Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid - Xin Shen; Yijing Zhang; Jiahao Li; Yitao Zhao; Jianlin Tang; Bin Qian; Xiaoming Lin (AIP Advances)
This study introduces a Learning-based Load Frequency Control (LB-LFC) approach to manage the challenges posed by renewable energy’s intermittency in microgrids, which often causes load disturbances, frequency fluctuations, and higher generation costs. The LB-LFC method employs reinforcement learning to balance generation costs and frequency stability effectively. In addition, a novel sort replay actor critic technique is proposed, leveraging the deep deterministic policy gradient algorithm and sort experience replay to enhance control efficiency and robustness. This dual-objective control strategy not only improves frequency management but also aims to reduce generation expenses. The effectiveness of this approach is validated through simulations on the isolated microgrid load frequency control model of China Southern Grid.