Automatic Generation Control for Distributed Multi-Region Interconnected Power System with Function Approximation

Solving the energy crisis and environmental pollution requires large-scale access to distributed energy and the popularization of electric vehicles. However, distributed energy sources and loads are characterized by randomness, intermittence and difficulty in accurate prediction, which bring great challenges to the security, stability and economic operation of power system. Therefore, this paper explores an integrated energy system model that contains a large amount of new energy and combined cooling heating and power (CCHP) from the perspective of automatic generation control (AGC). Then, a gradient Q(sigma,lambda) [GQ (sigma,lambda)] algorithm for distributed multi-region interconnected power system is proposed to solve it. The proposed algorithm integrates unified mixed sampling parameter and linear function approximation on the basis of the Q(lambda) algorithm with characteristics of interactive collaboration and self-learning. The GQ (sigma,lambda) algorithm avoids the disadvantages of large action spaces required by traditional reinforcement learning, so as to obtain multi-region optimal cooperative control. Under such control, the energy autonomy of each region can be achieved, and the strong stochastic disturbance caused by the large-scale access of distributed energy to grid can be resolved. In this paper, the improved IEEE two-area load frequency control (LFC) model and the integrated energy system model incorporating a large amount of new energy and CCHP are used for simulation analysis. Results show that compared with other algorithms, the proposed algorithm has optimal cooperative control performance, fast convergence speed and good robustness, which can solve the strong stochastic disturbance caused by the large-scale grid connection of distributed energy.