An Energy Storage Performance Improvement Model for Grid-Connected Wind-Solar Hybrid Energy Storage System
Joint Authors
Zhu, Rui
Zhao, An-lei
Wang, Guang-chao
Yang, Yaopan
Xia, Xin
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This study introduces a supercapacitor hybrid energy storage system in a wind-solar hybrid power generation system, which can remarkably increase the energy storage capacity and output power of the system.
In the specific solution, this study combines the distributed power generation system and the hybrid energy storage system, while using the static reactive power compensation system and the conductance-fuzzy dual-mode control method to increase output power in stages.
At the same time, the optimal configuration model of the wind-solar hybrid power generation system is established using MATLAB/Simulink software.
The output power of the microgrid to the wind-photovoltaic hybrid power generation system is calculated by simulation, and the optimization process of each component of the system is simulated.
This study mainly uses the static reactive power compensation system and the conductance-fuzzy dual-mode control method to optimize the wind-solar hybrid power generation system.
Using MATLAB software simulation verifies the feasibility and rationality of the optimal configuration of the system.
American Psychological Association (APA)
Zhu, Rui& Zhao, An-lei& Wang, Guang-chao& Xia, Xin& Yang, Yaopan. 2020. An Energy Storage Performance Improvement Model for Grid-Connected Wind-Solar Hybrid Energy Storage System. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1138950
Modern Language Association (MLA)
Zhu, Rui…[et al.]. An Energy Storage Performance Improvement Model for Grid-Connected Wind-Solar Hybrid Energy Storage System. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1138950
American Medical Association (AMA)
Zhu, Rui& Zhao, An-lei& Wang, Guang-chao& Xia, Xin& Yang, Yaopan. An Energy Storage Performance Improvement Model for Grid-Connected Wind-Solar Hybrid Energy Storage System. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1138950
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138950