互联新能源电力系统区内AGC机组分布式协同控制策略

doi:10.11930/j.issn.1004-9649.202405101

中国电力 ›› 2025, Vol. 58 ›› Issue (3): 8-19.DOI: 10.11930/j.issn.1004-9649.202405101

• 高比例新能源接入电网的协调控制与优化运行 • 上一篇下一篇

互联新能源电力系统区内AGC机组分布式协同控制策略

张磊¹(), 马晓伟²(), 王满亮³(), 陈力²(), 高丙团³()

1. 中国电力科学研究院有限公司，江苏南京　210003
2. 国家电网有限公司西北分部，陕西西安　710048
3. 东南大学电气工程学院，江苏南京　210096

收稿日期:2024-05-27 出版日期:2025-03-28 发布日期:2025-03-26
作者简介:
张磊（1982），男，高级工程师（教授级），从事新能源并网控制技术研究，E-mail：zhang-lei3@epri.sgcc.com.cn
马晓伟（1979），男，高级工程师（教授级），从事电力系统调度与运行控制技术研究，E-mail：maxw@nw.sgcc.com.cn
王满亮（1999），男，博士研究生，从事新能源并网控制技术研究，E-mail：wangmanliang@seu.edu.cn
陈力（1995），男，工程师，从事电力系统调度与运行控制技术研究，E-mail：1094502622@qq.com
高丙团（1981），男，通信作者，教授，博士生导师，从事新能源并网控制、机器人与智能电力运维技术研究，E-mail：gaobingtuan@seu.edu.cn
基金资助:
国家电网有限公司科技项目（4000-20236084A-1-1-ZN）。

Distributed Collaborative Control Strategy for Intra-regional AGC Units in Interconnected Power System with Renewable Energy

Lei ZHANG¹(), Xiaowei MA²(), Manliang WANG³(), Li CHEN²(), Bingtuan GAO³()

1. China Electric Power Research Institute, Nanjing 210003, China
2. Northwest Branch of State Grid Corporation of China, Xi'an 710048, China
3. School of Electrical Engineering, Southeast University, Nanjing 210096, China

Received:2024-05-27 Online:2025-03-28 Published:2025-03-26
Supported by:
This work is supported by Science and Technology Project of SGCC (No.4000-20236084A-1-1-ZN).

摘要/Abstract

摘要：

区域电网中具备自动发电控制（automatic generation control，AGC）功能的新能源场站数量多并且不同调频机组的响应特性差异明显，加重了集中控制器的计算负担，影响区域电网频率控制效果。针对上述挑战，提出了一种基于机组动态模型和分布式一致性的互联电力系统区域内AGC机组协同控制策略，区域间根据联络线功率频率偏差进行跨区功率互济。首先，建立不同类型调频机组的调频响应功率模型。在此基础上，考虑调频控制的经济性，以有功出力变化量的二次成本函数最小为目标，兼顾各调频机组出力约束和功率平衡约束，构建功率动态分配优化模型，并证明通过合理的系数设定即可满足调频经济性最优，无须求解该优化问题。然后，提出了基于分布式一致性算法的AGC调频控制策略，实现区域内各参与机组AGC指令的整定。最后，以三区域互联电力系统为例进行仿真验证，结果表明：所提控制策略可以有效提高调频性能，降低机组的调频成本。

关键词: 区域互联电力系统, 新能源, 自动发电控制, 分布式协同控制, 调频经济性

Abstract:

In the regional power grid where a large number of renewable energy stations are equipped with automatic generation control (AGC) functionality, the response characteristics of different frequency regulation units vary significantly, which may aggravate the computation load of the centralized controller and affect the frequency control performance of the regional power system. In response to the above challenges, a collaborative control strategy for intra-regional AGC units in interconnected power system based on unit dynamic model and distributed consistency is proposed, while power interchange is dispatched across different regional power systems according to tie-line power flow and the bias of frequency. Firstly, the frequency response models for different types of frequency regulation units are established. Then, considering the frequency regulation economy, a power dynamic allocation optimization model is constructed, with the objective of minimizing the quadratic cost function of active power variation constrained by active power output and power balance. It is also proved that the objective of economic optimization for frequency regulation can be fulfilled by setting reasonable coefficients, without directly solving the optimization problem. Hence, an AGC frequency control strategy based on distributed consistency algorithm is proposed to determine the settings of intra-regional AGC commands. Finally, taking a three-area interconnected power system as an example, the results show that the proposed strategy can effectively improve frequency regulation performance and reduce the frequency regulation cost.

