考虑新能源多重相关性的柔性配电网分布鲁棒优化策略

doi:10.11930/j.issn.1004-9649.202409078

中国电力 ›› 2024, Vol. 57 ›› Issue (12): 97-108.DOI: 10.11930/j.issn.1004-9649.202409078

考虑新能源多重相关性的柔性配电网分布鲁棒优化策略

刘志伟¹(), 马悦²(), 沙志成¹(), 邵云姝³, 牛远方¹, 董晓明², 王成福²()

1. 山东电力工程咨询院有限公司，山东济南　250013
2. 山东大学电气工程学院，山东济南　250061
3. 韩国高丽大学经济学院，韩国首尔　02841

收稿日期:2024-09-19 录用日期:2024-12-18 发布日期:2024-12-23 出版日期:2024-12-28
作者简介:刘志伟（1981—），男，学士，高级工程师，从事电力系统规划设计研究，E-mail：18660106622@163.com
王成福（1984—），男，通信作者，博士，副教授，博士生导师，从事电力与综合能源系统安全经济运行研究，E-mail：wangcf@sdu.edu.cn
基金资助:
国家自然科学基金资助项目（U2166208，52377108）。

Distributionally Robust Operation for Flexible Distribution Networks Considering Multi-correlation of Renewable Power Generation

Zhiwei LIU¹(), Yue MA²(), Zhicheng SHA¹(), Yunshu SHAO³, Yuanfang NIU¹, Xiaoming DONG², Chengfu WANG²()

1. Shandong Electric Power Engineering Consulting Institute Corporation Limited, Jinan 250013, China
2. School of Electrical Engineering, Shandong University, Jinan 250061, China
3. College of Business Administration, Korea University, Seorl 02841, South Korea

Received:2024-09-19 Accepted:2024-12-18 Online:2024-12-23 Published:2024-12-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.U2166208 and No.52377108).

摘要/Abstract

摘要：

高比例分布式新能源与全控型柔性电力电子元器件的大量接入，给传统配电网带来更多清洁电能与调控选择的同时，其出力的时空分布不均衡性与海量器件调节的复杂性，亦使得配电网的运行面临严峻挑战。对此，提出了一种考虑新能源多重相关性的柔性配电网分布鲁棒优化策略。首先，以配电网有功损耗与电压偏移最小为目标，推导并构建含多种源网荷协同调控措施的柔性配电网最优潮流模型。然后，计及新能源在时间空间与功率维度上的多重相关性，建立了基于数据驱动的柔性配电网两阶段分布鲁棒优化模型，采用1-范数与∞-范数进行源荷不确定性集合描述，并采用二阶锥将其线性化与凸松弛。最后，采用列与约束生成算法对该模型进行求解，并以改进的IEEE 33节点测试系统为例进行仿真分析，验证了该方法的有效性和实用性。

关键词: 柔性配电网, 新能源, 分布鲁棒优化, 数据驱动, 二阶锥规划, 列与约束生成算法

Abstract:

The integration of a high proportion of distributed renewable energy sources (DRES) and a substantial number of fully controllable flexible power electronic devices has brought more clean electrical energy and control options to traditional distribution networks. However, the uneven temporal and spatial distribution of their output power and the complexity of regulating a vast number of devices pose significant challenges to the operation of distribution networks. In response to this, a distributionally robust optimization strategy for flexible distribution networks, considering the multiple correlations of renewable energy, is proposed. Firstly, aiming to minimize active power loss and voltage deviation in the distribution network, an optimal power flow model for flexible distribution networks is derived and constructed, incorporating various coordinated control measures for sources, grids and loads. Secondly, taking into account the multiple correlations of renewable energy in terms of time, space and power dimensions, a two-stage distributionally robust optimization model for flexible distribution networks is established based on data-driven approaches. The 1-norm and ∞-norm are employed to describe the uncertainty sets of sources and loads, and second-order cones are utilized for linearization and convex relaxation. Finally, the column and constraint generation algorithm is adopted to solve the model, and a simulation analysis is conducted using an improved IEEE 33-bus test system as an example, verifying the effectiveness and practicality of the proposed method.

