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

doi:10.11930/j.issn.1004-9649.202405022

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202405022

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

Figures/Tables 12

References 16

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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

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

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

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

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