基于风光数据驱动不确定集合的配电网与多微网鲁棒经济调度

doi:10.12204/j.issn.1000-7229.2021.10.005

电力建设 ›› 2021, Vol. 42 ›› Issue (10): 40-50.doi: 10.12204/j.issn.1000-7229.2021.10.005

• 高渗透率可再生能源发电及其先进并网技术·栏目主持张兴教授、李飞副教授· • 上一篇下一篇

基于风光数据驱动不确定集合的配电网与多微网鲁棒经济调度

张宇威¹, 肖金星², 杨军¹(), 徐冰雁², 李蕊¹, 李勇汇¹

1.武汉大学电气与自动化学院,武汉市 430072
2.国网上海市电力公司,上海市 200122

收稿日期:2021-04-29 出版日期:2021-10-01 发布日期:2021-09-30
通讯作者: 杨军 E-mail:JYang@whu.edu.cn
作者简介:张宇威(1997),男,硕士研究生,主要研究方向为高比例电力系统调度运行。
肖金星(1983),男,高级工程师,主要研究方向为电力经济。
徐冰雁(1980),男,高级工程师,主要研究方向为电力系统。
李蕊(1974),女,博士,副教授,主要研究方向为电网安全监测。
李勇汇(1973),男,博士,副教授,主要研究方向为电力系统运行控制。
基金资助:
国家电网有限公司科技项目“含大规模分布式电源和多微网的配电系统柔性控制技术研究及应用”(52093220000H)

Data-Driven Robust Economic Dispatch for Distribution Network and Multiple Micro-grids Considering Correlativity of Wind and Solar Output

ZHANG Yuwei¹, XIAO Jinxing², YANG Jun¹(), XU Bingyan², LI Rui¹, LI Yonghui¹

1. School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
2. State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China

Received:2021-04-29 Online:2021-10-01 Published:2021-09-30
Contact: YANG Jun E-mail:JYang@whu.edu.cn
Supported by:
State Grid Corporation of China Research Program(52093220000H)

摘要/Abstract

摘要：

随着风电和光伏等可再生能源通过多微网(multiple microgrids,MMGs)的形式接入配电网(distribution network,DN),其不确定性会给配电网与多微网系统联合运行的可靠性、经济性带来挑战。对此,文章提出了一种考虑风光相关性的配电网与多微网数据驱动鲁棒调度方法。首先采用分布式调度方法建立配电网与多微网调度框架,分别建立配电网调度模型与微网二阶段鲁棒调度模型,以联络线功率作为两者的耦合参数;考虑风光出力的不确定性与时空相关性,采用数据驱动算法构建风-光出力不确定集合,从而建立微网数据驱动鲁棒调度模型;最后提出一种基于极限场景的改进列约束生成算法(column-and-constraint generation,C&CG)求解微网鲁棒调度问题,并采用目标级联分析法(analytical target cascading,ATC)对配电网与多微网整体调度问题进行求解。仿真结果表明,该配电网与多微网的数据驱动鲁棒调度策略可以捕捉风-光时空相关性,在保证系统调度鲁棒性时提高调度的经济性,并具有良好的收敛性。

关键词: 多微网(MMGs), 可再生能源, 数据驱动鲁棒调度, 极限场景

Abstract:

As renewable energy generations represented by wind and photovoltaic power connect to the distribution network (DN) through multiple micro-grids (MMGs), the uncertainty will bring challenges to the reliability and economy of the operation of the DN and MMGs. In response to this, this paper proposes a data-driven robust dispatch method for DN and MMGs considering correlation between wind and solar output. Firstly, a distributed dispatch method is adopted to establish the dispatch model of the DN and a two-stage dispatch model of the MMGs, with the tie-line power as the coupling parameter of the two. Aiming at the uncertainty of renewable energy output as well as the temporal and spatial correlations, the wind-solar output ellipsoid uncertain set is constructed based on the data-driven algorithm, thereby establishing the two-stage data-driven robust dispatch model of the micro-grid. Finally, an improved column and constraint generation algorithm based on extreme scenarios is proposed to solve the robust dispatch problem of the micro-grid, and the analytical target cascading method is used to solve the overall dispatch problem of DN and MMGs. The simulation results show that the proposed method can capture the spatial-temporal correlation between wind and solar, improve the economy of dispatch while ensuring the robustness of the DN and MMGs dispatch, and has good convergence.

