Tailor-made model reduction methods for integrated energy systems

The rapid increase of renewable/sustainable energy production leads to daunting challenges in the design, analysis, and control of the modern energy system. Namely, the large number and decentralized nature of renewable energy sources calls for a change of the current paradigm in which energy production is largely centralized in a small number of power plants.

Instead, future energy systems will constitute a network of a large number of heterogeneous dynamical systems, corresponding to a variety of agents such as large power plants, wind farms, solar collectors, industrial and household consumers.

One major challenge in the design of these highly integrated energy systems is that the complexity of the resulting mathematical models is immense, such that the analysis, simulation, and controller design become intractable. Therefore, satisfactory methods for the approximation of such complex models by lower-order, less complex models are needed. In order for these low-complexity models to be reliably employed as a substitute for the original model, they should not only accurately preserve the behavior of the original model, also inherit/preserve/reflect structural properties of the original model, in particular the underlying physical network structure. Consequently, this research will investigate network-structure preserving model reduction methods as well as a priori error bounds for such methods.