Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach
Junwei Cao,
Wanlu Zhang,
Zeqing Xiao and
Haochen Hua
Additional contact information
Junwei Cao: Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
Wanlu Zhang: Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
Zeqing Xiao: Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
Haochen Hua: Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
Energies, 2019, vol. 12, issue 8, 1-17
Abstract:
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.
Keywords: energy Internet; convolutional neural network; decision optimization; deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:8:p:1556-:d:225630
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