Power Flow Coordination Optimization Control Method for Power System with DG Based on DRL

Abstract

Aiming at the problem that traditional power flow coordination and optimization methods are difficult to apply to the situation that a large number of Distributed Generations (DG) are connected and can′t effectively control power flow, a power flow Coordination and Optimization Control(COC) method based on Deep Reinforcement Learning (DRL) for Power Grid (PG) with DGs is proposed. Firstly, the influence of DG grid connection on the Distribution Network node voltage distribution is analyzed, and the JFNG algorithm is used to calculate the distributed power flow considering the connection of DG. Then, by introducing the DRL algorithm DQN into the COC of power flow with DG, a power flow COC strategy based on DRL is proposed. Finally, the proposed method is compared with the other two methods under the same conditions through simulation experiments. The results show that the average optimization success rate of the proposed method is the highest, reaching 95.64%, and the voltage deviation of each node of the Distribution Network is the smallest, with the amplitude of 1.032. The overall time consumption and maximum frequency fluctuation are also the lowest, which are 2.33s and 0.002Hz respectively. The algorithm performance is better than the other two comparison algorithms.