Key words: multiple micro-grids (MMGs), renewable energy, data-driven robust optimization, extreme scenario

中图分类号:

TM73

张宇威, 肖金星, 杨军, 徐冰雁, 李蕊, 李勇汇. 基于风光数据驱动不确定集合的配电网与多微网鲁棒经济调度[J]. 电力建设, 2021, 42(10): 40-50.

ZHANG Yuwei, XIAO Jinxing, YANG Jun, XU Bingyan, LI Rui, LI Yonghui. Data-Driven Robust Economic Dispatch for Distribution Network and Multiple Micro-grids Considering Correlativity of Wind and Solar Output[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(10): 40-50.

图/表 15

图1 配电网与多微网结构

Fig.1 Structure of DN and MMGs

图2 风光区间不确定集以及椭球不确定集

Fig.2 The cubic set and the ellipsoid set

图3 数据驱动鲁棒优化算法流程

Fig.3 Flow chart of the data-driven robust optimization method

图4 基于ATC的配电网与多微网系统调度流程

Fig.4 Flow chart of dispatch solving based on ATC

图5 配电网日前调度方案

Fig.5 Day-ahead dispatching scheme of DN

图6 各微网中燃气轮机机组日前预出力与备用容量

Fig.6 Day-ahead scheduled output and reserve capacity of MT in MG

图7 ATC算法迭代收敛曲线

Fig.7 The iterative convergence curve of ATC

表1 Comparison of DN dispatch cost under different optimization methods元

Table 1

表2 Comparison of MMGs dispatch cost under different optimization methods元

Table 2

图A1 测试系统拓扑

Fig.A1 Topology of the test system

图A2 多微网以及配电网日负荷曲线

Fig.A2 Daily load curve of MMGs and DN

图A3 数据驱动集合中风机出力极限场景

Fig.A3 Extreme scenario of wind power output in data-driven set

图A4 数据驱动集合中光伏出力极限场景

Fig.A4 Extreme scenario of photovoltaic power output in data-driven set

图A5 区间不确定集合中风机出力极限场景

Fig.A5 Extreme scenario of wind power output in uncertain interval set

图A6 区间不确定集合中光伏出力极限场景

Fig.A6 Extreme scenario of photovoltaic power output in uncertain interval set

参考文献 30

[1]	OLUJOBI O J. The legal sustainability of energy substitution in Nigeria’s electric power sector: Renewable energy as alternative[J]. Protection and Control of Modern Power Systems, 2020, 5(1): 1-12. doi: 10.1186/s41601-019-0145-1 URL
[2]	ZHANG F, MU L H. New protection scheme for internal fault of multi-microgrid[J]. Protection and Control of Modern Power Systems, 2019, 4(1): 1-12. doi: 10.1186/s41601-019-0115-7 URL
[3]	刘迎澍, 陈曦, 李斌, 等. 多微网系统关键技术综述[J]. 电网技术, 2020, 44(10): 3804-3820.
	LIU Yingshu, CHEN Xi, LI Bin, et al. State of art of the key technologies of multiple microgrids system[J]. Power System Technology, 2020, 44(10): 3804-3820.
[4]	QIU H F, GU W, PAN J, et al. Multi-interval-uncertainty constrained robust dispatch for AC/DC hybrid microgrids with dynamic energy storage degradation[J]. Applied Energy, 2018, 228: 205-214. doi: 10.1016/j.apenergy.2018.06.089 URL
[5]	李云龙, 田书, 马亚光. 含多微网的主动配电网分层优化调度[J]. 电力系统及其自动化学报, 2020, 32(6): 88-93.
	LI Yunlong, TIAN Shu, MA Yaguang. Hierarchical optimal dispatching of active distribution network with multi-microgrid[J]. Proceedings of the CSU-EPSA, 2020, 32(6): 88-93.
[6]	包荣荣. 含多微网的主动配电网协调优化调度研究[D]. 济南: 山东大学, 2018.
	BAO Rongrong. Coordinated optimal scheduling of active distribution network with multi-microgrids[D]. Ji’nan: Shandong University, 2018.
[7]	艾欣, 许佳佳. 基于互动调度的微网与配电网协调运行模式研究[J]. 电力系统保护与控制, 2013, 41(1): 143-149.
