考虑资源储备度的主动配电网多目标集群划分与电压控制方法

doi:10.11930/j.issn.1004-9649.202307070

中国电力 ›› 2023, Vol. 56 ›› Issue (12): 69-79.DOI: 10.11930/j.issn.1004-9649.202307070

• 分布式智能电网的规划、运行和电力交易 • 上一篇下一篇

考虑资源储备度的主动配电网多目标集群划分与电压控制方法

王晶¹(), 袁懿¹(), 邵尹池², 张晋奇¹, 丁然³, 巩彦江⁴

1. 清华四川能源互联网研究院，四川成都　610042
2. 国网冀北电力有限公司电力科学研究院，北京　100045
3. 国网冀北电力有限公司，北京　100054
4. 国网冀北电力有限公司唐山供电公司，河北唐山　063000

收稿日期:2023-07-19 出版日期:2023-12-28 发布日期:2023-12-28
作者简介:王晶（1989—），女，通信作者，高级工程师，硕士，从事新能源规划、调度与运行控制研究，E-mail： wangjingepri@163.com
袁懿（2001—），男，硕士研究生，从事电力系统规划、电力电量平衡及其优化研究，E-mail： yy0473574@gmail.com
基金资助:
国家电网有限公司科技项目（高比例分布式光伏接入县域电网协同优化控制技术研究，52018K220015）。

Multi-Objective Cluster Classification and Voltage Control Approach for Active Distribution Network Considering Resource Reserve Degree

Jing WANG¹(), Yi YUAN¹(), Yinchi SHAO², Jinqi ZHANG¹, Ran DING³, Yanjiang GONG⁴

1. Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China
2. Electric Power Research Institute, State Grid Jibei Electric Power Co., Ltd., Beijing 100045, China
3. State Grid Jibei Electric Power Co., Ltd, Beijing 100054, China
4. Tangshan Power Supply Company of State Grid Jibei Electric Power Company Limited, Tangshan 063000, China

Received:2023-07-19 Online:2023-12-28 Published:2023-12-28
Supported by:
This work is supported by Science and Project of SGCC (Research on Synergistic Optimization and Control Technology for High Proportion of Distributed Photovoltaic Integration into County-level Power Grids, No.52018K220015).

摘要/Abstract

摘要：

针对分布式光伏高渗透率接入配电网后引起的节点电压越限问题，提出一种考虑资源储备度的主动配电网多目标集群划分与电压控制方法。首先，考虑分布式光伏发电接入时自身调节能力对集群耦合关系及整体控制能力的影响，建立考虑资源储备度的多维集群划分指标。然后，使用K-means聚类算法对离散粒子群算法（discrete particle swarm optimization，DPSO）进行改进，将集群划分转换为优化求解问题。接着，建立主动配电网集群电压控制模型，以主导节点的无功/有功功率为控制对象，确定分布式光伏的时序动作、功率；最后，以某整县屋顶分布式光伏开发试点内的电网为仿真算例，分析不同方案下电压的控制效果、系统网损和光伏利用率等，验证了本文所提电压控制方法的正确性和有效性。

关键词: 分布式光伏, 资源储备度, 集群划分, 电压控制, K-means聚类, 离散粒子群算法

Abstract:

A multi-objective cluster classification and voltage control approach for an active distribution network considering resource reserve degree is proposed to solve the problem that the node voltage exceeds the limit after the high penetration rate of distributed photovoltaic (PV) power generation is connected to the distribution network. Firstly, by considering the influence of its own regulation ability on cluster coupling relationship and global control ability when distributed PV power generation is connected, a multi-dimensional cluster classification indicator considering resource reserve degree is established. Then, the K-means clustering algorithm is used to improve the discrete particle swarm optimization (DPSO) algorithm to convert the cluster classification into an optimization solution problem. Then, a voltage control model of the active distribution network cluster is built. With the reactive/active power of the dominant node as the control object, the sequential action and power of distributed PV power generation are determined. Finally, the power grid of a rooftop distributed PV development pilot in a county is taken as a simulation example, and the voltage control effect, system network loss, and PV utilization rate under different schemes are analyzed. The correctness and effectiveness of the voltage control approach proposed in this paper are verified.

