ADP-Based Self-Triggered Optimal Control of Active Loads in DC Microgrid
Hanguang Su, Gan Zhi, Huaguang Zhang, Jiawei Wang, Goran Štrbac, He Ren
Abstract
In this brief, an adaptive dynamic programming (ADP)-based self-triggered control (STC) method was proposed to address the optimization control problem of power buffer systems in DC microgrids. The optimization control problem of power buffers is addressed in the framework of non-zero sum games to ensure mutual cooperation among power buffers. In the proposed STC mechanism, the next triggering moment is determined by the current triggering information, avoiding continuous monitoring of devices under the event-triggered control (ETC) and reducing the occupation of system communication and computing resources. Besides, an experience replay (ER) method is introduced when updating the weights of the critic neural networks (NNs). The proposed method ensures the stability of the system, eliminates the Zeno phenomenon, and leads to an adjustable positive minimum triggering interval. The effectiveness of the proposed method is ultimately verified by using a DC microgrid case study.