基于模型预测控制的风储联合电场参与电网二次调频策略

doi:10.16183/j.cnki.jsjtu.2022.217

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (1): 91-101.doi: 10.16183/j.cnki.jsjtu.2022.217

• 新型电力系统与综合能源 • 上一篇下一篇

基于模型预测控制的风储联合电场参与电网二次调频策略

刘传斌¹, 矫文书¹, 吴秋伟²(), 陈健¹, 周前³

1.山东大学电气工程学院,济南 250061
2.清华大学清华伯克利深圳学院;清华深圳国际研究生院, 广东深圳 518055
3.国网江苏省电力有限公司电力科学研究院,南京 211100

收稿日期:2022-06-13 修回日期:2022-07-06 接受日期:2022-07-13 出版日期:2024-01-28 发布日期:2024-01-16
通讯作者: 吴秋伟,教授,博士生导师;E-mail:qiuwu@sz.tsinghua.edu.cn.
作者简介:刘传斌(1997-),硕士生,从事风场控制研究.
基金资助:
国家自然科学基金(51877124)

Strategy of Wind-Storage Combined System Participating in Power System Secondary Frequency Regulation Based on Model Predictive Control

LIU Chuanbin¹, JIAO Wenshu¹, WU Qiuwei²(), CHEN Jian¹, ZHOU Qian³

1. School of Electrical Engineering, Shandong University, Jinan 250061, China
2. Tsinghua Berkeley Shenzhen Institute; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China
3. Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211100, China

Received:2022-06-13 Revised:2022-07-06 Accepted:2022-07-13 Online:2024-01-28 Published:2024-01-16

摘要/Abstract

摘要：

随着风力发电在电网中渗透率不断增加,需要风储联合电场参与电网调频服务,维持电网的频率稳定.针对中高风速下的风储联合电场,通过分析风力发电机的机械特性和储能系统的运行特性,确定了减载运行方式下风力发电机桨距角的控制方式,提出一种基于模型预测控制的风储联合电场参与电网二次调频的控制策略.建立风电场桨距角控制的预测模型和电化学储能系统的预测模型,优化风力发电机和储能系统的有功功率输出,在调频基础上更好地减少了风能损失.根据上级系统的有功功率指令值和风力发电机实际输出功率之间的差值对桨距角控制进行进一步修正,使得风力发电机在二次调频期间能够更好地追踪到上级系统的功率指令值,迅速响应频率变化值,减小动态频率偏差,完成二次调频任务.仿真结果表明,该控制策略综合考虑了风力发电机可控的二次调频能力和电化学储能系统响应快速、跟踪精确的特性,使风储联合电场能够主动响应系统频率的变化,更好地跟踪上级系统下发的有功功率指令值,参与电网二次调频.

关键词: 风储联合电场, 模型预测控制, 桨距角控制, 电化学储能系统, 二次调频, 有功功率追踪

Abstract:

With the increasing penetration of wind power in power grids, it is necessary for wind storage joint farms to participate in power grid frequency modulation to maintain frequency stability of the power grid. By analyzing the mechanical characteristics of the wind turbine and the operation characteristics of the energy storage system, this paper determines the adjustability of the wind turbine power output in the pitch angle load shedding operation mode, and proposes a control strategy for the wind farm with an energy storage system to participate in the secondary frequency regulation of the power grid based on model predictive control (MPC). It establishes a prediction model for pitch angle control of the wind farm and an electrochemical energy storage system, optimizing the active power output of the wind turbine and the energy storage system, and better reducing the wind energy loss based on frequency regulation. The pitch angle control is further corrected based on the difference between the active power command value of the superior system and the actual power output of the wind turbine, so that the wind turbine can better track the power command value of the superior system during secondary frequency regulation, quickly respond to the frequency changes, reduce the dynamic frequency deviation, avoid load rejection due to too low frequency drop, and complete the task of secondary frequency regulation. The simulation results show that under the control strategy proposed in this paper, the controllable secondary frequency regulation ability of the wind turbine and the characteristics of fast response and accurate tracking of the energy storage system are comprehensively considered, the active power command issued by the superior system is better tracked, and the task of the wind farm including the energy storage system participating in the secondary frequency regulation is realized.

