Deep Reinforcement Learning Based Multi-Objective Integrated Automatic Generation Control for Multiple Continuous Power Disturbances
Jiawen Li, Tao Yu
Abstract
This paper proposes a multi-objective integrated automatic generation control (MOI-AGC) that combines a controller with a dispatch together. This can contribute to improving both control performance and economy in a power grid with multiple continuous power disturbances. Subsequently, a distributed classification replay twin delayed deep deterministic policy gradient (DCR-TD3) is designed for MOI-AGC. On the one hand, DCR-TD3 introduces the classification replay method based on multiple explorers with different parameter actor networks for distributed optimization. On the other hand, the optimal control strategy is obtained through DCR-TD3 in an extremely random environment based on frequency deviation, regional control error together with frequency mileage payment as the reward function. This helps address the problem of frequency instability caused by multiple stochastic disturbance in a grid with a large number of distributed energies. Simulation verification is performed for the two-area load frequency control (LFC) model, with the result showing that the proposed algorithm has better control performance and economic benefits. Besides, compared with the existing algorithms, it can achieve a regional optimum control, reducing frequency mileage payment.