A Model Predictive Control Method for Simultaneous Load Sharing and Power Accommodating

With the increasing integration of distributed generation systems into power networks, greater variability and stochastic behavior have begun to impact energy quality and network stability. To address these challenges, this study investigates the control mechanisms of inverter-based distributed power units. The objective is not only to mitigate operational issues during grid integration but also to leverage the advantages of distributed energy systems in supporting environmental sustainability and economic development. A control framework combining droop regulation and model predictive control (MPC) is proposed to enhance power management and operating flexibility within microgrids. The work covers the design of an MPC scheme founded on droop characteristics for inverter control, strategies applicable to both grid-connected and autonomous modes, and load distribution using droop-based techniques. The proposed approach is expected to realize improved adaptability and robustness in microgrid operation.