[1]马兴,李俊杰,黎 博,等.分布式储能参与电压暂降补偿的优化配置与控制策略[J].郑州大学学报(工学版),2019,40(05):64-71.[doi:10.13705/j.issn.1671-6833.2019.05.004]
　Ma Xing,Li Junjie Libo,Xie Wei,et al.Optimal configuration and control strategy for distributed energy storage participating in voltage sag compensation[J].Journal of Zhengzhou University (Engineering Science),2019,40(05):64-71.[doi:10.13705/j.issn.1671-6833.2019.05.004]
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分布式储能参与电压暂降补偿的优化配置与控制策略()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40卷
期数:
2019年05期
页码:
64-71
栏目:
出版日期:
2019-10-23

文章信息/Info

Title:
Optimal configuration and control strategy for distributed energy storage participating in voltage sag compensation
作者:
马兴; 李俊杰; 黎 博; 谢 巍; 高盟凯; 陈民铀
国网重庆市电力公司电力科学研究院，重庆401120； 重庆大学电气工程学院，重庆400044
Author(s):
Ma Xing 1; Li Junjie 1Libo 2; Xie Wei 2; Gao Mengkai 2; Chen Minyou 2
1. Electric Power Research Institute of State Grid Chongqing Electric Power Company; 2. School of Electrical Engineering, Chongqing University
关键词:
分布式储能; 电压暂降; 补偿策略; 动态电压补偿器; 成本
Keywords:
distributed energy storage; voltage sag; compensation strategy; Dynamic voltage compensator; cost
DOI:
10.13705/j.issn.1671-6833.2019.05.004
文献标志码:
A
摘要:
针对分布式储能参与电网电压暂降补偿的补偿服务，提出了分布式储能优化配置模型、分布式 储能与动态电压补偿器联合补偿的控制策略.首先，以最小化分布式储能安装成本与敏感负荷的电压越 限成本为目标，建立了分布式储能双层优化配置模型.然后，以分布式储能、动态电压补偿器的补偿成本 和敏感负荷的电压满意度为目标，建立了分布式储能与电压动态补偿器联合补偿的控制模型.采用随机 变异的粒子群算法求解分布式储能优化配置模型与联合补偿的控制模型.最后以IEEE 33节点系统进行 算例仿真.仿真结果表明，所提方法能减少储能容量的配置，同时联合补偿策略能提高补偿效果和减少 补偿设备的投资.
Abstract:
In order to provide voltage sag compensation in distribution network, a model to optimize the alloca�1Ftion of distributed energy storage system ( DESS) and a control strategy incorporating DESS and dynamic volt�1Fage restorer (DVR) was formulated and solved have been formulated and solved. In this paper, a double-layer DESS allocation model based on minimize the installation cost of DESS, voltage sag detection equipment and voltage sag of sensitive load was formulated to find optimal configuration of DESS. Then, the minimization cost of DVR, DESS and maximization of voltage for sensitive loads were achieved by joint compensation control model combining DESS and DVR. Moreover, the particle swarm optimization algorithm with random mutatio was employed to seek optimal solution of the proposed model. This approach was tested on the IEEE 33 bus system integrated with DESS and sensitive load. The results revealed that the optimal allocation model could re�1Fduce storage capacity, and dispatch model could successfully meet the demands when considering voltage sag and further reduce the investment of the compensation equipment.
更新日期/Last Update: 2019-10-26