Indirect Multi-energy Transactions of Energy Internet with Deep Reinforcement Learning Approach

Author(s):  
Lingxiao Yang ◽  
Qiuye Sun ◽  
Ning Zhang ◽  
Yushuai Li
Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1556 ◽  
Author(s):  
Cao ◽  
Zhang ◽  
Xiao ◽  
Hua

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.


2020 ◽  
Vol 17 (10) ◽  
pp. 129-141
Author(s):  
Yiwen Nie ◽  
Junhui Zhao ◽  
Jun Liu ◽  
Jing Jiang ◽  
Ruijin Ding

2016 ◽  
Author(s):  
Dario di Nocera ◽  
Alberto Finzi ◽  
Silvia Rossi ◽  
Mariacarla Staffa

Author(s):  
Panagiotis Radoglou-Grammatikis ◽  
Konstantinos Robolos ◽  
Panagiotis Sarigiannidis ◽  
Vasileios Argyriou ◽  
Thomas Lagkas ◽  
...  

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