基于分层测量数据的高压变电站概率负荷预测方法

doi:10.11930/j.issn.1004-9649.202209110

中国电力 ›› 2023, Vol. 56 ›› Issue (8): 143-150.DOI: 10.11930/j.issn.1004-9649.202209110

基于分层测量数据的高压变电站概率负荷预测方法

唐旭辰, 潮铸, 段秦尉, 苏炳洪, 陈卉灿

广东电网有限责任公司电力调度控制中心, 广东广州 510000

收稿日期:2022-09-28 修回日期:2023-06-29 发布日期:2023-08-28
作者简介:唐旭辰（1993—），女，通信作者，硕士，工程师，从事电力系统运行控制，E-mail：2574266834@qq.com;潮铸（1988—），男，硕士，高级工程师，从事电力系统运行控制研究，E-mail：56544787@qq.com;段秦尉（1991—），男，博士，高级工程师，从事电力市场、需求侧响应、新能源优化研究，E-mail：9896545675@qq.com;苏炳洪（1985—），男，硕士，工程师，从事电力系统运行控制研究，E-mail：469853357@qq.com;陈卉灿（1989—），女，硕士，工程师，从事电力系统运行控制研究，E-mail：8965664437@qq.com
基金资助:
国家电网有限公司科技项目（kj-2019-028）

Probabilistic Load Forecasting Method of High Voltage Substation Based on Hierarchical Measurement Data

TANG Xuchen, CHAO Zhu, DUAN Qinwei, SU Binghong, CHEN Huican

Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510000, China

Received:2022-09-28 Revised:2023-06-29 Published:2023-08-28
Supported by:
This work is supported by Science and Technology Project of SGCC (No.kj-2019-028)

摘要/Abstract

摘要： 近年来，电力系统中各级量测系统的不断升级与完善使得高压变电站负荷预测精准度的进一步提高成为可能。以高压变电站以及附属的中压出线、中压配变的多级负荷数据为基础，提出一种基于中压出线与中压配电的自下而上的概率负荷预测方法，分析结果的分布特性并完成负荷的累加。此外，为提高预测精度，采用概率预测方法对网损与测量误差造成的预测偏差展开修正。算例分析结果表明，所提方法的综合评价较优，不仅能得到高准确度的负荷点预测结果，还能划定更小的预测区间。

关键词: 分层测量数据, 高压变电站, 自下而上, 概率负荷预测

Abstract: In recent years, the continuous upgrading and improvement of measurement systems at all levels in the power system makes it possible to further improve the accuracy of load forecasting in high-voltage substations. Based on the multi-level load data of high-voltage substation and its auxiliary medium voltage outgoing line and medium voltage distribution transformer, the paper proposes a bottom-up probabilistic load forecasting method based on medium voltage outgoing line and medium voltage distribution, analyzes the distribution characteristics of the results and completes the load accumulation. In addition, in order to improve the prediction accuracy, the probability prediction method is used to correct the prediction deviation caused by network loss and measurement error. The results of example analysis show that the comprehensive evaluation of the proposed method is better. It can not only get high accuracy load point forecasting results, but also delimit smaller forecasting intervals.

Key words: layered measurement data, high voltage substation, bottom up, probabilistic load forecasting

唐旭辰, 潮铸, 段秦尉, 苏炳洪, 陈卉灿. 基于分层测量数据的高压变电站概率负荷预测方法[J]. 中国电力, 2023, 56(8): 143-150.

TANG Xuchen, CHAO Zhu, DUAN Qinwei, SU Binghong, CHEN Huican. Probabilistic Load Forecasting Method of High Voltage Substation Based on Hierarchical Measurement Data[J]. Electric Power, 2023, 56(8): 143-150.

