Power Scheduling Optimization Method of Wind-Hydrogen Integrated Energy System Based on the Improved AUKF Algorithm

被引:43
作者
Wang, Yong [1 ]
Wen, Xuan [2 ]
Gu, Bing [1 ]
Gao, Fengkai [1 ]
机构
[1] Northeast Elect Power Univ, Minist Educ, Key Lab Modern Power Syst Simulat & Control & Ren, Jilin 132000, Jilin, Peoples R China
[2] Nanchang Univ, Sch Informat Engn, Nanchang 330027, Jiangxi, Peoples R China
关键词
AUKF algorithm; integration of wind and hydrogen; UKF; power scheduling; KALMAN FILTER; STATE;
D O I
10.3390/math10224207
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
With the proposal of China's green energy strategy, the research and development technologies of green energy such as wind energy and hydrogen energy are becoming more and more mature. However, the phenomenon of wind abandonment and anti-peak shaving characteristics of wind turbines have a great impact on the utilization of wind energy. Therefore, this study firstly builds a distributed wind-hydrogen hybrid energy system model, then proposes the power dispatching optimization technology of a wind-hydrogen integrated energy system. On this basis, a power allocation method based on the AUKF (adaptive unscented Kalman filter) algorithm is proposed. The experiment shows that the power allocation strategy based on the AUKF algorithm can effectively reduce the incidence of battery overcharge and overdischarge. Moreover, it can effectively deal with rapid changes in wind speed. The wind hydrogen integrated energy system proposed in this study is one of the important topics of renewable clean energy technology innovation. Its grid-connected power is stable, with good controllability, and the DC bus is more secure and stable. Compared with previous studies, the system developed in this study has effectively reduced the ratio of abandoned air and its performance is significantly better than the system with separate grid connected fans and single hydrogen energy storage. It is hoped that this research can provide some solutions for the research work on power dispatching optimization of energy systems.
引用
收藏
页数:16
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