Reinforcement learning-based event-triggered secondary control of DC microgrids

Abstract

In this paper, a reinforcement learning (RL)-based event-triggered mechanism (ETM) for employing in the secondary control layer (SCL) of DC microgrids is developed. The proposed RL-based ETM satisfies the SCL objectives, which is overcoming the disadvantages of primary control (such as voltage deviation and inappropriate current sharing among the distributed generating units). More importantly, it also aids in reducing the amount of transmitted data exchanged within all the distributed generators (DGs). The design parameters of the ETM scheme are regulated through a robust RL approach to provide adaptive ETM parameter tuning, enabling the ETM error vector threshold to quickly adapt to changes in the MG. The suggested RL-based ETM approach is implemented in a DC microgrid, and utilizing hardware in the loop (HIL) real-time OPAL-RT experimental tests, its performance in the SCL of DC microgrids is investigated. Experimental validations have confirmed the merits of the proposed approach.