基于分层关联性建模的分布式光伏功率超短期概率预测

doi:10.11930/j.issn.1004-9649.202405022

中国电力 ›› 2024, Vol. 57 ›› Issue (12): 50-59.DOI: 10.11930/j.issn.1004-9649.202405022

• 面向新型电力系统的源荷预测技术 • 上一篇下一篇

基于分层关联性建模的分布式光伏功率超短期概率预测

陈璨¹(), 苏紫诺²(), 马原¹(), 刘佳林¹(), 王玉庆²(), 王飞²^,³^,⁴()

1. 国网冀北电力有限公司电力科学研究院，北京　100045
2. 华北电力大学电力工程系，河北保定　071003
3. 新能源电力系统全国重点实验室（华北电力大学），北京　102206
4. 河北省分布式储能与微网重点实验室（华北电力大学），河北保定　071003

收稿日期:2024-05-09 录用日期:2024-08-07 发布日期:2024-12-23 出版日期:2024-12-28
作者简介:陈璨（1988—），女，通信作者，博士，高级工程师，从事新能源及分布式电源调控技术研究，E-mail：wscc0621@163.com
苏紫诺（2000—），女，硕士研究生，从事分布式光伏功率预测技术研究，E-mail：suzinuo@ncepu.edu.cn
马原（1998—），男，硕士，从事配电网数字化、运行优化控制技术研究，E-mail：ma.yuan.cn@outlook.com
基金资助:
国网冀北电力有限公司科技项目（基于智能融合终端的乡村有源配电网源荷协调调控技术研究与应用，52018K22001Z）。

Ultra-short-term Probabilistic Forecasting of Distributed Photovoltaic Power Generation Based on Hierarchical Correlation Modeling

Can CHEN¹(), Zinuo SU²(), Yuan MA¹(), Jialin LIU¹(), Yuqing WANG²(), Fei WANG²^,³^,⁴()

1. Electric Power Research Institute, State grid Jibei Electric Power Co., Ltd., Beijing 100045, China
2. Department of Power Engineering, North China Electric Power University, Baoding 071003, China
3. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China
4. Hebei Key Laboratory of Distributed Energy Storage and Microgrid (North China Electric Power University), Baoding 071003, China

Received:2024-05-09 Accepted:2024-08-07 Online:2024-12-23 Published:2024-12-28
Supported by:
This work is supported by Science and Technology Project of State Grid Jibei Electric Power Co., Ltd. (Research and Application of Source-Load Coordinated Control Technology for Rural Active Distribution Network Based on Intelligent Fusion Terminal, No.52018K22001Z).

摘要/Abstract

摘要：

准确的区域分布式光伏功率概率预测可为有源配电网优化运行提供更全面的信息支撑。当缺乏气象测量或预报数据时，对分布式光伏时空相关信息的挖掘利用可以有效提升功率预测精度，然而，现有研究或难以针对性挖掘时空关联信息，或在建模过程中丢失大量有效信息。为此，提出了一种基于分层关联建模的区域分布式光伏功率超短期概率预测方法。首先，采用基于深度一致性的聚类方法对分布式光伏集群进行子区域划分，支撑对子区域内部时空关联的针对性建模；在此基础上，构建分层图结构以同步建模子域内与子域间时空关联关系，有效利用不同层级间关联信息；然后，提出了基于分层图卷积神经网络的概率预测模型，挖掘光伏电站之间的深度时空关联特征，提升区域分布式光伏超短期功率概率预测精度。最后，利用实际分布式光伏功率数据集验证了该方法的有效性。

关键词: 分布式光伏, 概率预测, 分层关联建模, 深层时空关联性

Abstract:

Accurate probabilistic forecasting of regional distributed photovoltaic (PV) power can provide more comprehensive information support for the optimal operation of active distribution networks. When meteorological measurement or forecasting data is lacking, mining and utilizing spatio-temporal correlation information of distributed PV can effectively improve power forecasting accuracy. However, existing research either struggles to specifically mine spatio-temporal correlation information or loses a significant amount of valuable information during the modeling process. To address this, a method for ultra-short-term probabilistic forecasting of regional distributed PV power based on hierarchical correlation modeling is proposed. Firstly, a clustering method based on deep consistency is employed to divide the distributed PV clusters into subregions, which supports targeted modeling of the spatio-temporal correlations within the subregions. On this basis, a hierarchical graph structure is constructed to simultaneously model the intra-subregion and inter-subregion spatio-temporal correlations, enabling effective utilization of correlation information across different hierarchical levels. Then, a probabilistic forecasting model based on hierarchical graph convolutional neural networks (GCNs) is proposed to mine deep spatio-temporal correlation features between PV power stations, thereby enhancing the accuracy of ultra-short-term probabilistic forecasting of regional distributed PV power. Finally, the effectiveness of the proposed method is validated using actual distributed PV power data sets.

