考虑多时刻和压缩候选集合的配电网最小化采集优化方法

doi:10.11930/j.issn.1004-9649.202307052

摘要/Abstract

摘要：

当前配电网可观性不足，导致大规模分布式电源并网下的中低压配电网能量管理能力欠缺。配电网最小化采集技术能够以最小经济成本实现量测最优配置，对于提升系统可观性水平至关重要。提出一种考虑多时间断面的最小化采集优化方法，模型分两阶段求解。第一阶段压缩候选量测集，将无须迭代更新的Fisher信息矩阵（Fisher information matrix，FIM）值作为蚁群算法信息素更新参数，极大降低了算法的复杂度；在此基础上，第二阶段考虑状态估计精度需求，基于蚁群算法进一步确定最优配置方案。通过方案对比表明，所提最小化采集方法可充分考虑潮流分布变化对状态估计精度的影响，实现了采集终端集约化配置与高效计算，保障了终端投资经济性与配电网可观测性。

关键词: 配电网, 状态估计, 最小化采集, 蚁群算法, FIM值, 多时刻

Abstract:

At present, the insufficient observability of distribution networks leads to the lack of energy management ability of medium and low voltage distribution networks under large-scale distributed power grid connection. The minimized data collection technology for distribution networks can optimize the measurement configuration with the minimum economic cost, which plays an important role in improving the system observability. In this paper, a minimized data collection optimization method considering multiple time sections is proposed. The model of this method is solved in two stages: In the first stage, the candidate measurement set is compressed, with the Fisher information matrix (FIM) value used as the pheromone update parameter of ant colony algorithm. Since an iterative update is not required for the FIM, a significant decrease in algorithm complexity can be found. On this basis, the second stage involves a consideration of the accuracy requirement for state estimation, determining the optimal configuration scheme according to the ant colony algorithm. The comparison of schemes shows that the minimized data collection method in this paper, with a full consideration of the influence of power flow distribution changes on state estimation accuracy, realizes the intensive configuration and efficient calculation of data collection terminals, so as to ensure economical terminal investments and highly observable distribution networks.

Key words: distribution network, state estimation, minimized data collection, ant colony algorithm, Fisher information matrix (FIM), multiple-time

袁兆祥, 肖智宏, 王晶, 于燕玲, 黄炎, 高星乐. 考虑多时刻和压缩候选集合的配电网最小化采集优化方法[J]. 中国电力, 2023, 56(12): 20-30.

Zhaoxiang YUAN, Zhihong XIAO, Jing WANG, Yanling YU, Yan HUANG, Xingle GAO. A Minimized Data Collection Optimization Method for Distribution Networks Considering Multiple-Time and Compressed Candidate Sets[J]. Electric Power, 2023, 56(12): 20-30.

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https://www.electricpower.com.cn/CN/Y2023/V56/I12/20

图/表 18

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量测类型		量测布置位置
节点电压幅值量测		1
节点注入功率量测		2，3，4，5
支路功率量测		1—2，2—3，3—4，4—5

量测类型		量测布置位置
节点电压幅值量测		1
节点注入功率量测		2，3，4，5
支路功率量测		1—2，2—3，3—4，4—5

方案编号	配置1处量测时的配置方案	状态估计精度
0	未配置电流幅值量测	1.6602
1	$ {I_{{\text{12}}}} $	2.3212×10^–4
2	$ {I_{23}} $	5.1805×10^–4
3	$ {I_{34}} $	1.9589×10^–4
4	$ {I_{45}} $	2.4112
本文优化方案	$ {I_{34}} $	1.9589×10^–4

方案编号	配置1处量测时的配置方案	状态估计精度
0	未配置电流幅值量测	1.6602
1	$ {I_{{\text{12}}}} $	2.3212×10^–4
2	$ {I_{23}} $	5.1805×10^–4
3	$ {I_{34}} $	1.9589×10^–4
4	$ {I_{45}} $	2.4112
本文优化方案	$ {I_{34}} $	1.9589×10^–4

方案编号	配置2个电流幅值量测时的方案	状态估计精度
0	未配置电流幅值量测	1.6602
1	$ {I_{12}},{I_{23}} $	5.2133×10^–4
2	$ {I_{12}},{I_{34}} $	1.9674 ×10^–4
3	$ {I_{12}},{I_{45}} $	2.4065 ×10^–4
4	$ {I_{23}},{I_{34}} $	4.9873×10^–4
5	$ {I_{23}},{I_{45}} $	5.0931×10^–4
6	$ {I_{34}},{I_{45}} $	1.7140 ×10^–4
本文优化方案	$ {I_{34}},{I_{45}} $	1.7140×10^–4