Optimal Configuration Strategy of Energy Storage System Considering Wind Power Fluctuation Rate

被引：0
|
作者：
Chen Z. [1 ,2 ]
Sun Y. [3 ]
Zhang Y. [4 ]
Li M. [1 ]
机构：
[1] School of New Energy, North China Electric Power University, Beijing
[2] State Grid Beijing Urban Power Supply Company, Beijing
[3] Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing
[4] School of Economics and Management, North China Electric Power University, Beijing
来源：
Zhang, Yan (zhangyan8698@ncepu.edu.cn) | 1600年 / Hunan University卷 / 47期
基金：
中国国家自然科学基金;
关键词：
Cumulative fluctuation rate difference; Energy storage; Model predictive control(MPC); Positive and negative fluctuation rate; Wind power fluctuation;
D O I：
10.16339/j.cnki.hdxbzkb.2020.08.007
中图分类号：
学科分类号：
摘要：
In order to mitigate the impact of wind power fluctuation on the power grid, a model predictive control algorithm is proposed to analyze the optimal allocation of energy storage system with different fluctuation rate of wind power. Firstly, based on the model predictive control (MPC) algorithm, the control strategy of using energy storage to smooth the wind power fluctuation is studied, and an optimal configuration model is established. Secondly, the concepts of positive and negative fluctuation rate and cumulative fluctuation rate of wind power are proposed, and the power and capacity allocation strategies of energy storage system under three typical fluctuation rates are analyzed in steps. Finally, three typical scenarios, from the established standard sinusoidal wind power to the randomly selected wind power, are analyzed based on MATLAB software to verify the effectiveness of wind power fluctuation mitigation strategy based on model predictive control algorithm and the cumulative fluctuation rate difference as energy storage size evaluation index, which provides a reference for the friendly grid connection of renewable energy and energy storage optimal configuration. © 2020, Editorial Department of Journal of Hunan University. All right reserved.
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页码：60 / 68
页数：8
相关论文
共 17 条
  • [1] SORNSEN P, CUTULULISN A, VIGUERAS-RODRIGUEZ A, Et al., Power fluctuations from large wind farms, IEEE Transaction on Power System, 22, 3, pp. 958-965, (2007)
  • [2] YUAN X M., Overview of problems in large scale wind integrations, Journal of Modern Power Systems and Clean Energy, 1, 1, pp. 22-25, (2013)
  • [3] NIU D X, ZHAO D L, YANG S D, Et al., Collaborative optimization model of offshore wind power and remote clean energy considering comprehensive cost in power system, Journal of Hunan University (Natural Sciences), 46, 12, pp. 16-24, (2019)
  • [4] ESMAILI A, NOVAKOVIC B, NASIRI A, Et al., A hybrid system of Li-ion capacitors and flow battery for dynamic wind energy support, IEEE Transactions on Industry Applications, 49, 4, pp. 1649-1657, (2013)
  • [5] ZHANG Y X, DONG Z Y, LUO F J, Et al., Optimal allocation of battery energy storage systems in distribution networks with high wind power penetration, IET Renewable Power Generation, 10, 8, pp. 1105-1113, (2016)
  • [6] WANG X Y, VILATHGAMUWA D M, CHOI S S., Determination of battery storage capacity in energy buffer for wind farm, IEEE Transaction on Energy Conversion, 23, 3, pp. 868-878, (2008)
  • [7] LEI T, OUYANG Z K, LI Z, Et al., Coordinated control of battery SOC maintaining and filtering for wind power fluctuation smoothing, Electric Power Automation Equipment, 35, 7, pp. 126-131, (2015)
  • [8] WANG C S, YU B, XIAO J, Et al., Sizing of energy storage systems for output smoothing of renewable energy systems, Proceedings of the CSEE, 32, 16, pp. 1-8, (2012)
  • [9] ZHANG F, DONG X M, LIANG J, Et al., Capacity optimization of hybrid energy storage based on target decomposition and complementary fluctuations smoothing, Automation of Electric Power System, 38, 7, pp. 9-15, (2014)
  • [10] CHOI San Shing, SUN C C, YUAN Y, CHOI S S, Et al., Capacity optimization of hybrid energy storage systems in microgrid using empirical mode decomposition and neural network, Automation of Electric Power Systems, 39, 8, pp. 19-26, (2015)
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