考虑负荷多无功用电场景的城市配电网无功优化配置

doi:10.12204/j.issn.1000-7229.2022.08.005

电力建设 ›› 2022, Vol. 43 ›› Issue (8): 42-52.doi: 10.12204/j.issn.1000-7229.2022.08.005

• 新型电力系统下配电网规划与运行优化关键技术研究及应用·栏目主持王守相教授、赵倩宇博士· • 上一篇下一篇

考虑负荷多无功用电场景的城市配电网无功优化配置

杨秀¹, 焦楷丹¹(), 孙改平¹(), 陈小毅²(), 杜佳玮²(), 仇志鑫¹()

1.上海电力大学电气工程学院,上海市 200090
2.国网上海浦东供电公司,上海市 200122

收稿日期:2022-03-23 出版日期:2022-08-01 发布日期:2022-07-27
作者简介:杨秀(1972),男,博士,教授,主要研究方向为分布式发电与微网技术研究、HVDC与FACTS的运行与控制
焦楷丹(1997),女,硕士研究生,主要研究方向为电网无功优化配置与协调运维,E-mail: 457683402@qq.com
孙改平(1984),女,博士研究生,主要研究方向为电力系统调度优化,E-mail: sunfrog2002@163.com
陈小毅(1987),女,硕士,高级工程师,主要研究方向为电力营销,E-mail: chenxiaoyi-10@gmail.com
杜佳玮(1990),男,硕士,工程师,主要研究方向为电力营销,E-mail: djwieby@hotmail.com
仇志鑫(1996),女,硕士研究生,主要研究方向为配电网无功电压控制,E-mail: 2066192370@qq.com
基金资助:
上海市科委项目(18DZ1203200);上海市科委青年扬帆计划(21YF1414600);上海市教委青年教师培训计划(ZZDL20001)

Reactive Power Optimization of Urban Distribution Network Considering Multiple Reactive Power Scenarios of Loads

YANG Xiu¹, JIAO Kaidan¹(), SUN Gaiping¹(), CHEN Xiaoyi²(), DU Jiawei²(), QIU Zhixin¹()

1. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2. State Grid Shanghai Pudong Electric Power Supply Company,Shanghai 200122, China

Received:2022-03-23 Online:2022-08-01 Published:2022-07-27
Supported by:
Shanghai Science and Technology Program(18DZ1203200);Shanghai Sailing Program(21YF1414600);Shanghai Youth Teacher Training Program(ZZDL20001)

摘要/Abstract

摘要：

高比例电力电子设备与高比例分布式光伏的广泛接入以及城市电缆化率的提升,使配电网用户侧的无功特性变得复杂,导致负荷无功用电不确定性增加,不利于配电网安全运行。因此,为了更好地进行无功优化配置,文章采用不同负荷日功率因数变化曲线的组合场景及其概率来反映无功用电的不确定性,以运行成本的期望值最小为目标,建立多无功用电场景的期望值优化配置模型。首先,利用多重一维卷积自编码器(one-dimensional convolutional autoencoders,1D-CAEs)提取不同用户日功率因数数据的低维表征;随后,利用k-means方法进行场景缩减,获得典型日功率因数变化场景,并组合出多用户的场景集;最后,建立期望值无功优化模型,采用粒子群算法求解,确定出最优配置方案。依据上海市某配电网不同类型用户实际的无功用电信息,采用改进的IEEE 33节点系统进行仿真,以验证所提方法的有效性。

关键词: 电力电子化, 分布式光伏, 高电缆化率, 城市配电网, 负荷无功用电, 功率因数, 无功补偿, 数据驱动

Abstract:

The wide access of the high proportion of power electronic equipment and the high proportion of distributed photovoltaic power, and the improvement of the urban cabling rate make the reactive power characteristics on the user side of the distribution network complicate. The increased uncertainty of the load reactive power consumption is not conducive to the safe operation of distribution network. Therefore, to better optimize reactive power, the combined scenarios and their probabilities of daily power-factor variation curves of different loads are used to reflect the uncertainty of reactive power. Taking the minimum expected value of operation cost as the objective function, an optimal configuration model for the expected value of multiple reactive power scenarios is established. Firstly, multiple one-dimensional convolutional autoencoders (1D-CAEs) is used to extract the low-dimensional representation of the daily power factor data of different users. Then, the k-means method is used for scene reduction to obtain typical daily power-factor variation scenes, and multi-user scenario set is combined. Finally, the expected value reactive power optimization model is established, and the particle swarm algorithm is used to solve it to determine the optimal configuration scheme. According to the reactive power consumption scenarios of users in a distribution network in Shanghai, the modified IEEE 33-node system is taken as an example to verify the effectiveness of the proposed method.

Key words: power electronization, distributed photovoltaic power, high cable ratio, urban distribution network, load reactive power, power factor, reactive power optimization, data driven

中图分类号:

TM732

杨秀, 焦楷丹, 孙改平, 陈小毅, 杜佳玮, 仇志鑫. 考虑负荷多无功用电场景的城市配电网无功优化配置[J]. 电力建设, 2022, 43(8): 42-52.

YANG Xiu, JIAO Kaidan, SUN Gaiping, CHEN Xiaoyi, DU Jiawei, QIU Zhixin. Reactive Power Optimization of Urban Distribution Network Considering Multiple Reactive Power Scenarios of Loads[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(8): 42-52.

图/表 18

图1 总体优化框架

Fig.1 General optimization framework

图2 1D-CAEs数据降维原理

Fig.2 Data dimensionality reduction principle of 1D-CAEs

图3 粒子群算法求解无功优化模型流程

Fig.3 Flowchart of the PSO to solve the reactive power optimization model

表1 分布式光伏与负荷接入情况

Table 1 Parameters of distribution PV and load access

表2 基于1D-CAEs日功率因数序列降维过程

Table 2 Dimensionality reduction process of load’s power factor sequence based on 1D-CAEs

图4 负荷日功率因数变化曲线聚类结果

Fig.4 Clustering results of load power-factor curve

图5 负荷的典型无功用电场景

Fig.5 Typical reactive power scenarios of loads

图6 无功用电组合场景

Fig.6 Combination scenario sets

图7 线路充电功率与系统平均无功用电量对比

Fig.7 The line charging power and the system average reactive power

表3 各节点电容器配置情况

Table 3 Capacitor capacity of each model

表4 各节点电抗器配置情况

Table 4 Reactor capacity of each model

表5 不同模型平均网损

Table 5 Average network loss of different models

表6 不同模型所需费用对比

Table 6 Cost of different models

表7 不同模型平均电压波动幅度

Table 7 The average voltage fluctuation range of different models

图8 不同模型9个场景各节点平均电压对比

Fig.8 The average voltage of nodes in 9 scenarios of different models

图9 节点16电压幅值的概率分布

Fig.9 Probability distribution of voltage amplitudes at node 16

图10 节点31电压幅值的概率分布

Fig.10 Probability distribution of voltage amplitudes at node 31

表8 不同模型平均无功倒送量

Table 8 Average reactive power reverse of different models

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考虑负荷多无功用电场景的城市配电网无功优化配置

Reactive Power Optimization of Urban Distribution Network Considering Multiple Reactive Power Scenarios of Loads

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