上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 6: 855 - 862

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.485

新型电力系统与综合能源

融合拓扑信息的配电网电压-功率灵敏度估计数据驱动方法

  • 刘舒 ,
  • 周敏 ,
  • 高元海 ,
  • 徐潇源 ,
  • 严正
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  • 1.国网上海市电力公司电力科学研究院,上海 200437
    2.上海交通大学 电力传输与功率变换控制教育部重点实验室,上海 200240
    3.上海非碳基能源转换与利用研究院, 上海 200240
刘舒(1987-),高级工程师,从事智能配电网、分布式能源接入技术研究.
徐潇源,副教授,博士生导师,电话(Tel.):021-34204603;E-mail: xuxiaoyuan@sjtu.edu.cn.

收稿日期: 2022-11-28

  修回日期: 2022-12-27

  录用日期: 2023-02-02

  网络出版日期: 2023-05-04

基金资助

国网上海市电力公司科技项目(52094022003R)

收起

A Data-Driven Method Embedded with Topological Information for Voltage-Power Sensitivity Estimation in Distribution Network

  • LIU Shu ,
  • ZHOU Min ,
  • GAO Yuanhai ,
  • XU Xiaoyuan ,
  • YAN Zheng
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  • 1. State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China
    2. Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
    3. Shanghai Non-Carbon Energy Conversion and Utilization Institute, Shanghai 200240, China

Received date: 2022-11-28

  Revised date: 2022-12-27

  Accepted date: 2023-02-02

  Online published: 2023-05-04

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摘要

为解决配电网量测数据多重共线性导致电压-功率灵敏度估计精度低的问题,提出融合拓扑信息的数据驱动方法.首先,将电压-功率灵敏度矩阵分解为主成分矩阵和次成分矩阵两部分,其中主成分与配电网拓扑密切相关,次成分为主成分与实际值的误差.然后,分两阶段依次估计主成分和次成分,分别建立基于二次规划的数据驱动模型.第一阶段模型的关键是基于配电网拓扑信息的约束,第二阶段模型的关键是次成分与主成分比值为微小量的约束.最后,采用实际配电网量测数据和IEEE 33节点系统验证所提方法的精度和效率,并与常规最小二乘回归、岭回归、LASSO回归方法对比,仿真结果表明所提方法的精度具有数量级的显著提升.

关键词： 电压-功率灵敏度; 配电网拓扑; 多重共线性; 数据驱动; 主成分

本文引用格式

刘舒 , 周敏 , 高元海 , 徐潇源 , 严正 . 融合拓扑信息的配电网电压-功率灵敏度估计数据驱动方法[J]. 上海交通大学学报, 2024 , 58(6) : 855 -862 . DOI: 10.16183/j.cnki.jsjtu.2022.485

Abstract

The multicollinearity of measurement data leads to the low accuracy of the data-driven methods for estimating voltage-power sensitivity in distribution networks. In this paper, a data-driven method embedded with topological information is proposed to address the problem. First, the voltage-power sensitivity matrix is decomposed into principal and secondary components, where the principal component is closely related to the distribution network topology and the secondary component is the error between the principal component and the actual value. Then, the principal and secondary components are estimated sequentially in two stages, and their data-driven estimation models based on quadratic programming are established, respectively. The key of the model in the first stage is the constraint based on the distribution network topology information, and the key of the model in the second stage is the constraint that the ratio of the secondary component to the principal component is tiny. Finally, the accuracy and efficiency of the proposed method is validated in the IEEE 33-bus system with a set of measurement data, and comparisons are made with ordinary least square regression, ridge regression, and LASSO regression. The simulation results show that the accuracy of the proposed method is significantly improved by orders of magnitude.

Key words： voltage-power sensitivity; distribution network topology; multicollinearity; data-driven; principal component

