Intelligent adjustment for power system operation mode based on deep reinforcement learning - Wei Hu; Ning Mi; Shuang Wu; Huiling Zhang; Zhewen Hu; Lei Zhang (iEnergy)
Power flow adjustment is a sequential decision problem. The operator makes decisions to ensure that the power flow meets the system's operational constraints, thereby obtaining a typical operating mode power flow. However, this decision-making method relies heavily on human experience, which is inefficient when the system is complex. In addition, the results given by the current evaluation system are difficult to directly guide the intelligent power flow adjustment. In order to improve the efficiency and intelligence of power flow adjustment, this paper proposes a power flow adjustment method based on deep reinforcement learning. Combining deep reinforcement learning theory with traditional power system operation mode analysis, the concept of region mapping is proposed to describe the adjustment process, so as to analyze the process of power flow calculation and manual adjustment. Considering the characteristics of power flow adjustment, a Markov decision process model suitable for power flow adjustment is constructed. On this basis, a double Q network learning method suitable for power flow adjustment is proposed. This method can adjust the power flow according to the set adjustment route, thus improving the intelligent level of power flow adjustment. The method in this paper is tested on China Electric Power Research Institute (CEPRI) test system.