Coordinated Distributed-Predictive Control for Standalone Microgrid with Less Computational Burden

This paper focuses on a hybrid generation system which includes a battery bank, solar subsystem, wind subsystem, and an AC load. The challenges of designing a controller for this system are the nonlinearity of the system, the subsystems’ real constraints, disturbance rejection, the computational burden, and its dependence on wind speed, temperature, and illumination. In this study, the coordinated distributed predictive control approach is proposed for microgrid economic dispatch problems and power balance. In this approach, each generation unit is regulated by a local model-predictive controller (MPC). These local MPC controllers integrate the information between each other in their objective functions. The KKT (Karush–Kuhn–Tucker) conditions are involved in the coordinated distributed MPC strategy for the optimal solution of the optimization problem. Three different commercial solvers are used to determine the effective solver for the optimization problem. Different case studies are utilized to demonstrate the proposed controller’s superiority.