参考文献

[1] 胡宏, 陈新仪, 王利峰, 等. 面向新型电力系统的华东电网运行备用体系构建方法[J]. 上海交通大学学报, 2021, 55(12): 1640-1649.
  HU Hong, CHEN Xinyi, WANG Lifeng, et al. Construction method of an operating reserve system for East China power grid oriented to new power systems[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1640-1649.
[2] 黄强, 郭怿, 江建华, 等. “双碳” 目标下中国清洁电力发展路径[J]. 上海交通大学学报, 2021, 55(12): 1499-1509.
  HUANG Qiang, GUO Yi, JIANG Jianhua, et al. Development pathway of China’s clean electricity under carbon peaking and carbon neutrality goals[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1499-1509.
[3] 曾博, 穆宏伟, 董厚琦, 等. 考虑5G基站低碳赋能的主动配电网优化运行[J]. 上海交通大学学报, 2022, 56(3): 279-292.
  ZENG Bo, MU Hongwei, DONG Houqi, et al. Optimization of active distribution network operation considering decarbonization endowment from 5G base stations[J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 279-292.
[4] 徐志成, 赵波, 丁明, 等. 基于电压灵敏度的配电网光伏消纳能力随机场景模拟及逆变器控制参数优化整定[J]. 中国电机工程学报, 2016, 36(6): 1578-1587.
  XU Zhicheng, ZHAO Bo, DING Ming, et al. Photovoltaic hosting capacity evaluation of distribution networks and inverter parameters optimization based on node voltage sensitivity[J]. Proceedings of the CSEE, 2016, 36(6): 1578-1587.
[5] 熊正勇, 陈天华, 杜磊, 等. 基于改进灵敏度分析的有源配电网智能软开关优化配置[J]. 电力系统自动化, 2021, 45(8): 129-137.
  XIONG Zhengyong, CHEN Tianhua, DU Lei, et al. Optimal allocation of soft open point in active distribution network based on improved sensitivity analysis[J]. Automation of Electric Power Systems, 2021, 45(8): 129-137.
[6] 屈尹鹏, 孙元章, 徐箭, 等. 基于解析灵敏度的不平衡配电系统分布式发电出力优化[J]. 电力系统自动化, 2021, 45(19): 117-125.
  QU Yinpeng, SUN Yuanzhang, XU Jian, et al. Optimization of distributed generation output in unbalanced distribution system based on analytical sensitivity[J]. Automation of Electric Power Systems, 2021, 45(19): 117-125.
[7] 李振坤, 陈思宇, 符杨, 等. 基于时序电压灵敏度的有源配电网储能优化配置[J]. 中国电机工程学报, 2017, 37(16): 4630-4640.
  LI Zhenkun, CHEN Siyu, FU Yang, et al. Optimal allocation of ESS in distribution network containing DG base on timing-voltage-sensitivity analysis[J]. Proceedings of the CSEE, 2017, 37(16): 4630-4640.
[8] 刘琪, 王守相, 吉兴全. 主动配电网宽适应性潮流灵敏度分析模型[J]. 电力系统自动化, 2020, 44(13): 81-88.
  LIU Qi, WANG Shouxiang, JI Xingquan. Power flow sensitivity analysis model with wide adaptability for active distribution networks[J]. Automation of Electric Power Systems, 2020, 44(13): 81-88.
[9] 严正, 孔祥瑞, 徐潇源, 等. 微型同步相量测量单元在智能配电网运行状态估计中的应用[J]. 上海交通大学学报, 2018, 52(10): 1195-1205.
  YAN Zheng, KONG Xiangrui, XU Xiaoyuan, et al. Applications of micro synchronous phasor measurement units in state estimation of smart distribution network[J]. Journal of Shanghai Jiao Tong University, 2018, 52(10): 1195-1205.
[10] CHEN Y C, WANG J H, DOMíNGUEZ-GARCíA A D, et al. Measurement-based estimation of the power flow Jacobian matrix[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2507-2515.
[11] 李鹏, 宿洪智, 王成山, 等. 基于PMU量测的智能配电网电压-功率灵敏度鲁棒估计方法[J]. 电网技术, 2018, 42(10): 3258-3267.
  LI Peng, SU Hongzhi, WANG Chengshan, et al. Robust estimation method of voltage to power sensitivity for smart distribution networks based on PMU measurements[J]. Power System Technology, 2018, 42(10): 3258-3267.
[12] ZHANG J B, ZHENG X T, WANG Z, et al. Power system sensitivity identification—Inherent system properties and data quality[J]. IEEE Transactions on Power Systems, 2017, 32(4): 2756-2766.
[13] ZHANG J B, WANG Z, ZHENG X T, et al. Locally weighted ridge regression for power system online sensitivity identification considering data collinearity[J]. IEEE Transactions on Power Systems, 2018, 33(2): 1624-1634.
[14] DA SILVA E L, LIMA A M N, DE ROSSITER CORRêA M B, et al. Data-driven sensitivity coefficients estimation for cooperative control of PV inverters[J]. IEEE Transactions on Power Delivery, 2020, 35(1): 278-287.
[15] CHANG J W, KANG M, OH S. Data-driven estimation of voltage-to-power sensitivities considering their mutual dependency in medium voltage distribution networks[J]. IEEE Transactions on Power Systems, 2022, 37(4): 3173-3176.
[16] SAUNDERS C, GAMMERMAN A, VOVK V. Ridge regression learning algorithm in dual variables[C]//Proceedings of the Fifteenth International Conference on Machine Learning. Madison, USA: University of Wisconsin-Madison, 1998: 515-521.
[17] TIBSHIRANI R. Regression shrinkage and selection via the lasso: A retrospective[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2011, 73(3): 273-282.
[18] BARAN M, WU F F. Optimal sizing of capacitors placed on a radial distribution system[J]. IEEE Transactions on Power Delivery, 1989, 4(1): 735-743.
[19] DEKA D, BACKHAUS S, CHERTKOV M. Structure learning in power distribution networks[J]. IEEE Transactions on Control of Network Systems, 2018, 5(3): 1061-1074.
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