Power Adjustment Method for Transmission Section in Power Grid Combining Deep Reinforcement Learning and Artificial Experience

被引：0
作者：
Yang X. [1 ]
Yan J. [1 ]
Liu J. [1 ]
机构：
[1] China Electric Power Research Institute, Beijing
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2023年 / 47卷 / 15期
关键词：
deep reinforcement learning; power adjustment; power flow; proximal policy optimization; transmission section;
D O I：
10.7500/AEPS20220224003
中图分类号：
学科分类号：
摘要：
With the increasing proportion of renewable energy in power systems, the energy transmission change between regions is becoming more and more violent. Therefore, it is necessary to study the power adjustment methods for transmission sections in large power grids. However, due to the limitations of the traditional algorithms, such as the problems of the non-convergence of power flow and relying on expert experiences, it cannot well overcome the difficulties of poor convergence when the adjustment target changes greatly. Therefore, a power flow adjustment method for transmission section combining artificial experiences and deep reinforcement learning is proposed. Firstly, the basic concepts of deep reinforcement learning are introduced, and the generator pre-selection and power compensation mechanism is proposed. Secondly, the reinforcement learning state, action space, reward function, and deep neural network framework are designed. The knowledge experience is introduced in the model training process, and the action space of the agents is effectively reduced. Finally, the effectiveness of the method is verified by practical cases of IEEE 39-bus system and Northeast China power grid. © 2023 Automation of Electric Power Systems Press. All rights reserved.
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页码：133 / 141
页数：8
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