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参考文献

[1] 张斌, 庄池杰, 胡军, 等. 结合降维技术的电力负荷曲线集成聚类算法[J]. 中国电机工程学报, 2015, 35(15): 3741–3749
ZHANG Bin, ZHUANG Chijie, HU Jun, et al. Ensemble clustering algorithm combined with dimension reduction techniques for power load profiles[J]. Proceedings of the CSEE, 2015, 35(15): 3741–3749
[2] 李文武, 石强, 李丹, 等. 基于VMD和PSO-SVR的短期电力负荷多阶段优化预测[J]. 中国电力, 2022, 55(8): 171–177
LI Wenwu, SHI Qiang, LI Dan, et al. Multi-stage optimization forecast of short-term power load based on VMD and PSO-SVR[J]. Electric Power, 2022, 55(8): 171–177
[3] 李丹, 张远航, 李黄强, 等. 基于精准气象数据的地区电网短期负荷智能预测系统设计[J]. 中国电力, 2022, 55(7): 128–133
LI Dan, ZHANG Yuanhang, LI Huangqiang, et al. Design of a short-term load intelligent forecasting system for regional power grid based on accurate weather data[J]. Electric Power, 2022, 55(7): 128–133
[4] 梁露, 张智晟. 基于多尺度特征增强DHTCN的电力系统短期负荷预测研究[J]. 电力系统保护与控制, 2023, 51(10): 172–179
LIANG Lu, ZHANG Zhisheng. Research on short-term load forecasting of power system based on multi-scale feature enhancement DHTCN[J]. Power System Protection and Control, 2023, 51(10): 172–179
[5] CAO Z J, WAN C, ZHANG Z J, et al. Hybrid ensemble deep learning for deterministic and probabilistic low-voltage load forecasting[J]. IEEE Transactions on Power Systems, 2020, 35(3): 1881–1897.
[6] SHI H, XU M H, LI R. Deep learning for household load forecasting-a novel pooling deep RNN[J]. IEEE Transactions on Smart Grid, 2018, 9(5): 5271–5280.
[7] 狄曙光, 刘峰, 孙建宇, 等. 基于改进ABC和IDPC-MKELM的短期电力负荷预测[J]. 智慧电力, 2022, 50(9): 74–81
DI Shuguang, LIU Feng, SUN Jianyu, et al. Short-term power load forecasting based on improved ABC and IDPC-MKELM[J]. Smart Power, 2022, 50(9): 74–81
[8] 孔祥玉, 郑锋, 鄂志君, 等. 基于深度信念网络的短期负荷预测方法[J]. 电力系统自动化, 2018, 42(5): 133–139
KONG Xiangyu, ZHENG Feng, E Zhijun, et al. Short-term load forecasting based on deep belief network[J]. Automation of Electric Power Systems, 2018, 42(5): 133–139
[9] 李龙, 魏靖, 黎灿兵, 等. 基于人工神经网络的负荷模型预测[J]. 电工技术学报, 2015, 30(8): 225–230
LI Long, WEI Jing, LI Canbing, et al. Prediction of load model based on artificial neural network[J]. Transactions of China Electrotechnical Society, 2015, 30(8): 225–230
[10] MORDJAOUI M, HADDAD S, MEDOUED A, et al. Electric load forecasting by using dynamic neural network[J]. International Journal of Hydrogen Energy, 2017, 42(28): 17655–17663.
[11] 张智晟, 于道林. 考虑需求响应综合影响因素的RBF-NN短期负荷预测模型[J]. 中国电机工程学报, 2018, 38(6): 1631–1638, 1899
ZHANG Zhisheng, YU Daolin. RBF-NN based short-term load forecasting model considering comprehensive factors affecting demand response[J]. Proceedings of the CSEE, 2018, 38(6): 1631–1638, 1899
[12] KONG W C, DONG Z Y, JIA Y W, et al. Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 841–851.
[13] DING N, BENOIT C, FOGGIA G, et al. Neural network-based model design for short-term load forecast in distribution systems[J]. IEEE Transactions on Power Systems, 2016, 31(1): 72–81.
[14] WANG P, LIU B D, HONG T. Electric load forecasting with recency effect: a big data approach[J]. International Journal of Forecasting, 2016, 32(3): 585–597.
[15] WANG Y, XIA Q, KANG C Q. Unit commitment with volatile node injections by using interval optimization[J]. IEEE Transactions on Power Systems, 2011, 26(3): 1705–1713.
[16] CHEN C S, TZENG Y M, HWANG J C. The application of artificial neural networks to substation load forecasting[J]. Electric Power Systems Research, 1996, 38(2): 153–160.
[17] LI R, GU C H, LI F R, et al. Development of low voltage network templates—part I: substation clustering and classification[J]. IEEE Transactions on Power Systems, 2015, 30(6): 3036–3044.
[18] GUNGOR V C, SAHIN D, KOCAK T, et al. Smart grid technologies: communication technologies and standards[J]. IEEE Transactions on Industrial Informatics, 2011, 7(4): 529–539.
[19] QUILUMBA F L, LEE W J, HUANG H, et al. Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities[J]. IEEE Transactions on Smart Grid, 2015, 6(2): 911–918.
[20] STEPHEN B, TANG X Q, HARVEY P R, et al. Incorporating practice theory in sub-profile models for short term aggregated residential load forecasting[J]. IEEE Transactions on Smart Grid, 2017, 8(4): 1591–1598.
[21] WANG Y, CHEN Q X, SUN M Y, et al. An ensemble forecasting method for the aggregated load with subprofiles[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 3906–3908.
[22] 熊军华, 牛珂, 张春歌, 等. 基于小波变异果蝇优化支持向量机短期负荷预测方法研究[J]. 电力系统保护与控制, 2017, 45(13): 71–77
XIONG Junhua, NIU Ke, ZHANG Chunge, et al. LSSVM in short-term load forecasting based on wavelet transform and mutant fruit fly optimization algorithm[J]. Power System Protection and Control, 2017, 45(13): 71–77
[23] 鲁宗相, 李海波, 乔颖. 高比例可再生能源并网的电力系统灵活性评价与平衡机理[J]. 中国电机工程学报, 2017, 37(1): 9–20
LU Zongxiang, LI Haibo, QIAO Ying. Flexibility evaluation and supply/demand balance principle of power system with high-penetration renewable electricity[J]. Proceedings of the CSEE, 2017, 37(1): 9–20
[24] 周孝信, 陈树勇, 鲁宗相, 等. 能源转型中我国新一代电力系统的技术特征[J]. 中国电机工程学报, 2018, 38(7): 1893–1904, 2205
ZHOU Xiaoxin, CHEN Shuyong, LU Zongxiang, et al. Technology features of the new generation power system in China[J]. Proceedings of the CSEE, 2018, 38(7): 1893–1904, 2205
[25] 谢敏, 邓佳梁, 吉祥, 等. 基于信息熵和变精度粗糙集优化的支持向量机降温负荷预测方法[J]. 电网技术, 2017, 41(1): 210–214
XIE Min, DENG Jialiang, JI Xiang, et al. Cooling load forecasting method based on support vector machine optimized with entropy and variable accuracy roughness set[J]. Power System Technology, 2017, 41(1): 210–214

基于分层测量数据的高压变电站概率负荷预测方法

Probabilistic Load Forecasting Method of High Voltage Substation Based on Hierarchical Measurement Data

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基于分层测量数据的高压变电站概率负荷预测方法

Probabilistic Load Forecasting Method of High Voltage Substation Based on Hierarchical Measurement Data

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