Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

被引：21

作者：

Cao, Junwei ^{[1
]}

Zhang, Wanlu ^{[1
]}

Xiao, Zeqing ^{[1
]}

Hua, Haochen ^{[1
]}

机构：

[1] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Res Inst Informat Technol, Beijing 100084, Peoples R China

来源：

ENERGIES | 2019年 / 12卷 / 08期

基金：

中国国家自然科学基金;

关键词：

energy Internet; convolutional neural network; decision optimization; deep reinforcement learning; MANAGEMENT; NETWORK; SYSTEM;

D O I：

10.3390/en12081556

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The existence of high proportional distributed energy resources in energy Internet (EI) scenarios has a strong impact on the power supply-demand balance of the EI system. Decision-making optimization research that focuses on the transient voltage stability is of great significance for maintaining effective and safe operation of the EI. Within a typical EI scenario, this paper conducts a study of transient voltage stability analysis based on convolutional neural networks. Based on the judgment of transient voltage stability, a reactive power compensation decision optimization algorithm via deep reinforcement learning approach is proposed. In this sense, the following targets are achieved: the efficiency of decision-making is greatly improved, risks are identified in advance, and decisions are made in time. Simulations show the effectiveness of our proposed method.

引用

页数：17

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