Key words: distributed photovoltaic, resource reserve degree, cluster classification, voltage control, K-means clustering, discrete particle swarm algorithm

王晶, 袁懿, 邵尹池, 张晋奇, 丁然, 巩彦江. 考虑资源储备度的主动配电网多目标集群划分与电压控制方法[J]. 中国电力, 2023, 56(12): 69-79.

Jing WANG, Yi YUAN, Yinchi SHAO, Jinqi ZHANG, Ran DING, Yanjiang GONG. Multi-Objective Cluster Classification and Voltage Control Approach for Active Distribution Network Considering Resource Reserve Degree[J]. Electric Power, 2023, 56(12): 69-79.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202307070

https://www.electricpower.com.cn/CN/Y2023/V56/I12/69

图/表 12

图 1 K-means-DPSO算法分区流程

Fig.1 K-means-DPSO algorithm partitioning flow chart

图 2 10 kV配电线路拓扑

Fig.2 Topology of 10 kV distribution network

表 1 本文方法与传统K-means聚类集群划分方法对比

Table 1 Comparison of cluster classification approaches between proposed method and traditional K-means clustering

方法	集群划分结果		PV节点	主导节点
本文方法	集群1	36~47, 50	41, 46	41
	集群2	1~3, 24~35, 48, 49	28~33, 35	28
	集群3	18~23	18-20	20
	集群4	12, 13	13	13
	集群5	4~11, 14~17	11, 14, 15, 17	11
传统 K-means聚类	集群1	1, 24~50	28~33, 35, 41, 46	31
	集群2	18~23	18~20	20
	集群3	14~17	14, 15, 17	15
	集群4	4~13	11, 13	13
	集群5	2, 3

图 3 多目标离散粒子群优化分区结果

Fig.3 Multi-objective DPSO partitioning results

图 4 基于电气距离的K-means聚类分区结果

Fig.4 K-means clustering partitioning results based on electrical distance

表 2 两种方法分区指标值

Table 2 Partitioning index values by two methods

方法	集群	综合储备度	集群内强耦合	集群间弱耦合
本文方法	集群1	3	7.2946×10^–7	1.4792×10^–6
	集群2	1	0.005219	0. 01173
	集群3	1.904	6.2508×10^–7	1.2829×10^–6
	集群4	4	8.3025×10^–7	1.8587×10^–6
	集群5	4.151	1.6927×10^–6	1.2234×10^–6
	最大值	4.151	0.005219	0. 01173
传统 K-means聚类	集群1	6.055	0.01142	0.0235
	集群2	3	9.4892×10^–7	8.480×10^–11
	集群3	1	6.627×10^–7	1.3460×10^–6
	集群4	3	1.2897×10^–6	1.0261×10^–6
	集群5	0	∞	∞
	最大值	6.055	∞	∞

图 5 原始配电网节点电压情况

Fig.5 Node voltage of original distribution network

图 6 本文方法控制电压效果

Fig.6 Voltage control effect of proposed method

图 7 13:00时电压控制效果对比

Fig.7 Comparison of voltage control effects at 13:00

图 8 K-means分区控制电压效果

Fig.8 K-means partition voltage control effect

图 9 本文方法与K-means分区控制光伏发电情况对比

Fig.9 Comparison between proposed method and K-means partition control of PV power generation