Key words: flexible distribution network, renewable energy, distributionally robust optimization, data-driven, second-order cone programming, column-and-constraint generation algorithm

刘志伟, 马悦, 沙志成, 邵云姝, 牛远方, 董晓明, 王成福. 考虑新能源多重相关性的柔性配电网分布鲁棒优化策略[J]. 中国电力, 2024, 57(12): 97-108.

Zhiwei LIU, Yue MA, Zhicheng SHA, Yunshu SHAO, Yuanfang NIU, Xiaoming DONG, Chengfu WANG. Distributionally Robust Operation for Flexible Distribution Networks Considering Multi-correlation of Renewable Power Generation[J]. Electric Power, 2024, 57(12): 97-108.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202409078

https://www.electricpower.com.cn/CN/Y2024/V57/I12/97

图/表 14

图 1 H-FMSS接入配电网结构

Fig.1 Installation of FMSS with HESS in a distribution network

图 2 改进的IEEE 33节点测试系统

Fig.2 Improved IEEE 33 bus testing system

图 3 源荷预测出力曲线

Fig.3 Predicted output curve of source and load

图 4 改变β/α值时优化结果对比

Fig.4 Comparison of optimization results with changing β/α values

表 1 是否考虑储氢路由优化结果对比

Table 1 Optimization results comparison of hydrogen storage transfer with and without consideration

是否考虑储氢路由	网损值/kW	电压偏移/p.u.
是	1021.034	2.693
否	1054.447	2.841

表 2 改变βFL取值时优化结果对比

Table 2 Optimization results with changing βFL

β^FL	网损值/kW	电压偏移/p.u.
0.05	1042.718	2.765
0.10	1033.609	2.740
0.15	1028.218	2.719
0.20	1028.173	2.705
0.25	1025.400	2.698
0.30	1021.034	2.693

表 3 不同新能源渗透率对其消纳率的影响

Table 3 The influence of different new energy penetration rate on its absorption rate

渗透率/%		10.7		32.3		53.8		75.4		96.9
消纳率/%		100		100		98.6		97.6		86.1

图 5 3种方案下CB的无功功率输出

Fig.5 Reactive power output of CB under three schemes

表 4 3种方案下的优化结果对比

Table 4 Comparison of optimization results under three schemes

方案	网损值/kW	电压偏移/p.u.
1	620.826	4.251
2	618.363	4.238
3	615.853	4.220

表 5 不同置信水平下优化结果对比

Table 5 Optimization results under different confidence levels

α₁	目标函数值/kW
α₁	α_∞=0.5	α_∞=0.8	α_∞=0.95
0.50	869.866	869.868	869.882
0.80	869.866	869.873	869.888
0.95	869.866	869.886	869.891

表 6 综合范数与∞-范数优化结果对比

Table 6 Optimization results of synthetic and ∞-norm

α₁	目标函数值/kW
α₁	综合范数	∞-范数
0.50	869.868	871.157
0.80	869.873	871.157
0.95	869.873	871.157

表 7 综合范数与1-范数结果对比

Table 7 Optimization results of synthetic and 1-norm

α_∞	目标函数值/kW
α_∞	综合范数	1-范数
0.50	869.867	871.117
0.80	869.873	871.117
0.95	869.888	871.117

表 8 不同历史场景数下优化结果对比

Table 8 Optimization results under different historical scenarios

历史场景数/个		500		1000		2000		5000		10000
目标函数值/kW		879.418		874.348		871.330		869.873		860.636