Key words: regional interconnected power system, renewable energy, automatic generation control, distributed collaborative control, frequency regulation economy

张磊, 马晓伟, 王满亮, 陈力, 高丙团. 互联新能源电力系统区内AGC机组分布式协同控制策略[J]. 中国电力, 2025, 58(3): 8-19.

Lei ZHANG, Xiaowei MA, Manliang WANG, Li CHEN, Bingtuan GAO. Distributed Collaborative Control Strategy for Intra-regional AGC Units in Interconnected Power System with Renewable Energy[J]. Electric Power, 2025, 58(3): 8-19.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202405101

https://www.electricpower.com.cn/CN/Y2025/V58/I3/8

图/表 17

图 1 控制区域i的AGC控制模型框图

Fig.1 Block diagram of AGC model for the ith control area

图 2 火电机组的调频响应模型

Fig.2 Frequency regulation model of the thermal power unit

图 3 火电机组的调频响应模型等效形式

Fig.3 Equivalent form of frequency regulation model of the thermal power unit

图 4 新能源机组的调频响应模型

Fig.4 Frequency regulation model of the new energy unit

图 5 多机组通信网络及邻接矩阵示意

Fig.5 Schematic diagram of multi-unit communication network and adjacency matrix

图 6 所提AGC控制策略

Fig.6 The proposed AGC control strategy

图 7 所提控制策略流程

Fig.7 Flowchart of the proposed control strategy

图 8 仿真结构图

Fig.8 One-line diagram of simulated power grid

表 1 仿真参数

Table 1 Simulation parameters

区域	机组	容量/MW	调差系数/ (MW·Hz^–1)	T_R/s	F_H
1	G1	1250	20	12.0	0.35
	G2	347	20	6.5	0.15
	G3	812	20	10.0	0.32
	风电场1	750	25
2	G4	790	20	9.0	0.30
	G5	635	20	8.0	0.26
	G6	812	20	10.0	0.30
	G7	700	20	8.5	0.28
	风电场2	750	25
3	G8	675	20	8.0	0.25
	G9	1037	20	12.0	0.35
	G10	312	20	6.0	0.20
	光伏电站1	830	30
	光伏电站2	830	30

