DOI:10.35833/MPCE.2020.000240 Power System Flow Adjustment and Sample Generation Based on Deep Reinforcement Learning Page view: 0        Net amount: 149 Author: Shuang Wu1,Wei Hu1,Zongxiang Lu1,Yujia Gu2,Bei Tian2,Hongqiang Li2 Author Affiliation: 1.Department of Electrical Engineering, Tsinghua University, Beijing, China;2.State Grid Ningxia Electric Power Co. Ltd., Yinchuan, China Foundation: This work was supported by the Science and Technology Project of the State Grid Corporation of China (No. 5400-201935258A-0-0-00), and the National Natural Science Foundation of China (No. 51777104). Abstract: With the increasing complexity of power system structures and the increasing penetration of renewable energy, the number of possible power system operation modes increases dramatically. It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis. At present, problems of low efficiency and long time consumption are encountered in the formulation of operation modes, resulting in a very limited number of generated operation modes. In this paper, we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning. First, a discriminator is trained to judge the power flow convergence, and the output of this discriminator is used to construct a value function. Then, the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment. Finally, a large number of convergent power flow samples are generated using the learned adjustment strategy. Compared with the traditional flow adjustment method, the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model. Therefore, this strategy can be automatically learned without manual intervention, which allows a large number of different operation modes to be efficiently formulated. The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows. Keywords: Deep reinforcement learning ; power flow adjustment ; system operation mode ; sample generation Received:April 17, 2020               Online Time:2020/12/03 View Full Text       Download reader