表 9 不同方法下优化结果对比

Table 9 Optimization results under different methods

方法	系统网损/kW	电压偏移/p.u.
分布式鲁棒	615.853	4.220
随机规划	612.717	4.188
鲁棒优化	639.770	4.275

参考文献 31

1	吴思航, 沈弘, 齐磊, 等. 短路工况下柔直换流站直流场内瞬态电场预测分析[J]. 中国电力, 2021, 54 (9): 109- 118.
	WU Sihang, SHEN Hong, QI Lei, et al. Prediction and analysis of transient electric field in DC yard of flexible converter station under short-circuit conditions[J]. Electric Power, 2021, 54 (9): 109- 118.
2	刘宗, 何俊, 黄文涛, 等. 基于态势感知的高渗透率电动汽车接入电网后电压调整策略[J]. 中国电力, 2023, 56 (2): 32- 44.
	LIU Zong, HE Jun, HUANG Wentao, et al. Voltage adjustment strategy for high-penetration electric vehicles connected to power grid based on situation awareness[J]. Electric Power, 2023, 56 (2): 32- 44.
3	王典, 潘超, 鹿丽, 等. 计及风-光时序相关特性的源-储并网阶段式规划策略[J]. 东北电力大学学报, 2020, 40 (4): 1- 10.
	WANG Dian, PAN Chao, LU Li, et al. Source-storage staged planning strategy considering wind-photovoltaic timing related characteristics[J]. Journal of Northeast Electric Power University, 2020, 40 (4): 1- 10.
4	闫桂红, 李丹丹, 刘小恺, 等. 分布式调相机对大规模新能源直流送端系统的稳定特性影响[J]. 内蒙古电力技术, 2024, 42 (4): 80- 86.
	YAN Guihong, LI Dandan, LIU Xiaokai, et al. Impact of distributed regulator on stability characteristics of large-scale new energy DC sending-end system[J]. Inner Mongolia Electric Power, 2024, 42 (4): 80- 86.
5	郑凯, 蒋致乐, 刘明礼, 等. 流域梯级水电站负荷短期联合优化调度研究[J]. 电力科技与环保, 2023, 39 (4): 305- 313.
	ZHENG Kai, JIANG Zhile, LIU Mingli, et al. Study on short-term joint optimal operation of cascaded hydropower stations[J]. Electric Power Technology and Environmental Protection, 2023, 39 (4): 305- 313.
6	李晨, 张昊, 周济, 等. 考虑频率电压空间分布特性的电网实时减载及负荷恢复策略[J]. 电力科学与技术学报, 2021, 36 (2): 162- 171, 216.
	LI Chen, ZHANG Hao, ZHOU Ji, et al. Load shedding and load recovery strategy considering spatial distribution characteristics of frequency and voltage[J]. Journal of Electric Power Science and Technology, 2021, 36 (2): 162- 171, 216.
7	李希德, 张艳, 曹阳, 等. 基于精细化模拟的电力系统电力电量平衡研究[J]. 智慧电力, 2024, 52 (10): 70- 78.
	LI Xide, ZHANG Yan, CAO Yang, et al. Power and energy balance method for power system based on refined simulation[J]. Smart Power, 2024, 52 (10): 70- 78.
8	魏承志, 李俊豪, 涂春鸣, 等. 计及变压器与SOP损耗特性的柔性互联配电网优化调控策略[J]. 电力系统自动化, 2023, 47 (6): 69- 78. DOI
	WEI Chengzhi, LI Junhao, TU Chunming, et al. Optimal regulation and control strategy for flexible interconnected distribution network considering loss characteristics of transformers and soft open points[J]. Automation of Electric Power Systems, 2023, 47 (6): 69- 78. DOI
9	袁宇波, 张宸宇, 袁晓冬, 等. 基于柔性多状态开关的混合配电网能量管理策略与控制方法[J]. 电力系统自动化, 2021, 45 (8): 60- 67. DOI
	YUAN Yubo, ZHANG Chenyu, YUAN Xiaodong, et al. Energy management strategy and control method of hybrid distribution network based on soft open point[J]. Automation of Electric Power Systems, 2021, 45 (8): 60- 67. DOI
10	刘志文, 李岩, 邵冲, 等. 考虑柔性资源协同互动的配电网灵活性评估方法[J]. 中国电力, 2024, 57 (10): 158- 165.
