Open AccessLithium battery loss model and economic optimal control strategy for secondary frequency regulation in power system
Author(s) -
Xiao-Chi Zhang,
Xu Sha,
ZhiTong Zheng,
Jie Gong,
Yuan Li,
Kai Sun
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2108/1/012060
Subject(s) - control theory (sociology) , battery (electricity) , kalman filter , control variable , discretization , state of charge , variable (mathematics) , automatic frequency control , power (physics) , computer science , control (management) , engineering , mathematics , telecommunications , mathematical analysis , physics , quantum mechanics , artificial intelligence , machine learning
Lithium battery energy storage system (ESS) has fast response speed, but it is in service for a long time. Therefore, in the research of secondary frequency regulation, it is necessary to establish a battery model that can reflect the short-term polarization and estimate SOC accurately under long-term service. Therefore, we propose a method of variable parameter loss model of lithium battery suitable for secondary frequency modulation of power system and optimize its control strategy based on the relationship between the independent variable SOC and the internal electrical parameters of the battery. Firstly, we discretize the state equation of single cell to obtain the PNGV model with variable parameters. Then the SOC estimation method is improved by using the modified extended Kalman filter method. Finally, we did an analysis about the economy of the ESS participating in secondary frequency regulation by using the loss model, and the optimal control strategy is designed. The HPPC experiment of the ESS unit is designed by Simulink, and the accuracy of the simulation and experimental curves has been analyzed. Finally, the economic differences between the traditional control strategy and the optimal control strategy considering loss are compared. The results show that the error of the variable parameter model in the fitting experiment is no more than 0.41%, and the accuracy of SOC estimation is 12.96% higher than that of the traditional estimation. The economy of the optimized control strategy is 15.38% higher than that of the traditional control strategy.