Inertial Control Strategy for Wind Farm with Distributed Energy Storage System Based on Model Predictive Control

被引：0
|
作者：
Shen Y. [1 ]
Song X. [1 ]
Luo Z. [2 ]
Shen F. [2 ]
Huang S. [2 ]
机构：
[1] Electric Power Scientific Research Institute, State Grid Hunan Electric Power Co.Ltd., Changsha
[2] College of Electrical and Information Engineering, Hunan University, Changsha
来源：
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2022年 / 56卷 / 10期
关键词：
distributed energy storage (DES); inertial control; model predictive control; wind farm;
D O I：
10.16183/j.cnki.jsjtu.2022.134
中图分类号：
学科分类号：
摘要：
Distributed energy storage (DES) wind turbine is an effective means to solve the problem of system frequency stability caused by large-scale wind power connection. In this paper, an inertial control method for DES wind farms based on model predictive control (MPC) is proposed.First, the linearized prediction model of the DES wind farm is established. Then, on this basis, in combination with the control framework of MPC, an optimization model and strategy of MPC inertial control are proposed considering the cost of energy storage loss and the balanced change of wind turbine rotor speed,in order to achieve the balanced change of wind turbine rotor speed during inertia control. The simulation results show that the proposed control strategy can effectively coordinate the active power output of the wind power generation unit and the energy storage system unit in the DES wind turbine, reduce the cost of charging and discharging loss of the energy storage system, and ensure that the rotational speed of all wind turbines in the wind farm tends to be average during the inertial control period, avoiding the problem of wind turbines exiting frequency regulation due to excessive reduction of the rotational speed of wind turbines. The inertial control strategy of the DES wind farm is beneficial to improve the frequency stability of the power grid, which is of great significance to ensure the safe operation of the power grid. © 2022 Shanghai Jiao Tong University. All rights reserved.
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页码：1285 / 1293
页数：8
相关论文
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