	LIU Zhiwen, LI Yan, SHAO Chong, et al. Distribution network flexibility evaluation method considering collaborative interaction of flexible resources[J]. Electric Power, 2024, 57 (10): 158- 165.
11	胡鹏飞, 朱乃璇, 江道灼, 等. 柔性互联智能配电网关键技术研究进展与展望[J]. 电力系统自动化, 2021, 45 (8): 2- 12. DOI
	HU Pengfei, ZHU Naixuan, JIANG Daozhuo, et al. Research progress and prospects of key technologies of flexible interconnected smart distribution network[J]. Automation of Electric Power Systems, 2021, 45 (8): 2- 12. DOI
12	葛少云, 李俊锴, 刘洪, 等. 计及弹性响应的需求侧资源与软开关联合规划[J]. 天津大学学报(自然科学与工程技术版), 2022, 55 (5): 462- 470.
	GE Shaoyun, LI Junkai, LIU Hong, et al. Coordinated planning of soft open points and demand-side resources considering an elastic response[J]. Journal of Tianjin University (Science and Technology), 2022, 55 (5): 462- 470.
13	李岩, 陈夏, 李巍巍, 等. 基于智能软开关与联络开关并联的柔性配电网互联结构与控制技术研究[J]. 中国电机工程学报, 2022, 42 (13): 4749- 4759.
	LI Yan, CHEN Xia, LI Weiwei, et al. Research on interconnection structure and control technology of flexible distribution network based on soft open point in parallel with interconnection switch[J]. Proceedings of the CSEE, 2022, 42 (13): 4749- 4759.
14	王杰, 王维庆, 王海云, 等. 考虑越限风险的主动配电网中DG、SOP与ESS的两阶段协调规划[J]. 电力系统保护与控制, 2022, 50 (24): 71- 82.
	WANG Jie, WANG Weiqing, WANG Haiyun, et al. Two-stage coordinated planning of DG, SOP and ESS in an active distribution network considering violation risk[J]. Power System Protection and Control, 2022, 50 (24): 71- 82.
15	何叶, 杨晓东, 吴红斌, 等. 面向新型配电系统灵活性提升的智能软开关与储能系统协调规划[J]. 电力系统自动化, 2023, 47 (18): 142- 150. DOI
	HE Ye, YANG Xiaodong, WU Hongbin, et al. Coordinated planning of soft open point and energy storage system for flexibility enhancement of new distribution system[J]. Automation of Electric Power Systems, 2023, 47 (18): 142- 150. DOI
16	葛少云, 侯亭玉, 吴鸣, 等. 柔性互联配电网分布式电源承载能力分布鲁棒优化模型[J]. 电力系统自动化, 2023, 47 (13): 140- 148. DOI
	GE Shaoyun, HOU Tingyu, WU Ming, et al. Distributionally robust optimization model of hosting capability for distributed generators in flexible interconnected distribution network[J]. Automation of Electric Power Systems, 2023, 47 (13): 140- 148. DOI
17	李争, 张蕊, 孙鹤旭, 等. 可再生能源多能互补制-储-运氢关键技术综述[J]. 电工技术学报, 2021, 36 (3): 446- 462.
	LI Zheng, ZHANG Rui, SUN Hexu, et al. Review on key technologies of hydrogen generation, storage and transportation based on multi-energy complementary renewable energy[J]. Transactions of China Electrotechnical Society, 2021, 36 (3): 446- 462.
18	叶健强, 孙敦虎. 碳交易条件下基于鲁棒优化的电源规划研究[J]. 发电技术, 2024, 45 (3): 566- 574. DOI
	YE Jianqiang, SUN Dunhu. Research on power planning based on robust optimization under carbon trading condition[J]. Power Generation Technology, 2024, 45 (3): 566- 574. DOI
19	杨畅, 李正烁. 计及可变流量调节模式的电热综合能源系统条件分布鲁棒优化调度[J]. 中国电机工程学报, 2024, 44 (12): 4702- 4715.
	YANG Chang, LI Zhengshuo. Conditional distributionally robust optimization of integrated electricity and heat systems with variable flow regulation modes[J]. Proceedings of the CSEE, 2024, 44 (12): 4702- 4715.
20	齐彩娟, 陈宝生, 韦冬妮, 等. 考虑主从博弈定价模式的共享储能分布鲁棒优化配置方法研究[J]. 中国电力, 2024, 57 (7): 40- 53.