图 10 集中式方法控制电压效果

Fig.10 Voltage control effect of centralized method

参考文献 25

1	国家能源局综合司. 关于公布整县(市、区)屋顶分布式光伏开发试点名单的通知(国能综通新能〔2021〕84号)[Z
2	2022年光伏发电建设运行情况[EB/OL]. (2023-02-17) [2023-02-17]. http://www.nea.gov.cn/2023-02 /17 /c_1310698128.htm.
3	杨悦, 陈宇航, 成龙, 等. 考虑节点功率储备与GIN中心性的主动配电网动态集群电压控制[J/OL]. 电网技术: 1–13.[2023-06-25]. DOI: 10.13335/j.1000–3673.pst.2022.2520.
	YANG Yue, CHEN Yuhang, CHENG Long, et al. Power reserve and GIN centrality of buses considered dynamic cluster voltage control of active distribution networks[J/OL]. Power System Technology: 1–13[2023-06-25]. DOI:10.13335/j.1000–3673.pst.2022.2520.
4	时珉, 尹瑞, 姜卫同, 等. 分布式光伏灵活并网集群调控技术综述[J]. 电测与仪表, 2021, 58 (12): 1- 9.
	SHI Min, YIN Rui, JIANG Weitong, et al. Overview of flexible grid-connected cluster control technology for distributed photovoltaic[J]. Electrical Measurement & Instrumentation, 2021, 58 (12): 1- 9.
5	郑吉祥, 钟俊. 基于节点类型和分区耦合性的复杂网络无功电压快速分区方法[J]. 电网技术, 2020, 44 (1): 223- 230.
	ZHENG Jixiang, ZHONG Jun. Fast partitioning method of reactive power and voltage in complex networks based on node type and partition coupling[J]. Power System Technology, 2020, 44 (1): 223- 230.
6	CHAI Y, GUO L, WANG C, et al. Network partition and voltage coordination control for distribution networks with high penetration of distributed PV units[J]. IEEE Transactions on Power Systems, 2018: 3396–3407.
7	RUAN H, LIU Y, GAO H, et al. Distributed voltage control in active distribution network considering renewable energy: a novel network partitioning method[J]. IEEE Transactions on Power Systems, 2020, PP(99): 1–1.
8	李晓辉, 谭向红, 张轶君, 等. 基于最大供电能力的配电网分区方法[J]. 中国电力, 2017, 50 (3): 117- 123.
	LI Xiaohui, TAN Xianghong, ZHANG Yijun, et al. A distribution network partition method based on total supply capability[J]. Electric Power, 2017, 50 (3): 117- 123.
9	陈明, 顾伟, 李鹏, 等. 主动配电网分区分布式无功优化控制方法[J]. 现代电力, 2017, 34 (6): 40- 44.
	CHEN Ming, GU Wei, LI Peng, et al. Distributed reactive power optimization control method for active distribution network based on subarea division[J]. Modern Electric Power, 2017, 34 (6): 40- 44.
10	余乐, 张茜, 刘燕, 等. 含分布式电源的配电网无功补偿分区平衡优化调节方法[J]. 电力系统保护与控制, 2017, 45 (5): 58- 64.
	YU Le, ZHANG Qian, LIU Yan, et al. An adjustment method of distribution network reactive power compensation partition balance with distributed power sources[J]. Power System Protection and Control, 2017, 45 (5): 58- 64.
11	王育飞, 陈强, 郑云平, 等. 基于动态无功电压灵敏度的有源配电网移动式储能优化调度[J]. 电力自动化设备, 2023, 43 (2): 29- 35, 59.
	WANG Yufei, CHEN Qiang, ZHENG Yunping, et al. Optimal scheduling of mobile energy storage in active distribution network based on dynamic reactive power and voltage sensitivity[J]. Electric Power Automation Equipment, 2023, 43 (2): 29- 35, 59.
12	李建芳, 张路, 宋晓辉, 等. 含高渗透率分布式电源的配电网多目标无功分区及主导节点选择方法[J]. 可再生能源, 2017, 35 (11): 1664- 1671.
	LI Jianfang, ZHANG Lu, SONG Xiaohui, et al. Multi-objective reactive power partitioning and pilot node selection of distribution network with high penetration of DG[J]. Renewable Energy Resources, 2017, 35 (11): 1664- 1671.
13	窦晓波, 常莉敏, 倪春花, 等. 面向分布式光伏虚拟集群的有源配电网多级调控[J]. 电力系统自动化, 2018, 42 (3): 21- 31.
	DOU Xiaobo, CHANG Limin, NI Chunhua, et al. Multi-level dispatching and control of active distribution network for virtual cluster of distributed photovoltaic[J]. Automation of Electric Power Systems, 2018, 42 (3): 21- 31.
14	钟俊, 焦兴伟, 王志川. 基于电压临界稳定状态下的无功电压分区方法[J]. 电网技术, 2019, 43 (10): 3761- 3768.
	ZHONG Jun, JIAO Xingwei, WANG Zhichuan. Reactive voltage partition method based on voltage critical steady state[J]. Power System Technology, 2019, 43 (10): 3761- 3768.
