考虑源网协同支撑作用的含新能源电力系统网架重构决策优化方法

doi:10.11930/j.issn.1004-9649.202312022

中国电力 ›› 2024, Vol. 57 ›› Issue (10): 150-157.DOI: 10.11930/j.issn.1004-9649.202312022

• 应对非常规安全风险的新型电力系统规划、运行与控制关键技术 • 上一篇下一篇

考虑源网协同支撑作用的含新能源电力系统网架重构决策优化方法

周健¹(), 冯楠¹(), 季怡萍¹, 冯煜尧¹, 王帅², 李少岩²()

1. 国网上海市电力公司电力科学研究院，上海　200437
2. 华北电力大学电气与电子工程学院，河北保定　071003

收稿日期:2023-12-07 出版日期:2024-10-28 发布日期:2024-10-25
作者简介:周健（1974—），男，硕士，高级工程师，从事新型电力系统研究，E-mail：15121172367@163.com
冯楠（1989—），女，硕士，高级工程师，从事电力系统安全稳定运行分析、系统恢复与韧性提升等研究，E-mail：weixingzh2017@126.com
李少岩（1989—），男，通信作者，博士，副教授，从事新型电力系统安全防御与恢复控制、韧性评估与主动提升等研究，E-mail：shaoyan.li@ncepu.edu.cn
基金资助:
国家电网有限公司科技项目（52094022004S）。

A Decision-making Optimization Method for Network Reconfiguration of Power System With New Energy Considering the Synergistic Support of Source-grid

Jian ZHOU¹(), Nan FENG¹(), Yiping JI¹, Yuyao FENG¹, Shuai WANG², Shaoyan LI²()

1. Electric Power Research Institute of State Grid Shanghai Municipal Electric Power Company, Shanghai 200437, China
2. School of Electrical & Electronic Engineering, North China Electric Power University, Baoding 071003, China

Received:2023-12-07 Online:2024-10-28 Published:2024-10-25
Supported by:
This work is supported by Science and Technology Project of SGCC (No.52094022004S).

摘要/Abstract

摘要：

面对复杂多变的国际形势和日益增多的极端事件，研究新能源高比例接入下的系统恢复方案，对完善新型电力系统安全防御体系具有重要意义。在此背景下，提出一种含新能源电力系统网架重构决策优化方法。首先，基于核密度法对新能源出力不确定性进行分析建模；其次，考虑新能源并网和运行对网架强度的要求，实现了新能源多场站短路比约束的线性化建模；在此基础上，建立了能协同新能源、储能、常规机组和输电网络恢复的网架重构优化模型，并提出双层优化策略以提升模型求解效率。基于新英格兰10机39节点系统的算例结果验证了所提方法的有效性。

关键词: 电力系统恢复, 核密度法, 新能源多场站短路比, 双层优化

Abstract:

Facing the complex and changeable international situation and the increasing number of extreme events, it is of great significance to study the system recovery scheme under the high proportion of new energy access to improve the new power system security defense system. In this context, a decision-making optimization method for grid reconfiguration of power system with new energy is proposed. Firstly, the uncertainty of new energy output is analyzed and modeled based on the kernel density method. Secondly, considering the requirements of new energy grid connection and operation on the strength of the grid, the linearized model of multiple renewable energy stations short-circuit ratio constraint is realized. On this basis, a grid reconfiguration optimization model that can coordinate new energy, energy storage, conventional units and transmission network restoration is established and a bi-level optimization strategy is proposed to improve the efficiency of model solution. The example results based on the New England 10-machine 39-bus system verify the effectiveness of the proposed method.

Key words: power system restoration, nuclear density method, multiple renewable energy stations short-circuit ratio (MRSCR), bi-level optimization

中图分类号:

TM744

周健, 冯楠, 季怡萍, 冯煜尧, 王帅, 李少岩. 考虑源网协同支撑作用的含新能源电力系统网架重构决策优化方法[J]. 中国电力, 2024, 57(10): 150-157.

Jian ZHOU, Nan FENG, Yiping JI, Yuyao FENG, Shuai WANG, Shaoyan LI. A Decision-making Optimization Method for Network Reconfiguration of Power System With New Energy Considering the Synergistic Support of Source-grid[J]. Electric Power, 2024, 57(10): 150-157.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202312022

https://www.electricpower.com.cn/CN/Y2024/V57/I10/150

图/表 8

图 1 新能源预测误差概率分布

Fig.1 The probability distribution of new energy prediction error

图 2 双层优化模型求解流程

Fig.2 Bi-level optimization model solving process

表 1 新英格兰10机39节点系统机组参数

Table 1 Unit parameters of New England 10-unit 39-bus system

编号	$P_g^{\max } $/ MW	P_st,g/ MW	T_a,g/ h	P_r,g/ MW	$K_{p,g}^{\rm{max}} $/ (MW·h^–1)	$X'_d $(p.u.)
30	350	/	/	/	320	0.043
31	460	16.0	0.50	130	300	0.060
32	520	18.0	0.46	155	314	0.054
33	432	13.5	0.25	130	275	0.051
34	508	17.5	0.83	160	300	0.075
35	650	20.0	1.00	185	308	0.050
36	360	12.0	0.58	108	260	0.062
37	540	16.5	0.46	160	320	0.048
38	830	23.0	0.67	235	405	0.055

