考虑配电网静态电压稳定性的微电网优化配置

doi:10.12204/j.issn.1000-7229.2022.08.009

电力建设 ›› 2022, Vol. 43 ›› Issue (8): 87-101.doi: 10.12204/j.issn.1000-7229.2022.08.009

• 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

考虑配电网静态电压稳定性的微电网优化配置

徐艳春¹(), 张进¹(), 汪平¹(), MI Lu²()

1.梯级水电站运行与控制湖北省重点实验室(三峡大学),湖北省宜昌市 443002
2.德州农工大学电气与计算机工程系,美国德克萨斯州卡城 77840

收稿日期:2021-12-23 出版日期:2022-08-01 发布日期:2022-08-05
通讯作者: 张进 E-mail:xyc7309@163.com;sxdxzj@126.com;22813766@qq.com;mlu@ee.tamu.edu
作者简介:徐艳春(1973),女,博士,副教授,主要研究方向为分布式电源接入配电网扰动信号的检测与分析、电力系统谐波检测等,E-mail: xyc7309@163.com
汪平(1980),男,本科,讲师,主要研究方向为电力系统继电保护信号的测量与分析,E-mail: 22813766@qq.com
MI Lu(1949),女,博士,教授,主要研究方向为平行计算及分布处理,E-mail: mlu@ee.tamu.edu
基金资助:
国家自然科学基金项目(51707102)

Optimal Configuration of Microgrid Considering Static Voltage Stability of Distribution Network

XU Yanchun¹(), ZHANG Jin¹(), WANG Ping¹(), MI Lu²()

1. Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control (China Three Gorges University), Yichang 443002, Hubei Province, China
2. Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77840, USA

Received:2021-12-23 Online:2022-08-01 Published:2022-08-05
Contact: ZHANG Jin E-mail:xyc7309@163.com;sxdxzj@126.com;22813766@qq.com;mlu@ee.tamu.edu
Supported by:
National Natural Science Foundation of China(51707102)

摘要/Abstract

摘要：

考虑到风机、光伏两种分布式电源出力在时间上具有互补的特征,利用储能装置功率双向流动的特点,将可再生能源和储能装置以微电网的形式接入配电网实现可再生能源的高比例接入。对现有的配电网静态电压稳定性指标进行改进,以配电网的静态电压稳定性和运行经济性为目标研究微电网系统的定容选址问题。采用基于K-means++方法的多场景技术处理可再生能源出力不确定性问题,针对配电网不同地区的可再生能源出力特点,计算出风光储三种装置不同的最佳容量配置比例。对平衡优化器(equilibrium optimizer, EO)算法进行改进,利用Tent映射产生的混沌序列代替随机生成的初始种群,根据拥挤度和非支配排序实现多目标问题寻优。最后,在由三种地形组成的IEEE 33节点系统和PG&E-69系统中验证了所提模型和算法的有效性。

关键词: 配电网, 静态电压稳定性, 多目标优化, 分布式电源, 多场景分析, 微电网

Abstract:

Considering that the output of photovoltaic and wind power is complementary in time, and that the energy storage devices may provide bidirectional power flow, the renewable energy and storage devices are often connected to the distribution grid in the form of microgrid to realize the high proportion of renewable energy access. The static voltage stability index of the existing distribution grid is improved, and the static voltage stability and operation economy of the distribution network are used as the target to study the location and capacity determination of the energy storage in the microgrid. The multi-scenario technology based on K-means++ method is used to deal with the uncertainty of renewable energy output, and the optimal capacity allocation ratio of three types of scenery storage devices is calculated for the renewable energy output characteristics of the distribution network in different areas. The equilibrium optimizer (EO) algorithm is improved by using chaotic sequences generated by Tent mapping instead of randomly generated initial populations to achieve multi-objective problem solution according to congestion and non-dominated ranking. The effectiveness of the model and algorithm in this paper is verified by simulation in the IEEE 33-node and PG&E-69 system.

Key words: distribution network, static voltage stability, multi-objective optimization, distributed generation, multi-scenario analysis, microgrid

中图分类号:

TM76

徐艳春, 张进, 汪平, MI Lu. 考虑配电网静态电压稳定性的微电网优化配置[J]. 电力建设, 2022, 43(8): 87-101.

XU Yanchun, ZHANG Jin, WANG Ping, MI Lu. Optimal Configuration of Microgrid Considering Static Voltage Stability of Distribution Network[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(8): 87-101.

图/表 26

图1 PV曲线

Fig.1 P-V curve

图2 函数f(x)特性

Fig.2 Function characteristics of f(x)

表A1 适用于不同电压限值的参数设置

Table A1 Parameter settings suitable for different voltage limits

表A2 不同算法在测试函数UF1中指标对比

Table A2 Comparison of indicators of different algorithms in the test function UF1

表A3 不同算法在测试函数UF2中指标对比

Table A3 Comparison of indicators of different algorithms in the test function UF2

表A4 不同算法在测试函数UF3中指标对比

Table A4 Comparison of indicators of different algorithms in the test function UF3

表A5 不同算法在测试函数UF4中指标对比

Table A5 Comparison of indicators of different algorithms in the test function UF4

表A6 不同算法在测试函数UF5中指标对比

Table A6 Comparison of indicators of different algorithms in the test function UF5

表A7 不同算法在测试函数UF6中指标对比

Table A7 Comparison of indicators of different algorithms in the test function UF6

图3 微电网优化配置流程

Fig.3 Flow chart of microgrid optimal configuration

图B1 IEEE 33节点系统图

Fig.B1 IEEE 33-node system diagram

图4 IEEE 33节点系统中指标及电压极值随负荷增长变化曲线

Fig.4 Indices and voltage extreme value change with load growth in IEEE 33-node system

表1 不同方法的计算时间对比

Table 1 Computing time comparison of different methods

图B2 高海拔地区可再生能源全年有功功率

Fig.B2 Annual active power output of renewable energy in high altitude areas

图B3 沟壑地区可再生能源全年有功功率

Fig.B3 Annual active power output of renewable energy in gully areas

图B4 正常地区可再生能源全年有功功率

Fig.B4 Annual active power output of renewable energy in normal areas

图5 不同K值下簇内平方误差和

Fig.5 Sum of square errors in clusters with different K values

图6 每种场景占比

Fig.6 Specific proportion of each scenario

图7 帕累托最优前沿

Fig.7 Pareto optimal frontier

表2 IEEE 33节点系统中MOEO算法优化结果

Table 2 Optimization results of MOEO algorithm in IEEE 33-node system

表3 不考虑电压稳定性时IEEE 33节点系统中优化结果

Table 3 Optimization results in IEEE 33-node system without considering voltage stability

表4 IEEE 33节点系统中MOGWO算法优化结果

Table 4 Optimization results of MOGWO algorithm in IEEE 33-node system

图B5 PG&E-69节点系统图

Fig.B5 PG&E-69 node system diagram

表5 PG&E-69中MOEO算法优化结果

Table 5 Optimization results of MOEO algorithm in PG&E-69 system

表6 PG&E-69中MOGWO算法优化结果

Table 6 Optimization results of MOGWO algorithm in PG&E-69 system

表7 两种算法获得Pareto解的数量

Table 7 The number of Pareto solutions obtained by the two algorithms

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考虑配电网静态电压稳定性的微电网优化配置

Optimal Configuration of Microgrid Considering Static Voltage Stability of Distribution Network

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