Key words: wind-storage combined system, model predictive control (MPC), pitch angle control, second frequency regulation, electrochemical energy storage system, active power tracking

中图分类号:

TM614

刘传斌, 矫文书, 吴秋伟, 陈健, 周前. 基于模型预测控制的风储联合电场参与电网二次调频策略[J]. 上海交通大学学报, 2024, 58(1): 91-101.

LIU Chuanbin, JIAO Wenshu, WU Qiuwei, CHEN Jian, ZHOU Qian. Strategy of Wind-Storage Combined System Participating in Power System Secondary Frequency Regulation Based on Model Predictive Control[J]. Journal of Shanghai Jiao Tong University, 2024, 58(1): 91-101.

图/表 16

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

图14

图15

图16

参考文献 24

[1]	岳涵, 郑宽, 于洋, 等. 大规模双馈风电接入对东北电网稳定性的影响[J]. 电工电能新技术, 2013, 32(4): 1-6.
	YUE Han, ZHENG Kuan, YU Yang, et al. Impact of large-scale DFIG based wind power on stability of Northeast Power Grid[J]. Advanced Technology of Electrical Engineering and Energy, 2013, 32(4): 1-6.
[2]	DÍAZ-GONZÁLEZ F, HAU M, SUMPER A, et al. Participation of wind power plants in system frequency control: Review of grid code requirements and control methods[J]. Renewable and Sustainable Energy Reviews, 2014, 34: 551-564. doi: 10.1016/j.rser.2014.03.040 URL
[3]	白雁翔. 双馈风电机组的运行控制及其参与电网调频的控制策略研究[D]. 成都: 西南交通大学, 2018.
	BAI Yanxiang. Research on operation control of doubly-fed wind turbine and control strategy of participating in power grid frequency modulation[D]. Chengdu: Southwest Jiaotong University, 2018.
[4]	李建林, 马会萌, 惠东. 储能技术融合分布式可再生能源的现状及发展趋势[J]. 电工技术学报, 2016, 31(14): 1-10.
	LI Jianlin, MA Huimeng, HUI Dong. Present development condition and trends of energy storage technology in the integration of distributed renewable energy[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 1-10.
[5]	薛迎成, 邰能灵, 刘立群, 等. 双馈风力发电机参与系统频率调节新方法[J]. 高电压技术, 2009, 35(11): 2839-2845.
	XUE Yingcheng, TAI Nengling, LIU Liqun, et al. New method of system frequency regulation with doubly fed induction generator(DFIG)[J]. High Voltage Engineering, 2009, 35(11): 2839-2845.
[6]	MORREN J, DE HAAN S W H, KLING W L, et al. Wind turbines emulating inertia and supporting primary frequency control[J]. IEEE Transactions on Power Systems, 2006, 21(1): 433-434. doi: 10.1109/TPWRS.2005.861956 URL
[7]	田新首, 王伟胜, 迟永宁, 等. 基于双馈风电机组有效储能的变参数虚拟惯量控制[J]. 电力系统自动化, 2015, 39(5): 20-26.
	TIAN Xinshou, WANG Weisheng, CHI Yongning, et al. Variable parameter virtual inertia control based on effective energy storage of DFIG-based wind turbines[J]. Automation of Electric Power Systems, 2015, 39(5): 20-26.
[8]	丁磊, 尹善耀, 王同晓, 等. 结合超速备用和模拟惯性的双馈风机频率控制策略[J]. 电网技术, 2015, 39(9): 2385-2391.
	DING Lei, YIN Shanyao, WANG Tongxiao, et al. Integrated frequency control strategy of DFIGs based on virtual inertia and over-speed control[J]. Power System Technology, 2015, 39(9): 2385-2391.
[9]	BAO W Y, WU Q W, DING L, et al. A hierarchical inertial control scheme for multiple wind farms with BESSs based on ADMM[J]. IEEE Transactions on Sustainable Energy, 2021, 12(2): 751-760. doi: 10.1109/TSTE.2020.2995101 URL
[10]	GHOSH S, KAMALASADAN S, SENROY N, et al. Doubly fed induction generator (DFIG)-based wind farm control framework for primary frequency and inertial response application[J]. IEEE Transactions on Power Systems, 2015, 31(3): 1861-1871. doi: 10.1109/TPWRS.2015.2438861 URL
[11]	高明杰, 惠东, 高宗和, 等. 国家风光储输示范工程介绍及其典型运行模式分析[J]. 电力系统自动化, 2013, 37(1): 59-64.
	GAO Mingjie, HUI Dong, GAO Zonghe, et al. Presentation of national wind/photovoltaic/energy storage and transmission demonstration project and analysis of typical operation modes[J]. Automation of Electric Power Systems, 2013, 37(1): 59-64.