	AI Xin, XU Jiajia. Study on the microgrid and distribution network co-operation model based on interactive scheduling[J]. Power System Protection and Control, 2013, 41(1): 143-149.
[8]	谢敏, 吉祥, 柯少佳, 等. 基于目标级联分析法的多微网主动配电系统自治优化经济调度[J]. 中国电机工程学报, 2017, 37(17): 4911-4921, 5210.
	XIE Min, JI Xiang, KE Shaojia, et al. Autonomous optimized economic dispatch of active distribution power system with multi-microgrids based on analytical target cascading theory[J]. Proceedings of the CSEE, 2017, 37(17): 4911-4921, 5210.
[9]	LIU Y X, GUO L, WANG C S. A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids[J]. Applied Energy, 2018, 228: 130-140. doi: 10.1016/j.apenergy.2018.04.087 URL
[10]	梁子鹏, 陈皓勇, 王勇超, 等. 含电动汽车的微网鲁棒经济调度[J]. 电网技术, 2017, 41(8): 2647-2658.
	LIANG Zipeng, CHEN Haoyong, WANG Yongchao, et al. Robust economic dispatch of microgrids containing electric vehicles[J]. Power System Technology, 2017, 41(8): 2647-2658.
[11]	WANG L H, ZHANG B Y, LI Q Q, et al. Robust distributed optimization for energy dispatch of multi-stakeholder multiple microgrids under uncertainty[J]. Applied Energy, 2019, 255: 113845. doi: 10.1016/j.apenergy.2019.113845 URL
[12]	QIU H F, YOU F Q. Decentralized-distributed robust electric power scheduling for multi-microgrid systems[J]. Applied Energy, 2020, 269: 115146. doi: 10.1016/j.apenergy.2020.115146 URL
[13]	周丹, 孙可, 张全明, 等. 含多个综合能源联供型微网的配电网日前鲁棒优化调度[J]. 中国电机工程学报, 2020, 40(14): 4473-4485, 4727.
	ZHOU Dan, SUN Ke, ZHANG Quanming, et al. Day-ahead robust dispatch of distribution network with multiple integrated energy system-based micro-grids[J]. Proceedings of the CSEE, 2020, 40(14): 4473-4485, 4727.
[14]	周晓倩, 艾芊. 配电网与多微网联合分布式鲁棒经济调度[J]. 电力系统自动化, 2020, 44(7): 23-30.
	ZHOU Xiaoqian, AI Qian. Combined distributed robust economic dispatch of distribution network and multiple microgrids[J]. Automation of Electric Power Systems, 2020, 44(7): 23-30.
[15]	张兴友. 微网及区域多微网系统随机性建模与评估[D]. 天津: 天津大学, 2016.
	ZHANG Xingyou. Study on stochastic modelling and assessment of microgrid and multi-microgrids system[D]. Tianjin: Tianjin University, 2016.
[16]	吴成辉, 林声宏, 夏成军, 等. 基于模型预测控制的微电网群分布式优化调度[J]. 电网技术, 2020, 44(2): 530-538.
	WU Chenghui, LIN Shenghong, XIA Chengjun, et al. Distributed optimal dispatch of microgrid cluster based on model predictive control[J]. Power System Technology, 2020, 44(2): 530-538.
[17]	冯麒铭, 刘继春, 杨阳方, 等. 含不同类型能源的多局域电网优化经济调度[J]. 电网技术, 2019, 43(2): 452-461.
	FENG Qiming, LIU Jichun, YANG Yangfang, et al. Multi-local grid optimization economic dispatch with different types of energy[J]. Power System Technology, 2019, 43(2): 452-461.
[18]	SAYED A R, WANG C, ZHAO J B, et al. Distribution-level robust energy management of power systems considering bidirectional interactions with gas systems[J]. IEEE Transactions on Smart Grid, 2020, 11(3): 2092-2105. doi: 10.1109/TSG.2019.2947219 URL
[19]	童小娇, 易国伟, 周鹏, 等. 考虑功率调整优先策略的微网动态经济调度[J]. 电网技术, 2015, 39(8): 2173-2179.
