MPC-Based Distributed Control for Intelligent Energy Management of AC Microgrids

Power scheduling of distributed storage devices and renewable energy resources in microgrids is crucial for their reliable and optimal operation. Conventionally, the power scheduling problem is solved with a centralized energy management control, which has access to all the information from the overall system. This type of control has limitations related to computational burden, communication latencies, and missing information. Therefore, the primary objective of this paper is to propose a distributed energy management strategy, to reduce the operational cost of microgrids, and to enhance the computation time. The strategy is formulated based on the model predictive control method since it allows the control action in the current time-step to be optimized while taking into account the future time-steps. Outputs of the energy management system are the optimal power references generated for all energy resources based on the system's demand, constraints, and objective functions. To illustrate the effectiveness of the distributed model predictive control, the proposed algorithm is validated through simulation, on a microgrid candidate via MATLAB/Simulink, and controller hardware-in-the-loop experiment on a LabVIEW platform using National Instruments control devices (myRIO). The results will show the computation time in the distributed IEM method is improved compared to centralized IEM. Additionally, the results will show the robustness of the distributed IEM in the plug and play scenario.