers are mostly independent from each other. However, crises or other events (e.g. the TV program [3]) can cause a synchronization of people, which can lead to critically high electricity use. Synchronization can be induced by external factors (like the daily cycle or weather influences) or internal effects emerging from the people (e.g. panic behavior). A general model to describe the synchronization of consumers has been developed in [1].

In the thesis, this model shall be tested/validated with measured consumption data by using statistical or machine learning methods. The dataset [2] contains the electric load of 38 households in 10 seconds resolution over more than 2 years. With this, detailed load profiles of the individual as well as the aggregated demand can be constructed, while being able to distinguish between season, weekday, and time of the day. It is to be investigated whether the synchronization model [1] can be reasonably applied to the data. As the dataset also contains weather data, the representation of known/predicted synchronization from external factors could be examined.

 

Requirements:

  • Basic Knowledge in (multivariate) statistics
  • Attendance in lecture “Machine Learning & Energy” helpful
  • Interest in working with data

[1] Tobias Gebhard; Eva Brucherseifer; Florian Steinke:  Monitoring Electricity Demand Synchronization Using Copulas.

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

[3] https://en.wikipedia.org/wiki/TV_pickup

 

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.

 

Other projects

Unfortunately, there is nothing available in the moment.

No open topics? No problem

Even if we have no open topics, but you are interested in writing a thesis at our institut dont hesitate to ask us. Just send us a mail with your latest transcript of records and a current CV.

Pro Seminar / Project Seminar

You would like to complete your Proseminar / Projectseminar with us and think that our published bachelor and master theses sound interesting? 

You have some own ideas and think they would fit in in our institute profile? 

Get in touch. We can surely sit down and talk about it.

Project seminar: Second-life Battery

In collboration with the Darmstadt-based startup ReLi you can participate in the development of a second life battery systems for homes with solar energy. Several tasks can be defined as a project seminar. This includes both programming work and eletrical / electronics hardware development. If you are interested, pease get in touch with Laura ().