  The increasing share of renewable generation leads to new challenges in
reliable power system operation, such as the rising volatility of power
generation, which leads to time-varying dynamics and behavior of the system. To
counteract the changing dynamics, we propose to adapt the parameters of
existing controllers to the changing conditions. Doing so, however, is
challenging, as large power systems often involve multiple subsystem operators,
which, for safety and privacy reasons, do not want to exchange detailed
information about their subsystems. Furthermore, centralized tuning of
structured controllers for large-scale systems, such as power networks, is
often computationally very challenging. For this reason, we present a
hierarchical decentralized approach for controller tuning, which increases data
security and scalability. The proposed method is based on the exchange of
structured reduced models of subsystems, which conserves data privacy and
reduces computational complexity. For this purpose, suitable methods for model
reduction and model matching are introduced. Furthermore, we demonstrate how
increased renewable penetration leads to time-varying dynamics on the IEEE 68
bus power system, which underlines the importance of the problem. Then, we
apply the proposed approach on simulation studies to show its effectiveness. As
shown, a similar system performance as with a centralized method can be
obtained. Finally, we show the scalability of the approach on a large power
system with more than 2500 states and about 1500 controller parameters.
