Dr. Raphael Kruse
Prof. Dr. Volker Mehrmann
Dr. Matthias Voigt
Technische Universität Berlin
In this project we will study the modeling of power networks by employing the port-Hamiltonian framework. Energy based modeling with port-Hamiltonian descriptor systems has many advantages, e. g., it accounts for the physical interpretation of its variables, it is best suited for the modular structure of the network, since coupled port-Hamiltonian systems form again a port-Hamiltonian system and it encodes these properties in algebraic and geometric properties that simplify Galerkin type model reduction, stability analysis, and also efficient discretization techniques. To improve the predictions that one obtains from such models we suggest to employ data assimilation and state estimation techniques by incorporating the measurement data. These would allow to take the uncertainty in the measurements and the presence of unmodeled dynamics as well as data and modeling errors into account. The improved predictions can then be used to control the network such that (the expected value of) the load is kept as constant as possible. To control the network we propose to use techniques of model predictive control (MPC) which solve a sequence of finite horizon optimal control problems. The method uses predictions of the state and computes a local optimal control which is then used for the model simulation in the next iteration. This framework is very flexible, since it allows control in real time and the incorporation of nonlinear dynamics and/or inequality constraints. It has already been used successfully within other areas of energy network control. Our new ansatz will also incorporate the stochastic effects into the model predictive control framework using data assimilation.
Our vision is to develop numerical methods for network operators that allows the incorporation of model uncertainities for improving simulation and control of power networks.