分布式光伏集群发电功率波动模式识别与超短期概率预测

doi:10.16183/j.cnki.jsjtu.2023.048

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (9): 1334-1343.doi: 10.16183/j.cnki.jsjtu.2023.048

• 新型电力系统与综合能源 • 上一篇下一篇

分布式光伏集群发电功率波动模式识别与超短期概率预测

王于波¹, 郝玲²^,³(), 徐飞²^,³, 陈文彬¹, 郑利斌¹, 陈磊²^,³, 闵勇²^,³

1.北京智芯微电子科技有限公司, 北京 102200
2.清华大学电机工程与应用电子技术系, 北京 100084
3.清华大学新型电力系统运行与控制全国重点实验室,北京 100084

收稿日期:2023-02-13 修回日期:2023-05-06 接受日期:2023-05-12 出版日期:2024-09-28 发布日期:2024-10-11
通讯作者: 郝玲,博士,助理研究员;E-mail:haolg@foxmail.com.
作者简介:王于波(1969—),博士生,正高级工程师,从事电力物联网研究.
基金资助:
北京智芯微电子科技有限公司院士专家基金项目资助(SGITZXDTZPQT2201165)

Pattern Recognition and Ultra-Short-Term Probabilistic Forecasting of Power Fluctuating in Aggregated Distributed Photovoltaics Clusters

WANG Yubo¹, HAO Ling²^,³(), XU Fei²^,³, CHEN Wenbin¹, ZHENG Libin¹, CHEN Lei²^,³, MIN Yong²^,³

1. Beijing SmartChip Microelectronics Technology Co., Ltd., Beijing 102200, China
2. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
3. State Key Laboratory of Power System Operation and Control, Tsinghua University, Beijing 100084, China

Received:2023-02-13 Revised:2023-05-06 Accepted:2023-05-12 Online:2024-09-28 Published:2024-10-11

摘要/Abstract

摘要：

分布式光伏功率不确定性的量化评估对于配电网安全稳定运行具有重要意义.考虑到不同出力波动模式下功率特性差异显著,为得出适应功率波动模式的预测模型并对功率不确定性进行精细化评估,提出一种分布式光伏集群发电功率波动模式识别与超短期概率预测方法.首先,综合卫星云图和光伏功率数据,通过出力波动的特征提取构建波动模式识别模型,实现对波动规律的挖掘.在此基础上,通过分类建模考虑不同波动模式的可预测性差异及波动模式与预测误差的关联关系,使预测区间宽度能更好地适应预测误差分布特征.由此实现对不同波动模式下功率不确定性的精细化考虑,从而提高概率预测精度,为电网调度提供更多参考,削弱分布式光伏功率强波动性对电力系统的影响.

关键词: 分布式光伏, 功率预测, 深度学习, 模式识别, 概率预测

Abstract:

The quantitative evaluation of the uncertainty in distributed photovoltaic power is significant for the safe and stable operation of distribution network. Considering the significant differences in power characteristics of different output fluctuation patterns, in order to obtain a prediction model suitable for different fluctuation patterns and to perform a refined assessment of power uncertainty, this paper proposes a method for pattern recognition and ultra-short-term probabilistic forecasting of power fluctuating in aggregated distributed photovoltaic clusters. First, the satellite cloud images and photovoltaic power data are integrated, and the pattern recognition model of fluctuation is constructed via the feature extraction of power fluctuation, realizing the mining of fluctuation rules. On this basis, the difference in predictability of different fluctuation patterns and the correlation between fluctuation patterns and prediction errors are considered via classification modeling, so that the width of prediction interval can better adapt to the characteristics of prediction error distribution. Thus, refined consideration of power uncertainty of different fluctuation patterns is realized to improve the precision of probabilistic prediction, provide more references for power grid dispatching, and weaken the influence of the strong volatility in distributed photovoltaic power on the power system.

Key words: distributed photovoltaic, power forecasting, deep learning, pattern recognition, probabilistic forecasting

中图分类号:

TM732

王于波, 郝玲, 徐飞, 陈文彬, 郑利斌, 陈磊, 闵勇. 分布式光伏集群发电功率波动模式识别与超短期概率预测[J]. 上海交通大学学报, 2024, 58(9): 1334-1343.

WANG Yubo, HAO Ling, XU Fei, CHEN Wenbin, ZHENG Libin, CHEN Lei, MIN Yong. Pattern Recognition and Ultra-Short-Term Probabilistic Forecasting of Power Fluctuating in Aggregated Distributed Photovoltaics Clusters[J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1334-1343.

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时间尺度	模型	e_ACE/%	δ_mean/MW	S/MW
15 min	原始模型	2.91	125.60	-31.29
15 min	所提模型	5.18	103.47	-12.04
1 h	原始模型	-2.42	147.33	-48.49
1 h	所提模型	1.35	120.94	-29.26
2 h	原始模型	-6.37	174.61	-74.86
2 h	所提模型	-2.38	139.78	-56.25
4 h	原始模型	-12.41	191.38	-162.94
4 h	所提模型	-7.96	154.72	-128.18

置信度/%	模型	e_ACE/%	δ_mean/MW	S/MW
99	原始模型	-5.46	367.37	-41.11
99	所提模型	-5.06	275.59	-24.20
95	原始模型	-8.28	241.42	-112.88
95	所提模型	-7.11	183.08	-83.96
90	原始模型	-12.41	191.38	-162.94
90	所提模型	-7.96	154.72	-128.18
85	原始模型	-16.50	124.22	-192.93
85	所提模型	-11.03	93.85	-149.71

模型	e_ACE/%	δ_mean/ MW	S/MW
LSTM	-12.41	191.38	-162.94
LSTM+DWT	-9.56	167.94	-139.90
LSTM+WPD	-9.10	166.58	-137.73
LSTM+EMD	-9.33	165.81	-136.78
LSTM+VMD	-8.91	160.42	-131.77
LSTM+PR	-7.96	154.72	-128.18

分布式光伏集群发电功率波动模式识别与超短期概率预测

Pattern Recognition and Ultra-Short-Term Probabilistic Forecasting of Power Fluctuating in Aggregated Distributed Photovoltaics Clusters

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