Design of Dynamic Virtual Power Plants based on Model Predictive Controller

Feeding power systems with more inverter-based resources (IBRs) diminishes their inertia, leading to instability. They also usually produce less power than the conventional fossil-based power plants, making them market price followers. To ensure the power system stability in the presence of IBRs and make renewable energies an influencing factor in market price, the dynamic virtual power plant (DVPP) has emerged. This paper presents a novel hierarchical model predictive control (MPC) for the DVPPs. Instead of involving the dynamical references in the controller design, the proposed controller uses them to generate the sequence of future desired outputs and design the control input optimally. Therefore, the states of the references are not needed. Compared to available controllers, the proposed approach has a low online implementation burden. The control actions can be derived offline as a function of the dynamical reference, therefore, if the references change due to any change in the power system topology, the controller is still valid. The effectiveness of the approach is validated through simulation studies to improve the slow transient response of hydro energy resources utilizing IBRs.