图 3 新能源预测出力曲线

Fig.3 New energy forecast output curve

图 4 新英格兰10机39节点系统的网架重构方案

Fig.4 The grid reconstruction scheme of New England 10-machine 39-node system

表 2 各时步恢复数据

Table 2 The restoration data of each time step

时步	新能源出力/MW	常规机组出力/MW	储能电站充电功率/MW		负荷恢复率/%
时步	新能源出力/MW	常规机组出力/MW	C1	C2	负荷恢复率/%
1	0	80	0	0	1.10
2	0	160	0	0	2.49
3	281	240	40	100	5.79
4	362	395	0	60	10.64
5	620	580	50	80	16.89
6	759	803	20	0	24.67
7	835	1096	–40	–60	33.14
8	1208	1467	0	50	43.12
9	1360	1950	0	50	54.12
10	1533	2476	20	70	65.54

图 5 新能源调度出力曲线

Fig.5 New energy dispatching output curve

图 6 各时步新能源多场站短路比指标

Fig.6 The multiple renewable energy stations short-circuit ratio index at each time step

参考文献 23

1	李明明, 孙磊, 马英浩. 大停电事故后计及信息系统故障的机组启动次序优化策略[J]. 中国电力, 2022, 55 (9): 146- 155.
	LI Mingming, SUN Lei, MA Yinghao. An optimization strategy for generator start-up sequence after blackouts considering the cyber system fault[J]. Electric Power, 2022, 55 (9): 146- 155.
2	李婧斐, 黎琦, 罗萍萍, 等. 计及停电损失的电力系统黑启动分区策略[J]. 南方电网技术, 2022, 16 (3): 58- 66.
	LI Jingfei, LI Qi, LUO Pingping, et al. Partition strategies of power system black-start considering outage loss[J]. Southern Power System Technology, 2022, 16 (3): 58- 66.
3	魏瑞增, 王磊, 梁永超, 等. 台风灾害下电力抢修队伍智能调拨技术研究[J]. 智慧电力, 2023, 51 (1): 123- 130.
	WEI Ruizeng, WANG Lei, LIANG Yongchao, et al. Intelligent allocation technology for electric emergency repair crew under typhoon disaster[J]. Smart Power, 2023, 51 (1): 123- 130.
4	周勤勇, 李根兆, 秦晓辉, 等. 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.
	ZHOU Qinyong, LI Genzhao, QIN Xiaohui, et al. Analysis of power system paradigm shift under energy revolution[J]. Electric Power, 2024, 57 (3): 1- 11.
5	吴应双, 冯祥勇, 王寅, 等. 一种考虑新能源电站出力不确定性的采样鲁棒无功优化方法[J]. 电力科学与技术学报, 2023, 38 (2): 84- 95.
	WU Yingshuang, FENG Xiangyong, WANG Yin, et al. A sample robust reactive power optimization approach considering the power output uncertainty of renewable energy stations[J]. Journal of Electric Power Science and Technology, 2023, 38 (2): 84- 95.
6	李吉峰, 唐克, 王孜航, 等. 计及多源互补特性的新型电力系统分布式电源承载能力评估[J]. 东北电力大学学报, 2023, 43 (1): 62- 68.
	LI Jifeng, TANG Ke, WANG Zihang, et al. Assessment of distributed power generations bearing capacity of modern power systems with multi-sources complementary characteristics[J]. Journal of Northeast Electric Power University, 2023, 43 (1): 62- 68.
7	翟晶晶, 吴晓蓓, 傅质馨, 等. 考虑需求响应与光伏不确定性的综合能源系统鲁棒优化[J]. 中国电力, 2020, 53 (8): 9- 18.
	ZHAI Jingjing, WU Xiaobei, FU Zhixin, et al. Robust optimization of integrated energy systems considering demand response and photovoltaic uncertainty[J]. Electric Power, 2020, 53 (8): 9- 18.
8	Song Z, Huang Y, Xie H, et al. Generation method of multi-regional photovoltaic output scenarios-set using conditional generative adversarial networks[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2023, 13 (3): 861- 870.
9	张璨, 孙磊, 林振智, 等. 含风电场的电力系统最优网络重构策略[J]. 电力系统及其自动化学报, 2016, 28 (2): 22- 29.
	ZHANG Can, SUN Lei, LIN Zhenzhi, et al. Optimal network reconfiguration strategy for power systems with integrated wind farms[J]. Proceedings of the CSU-EPSA, 2016, 28 (2): 22- 29.
10	梁海平, 程子玮, 孙海新, 等. 计及风电预测误差不确定性的风电参与网架重构优化[J]. 电力系统自动化, 2019, 43 (7): 151- 158, 184.
	LIANG Haiping, CHENG Ziwei, SUN Haixin, et al. Optimization of power network reconstruction with wind farm considering uncertainty of wind power prediction error[J]. Automation of Electric Power Systems, 2019, 43 (7): 151- 158, 184.
11	叶茂, 刘艳, 顾雪平, 等. 基于动态风电穿透功率极限的黑启动方案制定[J]. 中国电机工程学报, 2018, 38 (3): 744- 752.
