M.Sc. Andreas Bott
- Modelling of district heating networks
- Probabilistic State Estimation
- Flexible Operation and Scheduling of District Heating Networks
- Machine learning and data analysis in the energy domain
Space heating accounts for more than 50% of the final energy consumption in private housing. Therefore, means to provide CO2-neutral heat is an important step towards a Carbon neutral future. District heating networks of the 4th generation can provide this energy in densely populated areas and cities as they connect decentralized heat sources such as industrial waste heat and large-scale heat pumps with flexible consumers and heat storages. As heat can be stored cheaply and easily, and the heating sector is coupled with the electrical sector via heat pumps and combined heat and power plants, flexible operation of district heating grids can also help balancing the fluctuating feed in in the electrical grid by renewable energy sources.
Traditionally, district heating grids were used as distribution grids with a low number of feed-in points and easy but robust control schemes, that did not utilise its flexibility potential. In the project MeFlexWärme new methods for the flexible operation of district heating grids are developed, focusing on an efficient scheduling of different heating sources and on providing incentives for participants to adapt to the grids state through a local energy marked.
Network transparency is key in enabling a flexible grid operation while ensuring the supply of all customers. Measurements are sparsely placed in district heating grids, such that the temperatures and pressures have to be estimated based on the available information as best as possible. Statistical methods and machine learning approaches are utilised to develop new models for this state estimation.
since 2020: PhD student at EINS
2016 - 2019: M.Sc. Energy Science and Engineering at TU Darmstadt
2012 - 2016: B.Sc. Physics at TU Darmstadt
Andreas Bott; Steinke Florian :
Efficient Training Data Generation for Learning-Based State Estimation in 4th generation District Heating Grids.
In: 9th International Conference on Smart Energy Systems (SESAAU 2023), Copenhagen, Denmark, 2023
Andreas Bott; Tim Janke; Florian Steinke :
Deep learning-enabled MCMC for probabilistic state estimation in district heating grids.
In: Elsevier Applied Energy 336 , P. 120837, prePrint, 2023
Andreas Bott; Pascal Friedrich; Lea Rehlich; Florian Steinke :
Model Reduction for Heat Grid State Estimation.
In: 2021 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), virtual Conference, 2021
Andreas Bott; Alexander Matei; Florian Steinke; Stefan Ulbrich :
Methodenbaukasten für Flexible Wärmenetze der Zukunft.
In: Konferenzreader Digitalisieren, Sektoren koppeln, Flexibilisieren: Systemische Integration der Bioenergie und weiterer erneuerbarer Energien in Gebäuden & Quartieren, virtual Conference, 2020