Paper
16 December 2022 Research on energy storage capacity planning in multi-energy complementary form of park
Zelong Zhang, Xinyu He, Bin Che, Haiyan Yang, Lin Zhu, Dunnan Liu
Author Affiliations +
Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 1250005 (2022) https://doi.org/10.1117/12.2660626
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China
Abstract
As a flexible resource, energy storage plays a role in promoting the absorption of new energy and the safe and stable operation of power system. However, limited by the cost of energy storage investment, it is difficult to rely on large-scale energy storage to meet the flexibility requirements of the system. In order to solve the problem of electric/thermal energy storage configuration of integrated energy, a method of electric/thermal energy storage configuration of regional integrated energy system with wind power access was proposed. Firstly, the dynamic neural network prediction model for wind power prediction is constructed to effectively predict the wind farm output in the regional integrated energy system. Then, the charging and discharging behavior of electric/thermal energy storage in regional integrated energy system is analyzed, the relationship between electric and thermal storage and regional wind farm power is established, and the electric/thermal coupling relationship is considered to form a joint optimal configuration model of electric/thermal energy storage.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zelong Zhang, Xinyu He, Bin Che, Haiyan Yang, Lin Zhu, and Dunnan Liu "Research on energy storage capacity planning in multi-energy complementary form of park", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 1250005 (16 December 2022); https://doi.org/10.1117/12.2660626
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KEYWORDS
Wind energy

System integration

Neural networks

Wind measurement

Internet

Optimization (mathematics)

Clouds

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