Smart generation control based on deep reinforcement learning with the ability of action self-optimization

Abstract

Random disturbance or noise caused by the large-scale integration of new energy and distributed energy affects the safe and economical operation of the interconnected power grids. This study aims to propose DDRQN-AD, which was based on action self-optimization. In comparison with the traditional centralized automatic generation control systems, DDRQN-AD identified the optimal strategies easily and addressed the stochastic disturbance caused by the extensive integration of new energy and distributed energy sources into interconnected power grids to maximize the utilization of new energy. Simulation results for a two-area microgrid load-frequency control power system model and the Guangdong Power Grid model showed that the proposed algorithm can reduce carbon emissions can enhance the utilization rates of new energy sources. Moreover, the robustness and learning ability of DDRQN-AD were stronger than those of the traditional smart methods.