供电分区场景下基于数据驱动的负荷密度综合评估及预测方法

doi:10.11930/j.issn.1004-9649.202205025

中国电力 ›› 2023, Vol. 56 ›› Issue (6): 71-81.DOI: 10.11930/j.issn.1004-9649.202205025

供电分区场景下基于数据驱动的负荷密度综合评估及预测方法

贾巍, 雷才嘉, 方兵华, 刘涌

广东电网有限责任公司广州供电局，广东广州 510620

收稿日期:2022-05-10 修回日期:2023-05-06 出版日期:2023-06-28 发布日期:2023-07-04
作者简介:贾巍（1981—），男，硕士，工程师，从事配电网规划研究，E-mail：308906793@qq.com;雷才嘉（1982—），男，硕士，高级工程师，从事配电网规划设计，E-mail：eptiger@126.com;方兵华（1986—），男，硕士，高级工程师，从事配电网规划及配网项目管理工作，E-mail：289128190@qq.com;刘涌（1976—），男，通信作者，博士，高级工程师，从事配电网智能规划和电力大数据研究，Email：wangsenhit@163.com
基金资助:
中国南方电网有限责任公司科技项目（大数据环境下规划负荷特性及供电分区相关影响因素的分析与应用，GZHKJXM20180011）。

A Comprehensive Evaluation and Prediction Method for Load Density Based on Big Data under Power Supply Partition Scenarios

JIA Wei, LEI Caijia, FANG Binghua, LIU Yong

Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510620, China

Received:2022-05-10 Revised:2023-05-06 Online:2023-06-28 Published:2023-07-04
Supported by:
This work is supported by Science & Technology Project of China Southern Power Grid Co., Ltd. (Analysis and Application of Planning Load Characteristics and Power Supply Division Relevant Influencing Factors of Power Supply Zoning in Big Data Environment, No.GZHKJXM20180011).

摘要/Abstract

摘要： 为满足配电网供电分区和网格化规划需求，提出一种基于数据驱动的负荷密度综合评估及中长期精细化预测方法，通过改进Agglomerative算法，实现了相似单元的聚类。所提方法可有效提取出各类负荷密度的典型特征，进而降低系统对数据样本的要求，为后续各类负荷的分类精细化预测提供支持。首先，基于数据思维，通过核密度估计方法对网格内地块样本进行负荷密度特征提取；其次，采用$\bar E $熵权法对各类负荷密度的特征值进行赋权，实现各供电单元不同类型负荷密度的评估，并进一步对供电单元和供电网格的综合负荷密度水平进行计算；最后，通过供电单元聚类，采用最小二乘法对负荷密度S型增长曲线的参数进行分类求解，实现供电单元各类负荷密度的中长期预测。在算例部分，进行了详细分析，结合工程实例验证了该方法的可行性。

关键词: 配电网网格化, 负荷密度, 核密度估计, ē熵权法, 中长期预测

Abstract: In order to meet the requirements of power supply partition and grid planning, a comprehensive evaluation and mid-long term refined prediction method for load density based on big data under power supply scenarios is proposed, and similar units are clustered through the improved Agglomerative algorithm. The proposed method can effectively extract the typical features of various load densities, so as to reduce the requirement of the system for data sampling and provide support for the classified refined forecasting of various loads. Firstly, based on the data samples, the load density features of the plot samples in the grid are extracted with the kernel density estimation (KDE) method. Then, the entropy method is used to weight the eigenvalues to realize the evaluation of different types of load densities in each power supply unit, and further calculate the integrated load density level of the power supply units and power grids. Finally, the power supply units are clustered, and the parameters of the S-shaped growth curve are solved by the least square method, so as to realize the mid-long term prediction of various load densities. In case study, a detailed analysis is carried out, and the effectiveness of the method is verified by engineering examples.

Key words: distribution network grid, load density, KDE, entropy weight method, mid- long term forecast

贾巍, 雷才嘉, 方兵华, 刘涌. 供电分区场景下基于数据驱动的负荷密度综合评估及预测方法[J]. 中国电力, 2023, 56(6): 71-81.

JIA Wei, LEI Caijia, FANG Binghua, LIU Yong. A Comprehensive Evaluation and Prediction Method for Load Density Based on Big Data under Power Supply Partition Scenarios[J]. Electric Power, 2023, 56(6): 71-81.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202205025

