Distributed event-triggered average consensus control strategy with fractional-order local controllers for DC microgrids

Abstract

Recently, direct current (DC) microgrids have become prominent due to their less complicated architecture and better integration of renewable energy sources (RES). Energy storage systems (ESSs) and RES, such as photovoltaic (PV) panels and fuel cells (FCs), are internally DC. Hence, it is easier for them to be integrated into DC microgrids compared to AC systems. However, DC microgrids have low inertia, and voltage stability is harder to achieve due to the inexistence of reactive power compensators. Therefore, this paper suggests a novel distributed control system for DC microgrids to achieve voltage stabilization and accurate energy balancing of ESSs based on event-triggered average consensus protocol and fractional-order proportional-integral (FOPI) local controllers. The fractional-order controller has many advantages over the integer-order classical controller, such as achieving better performance, flexibility, and a higher degree of freedom in the controller design. In this regard, the FOPI local controllers provide accurate load sharing for the droop mechanism. The distributed secondary layer controller compensates for the voltage offset, meanwhile balancing the energy level of ESSs. In the conventional distributed control strategies, the communication between the agents is periodically at discrete sampling time, resulting in network traffic increase and excessive energy consumption in the devices. Therefore, in this paper, the average consensus protocol is event-based, saving considerable network capacity for other microgrid communication needs. The effectiveness of the proposed control strategy is confirmed by the simulation of a 380V DC microgrid in MATLAB/Simulink that shows the achievement of the control objectives. Simulation results show the proper voltage stabilization and accurate energy balancing of ESSs in both islanded and grid-connected modes and a significant reduction in the transmitted information between agents.