15	胡雪凯, 尹瑞, 时珉, 等. 基于改进粒子群算法的分布式光伏集群划分与无功优化策略[J]. 电力电容器与无功补偿, 2021, 42 (4): 14- 2.
	HU Xuekai, YIN Rui, SHI Min, et al. Distributed photovoltaic cluster partition and reactive power optimization strategy based on improved particle swarm optimization algorithm[J]. Power Capacitor & Reactive Power Compensation, 2021, 42 (4): 14- 2.
16	李香龙, 付晓, 朱洁, 等. 含分布式电源的配电网分区和主导节点选取方法研究[J]. 电力系统保护与控制, 2019, 47 (13): 24- 29.
	LI Xianglong, FU Xiao, ZHU Jie, et al. Research on partition method and selection of dominant nodes for distribution network with distributed generation[J]. Power System Protection and Control, 2019, 47 (13): 24- 29.
17	汪泽州, 张明明, 钱峰强, 等. 含光伏接入的中压配电网集中调控优化策略[J]. 中国电力, 2023, 56 (2): 15- 22.
	WANG Zezhou, ZHANG Mingming, QIAN Fengqiang, et al. Centralized regulation and optimization strategy for MV distribution network with PV integration[J]. Electric Power, 2023, 56 (2): 15- 22.
18	李军徽, 孙大朋, 朱星旭, 等. 光伏高渗透率下分布式储能群间协同的电压控制策略[J]. 电力系统自动化, 2023, 47 (10): 47- 56.
	LI Junhui, SUN Dapeng, ZHU Xingxu, et al. Voltage regulation strategy for distributed energy storage considering coordination among clusters with high penetration of photovoltaics[J]. Automation of Electric Power Systems, 2023, 47 (10): 47- 56.
19	张杰, 王恒凤, 刘生春, 等. 基于内点法和邻域搜索解耦动态规划法的区域电网动态无功优化方法[J]. 中国电力, 2023, 56 (2): 59- 67.
	ZHANG Jie, WANG Hengfeng, LIU Shengchun, et al. Algorithm for dynamic reactive power optimization of regional power grid based on interior point method and neighborhood search decoupling dynamic programming method[J]. Electric Power, 2023, 56 (2): 59- 67.
20	WANG Z G, WANG Y Z, LIU G W, et al. Fast distributed voltage control for PV generation clusters based on approximate Newton method[J]. IEEE Transactions on Sustainable Energy, 2021, 12 (1): 612- 622. DOI
21	祖其武, 牛玉刚, 陈蓓. 基于改进粒子群算法的微网多目标经济运行策略研究[J]. 电力系统保护与控制, 2017, 45 (14): 57- 63.
	ZU Qiwu, NIU Yugang, CHEN Bei. Study on multi-objective economic operating strategy of microgrid based on improved particle swarm optimization algorithm[J]. Power System Protection and Control, 2017, 45 (14): 57- 63.
22	叶畅, 伊华茂, 朱炯达, 等. 考虑灵活性供需平衡及响应速度的分布式电源集群划分方法[J]. 中国电力, 2023, 56 (2): 150- 156.
	YE Chang, YI Huamao, ZHU Jiongda, et al. A cluster partition method for distributed generation considering flexibility supply-demand balance and response speed[J]. Electric Power, 2023, 56 (2): 150- 156.
23	于琳, 孙莹, 徐然, 等. 改进粒子群优化算法及其在电网无功分区中的应用[J]. 电力系统自动化, 2017, 41 (3): 89- 95, 128.
	YU Lin, SUN Ying, XU Ran, et al. Vibration signal based diagnosis method for looseness fault of transformer winding[J]. Automation of Electric Power Systems, 2017, 41 (3): 89- 95, 128.
24	闫群民, 董新洲, 穆佳豪, 等. 基于改进多目标粒子群算法的有源配电网储能优化配置[J]. 电力系统保护与控制, 2022, 50 (10): 11- 19. DOI
	YAN Qunmin, DONG Xinzhou, MU Jiahao, et al. Optimal configuration of energy storage in an active distribution network based on improved multi-objective particle swarm optimization[J]. Power system protection and control, 2022, 50 (10): 11- 19. DOI
25	国家标准化管理委员会. 电力系统电压和无功电力技术导则: GB/T 40427—2021 [S]. 北京: 中国标准出版社, 2022.

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考虑资源储备度的主动配电网多目标集群划分与电压控制方法

Multi-Objective Cluster Classification and Voltage Control Approach for Active Distribution Network Considering Resource Reserve Degree

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Multi-Objective Cluster Classification and Voltage Control Approach for Active Distribution Network Considering Resource Reserve Degree

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