MASTER’S THESIS – MANAGING GRID CONGESTION WITH FLEXIBLE LOAD DEVICE CONTROL AND GRID STATE FORECASTS (M/F/D)

fortiss is the research institute of the Free State of Bavaria for software-intensive systems and services with headquarters in Munich. The institute currently employs around 150 employees, who collaborate on research, development and transfer projects with universities and technology companies in Bavaria, Germany and Europe. Research is focused on state of the art methods, techniques and tools of software development, systems & service engineering and their application to reliable, secure cyber-physical systems, such as the Internet of Things (IoT). fortiss has the legal structure of a non-profit limited liability company (GmbH). Its shareholders are the Free State of Bavaria (as majority shareholder) and the Fraunhofer Society for the Promotion of Applied Research. 

To extend our research lines in our Architectures and Services for Critical Infrastructures team, we are offering a: 

M.Sc. Thesis on Managing grid congestion with flexible load device control and grid state forecasts (m/f/d)

In recent years, the landscape of energy distribution systems has undergone a remarkable transformation. The modernization and diversification of the energy networks has come with severe challenges and opportunities, and the advent of smart grids have initiated an era of massive data generation and exchange in this domain. Data from several measuring devices distributed across the network are now available, and this together with the power of modern machine learning techniques present opportunities for a more resilient, efficient, and sustainable energy infrastructure.

Time synchronized loads such as electric vehicles, heat pumps, and variations on weather dependent energy generation can lead the energy grid state towards high peaks. This can be a detriment for the grid infrastructure, as it can cause cable and transformer overloads. Preventing and avoiding such scenarios is crucial for maintaining grid stability, specially as modern energy networks follow the trend of increasing variable loads and energy sources. There are different approaches to influence the grid to avoid congestion events from happening. Altering market prices, applying re-dispatch schedules, incorporating shedding schemes, among many others. Based on the legal and technological landscape of the energy infrastructure in Germany, we focus on the scenario where the controllable actions are dimming signals, which is a mechanism that can be easily implemented by the networks operators to reduce the consumption of controlable load devices in the grid, according to the energy industry act §14a. The aim of the project is to develop compare and validate different flexibility management schemes. 

We will use existing SotA deep learning methods to forecast active and reactive loads and estimate possible states of the energy grid. Exploring different approaches to use those forecasts to define strategies or suggestions to avoid congestion events. For the validation we plan a lab integration of controllable load devices. The communication of the dimming signals to the appliances will be implemented via the OpenEMS framework. This will serve to assess the efficacy of the proposed control approach to deal with expected congestion events.