	QI Caijuan, CHEN Baosheng, WEI Dongni, et al. Distributionally robust optimal configuration for shared energy storage based on stackelberg game pricing model[J]. Electric Power, 2024, 57 (7): 40- 53.
21	BAGHERI A, WANG J H, ZHAO C Y. Data-driven stochastic transmission expansion planning[J]. IEEE Transactions on Power Systems, 2016, 32 (5): 3461- 3470.
22	钱仲豪, 胡骏, 沈思辰, 等. 考虑条件风险价值的多源协调优化运行策略[J]. 发电技术, 2023, 44 (6): 781- 789. DOI
	QIAN Zhonghao, HU Jun, SHEN Sichen, et al. Multi-power coordinated optimization operation strategy considering conditional value at risk[J]. Power Generation Technology, 2023, 44 (6): 781- 789. DOI
23	孙东磊, 王宪, 孙毅, 等. 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.
	SUN Donglei, WANG Xian, SUN Yi, et al. Polyhedral uncertainty set based power system flexibility quantitative assessment[J]. Electric Power, 2024, 57 (9): 146- 155.
24	ZULPO R S, LEBORGNE R C, BRETAS A S. Optimal siting and sizing of distributed generation through power losses and voltage deviation[C]//2014 16th International Conference on Harmonics and Quality of Power (ICHQP). Bucharest, Romania. IEEE, 2014: 871–875.
25	DING T, LIN Y L, LI G F, et al. A new model for resilient distribution systems by microgrids formation[J]. IEEE Transactions on Power Systems, 2017, 32 (5): 4145- 4147. DOI
26	ZHAO C Y, GUAN Y P. Data-driven stochastic unit commitment for integrating wind generation[J]. IEEE Transactions on Power Systems, 2015, 31 (4): 2587- 2596.
27	FARIVAR M, LOW S H. Branch flow model: relaxations and convexification: part I[J]. IEEE Transactions on Power Systems, 2013, 28 (3): 2554- 2564. DOI
28	GU Y X, WANG J X, ZHENG K D, et al. Cost sharing mechanism for reactive power management amidst P2P energy sharing[C]//2020 IEEE Power & Energy Society General Meeting (PESGM). Montreal, QC, Canada. IEEE, 2020: 1–5.
29	LI P, JI H R, YU H, et al. Combined decentralized and local voltage control strategy of soft open points in active distribution networks[J]. Applied Energy, 2019, 241, 613- 624. DOI
30	LOFBERG J. YALMIP: a toolbox for modeling and optimization in MATLAB[C]//2004 IEEE International Conference on Robotics and Automation. Taipei, China. IEEE, 2004: 284–289.
31	NICK M, CHERKAOUI R, PAOLONE M. Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support[J]. IEEE Transactions on Power Systems, 2014, 29 (5): 2300- 2310. DOI

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考虑新能源多重相关性的柔性配电网分布鲁棒优化策略

Distributionally Robust Operation for Flexible Distribution Networks Considering Multi-correlation of Renewable Power Generation

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考虑新能源多重相关性的柔性配电网分布鲁棒优化策略

Distributionally Robust Operation for Flexible Distribution Networks Considering Multi-correlation of Renewable Power Generation

RichHTML

PDF (PC)

可视化

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参考文献 31

相关文章 15

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