Frequency control for islanded AC microgrid based on deep reinforcement learning
Xianggang Liu, Zhi‐Wei Liu, Ming Chi, Guixi Wei
Abstract
The incorporation of intermittent and stochastic renewable energy into a microgrid creates frequent fluctuations, which provides new challenges in frequency control. This paper deals with the frequency control problem in the islanded AC microgrid (IACMG) via a model-free deep reinforcement learning (DRL) method, which includes offline learning and online control. Twin-delayed deep deterministic policy gradient is involved to improve the performance of the agent to minimise the frequency deviation. The advantage of the proposed method is self-adaptive to the uncertain IACMG model including renewable energy sources. Finally, the effectiveness and robustness of the proposed controller is demonstrated by four simulation scenarios.