Application of Model Order Reduction in Evaluation of Small-Signal Stability of Power Systems With Virtual Power Plants

Virtual Power Plants (VPPs) represent an aggregation of distributed generators and loads that are capable of following power references sent by grid operators. To guarantee system stability, grid operators need to verify the impact of VPPs on the small-signal stability of electrical transmission systems. Since dynamic models of VPPs have a large number of states, the application of model order reduction (MOR) techniques should be considered for stability analyses. In this paper, the influence of VPPs on the small-signal stability of electrical transmission systems is evaluated. For that purpose, VPPs are modelled in detail, including the dynamics of renewable sources such as wind and solar. Then, the models are reduced by using two MOR techniques, namely state relevance and balanced truncation. The VPPs are connected to the benchmark two-area power system model and their impact on the small-signal stability assessed. The results show that renewable energy-based VPPs have a significant impact on the system eigenvalues and that they provide additional damping to the system. It is shown that the low-frequency dynamic properties can be preserved using low-order reduced models, while higher-order ones are needed for the medium-frequency dynamics. Eigenvalue analysis is used to validate the theoretical developments.