A novel automatic generation control method with hybrid sampling for multi-area interconnected girds - Zhang, Shengxi; Lan, Feng; Xue, Binglei; Chen, Qingwei; Qiu, Xuanyu (Frontiers in Energy Research, 2023)
The emerging "net-zero carbon" police will accelerate the large-scale penetration of the renewable energies in the power grid, which would bring strong random disturbances due to the unpredictable power output. It would affect the coordinated control performance of the distributed grids. From the quadratic frequency modulation perspective, this paper proposes a fast Q-learning based automatic generation control (AGC) algorithm which combines the full sampling with full expectation for multi-area coordination. A parameter σ is used to balance the state between the full sampling update and only expectation update so as to improve the convergence accuracy. Meanwhile, the fast Qlearning is incorporated by replacing the historical estimation function with the current state estimation function to accelerate the convergence speed. Simulations on the IEEE two-region load frequency control model and Hubei power grid model in China have been performed to validate that the proposed algorithm can achieve optimal multi-area coordination and improve the control performance of the frequency deviations caused by the strong random disturbances. The proposed Qlearning based AGC method outperforms the convergence accuracy, speed and control performance compared with other reinforcement learning algorithms.