Key words: distributed photovoltaic, probabilistic forecasting, hierarchical association modeling, deep temporal and spatial correlation

陈璨, 苏紫诺, 马原, 刘佳林, 王玉庆, 王飞. 基于分层关联性建模的分布式光伏功率超短期概率预测[J]. 中国电力, 2024, 57(12): 50-59.

Can CHEN, Zinuo SU, Yuan MA, Jialin LIU, Yuqing WANG, Fei WANG. Ultra-short-term Probabilistic Forecasting of Distributed Photovoltaic Power Generation Based on Hierarchical Correlation Modeling[J]. Electric Power, 2024, 57(12): 50-59.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202405022

https://www.electricpower.com.cn/CN/Y2024/V57/I12/50

图/表 12

图 1 区域分布式光伏功率超短期概率预测流程

Fig.1 Flowchart of ultra-short-term probabilistic forecasting of regional distributed PV power

图 2 子图和全局图的分层结构示意

Fig.2 Schematic of the hierarchical structure of subgraphs and global graphs.

图 3 分层图卷积神经网络预测模型结构示意

Fig.3 The structure of the proposed HGCN prediction model

表 1 不同聚类方法下的点预测精度

Table 1 Forecasting accuracy of different clustering methods

方法	E_MA/MW	E_RMS/MW
K-means聚类+GCN	23.8284	30.5803
深度一致性聚类+GCN	23.6658	30.2521
K-means聚类+HGCN	22.8821	30.0161
深度一致性聚类+HGCN	22.8191	29.8063

图 4 不同方法下点预测的预测曲线

Fig.4 Prediction curves for point prediction under different methods

表 2 提前4 h下光伏发电功率的预测性能

Table 2 Predictive performance of PV power under 4 h lead time

方法	置信度95%			置信度85%
方法	E_PICP/%	E_PINAW	E_ICE	E_PICP/%	E_PINAW	E_ICE
本文方法	91.72	93.11	102.31	78.52	66.27	84.40
方法1	92.86	98.28	105.83	80.80	73.53	91.01
方法2	94.79	105.91	111.73	86.94	81.69	93.96
方法3	95.00	108.10	113.78	86.50	82.16	94.97
方法4	98.69	130.75	132.48	92.38	96.38	104.33

表 4 阴天天气提前4 h下光伏发电功率的预测性能

Table 4 Predictive performance of photovoltaic power generation under cloudy weather with 4 h lead time

方法	置信度95%			置信度85%
方法	E_PICP/%	E_PINAW	E_ICE	E_PICP/%	E_PINAW	E_ICE
本文方法	93.42	94.05	100.66	78.68	63.24	80.38
方法1	94.39	100.86	106.85	84.77	74.23	87.56
方法2	95.99	108.76	113.30	89.42	81.79	91.47
方法3	96.15	111.31	115.76	88.46	82.12	92.83
方法4	99.83	131.12	131.33	93.26	95.54	102.43

图 5 本文方法3种天气场景的区间预测结果

Fig.5 Interval forecasting results for three weather scenarios of the method in this paper

图 6 方法1下3种天气场景的区间预测结果

Fig.6 Interval forecasting results for three weather scenarios of the method 1

图 7 方法2下3种天气场景的区间预测结果

Fig.7 Interval forecasting results for three weather scenarios of the method 2

表 3 晴天天气提前4 h下光伏发电功率的预测性能

Table 3 Predictive performance of photovoltaic power generation under sunny weather with 4 h lead time

方法	置信度95%			置信度85%
方法	E_PICP/%	E_PINAW	E_ICE	E_PICP/%	E_PINAW	E_ICE
本文方法	92.44	94.09	101.78	79.16	59.10	74.65
方法1	97.65	100.79	103.21	80.41	68.43	85.09
方法2	93.12	103.57	111.24	85.41	75.04	87.85
方法3	98.95	110.43	111.59	84.16	76.34	90.70
方法4	95.42	126.51	132.59	90.17	91.97	101.99

表 5 雨天天气提前4 h下光伏发电功率的预测性能

Table 5 Predictive performance of photovoltaic power generation under rainy weather with 4 h lead time

方法	置信度95%			置信度85%
方法	E_PICP/%	E_PINAW	E_ICE	E_PICP/%	E_PINAW	E_ICE
本文方法	90.41	95.01	105.07	78.54	74.31	94.61
方法1	91.25	97.97	107.36	75.83	75.53	99.60
方法2	93.54	105.54	112.83	84.46	84.37	99.89
方法3	93.54	106.79	114.16	83.95	85.78	102.17
方法4	99.63	132.33	132.84	92.36	100.38	108.68

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基于分层关联性建模的分布式光伏功率超短期概率预测

Ultra-short-term Probabilistic Forecasting of Distributed Photovoltaic Power Generation Based on Hierarchical Correlation Modeling

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基于分层关联性建模的分布式光伏功率超短期概率预测

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