Voltage Dynamic Response Optimization of DC Microgrid Based on Model Predictive Control

被引：0

作者：

Zhu X. ^{[1
]}

Hou S. ^{[1
]}

Li Z. ^{[1
]}

机构：

[1] Hebei Key Laboratory of Distributed Energy Storage and Micro-grid, North China Electric Power University, Baoding, 071003, Hebei Province

来源：

Dianwang Jishu/Power System Technology | 2020年 / 44卷 / 06期

关键词：

Bidirectional DC-DC converter; DC micro-grid; Dynamic optimization; Model predictive control; Virtual capacitor;

D O I：

10.13335/j.1000-3673.pst.2019.2071

中图分类号：

学科分类号：

摘要：

The DC voltage of the DC microgrid based on the droop control will fluctuate sharply when the load is frequently switched, which seriously affects the power quality of the DC voltage. DC capacitors can block the sudden changes in the DC bus voltage, while the excessive DC capacitors will extend the duration that the system reaches its steady state. This paper proposes a voltage dynamic response optimization method of DC microgrid based on model predictive control. When the load is abrupt, a large virtual capacitor is used to slow down the change of the DC voltage. However, in the later stage of the dynamic response, a small virtual capacitor is used to reach the DC voltage to the steady state quickly. Firstly, the working principle of the battery side virtual capacitor is given. The optimal virtual capacitor value is obtained through model predictive control and applied to the additional virtual capacitor control of the DC/DC converter. Secondly, the model predictive controller of the current inner loop is designed. Finally, the effects of the current inner loop on the dynamic response of the DC bus voltage during PI control and FCS-MPC(finite control set model predictive control) is compared. The simulation model of DC microgrid is established based on RT-LAB. The simulation results verify the effectiveness of the proposed control. © 2020, Power System Technology Press. All right reserved.

引用

页码：2187 / 2195

页数：8

共 18 条

[1]

Wang Yi, Yu Ming, Li Yonggang, Model predictive controller-based distributed control of wind turbine DC microgrid, Transactions of China Electrotechnical Society, 31, 21, pp. 57-66, (2016)

[2]

Zhu Xiaorong, Cai Jie, Wang Yi, Et al., Virtual inertia control of wind- battery-based DC micro-grid, Proceedings of the CSEE, 36, 1, pp. 49-58, (2016)

[3]

Zhu Xiaorong, Xie Zhiyun, Jing Shuzhi, Virtual inertia control and stability analysis of DC micro-grid, Power System Technology, 41, 12, pp. 3884-3893, (2017)

[4]

Zou Peigen, Meng Jianhui, Wang Yi, Et al., A flexible virtual inertia control strategy for DC microgrid, Electric Power Construction, 39, 6, pp. 56-62, (2018)

[5]

Xu Haizhen, Zhang Xing, Liu Fang, Et al., Reactive power sharing control strategy for microgrid inverters based on virtual capacitor [J], Automation of Electric Power Systems, 40, 19, pp. 59-65, (2016)

[6]

Mao Fubin, Zhang Xuemeng, Li Yuliang, Et al., Research on optimal virtual inertia of micro-grid inverter based on VSG control, Journal of Electrical Engineering, 13, 3, pp. 9-16, (2018)

[7]

Dizqah A M, Maheri A, Busawon K, Et al., A multivariable optimal energy management strategy for standalone DC microgrids[J], IEEE Transactions on Power Systems, 30, 5, pp. 2278-2287, (2015)

[8]

Yu Ming, Wang Yi, Li Yonggang, Virtual inertia control of hybrid energy storage in DC microgrid based on predictive method, Power System Technology, 41, 5, pp. 1526-1532, (2017)

[9]

Zhang Guorong, Peng Bo, Xie Runsheng, Et al., Predictive synergy control strategy for flexible multi-state switch model, Automation of Electric Power Systems, 42, 20, pp. 123-129, (2018)

[10]

Li Nan, Gao Feng, A hybrid model predictive control method for modular multilevel converter of battery energy storage system [J], Transactions of China Electrotechnical Society, 32, 14, pp. 165-174, (2017)

← 1 2 →