	TONG Xiaojiao, YI Guowei, ZHOU Peng, et al. Dynamic economic dispatch for microgrid considering priority strategy of power adjustment[J]. Power System Technology, 2015, 39(8): 2173-2179.
[20]	GROSKLOS G, THEILER J. Ellipsoids for anomaly detection in remote sensing imagery[C]// Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI. Baltimore, Maryland, USA: SPIE, 2015.
[21]	DING T, LV J, BO R, et al. Lift-and-project MVEE based convex hull for robust SCED with wind power integration using historical data-driven modeling approach[J]. Renewable Energy, 2016, 92: 415-427. doi: 10.1016/j.renene.2016.01.001 URL
[22]	ZHANG Y P, AI X M, WEN J Y, et al. Data-adaptive robust optimization method for the economic dispatch of active distribution networks[J]. IEEE Transactions on Smart Grid, 2019, 10(4): 3791-3800. doi: 10.1109/TSG.2018.2834952 URL
[23]	梁子鹏, 陈皓勇, 郑晓东, 等. 考虑风电极限场景的输电网鲁棒扩展规划[J]. 电力系统自动化, 2019, 43(16): 58-67.
	LIANG Zipeng, CHEN Haoyong, ZHENG Xiaodong, et al. Robust expansion planning of transmission network considering extreme scenario of wind power[J]. Automation of Electric Power Systems, 2019, 43(16): 58-67.
[24]	张艺镨, 艾小猛, 方家琨, 等. 基于极限场景的两阶段含分布式电源的配网无功优化[J]. 电工技术学报, 2018, 33(2): 380-389.
	ZHANG Yipu, AI Xiaomeng, FANG Jiakun, et al. Two-stage reactive power optimization for distribution network with distributed generation based on extreme scenarios[J]. Transactions of China Electrotechnical Society, 2018, 33(2): 380-389.
[25]	吕天光, 艾芊, 孙树敏, 等. 含多微网的主动配电系统综合优化运行行为分析与建模[J]. 中国电机工程学报, 2016, 36(1): 122-132.
	LÜ Tianguang, AI Qian, SUN Shumin, et al. Behavioural analysis and optimal operation of active distribution system with multi-microgrids[J]. Proceedings of the CSEE, 2016, 36(1): 122-132.
[26]	罗毅, 邵周策, 张磊, 等. 考虑风电不确定性和气网运行约束的鲁棒经济调度和备用配置[J]. 电工技术学报, 2018, 33(11): 2456-2467.
	LUO Yi, SHAO Zhouce, ZHANG Lei, et al. Robust economic dispatch and reserve configuration considering wind uncertainty and gas network constraints[J]. Transactions of China Electrotechnical Society, 2018, 33(11): 2456-2467.
[27]	林顺富, 刘持涛, 李东东, 等. 考虑电能交互的冷热电区域多微网系统双层多场景协同优化配置[J]. 中国电机工程学报, 2020, 40(5): 1409-1421.
	LIN Shunfu, LIU Chitao, LI Dongdong, et al. Bi-level multiple scenarios collaborative optimization configuration of CCHP regional multi-microgrid system considering power interaction among microgrids[J]. Proceedings of the CSEE, 2020, 40(5): 1409-1421.
[28]	GAO H J, WANG R J, LIU Y B, et al. Data-driven distributionally robust joint planning of distributed energy resources in active distribution network[J]. IET Generation, Transmission & Distribution, 2020, 14(9): 1653-1662. doi: 10.1049/iet-gtd.2019.1565 URL
[29]	Belgian grid operator Elia discloses data[EB/OL]. Belgium: Elia Group, 2020 [2020-11-18]. http://www.elia.be/en/grid-data/.
[30]	梁俊文, 林舜江, 刘明波, 等. 主动配电网分布式鲁棒优化调度方法[J]. 电网技术, 2019, 43(4): 1336-1344.
	LIANG Junwen, LIN Shunjiang, LIU Mingbo, et al. Distributed robust optimal dispatch in active distribution networks[J]. Power System Technology, 2019, 43(4): 1336-1344.

