基于模型预测控制的分布式储能型风力发电场惯性控制策略

doi:10.16183/j.cnki.jsjtu.2022.134

摘要/Abstract

摘要：

分布式储能型(DES)风力发电机组是解决规模化风力发电接入引起系统频率稳定问题的有效手段.提出一种基于模型预测控制(MPC)的分布式储能型风力发电场惯性控制方法,首先建立分布式储能型风力发电场的线性化预测模型,在此基础上结合MPC控制框架,设计考虑储能损耗成本和风机转子转速均衡变化的MPC惯性控制优化模型和策略,以实现惯量控制期间风力发电机组转子转速的均衡变化.仿真结果表明:所提控制策略可以有效协调分布式储能型风力发电机组中风力发电单元和储能系统单元的有功功率输出,降低储能系统的充放电损耗成本,并保证风力发电场内所有风机转速在惯性控制期间趋于平均,避免由于风机转速下降过度而导致风力发电机组退出调频的问题.分布式储能型风力发电场惯性控制策略有利于提高电网频率稳定性,对保障电网的安全运行具有重要意义.

关键词: 风力发电场, 惯性控制, 分布式储能, 模型预测控制

Abstract:

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.

Key words: wind farm, inertial control, distributed energy storage (DES), model predictive control

中图分类号:

TM614

沈阳武, 宋兴荣, 罗紫韧, 沈非凡, 黄晟. 基于模型预测控制的分布式储能型风力发电场惯性控制策略[J]. 上海交通大学学报, 2022, 56(10): 1285-1293.

SHEN Yangwu, SONG Xingrong, LUO Ziren, SHEN Feifan, HUANG Sheng. Inertial Control Strategy for Wind Farm with Distributed Energy Storage System Based on Model Predictive Control[J]. Journal of Shanghai Jiao Tong University, 2022, 56(10): 1285-1293.