图 9 区域2负荷突增0.2 p.u.系统频率

Fig.9 System frequency when the load increases by 0.2 p.u. in area 2

图 10 区域2负荷突增0.2 p.u.联络线功率

Fig.10 Tie-line power flow changes when the load increases by 0.2 p.u. in area 2

图 11 区域2各机组AGC指令变化

Fig.11 Results of the AGC command for each unit in area 2

图 12 区域2负荷突增0.4 p.u.系统频率

Fig.12 System frequency when the load increases by 0.4 p.u. in area 2

图 13 区域2负荷突增0.4 p.u.联络线功率

Fig.13 Tie-line power flow when the load increases by 0.4 p.u. in area 2

图 14 不同区域各机组AGC指令变化

Fig.14 Results of the AGC command for each unit in different area

表 2 不同AGC方法调频成本对比结果

Table 2 Comparison of frequency modulation cost for different AGC methods 单位：元

AGC方法	负荷突增0.2 p.u.	负荷突增0.4 p.u.
所提策略	1900	3330
集中式PI控制	2456	4211

图 15 不同新能源渗透率下系统频率结果

Fig.15 System frequency under different penetration rates of new energy

参考文献 30

1	辛保安, 李明节, 贺静波, 等. 新型电力系统安全防御体系探究[J]. 中国电机工程学报, 2023, 43 (15): 5723- 5732.
	XIN Baoan, LI Mingjie, HE Jingbo, et al. Research on security defense system of new power system[J]. Proceedings of the CSEE, 2023, 43 (15): 5723- 5732.
2	朱一兵, 丛野, 张粒子, 等. 跨国跨州/跨省跨区输电定价机制及对中国专项工程定价的启示[J]. 电力系统自动化, 2024, 48 (4): 86- 100. DOI
	ZHU Yibing, CONG Ye, ZHANG Lizi, et al. Transnational/interstate and trans-provincial/trans-regional transmission pricing mechanism and its enlightenment for pricing of dedicated projects in China[J]. Automation of Electric Power Systems, 2024, 48 (4): 86- 100. DOI
3	梁双, 王涉, 徐辉. 中国西电东送40年发展成效与政策建议[J]. 中国电力, 2024, 57 (11): 88- 93.
	LIANG Shuang, WANG She, XU Hui. Development achievements and policy suggestions of China's west to east power transmission for 40 Years[J]. Electric Power, 2024, 57 (11): 88- 93.
4	王鹤, 丁晨, 范高锋, 等. 基于AGC调频分区控制的光储联合系统参与市场投标策略[J]. 电力系统保护与控制, 2023, 51 (2): 121- 131.
	WANG He, DING Chen, FAN Gaofeng, et al. Photovoltaic and storage system participating in the electricity market based on AGC zoning control[J]. Power System Protection and Control, 2023, 51 (2): 121- 131.
5	王益斌, 陈飞雄, 邵振国, 等. 基于权重系数校正的火储两阶段联合调频方法[J]. 中国电力, 2024, 57 (3): 83- 94.
	WANG Yibin, CHEN Feixiong, SHAO Zhenguo, et al. Two-stage frequency regulation method for energy storage coordinated with thermal power unit based on weight coefficient correction[J]. Electric Power, 2024, 57 (3): 83- 94.
6	徐飞阳, 薛安成, 常乃超, 等. 电力系统自动发电控制网络攻击与防御研究现状与展望[J]. 电力系统自动化, 2021, 45 (3): 3- 14.
	XU Feiyang, XUE Ancheng, CHANG Naichao, et al. Research status and prospect of cyber attack and defense on automatic generation control in power system[J]. Automation of Electric Power Systems, 2021, 45 (3): 3- 14.
7	MA Y X, HU Z C, SONG Y H. Hour-ahead optimization strategy for shared energy storage of renewable energy power stations to provide frequency regulation service[J]. IEEE Transactions on Sustainable Energy, 2022, 13 (4): 2331- 2342. DOI
8	KARANAM A N, SHAW B. A new two-degree of freedom combined PID controller for automatic generation control of a wind integrated interconnected power system[J]. Protection and Control of Modern Power Systems, 2022, 7 (2): 1- 16.
9	LI Z W, CHENG Z P, LIANG J, et al. Distributed cooperative AGC method for new power system with heterogeneous frequency regulation resources[J]. IEEE Transactions on Power Systems, 2023, 38 (5): 4928- 4939. DOI
10	姚琦, 胡阳, 柳玉, 等. 考虑载荷抑制的风电场分布式自动发电控制[J]. 电工技术学报, 2022, 37 (3): 697- 706.
	YAO Qi, HU Yang, LIU Yu, et al. Distributed automatic generation control of wind farm considering load suppression[J]. Transactions of China Electrotechnical Society, 2022, 37 (3): 697- 706.
11	崔童飞, 李晓明, 王硕, 等. 区域新能源电力系统AGC调频容量实时评估方法[J]. 电力系统及其自动化学报, 2023, 35 (11): 43- 49.
	CUI Tongfei, LI Xiaoming, WANG Shuo, et al. Real-time evaluation method for AGC frequency modulation capacity of regional new energy power system[J]. Proceedings of the CSU-EPSA, 2023, 35 (11): 43- 49.
12	杜刚, 赵冬梅, 刘鑫. 含高比例风电系统随机备用调度研究[J]. 电网技术, 2023, 47 (4): 1378- 1387.
	DU Gang, ZHAO Dongmei, LIU Xin. Stochastic reserve scheduling of high proportion wind power system[J]. Power System Technology, 2023, 47 (4): 1378- 1387.
13	杨蕾, 李胜男, 黄伟, 等. 考虑风光新能源参与二次调频的多源最优协同控制[J]. 电力系统保护与控制, 2020, 48 (19): 43- 49.
	YANG Lei, LI Shengnan, HUANG Wei, et al. Optimal coordinated control of multi-source for AGC with participation of wind and solar energy[J]. Power System Protection and Control, 2020, 48 (19): 43- 49.
