AGC command tracking control strategy for battery energy storage power station based on optimized dynamic grouping technology

doi:10.19799/j.cnki.2095-4239.2021.0619

Abstract

Abstract:

Aiming at the problem of low consistency of charge state and high action times of battery cells when battery energy storage power station tracks AGC command, a new control strategy for battery energy storage power station to track AGC command is studied in this paper. Based on the brief discussion of the working principle of the Beetle Antennae Search, this paper puts forward the tracking AGC command control strategy of battery energy storage power station based on dynamic grouping technology. Firstly, the battery cells are sorted according to their charge state, and the battery cells are grouped once according to the sorting results; Then, determine the power response command of each battery pack according to its power distribution principle; Thirdly, the battery cells are grouped twice according to the difference in the action times of the battery cells; Finally, according to the principle of power distribution in the battery pack, the battery unit power response command is obtained, and the battery unit responds to the distributed power. In addition, this paper designs the evaluation system according to the effect of battery energy storage power station tracking AGC command, then constructs the fitness function required by Beetle Antennae Search, and uses Beetle Antennae Search to optimize the forced update threshold of dynamic grouping technology. The simulation results show that the control strategy improves the effect of battery energy storage power station tracking AGC command, improves the consistency of battery cell charge state, and reduces the action times of battery cell.

Key words: battery energy storage, dynamic grouping technology, AGC, beetle antennae search

CLC Number:

TM 911

Xinlei CAI, Kai DONG, Zijie MENG, Zhenfan YU, Boxiao WANG, Yang YU. AGC command tracking control strategy for battery energy storage power station based on optimized dynamic grouping technology[J]. Energy Storage Science and Technology, 2022, 11(5): 1475-1481.

Figures/Tables 6

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动作次数/次			比较结果
本文控制策略	传统控制策略		减少次数/次	减少比例/%
5896	9000		3104	34.49

动作次数/次			比较结果
经BAS算法优化的控制策略	未经BAS算法优化的控制策略	减少次数 /次		减少比例 /%
81	114	33		28.95

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