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

doi:10.11930/j.issn.1004-9649.202307070

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202307070

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

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References 25

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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

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

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	∞	∞

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	∞	∞

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