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参考文献 21

[1]	MORREN J, PIERIK J, DE HAAN S W H. Inertial response of variable speed wind turbines[J]. Electric Power Systems Research, 2006, 76(11): 980-987. doi: 10.1016/j.epsr.2005.12.002 URL
[2]	唐西胜, 苗福丰, 齐智平, 等. 风力发电的调频技术研究综述[J]. 中国电机工程学报, 2014, 34(25): 4304-4314.
	TANG Xisheng, MIAO Fufeng, QI Zhiping, et al. Survey on frequency control of wind power[J]. Proceedings of the CSEE, 2014, 34(25): 4304-4314.
[3]	REZA M. Power electronic interfaced DG units: Impact of control strategy on power system transient stability[C]//3rd IEE International Conference on Reliability of Transmission and Distribution Networks. London, UK: IEE, 2005: 179-182.
[4]	付媛, 王毅, 张祥宇, 等. 基于多端直流联网的风电功率协调控制[J]. 高电压技术, 2014, 40(2): 611-619.
	FU Yuan, WANG Yi, ZHANG Xiangyu, et al. Coordinated control of wind power in multi-terminal DC transmission system[J]. High Voltage Engineering, 2014, 40(2): 611-619.
[5]	LIU F, LIU Z W, MEI S W, et al. ESO-based inertia emulation and rotor speed recovery control for DFIGs[J]. IEEE Transactions on Energy Conversion, 2017, 32(3): 1209-1219. doi: 10.1109/TEC.2017.2698212 URL
[6]	WANG D X, GAO X D, MENG K, et al. Utilisation of kinetic energy from wind turbine for grid connections: A review paper[J]. IET Renewable Power Ge-neration, 2018, 12(6): 615-624.
[7]	苗福丰, 唐西胜, 齐智平. 风储联合调频下的电力系统频率特性分析[J]. 高电压技术, 2015, 41(7): 2209-2216.
	MIAO Fufeng, TANG Xisheng, QI Zhiping. Analysis of frequency characteristics of power system based on wind farm-energy storage combined frequency re-gulation[J]. High Voltage Engineering, 2015, 41(7): 2209-2216.
[8]	BAO W Y, WU Q W, DING L, et al. Synthetic inertial control of wind farm with BESS based on model predictive control[J]. IET Renewable Power Generation, 2020, 14(13): 2447-2455. doi: 10.1049/iet-rpg.2019.0885 URL
[9]	FANG J Y, ZHANG R Q, LI H C, et al. Frequency derivative-based inertia enhancement by grid-connected power converters with a frequency-locked-loop[J]. IEEE Transactions on Smart Grid, 2019, 10(5): 4918-4927. doi: 10.1109/TSG.2018.2871085 URL
[10]	刘巨, 姚伟, 文劲宇, 等. 一种基于储能技术的风电场虚拟惯量补偿策略[J]. 中国电机工程学报, 2015, 35(7): 1596-1605.
	LIU Ju, YAO Wei, WEN Jinyu, et al. A wind farm virtual inertia compensation strategy based on energy storage system[J]. Proceedings of the CSEE, 2015, 35(7): 1596-1605.
[11]	WU Z P, GAO D W, ZHANG H G, et al. Coordinated control strategy of battery energy storage system and PMSG-WTG to enhance system frequency regulation capability[J]. IEEE Transactions on Sustainable Energy, 2017, 8(3): 1330-1343. doi: 10.1109/TSTE.2017.2679716 URL
[12]	JIANG Q Y, GONG Y Z, WANG H J. A battery energy storage system dual-layer control strategy for mitigating wind farm fluctuations[J]. IEEE Transactions on Power Systems, 2013, 28(3): 3263-3273. doi: 10.1109/TPWRS.2013.2244925 URL
[13]	ZHAO H R, WU Q W, GUO Q L, et al. Optimal active power control of a wind farm equipped with energy storage system based on distributed model predictive control[J]. IET Generation, Transmission & Distribution, 2016, 10(3): 669-677. doi: 10.1049/iet-gtd.2015.0112 URL
[14]	付红军, 陈惠粉, 赵华, 等. 高渗透率下风电的调频技术研究综述[J]. 中国电力, 2021, 54(1): 104-115.
	FU Hongjun, CHEN Huifen, ZHAO Hua, et al. Review on frequency regulation technology with high wind power penetration[J]. Electric Power, 2021, 54(1): 104-115.
[15]	ZHAO H R, WU Q W, GUO Q L, et al. Distributed model predictive control of a wind farm for optimal active power control—Part I: Clustering-based wind turbine model linearization[J]. IEEE Transactions on Sustainable Energy, 2015, 6(3): 831-839. doi: 10.1109/TSTE.2015.2418282 URL
[16]	GUO Y F, GAO H L, WU Q W, et al. Enhanced voltage control of VSC-HVDC-connected offshore wind farms based on model predictive control[J]. IEEE Transactions on Sustainable Energy, 2018, 9(1): 474-487. doi: 10.1109/TSTE.2017.2743005 URL
[17]	刘淼. 参与电网调频的风电场有功功率控制方法研究[D]. 成都: 电子科技大学, 2018.
	LIU Miao. Wind farm active power control method in power system frequency regulation[D]. Chengdu: University of Electronic Science and Technology of China, 2018.
[18]	WANG S Q, TOMSOVIC K. A novel active power control framework for wind turbine generators to improve frequency response[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6579-6589. doi: 10.1109/TPWRS.2018.2829748 URL
[19]	HUANG S, WU Q W, GUO Y F, et al. Hierarchical active power control of DFIG-based wind farm with distributed energy storage systems based on ADMM[J]. IEEE Transactions on Sustainable Energy, 2020, 11(3): 1528-1538. doi: 10.1109/TSTE.2019.2929820 URL
[20]	HUANG S, WU Q W, GUO Y F, et al. Bi-level decentralised active power control for large-scale wind farm cluster[J]. IET Renewable Power Generation, 2018, 12(13): 1486-1492. doi: 10.1049/iet-rpg.2017.0871 URL
[21]	ANDERSON P M, BOSE A. Stability simulation of wind turbine systems[J]. IEEE Transactions on Power Apparatus and Systems, 1983, 102(12): 3791-3795.