	YE Mao, LIU Yan, GU Xueping, et al. Black start scheme formation considering dynamic wind power penetration limit[J]. Proceedings of the CSEE, 2018, 38 (3): 744- 752.
12	冯丽, 金黎明, 张同尊, 等. 考虑恢复路径充电时间的机组启动次序决策方法[J]. 中国电机工程学报, 2016, 36 (18): 4904- 4910, 5114.
	FENG Li, JIN Liming, ZHANG Tongzun, et al. A new method to determine non-blackstart units restoration sequence with consideration of charging time of restoration paths[J]. Proceedings of the CSEE, 2016, 36 (18): 4904- 4910, 5114.
13	覃智君, 侯云鹤, 李大虎, 等. 输电网负荷恢复方案的优化计算方法[J]. 电工技术学报, 2016, 31 (8): 116- 124.
	QIN Zhijun, HOU Yunhe, LI Dahu, et al. Optimization method for constructing load restoration strategy of transmission systems[J]. Transactions of China Electrotechnical Society, 2016, 31 (8): 116- 124.
14	顾雪平, 王丽媛, 李少岩, 等. 风电参与系统恢复的目标网架决策优化[J]. 华北电力大学学报(自然科学版), 2021, 48 (4): 1- 10, 30.
	GU Xueping, WANG Liyuan, LI Shaoyan, et al. Decision-making optimization of target network considering wind power participating in system restoration[J]. Journal of North China Electric Power University (Natural Science Edition), 2021, 48 (4): 1- 10, 30.
15	孙华东, 徐式蕴, 许涛, 等. 新能源多场站短路比定义及指标[J]. 中国电机工程学报, 2021, 41 (2): 497- 506.
	SUN Huadong, XU Shiyun, XU Tao, et al. Definition and index of short circuit ratio for multiple renewable energy stations[J]. Proceedings of the CSEE, 2021, 41 (2): 497- 506.
16	于琳, 孙华东, 赵兵, 等. 新能源并网系统短路比指标分析及临界短路比计算方法[J]. 中国电机工程学报, 2022, 42 (3): 919- 929.
	YU Lin, SUN Huadong, ZHAO Bing, et al. Short circuit ratio index analysis and critical short circuit ratio calculation of renewable energy grid-connected system[J]. Proceedings of the CSEE, 2022, 42 (3): 919- 929.
17	徐玉琴, 陈坤, 聂暘. 基于非参数核密度估计的风速短期相依模型[J]. 太阳能学报, 2017, 38 (2): 450- 456.
	XU Yuqin, CHEN Kun, NIE Yang. Temporal time dependence model of wind speed series based on non-parametric kernel density estimation method[J]. Acta Energiae Solaris Sinica, 2017, 38 (2): 450- 456.
18	李少岩, 曹珂, 顾雪平, 等. 基于伴随网络的受端电网多馈入短路比约束线性化建模[J]. 电力系统自动化, 2022, 46 (15): 75- 84.
	LI Shaoyan, CAO Ke, GU Xueping, et al. Linearized modeling of multi-infeed short circuit ratio constraints for receiving-end power grid based on accompanying network[J]. Automation of Electric Power Systems, 2022, 46 (15): 75- 84.
19	李明明, 孙磊, 丁明. 考虑系统恢复能力的快速切负荷机组最优布点策略[J]. 电力系统保护与控制, 2023, 51 (5): 84- 95.
	LI Mingming, SUN Lei, DING Ming. An optimal location strategy of units with FCB function considering the restorability of power systems[J]. Power System Protection and Control, 2023, 51 (5): 84- 95.
20	顾雪平, 白岩松, 李少岩, 等. 考虑风电不确定性的电力系统恢复全过程两阶段鲁棒优化方法[J]. 电工技术学报, 2022, 37 (21): 5462- 5477.
	GU Xueping, BAI Yansong, LI Shaoyan, et al. Two stage robust optimization method for the whole-process power system restoration considering wind power uncertainty[J]. Transactions of China Electrotechnical Society, 2022, 37 (21): 5462- 5477.
21	YANG J W, ZHANG N, KANG C Q, et al. A state-independent linear power flow model with accurate estimation of voltage magnitude[J]. IEEE Transactions on Power Systems, 2017, 32 (5): 3607- 3617.
22	宋坤隆, 谢云云, 殷明慧, 等. 应用网络流理论的停电系统恢复路径混合整数线性优化模型[J]. 电力系统自动化, 2017, 41 (3): 25- 32.
	SONG Kunlong, XIE Yunyun, YIN Minghui, et al. Mixed integer linear optimization model for path restoration of blackout system based on network flow theory[J]. Automation of Electric Power Systems, 2017, 41 (3): 25- 32.
23	李少岩, 曹珂, 顾雪平, 等. 多直流馈入受端系统与直流联络线协调恢复的一体化建模与求解[J]. 电工技术学报, 2023, 38 (21): 5862- 5877.
	LI Shaoyan, CAO Ke, GU Xueping, et al. Integrated modeling and solution for coordinated restoration of multi-infeed receiving-end systems and HVDC Tie lines[J]. Transactions of China Electrotechnical Society, 2023, 38 (21): 5862- 5877.