基于风光数据驱动不确定集合的配电网与多微网鲁棒经济调度

Data-Driven Robust Economic Dispatch for Distribution Network and Multiple Micro-grids Considering Correlativity of Wind and Solar Output

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 30

相关文章 15

编辑推荐

Metrics

本文评价

[1]	丁峰, 李晓刚, 梁泽琪, 吴敏, 朱雨蕙, 冯冬涵, 周云. 国外可再生能源发展经验及其对我国相关扶持政策的启示[J]. 电力建设, 2022, 43(9): 1-11.
[2]	杨雪, 金孝俊, 王海洋, 魏翼飞. 基于区块链的绿证和碳交易市场联合激励机制[J]. 电力建设, 2022, 43(6): 24-33.
[3]	兰洲, 蒋晨威, 谷纪亭, 文福拴, 杨侃, 王坤. 促进可再生能源发电消纳和碳减排的数据中心优化调度与需求响应策略[J]. 电力建设, 2022, 43(4): 1-9.
[4]	董昱, 董存, 于若英, 夏俊荣, 王会超. 基于线性潮流的电力系统长时间尺度快速时序仿真方法[J]. 电力建设, 2022, 43(2): 126-134.
[5]	欧阳翰, 吕林, 刘俊勇, 高红均. 考虑可再生能源不确定性的热电联供型微网随机鲁棒经济调度[J]. 电力建设, 2022, 43(1): 19-28.
[6]	谢康胜, 李华强, 王俊翔, 邓靖微. 考虑输配电网协同的源荷储资源统筹规划[J]. 电力建设, 2021, 42(9): 41-52.
[7]	白昱阳, 张俊, 王晓辉, 陈思远, 高天露, 刘鹏. 考虑分布式可再生能源的通证激励机制设计与分散式电力市场研究[J]. 电力建设, 2021, 42(6): 29-43.
[8]	王一铮, 李忠憓, 黄晨宏, 胡亦玺, 郑真, 马小丽, 颜华敏, 文福拴. 促进间歇性可再生能源发电消纳的水电定价与能量管理策略[J]. 电力建设, 2021, 42(3): 107-116.
[9]	李金城, 蒋轶澄, 江海龙, 王海超, 林振智, 文福拴. 消纳保障机制下增量配售电公司的投资组合决策[J]. 电力建设, 2021, 42(10): 139-148.
[10]	陈荃, 吴科成, 曲毅, 何春庚, 黄彬彬, 谢敏. 基于多学科协同理论的可再生能源消纳责任权重测算方法[J]. 电力建设, 2021, 42(1): 49-58.
[11]	黄家祺，张宇威，贺继锋，颜炯，朱旭，杨军. 一种考虑极限场景的配电网鲁棒扩展规划方法[J]. 电力建设, 2020, 41(7): 67-74.
[12]	董凌1， 2，年珩3，范越2，赵建勇3，张智3，林振智3. 能源互联网背景下共享储能的商业模式探索与实践[J]. 电力建设, 2020, 41(4): 38-44.
[13]	郭莉，吴晨，陈立，丛小涵，苗曦云，王蓓蓓. 可再生能源跨区消纳的受端电网阻塞机理分析[J]. 电力建设, 2020, 41(2): 21-29.
[14]	潘尔生, 宋毅, 原凯, 郭玥, 程浩忠, 张沈习. 考虑可再生能源接入的综合能源系统规划评述与展望[J]. 电力建设, 2020, 41(12): 3-13.
[15]	吕振华, 李强, 韩华春, 王大朔, 马瑞. 区域综合能源系统双层多目标模糊优化模型预测控制方法[J]. 电力建设, 2020, 41(12): 123-132.