Frequency regulation of multi-microgrid with shared energy storage based on deep reinforcement learning

Abstract

• A frequency regulation model for microgrid with share energy storage is established. • A DRL-based economic frequency regulation method is proposed. • Performance and operating cost of frequency regulation are considered together. • Multiple frequency regulation methods are compared and analyzed. • Results show that the proposed method has obvious advantages in integrated benefit. The microgrid is one of the fundamental ways to consume renewable energy, and the safety and economy of its frequency regulation are widely concerned and studied. For the microgrid with shared energy storage, a new frequency regulation method based on deep reinforcement learning (DRL) is proposed to cope with the uncertainty of source load, which considers both frequency performance and the operational economy of the microgrid. Firstly, a frequency regulation model for the microgrid is developed by sharing the frequency regulation potential of energy consumers. Secondly, a command allocation model for smart generation control (SGC) based on the integrated benefit is proposed, where frequency safety and economy are combined. Then, a DRL framework is designed, and the twin delayed deep deterministic policy gradient algorithm is used to implement the SGC of the microgrid in the continuous action space. Finally, the effectiveness of the proposed frequency regulation method is demonstrated by designing comparative simulations in the isolated multi-microgrid, and the cost weight and responsiveness of energy consumers are analyzed.