https://www.electricpower.com.cn/CN/Y2023/V56/I6/71

参考文献

[1] 张漫, 王主丁, 李强, 等. 中压目标网架规划中供电分区优化模型和方法[J]. 电力系统自动化, 2019, 43(16): 125–135
ZHANG Man, WANG Zhuding, LI Qiang, et al. Optimization model and method of power supply area division for planning of medium-voltage target network[J]. Automation of Electric Power Systems, 2019, 43(16): 125–135
[2] 陈哲, 王主丁, 黄河, 等. 中压配电网规划中计及多因素影响的供电分区划分方法[J]. 电网技术, 2020, 44(12): 4636–4643
CHEN Zhe, WANG Zhuding, HUANG He, et al. Power supply area division method with multiple factors’ influence for medium voltage distribution network planning[J]. Power System Technology, 2020, 44(12): 4636–4643
[3] ZHANG B, ZHAO X H, DOU Z H, et al. A new medium and long-term power load forecasting method considering policy factors[J]. IEEE Access, 2021, 9: 160021–160034.
[4] 黄存强, 赵雪, 刘兴文, 等. 考虑负荷时空特性的配电系统运行效率评价方法[J]. 南方电网技术, 2021, 15(2): 20–25
HUANG Cunqiang, ZHAO Xue, LIU Xingwen, et al. Evaluation method of distribution system operating efficiency considering load temporal and spatial characteristics[J]. Southern Power System Technology, 2021, 15(2): 20–25
[5] 肖白, 张小娜, 姜卓, 等. 考虑本位元胞接受能力和相邻元胞负荷影响的空间负荷预测[J]. 电力系统自动化, 2021, 45(12): 57–64
XIAO Bai, ZHANG Xiaona, JIANG Zhuo, et al. Spatial load forecasting considering acceptability of standard cell and influence of load of adjacent cells[J]. Automation of Electric Power Systems, 2021, 45(12): 57–64
[6] 杨军胜, 彭石, 王承民, 等. 基于城市用地性质的配网空间负荷预测研究[J]. 电测与仪表, 2018, 55(11): 30–34
YANG Junsheng, PENG Shi, WANG Chengmin, et al. Research of spatial load forecasting in distribution network based on grid partition of urban land-use property[J]. Electrical Measurement & Instrumentation, 2018, 55(11): 30–34
[7] LIANG J H, GAO Y, LIU D N. Research on power medium-and long-term load forecasting based on forecasting empirical partial least squares regression[C]//2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA). Guangzhou, China. IEEE, 2021: 395–398.
[8] YE C J, DING Y, WANG P, et al. A data-driven bottom-up approach for spatial and temporal electric load forecasting[J]. IEEE Transactions on Power Systems, 2019, 34(3): 1966–1979.
[9] 杨智伟, 刘灏, 毕天姝, 等. 基于长短期记忆网络的PMU不良数据检测方法[J]. 电力系统保护与控制, 2020, 48(7): 1–9
YANG Zhiwei, LIU Hao, BI Tianshu, et al. PMU bad data detection method based on long short-term memory network[J]. Power System Protection and Control, 2020, 48(7): 1–9
[10] 林勇, 陈允鹏, 王志勇, 等. 广东电网目标网架方案论证与建议[J]. 南方电网技术, 2020, 14(3): 42–48
LIN Yong, CHEN Yunpeng, WANG Zhiyong, et al. Demonstration and suggestion on network scheme of Guangdong power system[J]. Southern Power System Technology, 2020, 14(3): 42–48
[11] 王骁. 电力系统空间负荷密度预测研究[D]. 上海: 上海交通大学, 2018.
WANG Xiao. Study on spatial load density prediction of power system[D]. Shanghai: Shanghai Jiao Tong University, 2018.
[12] 徐鑫裕, 边晓燕, 张骞, 等. 基于数据驱动的双馈风电场经VSC-HVDC并网次同步振荡影响因素分析[J]. 电力系统保护与控制, 2021, 49(21): 80–87
XU Xinyu, BIAN Xiaoyan, ZHANG Qian, et al. Analysis of influencing factors of subsynchronous oscillation caused by a DFIG-based wind farm via the VSC-HVDC grid-connected system based on a data driven method[J]. Power System Protection and Control, 2021, 49(21): 80–87
[13] WAHBAH M, EL-FOULY T H M, ZAHAWI B, et al. Hybrid beta-KDE model for solar irradiance probability density estimation[J]. IEEE Transactions on Sustainable Energy, 2020, 11(2): 1110–1113.
[14] 罗新, 牛海清, 宋廷汉, 等. 基于S变换和概率神经网络的局部放电特征提取及放电识别方法[J]. 南方电网技术, 2020, 14(7): 17–23
LUO Xin, NIU Haiqing, SONG Tinghan, et al. S transform and probabilistic neural network based partial discharge feature extraction and discharge recognition method[J]. Southern Power System Technology, 2020, 14(7): 17–23
