School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
This work is supported by National Key R&D Program of China (No. 2016YFB0900100), Excellent Youth Foundation of Hubei Province of China (No. 2018CFA080) and National Natural Science Foundation of China (No. 51707136).
When the large-scale intermittent generation sources including wind and photovoltaic generation connects to the power system, in order to cope with their uncertainty, the economic dispatch model for power system needs to be built based on the uncertainty modeling. The accuracy of modeling will directly affect the accuracy of the dispatch results. However, when considering the complex uncertainty of both load and intermittent generation sources such as wind and photovoltaic generation, it is particularly difficult to accurately model the overall uncertainty of the system. In view of this problem, the deep deterministic policy gradient (DDPG) algorithm in the deep reinforcement learning is introduced. The work of uncertainty modeling is avoided. Instead, the uncertainty is adapted by the DDPG algorithm relied on the mechanism of interacting with the environment and improving the strategy based on feedbacks. In order to guarantee the applicability of the algorithm, the generalization method for the DDPG model is proposed. Aiming at the stability problem of the algorithm, two mechanisms are designed, including perception-learning ratio adjustment and experience replay improvement. The result of example shows that the dynamic economic dispatch problem of power systems in any scenario can be solved by the proposed method based on adapting the system uncertainty.
PENG Liuyang,SUN Yuanzhang,XU Jian,et al.Self-adaptive Uncertainty Economic Dispatch Based on Deep Reinforcement Learning[J].Automation of Electric Power Systems,2020,44(9):33-42.DOI:10.7500/AEPS20190706003Copy