Frequency control for islanded AC microgrid based on deep reinforcement learning

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.

KEYWORDS:

• AC microgrid
• frequency control
• deep reinforcement learning
• automatic generation control
• twin-delayed deep deterministic policy gradient

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the State Grid Corporation of China Headquarters Science and Technology Project [5100-202099522A-0-0-00].