Speaker
Description
Operators of district heating networks are facing numerous technical challenges in course of the energy system transformation, which require innovations in network control. A realistic mapping of the energy transport through the distribution network is key to optimizing the use of operating resources. The underlying thermo-hydraulic PDE system and optimization problems constrained by this PDE system strongly depend on the demand resulting from the heat consumers’ behavior as a boundary condition.
For predictive optimization, a realistic demand forecast model needs to be provided. To identify underlying patterns, we use a neuronal network, namely a transformer model for time series forecasting, and historical real monitoring data to generate temperature-dependent, time-resolved, characteristic demand profiles of heat consumers. Next, we model the inherent stochastic nature of demand by incorporating stochastic processes with time-dependent mean reversion levels as the Ornstein-Uhlenbeck process into our previous results.
The optimal control problem under consideration is to find the optimal input into the system such that not only the cost is minimized but in addition the stochastic demands are satisfied. We employ different optimization strategies and compare them for our application example of a district heating network.