14	ZHANG X S, TAN T, ZHOU B, et al. Adaptive distributed auction-based algorithm for optimal mileage based AGC dispatch with high participation of renewable energy[J]. International Journal of Electrical Power & Energy Systems, 2021, 124, 106371.
15	杨冬锋, 朱军豪, 姜超, 等. 基于分布式模型预测的高比例风电系统多源协同负荷频率控制策略[J]. 电网技术, 2024, 48 (7): 2804- 2814.
	YANG Dongfeng, ZHU Junhao, JIANG Chao, et al. Highly-proportional wind power system multi-source collaborate load frequency control strategy based on distributed model prediction[J]. Power System Technology, 2024, 48 (7): 2804- 2814.
16	KASIS A, MONSHIZADEH N, LESTAS I. A distributed scheme for secondary frequency control with stability guarantees and optimal power allocation[J]. Systems & Control Letters, 2020, 144, 104755.
17	李彦营, 席磊, 郭宜果, 等. 基于权重双Q-时延更新学习算法的自动发电控制[J]. 中国电机工程学报, 2022, 42 (15): 5459- 5471.
	LI Yanying, XI Lei, GUO Yiguo, et al. Automatic generation control based on the weighted double Q-delayed update learning algorithm[J]. Proceedings of the CSEE, 2022, 42 (15): 5459- 5471.
18	王晶晶, 廖思阳, 姚良忠, 等. 基于一致性算法的直流受端电网分布式调频资源协同频率控制[J]. 电网技术, 2022, 46 (3): 888- 900.
	WANG Jingjing, LIAO Siyang, YAO Liangzhong, et al. Coordinated frequency control strategy for DC receiving-end power grid with distributed frequency regulation resources using consensus algorithm[J]. Power System Technology, 2022, 46 (3): 888- 900.
19	李忠文, 柏宁宁, 程志平, 等. 新型电力系统中异质调频机组分布式协同AGC方法研究[J]. 电网技术, 2024, 48 (6): 2327- 2335.
	LI Zhongwen, BAI Ningning, CHENG Zhiping, et al. Distributed collaborative AGC method for heterogeneous frequency regulation units in new power systems[J]. Power System Technology, 2024, 48 (6): 2327- 2335.
20	田野, 李正烁, 吴文传. 计及调节资源AGC动态特性的离散-连续时间随机前瞻经济调度[J]. 中国电机工程学报, 2024, 44 (7): 2578- 2590.
	TIAN Ye, LI Zhengshuo, WU Wenchuan. Discrete-continuous-time stochastic look-ahead economic dispatch model considering the dynamic characteristics of AGC provided by regulating resources[J]. Proceedings of the CSEE, 2024, 44 (7): 2578- 2590.
21	TSYDENOV E, PROKHOROV A, WANG L. Online estimation of plant participation factors for automatic generation control in power systems with variable energy resources[J]. IEEE Transactions on Industry Applications, 2022, 58 (4): 4401- 4410. DOI
22	杨丽, 孙元章, 徐箭, 等. 基于在线强化学习的风电系统自适应负荷频率控制[J]. 电力系统自动化, 2020, 44 (12): 74- 83. DOI
	YANG Li, SUN Yuanzhang, XU Jian, et al. Adaptive load frequency control of wind power system based on online reinforcement learning[J]. Automation of Electric Power Systems, 2020, 44 (12): 74- 83. DOI
23	梁煜东, 陈峦, 张国洲, 等. 基于深度强化学习的多能互补发电系统负荷频率控制策略[J]. 电工技术学报, 2022, 37 (7): 1768- 1779.
	LIANG Yudong, CHEN Luan, ZHANG Guozhou, et al. Load frequency control strategy of hybrid power generation system: a deep reinforcement learning: based approach[J]. Transactions of China Electrotechnical Society, 2022, 37 (7): 1768- 1779.
24	MA Y X, HU Z C, SONG Y H. A reinforcement learning based coordinated but differentiated load frequency control method with heterogeneous frequency regulation resources[J]. IEEE Transactions on Power Systems, 2024, 39 (1): 2239- 2250. DOI
25	LI J W, YU T, CUI H Y. A multi-agent deep reinforcement learning-based "Octopus" cooperative load frequency control for an interconnected grid with various renewable units[J]. Sustainable Energy Technologies and Assessments, 2022, 51, 101899. DOI
26	ZHANG X S, LI C Z, XU B, et al. Dropout deep neural network assisted transfer learning for bi-objective Pareto AGC dispatch[J]. IEEE Transactions on Power Systems, 2023, 38 (2): 1432- 1444. DOI
27	李卫东, 常烨骙, 陈兆庆, 等. 区域控制偏差的动态内涵[J]. 电力系统自动化, 2016, 40 (24): 146- 150, 163. DOI
	LI Weidong, CHANG Yekui, CHEN Zhaoqing, et al. Dynamic contents of area control error[J]. Automation of Electric Power Systems, 2016, 40 (24): 146- 150, 163. DOI
28	刘子琦, 刘洋, 李卫东, 等. 火电机组一次调频和二次调频响应功率数据剥离方法[J]. 电力系统自动化, 2023, 47 (16): 132- 141. DOI
	LIU Ziqi, LIU Yang, LI Weidong, et al. Data separation method for primary and secondary frequency regulation response power of thermal power units[J]. Automation of Electric Power Systems, 2023, 47 (16): 132- 141. DOI
29	LI J P, LI Y J, LI W B, et al. System power imbalance estimation utilizing linear component of local frequency[J]. IEEE Transactions on Power Systems, 2024, 39 (1): 2373- 2376. DOI
30	闵勇, 陈磊, 刘瑞阔, 等. 电力系统频率动态中惯量与惯量响应特性辨析[J]. 中国电机工程学报, 2023, 43 (3): 855- 868.
	MIN Yong, CHEN Lei, LIU Ruikuo, et al. Analysis on characteristics of inertia and inertial response in power system frequency dynamics[J]. Proceedings of the CSEE, 2023, 43 (3): 855- 868.

