• 4th generation district heating
• Learning-based hybrid models
• State estimation and generation optimisation

Research Project:

It is anticipated that district heating networks will undergo a substantial transformation in the forthcoming years. The conventional approach to district heating involves distributing heat from a single, high-temperature heating plant to dispersed consumers through a mostly tree-like network. Upcoming, so-called 4th generation district heating networks, on the other hand, are characterised by lower supply temperatures, distributed and flexibly operated heat sources, and loop-based grid layouts. This system transition necessitates novel approaches to address grid operation tasks.

A particular challenge in this endeavour is the lack of real-time consumer demand measurements. The acquisition of real-time consumption data is costly, both financially and in terms of organisational resources. If these measurements are not available, the resulting thermal demand uncertainty has to be reflected in the operation algorithms. Moreover, since the physical equations describing heating grids are nonlinear, mapping the demand's uncertainty onto the grid states can result in multimodal probability distributions, even if the initial demand uncertainty has a simple Gaussian form. It is, therefore, infeasible to find adequate parametric representations for the grid state's uncertainty.

Therefore, we inestigate the utilisation of learning-based hybrid models to model state uncertainites in state estimation and heating plant control tasks for 4th genertion district heating systems. In particular, we employ surrogate models that approximate thermal power flow calculations, i.e., the mapping from the thermal powers and feed-in temperatures at producers and consumers onto the grid states. The possibility of rapid evaluation of these surrogate models then enables the usage of classic sampling-based stochastic algorithms, omitting the necessity for parametric uncertainty representations. 

The reaserch in this field encopases three reasearch strands:

• the efficient generation of training samples for the surrogate models;
• hybrid models for state estimation in district heating grids;
• hybrid models to optimise the power plant's set-points.

These hybrid models allow for estimating the probability of grid constraint violations as well as deducing optimal responses to minimise this probability. The approaches have low computation times, which allows them to be used in online operational settings. Overall, the proposed hybrid models allow the utilisation of the flexibility of 4\Th generation district heating networks without requiring a complete set of measurements first.

• 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