[12]	李建林, 王上行, 袁晓冬, 等. 江苏电网侧电池储能电站建设运行的启示[J]. 电力系统自动化, 2018, 42(21): 1-9.
	LI Jianlin, WANG Shangxing, YUAN Xiaodong, et al. Enlightenment from construction and operation of battery energy storage station on grid side in Jiangsu power grid[J]. Automation of Electric Power Systems, 2018, 42(21): 1-9.
[13]	沈阳武, 宋兴荣, 罗紫韧, 等. 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, et al. 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.
[14]	游广增, 杭志, 陈凯, 等. 基于改进粒子群算法的风机频率控制研究[J]. 电力工程技术, 2020, 39(3): 43-50.
	YOU Guangzeng, HANG Zhi, CHEN Kai, et al. Wind turbine generator frequency control based on improved particle swarm optimization[J]. Electric Power Engineering Technology, 2020, 39(3): 43-50.
[15]	刘柳, 王德林, 杨仁杰, 等. 基于桨距角控制的双馈风机参与电网二次调频控制策略研究[J]. 电工电能新技术, 2020, 39(5): 10-16. doi: 10.12067/ATEEE1903039
	LIU Liu, WANG Delin, YANG Renjie, et al. Research on control strategy of DFIG participating in secondary frequency regulation based on pitch control[J]. Advanced Technology of Electrical Engineering and Energy, 2020, 39(5): 10-16. doi: 10.12067/ATEEE1903039
[16]	董天翔, 翟保豫, 李星, 等. 风储联合系统参与频率响应的优化控制策略[J]. 电网技术, 2022, 46(10): 3980-3988.
	DONG Tianxiang, ZHAI Baoyu, LI Xing, et al. Optimal control strategy for combined wind-storage system to participate in frequency response[J]. Power System Technology, 2022, 46(10): 3980-3988.
[17]	孙诚斌, 李兆伟, 李碧君, 等. 电化学储能参与电网低频第三道防线的控制策略[J]. 电力工程技术, 2021, 40(3): 27-34.
	SUN Chengbin, LI Zhaowei, LI Bijun, et al. A control strategy for the low frequency third defense line of power grid containing the electrochemical energy storage[J]. Electric Power Engineering Technology, 2021, 40(3): 27-34.
[18]	裴振坤, 王学梅, 康龙云. 考虑用户充电计划的电动汽车辅助调频控制策略[J]. 电力工程技术, 2023, 42(1): 88-97.
	PEI Zhenkun, WANG Xuemei, KANG Longyun. Auxiliary frequency regulation control strategy for electric vehicles considering users’ charging plans[J]. Electric Power Engineering Technology, 2023, 42(1): 88-97.
[19]	杨罡. 电力系统模型预测控制技术研究[D]. 北京: 北京交通大学, 2013.
	YANG Gang. Research on model predictive control technology of power system[D]. Beijing: Beijing Jiaotong University, 2013.
[20]	张怡, 刘向杰. 互联电力系统鲁棒分布式模型预测负荷频率控制[J]. 控制理论与应用, 2016, 33(5): 621-630.
	ZHANG Yi, LIU Xiangjie. Robust distributed model predictive control for load frequency control of uncertain power systems[J]. Control Theory & Applications, 2016, 33(5): 621-630.
[21]	AL-GHERWI W, BUDMAN H, ELKAMEL A. A robust distributed model predictive control algorithm[J]. Journal of Process Control, 2011, 21(8): 1127-1137. doi: 10.1016/j.jprocont.2011.07.002 URL
[22]	沈坤, 张少云, 刘录光. 双馈风力发电系统模型预测控制算法研究[J]. 电力电子技术, 2019, 53(9): 86-89.
	SHEN Kun, ZHANG Shaoyun, LIU Luguang. Research on MPC algorithm for doubly-fed wind power generation system[J]. Power Electronics, 2019, 53(9): 86-89.
[23]	严干贵, 刘莹, 段双明, 等. 电池储能单元群参与电力系统二次调频的功率分配策略[J]. 电力系统自动化, 2020, 44(14): 26-34.
	YAN Gangui, LIU Ying, DUAN Shuangming, et al. Power distribution strategy for battery energy storage unit group participating in secondary frequency regulation of power system[J]. Automation of Electric Power Systems, 2020, 44(14): 26-34.
[24]	任洛卿, 白泽洋, 于昌海, 等. 风光储联合发电系统有功控制策略研究及工程应用[J]. 电力系统自动化, 2014, 38(7): 105-111.
	REN Luoqing, BAI Zeyang, YU Changhai, et al. Research on active power control strategy for wind/photovoltaic/energy storage hybrid power system and its engineering application[J]. Automation of Electric Power Systems, 2014, 38(7): 105-111.

基于模型预测控制的风储联合电场参与电网二次调频策略

Strategy of Wind-Storage Combined System Participating in Power System Secondary Frequency Regulation Based on Model Predictive Control

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 16

参考文献 24

相关文章 14

编辑推荐

Metrics

本文评价

[1]	姜恩宇, 陈宇, 施峥靖, 吴哲城, 林顺富, 李东东. 考虑碳配额引导需求响应的微电网能量管理策略[J]. 上海交通大学学报, 2023, 57(9): 1126-1136.
[2]	奚鑫泽, 邢超, 覃日升, 何廷一, 和鹏, 孟贤, 程春辉. 含双馈风力发电系统的配电网短路电流特性[J]. 上海交通大学学报, 2023, 57(7): 921-927.
[3]	张晨宇, 孟帅. 基于模型预测控制的深海钻井立管再入井仿真分析[J]. 上海交通大学学报, 2023, 57(11): 1389-1399.
[4]	姜俊豪, 陈刚. 驾驶机器人转向操纵的动态模型预测控制方法[J]. 上海交通大学学报, 2022, 56(5): 594-603.
[5]	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.
[6]	李彪, 王立文, 邢志伟, 王思博, 罗谦. 飞机地面除冰资源协同控制[J]. 上海交通大学学报, 2021, 55(11): 1362-1370.
[7]	何德峰, 彭彬彬, 顾煜佳, 余世明. 基于高斯过程回归的车辆巡航系统学习预测控制[J]. 上海交通大学学报, 2020, 54(9): 904-909.
[8]	王琳，邹媛媛，李少远. 基于性能触发的双层结构模型预测控制[J]. 上海交通大学学报（自然版）, 2018, 52(10): 1324-1332.
[9]	王玄，陶建峰，张峰榕，吴亚瑾，刘成良. 基于预测控制的单向比例泵控非对称液压缸系统控制方法[J]. 上海交通大学学报（自然版）, 2016, 50(05): 696-701.
[10]	罗莉华. 基于MPC的车道保持系统转向控制策略[J]. 上海交通大学学报（自然版）, 2014, 48(07): 1015-1020.
[11]	鲍荣, 何德峰, 郑凯华. 基于黄金分割法的阶梯式约束预测控制[J]. 上海交通大学学报（自然版）, 2012, 46(12): 1940-1944.
[12]	李刚1, 2, 宗长富1, 张泽星1, 段明序1, 梁赫奇3, 洪伟1. 车辆底盘集成控制研究[J]. 上海交通大学学报（自然版）, 2012, 46(08): 1291-1296.
[13]	黄昆, 喻凡, 张勇超. 基于能量流动分析的电磁式馈能型主动悬架控制[J]. 上海交通大学学报（自然版）, 2011, 45(07): 1068-1073.
[14]	黄昆，喻凡，张勇超. 电磁式主动悬架模型预测控制器设计 [J]. 上海交通大学学报（自然版）, 2010, 44(11): 1619-1624.