[1]	张旭, 王淳, 胡奕涛, 陈锐凯, 刘昆, 郭志东, 钟俊勋. 面向短时过载及长期轻载的配变侧储能配置与调度双层优化[J]. 中国电力, 2025, 58(1): 174-184.
[2]	鲁玲, 苑涛, 杨波, 李欣, 鲁洋, 蒲秋平, 张鑫. 计及㶲效率和多重不确定性的区域综合能源系统双层优化[J]. 中国电力, 2025, 58(1): 128-140.
[3]	王世杰, 冯天波, 孙宁, 何可, 李嘉文, 杨程, 崔昊杨. 考虑电-气-热耦合和需求响应的虚拟电厂优化调度策略[J]. 中国电力, 2024, 57(1): 101-114.
[4]	薛贵元, 吴垠, 诸晓骏, 谈健, 明昊. 计及用能权配额约束的发电商竞价策略双层优化方法[J]. 中国电力, 2023, 56(5): 51-61.
[5]	李明明, 孙磊, 马英浩. 大停电事故后计及信息系统故障的机组启动次序优化策略[J]. 中国电力, 2022, 55(9): 146-155.
[6]	卢炳文, 魏震波, 魏平桉, 郭毅, 胡蓉. 考虑多重区间不确定性的用户侧综合能源系统双层优化配置[J]. 中国电力, 2022, 55(3): 193-202.
[7]	谭鸣骢, 王玲玲, 蒋传文, 刘航航, 巫里尔沙, 唐炯. 考虑负荷聚合商调节潜力的需求响应双层优化模型[J]. 中国电力, 2022, 55(10): 32-44.
[8]	白中华, 李强, 陈晶, 袁福生, 徐文波, 孙奉昌, 余宗泽. 储能电站在多站融合场景下的运行策略优化[J]. 中国电力, 2021, 54(6): 136-144.
[9]	张海静, 杨雍琦, 赵昕, 徐楠, 李晨辉, 薛万磊. 计及需求响应的区域综合能源系统双层优化调度策略[J]. 中国电力, 2021, 54(4): 141-150.
[10]	刘鑫, 吴红斌, 王鲸杰, 卢俊花. 市场环境下考虑需求响应的虚拟电厂经济调度[J]. 中国电力, 2020, 53(9): 172-180.
[11]	寇凌峰, 季宇, 吴鸣, 牛耕. 多能互补系统全寿命周期优化配置方法[J]. 中国电力, 2020, 53(12): 75-82.
[12]	赵楠, 王蓓蓓. 计及多类型需求响应资源的配电网分布式电源优化配置[J]. 中国电力, 2019, 52(11): 51-59,67.
[13]	刘欣宇，吴迎霞，冯丽，史成钢，李长城，张沛，刘聪，王小君，刘海，王澍. 基于D5000调度平台的在线智能恢复决策系统[J]. 中国电力, 2016, 49(6): 137-140.

考虑源网协同支撑作用的含新能源电力系统网架重构决策优化方法

A Decision-making Optimization Method for Network Reconfiguration of Power System With New Energy Considering the Synergistic Support of Source-grid

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 23

相关文章 13

编辑推荐

Metrics

模态框（Modal）标题

考虑源网协同支撑作用的含新能源电力系统网架重构决策优化方法

A Decision-making Optimization Method for Network Reconfiguration of Power System With New Energy Considering the Synergistic Support of Source-grid

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 23

相关文章 13

编辑推荐

Metrics