[1]	汪林光, 李旭涛, 任勇, 谢小荣. 基于元启发式算法的新能源电力系统振荡稳定性最差工况搜索方法[J]. 中国电力, 2025, 58(3): 65-72.
[2]	邹小燕, 张瑞宏. 考虑政府干预的可再生能源与储能企业合作模式演化博弈研究[J]. 中国电力, 2025, 58(1): 153-163.
[3]	王雨晴, 张敏, 王嘉兴, 李泊皓, 杨天阳, 曾鸣. 基于超模博弈的共享储能容量租赁价格决策[J]. 中国电力, 2025, 58(1): 164-173.
[4]	孙东磊, 王宪, 孙毅, 孟祥飞, 张涌琛, 张玉敏. 基于多面体不确定集合的电力系统灵活性量化评估方法[J]. 中国电力, 2024, 57(9): 146-155.
[5]	孙东磊, 孙毅, 刘蕊, 孙鹏凯, 张玉敏. 计及多层级配电网的分布式新能源最大消纳空间分解测算[J]. 中国电力, 2024, 57(8): 108-116.
[6]	陈胜, 张洪略, 夏天, 杨雪, 张文军, 颜伟. 考虑TCPS考核约束的AGC机组动态优化模型[J]. 中国电力, 2024, 57(8): 145-151.
[7]	李鲁阳, 陈龙翔, 陈磊, 孙大卫, 吴林林, 闵勇. 用于新能源一次调频的储能经济配置研究[J]. 中国电力, 2024, 57(7): 54-65.
[8]	李佳蔚, 张冠宇. 大规模分布式新能源接入对省级电网稳定性影响[J]. 中国电力, 2024, 57(6): 174-180.
[9]	高政南, 姜楠, 陈启鑫, 徐江, 王海利, 辛力, 徐青贵. 德国电力市场能源转型建设及启示[J]. 中国电力, 2024, 57(6): 204-214.
[10]	李咸善, 丁胜彪, 李飞, 李欣. 考虑水电调节费用补偿的风光水联盟优化调度策略[J]. 中国电力, 2024, 57(5): 26-38.
[11]	朱子民, 张锦芳, 常清, 周专, 张晓林. 大规模新能源接入弱同步支撑柔直系统的送端自适应VSG控制策略[J]. 中国电力, 2024, 57(5): 211-221.
[12]	王帅, 黄越辉, 聂元弘, 刘思扬. 基于生产模拟的受端电网新能源发展场景研究[J]. 中国电力, 2024, 57(5): 240-250.
[13]	李虎军, 张栋, 吕梦璇, 邓方钊, 杨萌, 元博. 考虑碳排放约束的新能源与调峰资源优化配置方法[J]. 中国电力, 2024, 57(4): 42-51.
[14]	叶小宁, 王彩霞, 李琼慧, 杨超. 国外新能源高占比电力系统电力供应保障措施及启示[J]. 中国电力, 2024, 57(4): 61-67.
[15]	周勤勇, 李根兆, 秦晓辉, 施浩波, 陈文静, 龚浩岳. 能源革命下的电力系统范式转换分析[J]. 中国电力, 2024, 57(3): 1-11.

互联新能源电力系统区内AGC机组分布式协同控制策略

Distributed Collaborative Control Strategy for Intra-regional AGC Units in Interconnected Power System with Renewable Energy

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 30

相关文章 15

编辑推荐

Metrics

模态框（Modal）标题

互联新能源电力系统区内AGC机组分布式协同控制策略

Distributed Collaborative Control Strategy for Intra-regional AGC Units in Interconnected Power System with Renewable Energy

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 30

相关文章 15

编辑推荐

Metrics