[15] 曹华珍, 王天霖, 张黎明, 等. 基于组合赋权-TOPSIS交直流配电网能效评估[J]. 南方电网技术, 2021, 15(3): 55–67
CAO Huazhen, WANG Tianlin, ZHANG Liming, et al. Energy efficiency evaluation of AC/DC distribution network based on combined weighting TOPSIS[J]. Southern Power System Technology, 2021, 15(3): 55–67
[16] 黄锋, 沈华, 甄昊涵, 等. 基于AHP-灰色定权聚类的低压计量箱状态评估[J]. 电测与仪表, 2019, 56(3): 64–69
HUANG Feng, SHEN Hua, ZHEN Haohan, et al. Condition assessment of low voltage metering box based on AHP-gray fixed weight clustering[J]. Electrical Measurement & Instrumentation, 2019, 56(3): 64–69
[17] LEIBOVITZ G, GIRYES R. Efficient least residual greedy algorithms for sparse recovery[J]. IEEE Transactions on Signal Processing, 2020, 68: 3707–3722.
[18] 陈朝晖, 漆家炜, 郑茂然, 等. 基于拓扑分群等值的不同拓扑下方向过流保护整定计算方法[J]. 南方电网技术, 2021, 15(4): 81–89
CHEN Zhaohui, QI Jiawei, ZHENG Maoran, et al. Coordination method of directional overcurrent relays under different topologies based on topology grouping and equivalence[J]. Southern Power System Technology, 2021, 15(4): 81–89
[19] 杨志淳, 沈煜, 杨帆, 等. 基于数据关联分析的低压配电网拓扑识别方法[J]. 电测与仪表, 2020, 57(18): 5–11, 35
YANG Zhichun, SHEN Yu, YANG Fan, et al. Topology identification method of low voltage distribution network based on data association analysis[J]. Electrical Measurement & Instrumentation, 2020, 57(18): 5–11, 35
[20] 李富鹏, 沈秋英, 王森, 等. 基于大数据和多因素组合分析的单元制配电网精细化负荷预测[J]. 智慧电力, 2020, 48(1): 55–62
LI Fupeng, SHEN Qiuying, WANG Sen, et al. Refined load forecasting method for unit distribution network based on big data and multiple factors[J]. Smart Power, 2020, 48(1): 55–62
[21] AFRASIABI M, MOHAMMADI M, RASTEGAR M, et al. Deep-based conditional probability density function forecasting of residential loads[J]. IEEE Transactions on Smart Grid, 2020, 11(4): 3646–3657.
[22] 邵宇鹰, 彭鹏, 张秋桥, 等. 基于极限学习机与负荷密度指标法的空间负荷预测[J]. 电力工程技术, 2021, 40(1): 86–91
SHAO Yuying, PENG Peng, ZHANG Qiuqiao, et al. Spatial load forecasting based on ELM and clustering algorithm[J]. Electric Power Engineering Technology, 2021, 40(1): 86–91
[23] 刘思, 傅旭华, 叶承晋, 等. 考虑地域差异的配电网空间负荷聚类及一体化预测方法[J]. 电力系统自动化, 2017, 41(3): 70–75,82
LIU Si, FU Xuhua, YE Chengjin, et al. Spatial load clustering and integrated forecasting method of distribution network considering regional difference[J]. Automation of Electric Power Systems, 2017, 41(3): 70–75,82
[24] 杨宏宇, 吕万, 关雅静, 等. 基于高阶累积量与ESPRIT算法的功率振荡检测方法[J]. 电测与仪表, 2021, 58(1): 37–45
YANG Hongyu, LV Wan, GUAN Yajing, et al. Power oscillation detection method based on higher order cumulant and ESPRIT algorithm[J]. Electrical Measurement & Instrumentation, 2021, 58(1): 37–45
[25] 刘思议, 张程, 金涛. 基于相邻系数TQWT与改进TLS-ESPRIT算法的电力系统低频振荡模态辨识[J]. 高电压技术, 2019, 45(3): 890–898
LIU Siyi, ZHANG Cheng, JIN Tao. Research on modes identification of low-frequency oscillation of power system based on adjacent coefficient TQWT and improved TLS-ESPRIT algorithm[J]. High Voltage Engineering, 2019, 45(3): 890–898

供电分区场景下基于数据驱动的负荷密度综合评估及预测方法

A Comprehensive Evaluation and Prediction Method for Load Density Based on Big Data under Power Supply Partition Scenarios

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

[1]	陈茜, 李录平, 刘瑞, 杨波, 邓子豪, 李重桂. 基于PCA-KNN融合算法的风力机变桨角度故障诊断方法[J]. 中国电力, 2021, 54(11): 190-198.
[2]	汪斌, 张欣欣, 嵇灵, 解玉磊. 北京市电力消耗驱动因素分析及需求预测[J]. 中国电力, 2018, 51(6): 178-184.
[3]	程正刚，霍艳萍，席云华，梁杰. 一种提升城网220 kV变电站10 kV侧直供能力的方案[J]. 中国电力, 2015, 48(11): 99-102.
[4]	吴振威, 蒋小平, 马会萌, 马速良, 高志强. 基于非参数核密度估计法的光伏出力随机分布模型[J]. 中国电力, 2013, 46(9): 126-130.

模态框（Modal）标题

供电分区场景下基于数据驱动的负荷密度综合评估及预测方法

A Comprehensive Evaluation and Prediction Method for Load Density Based on Big Data under Power Supply Partition Scenarios

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics