Distributed Model Predictive Control-Based Power Management Scheme for Grid-Integrated Microgrids

Transitioning from traditional electrical grids to smart grids is currently an ongoing process that many nations are striving for due to their access to renewable resources. Energy management is one of the key parameters that decides the performance of such complex systems. Distributed Model Predictive Control (DMPC) is a promising technique that can be used to improve the energy management of grid-connected systems. This paper analyzes a grid-connected inverter system with DMPC that exchanges key operating parameters with the grid to optimize coordinated power sharing between its respective loads. The state-space model for the inverter is derived and verified to ensure controllability and observability. A state observer for an inverter system is then developed to estimate the nominal states in the derived state-space model. The system performance is evaluated with MATLAB simulation by implementing load disturbances, which validate the effectiveness of the proposed power management control algorithm.