Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics

Xinghua Wang, Fucheng Zhong (), Yilin Xu, Xixian Liu, Zezhong Li, Jianan Liu and Zhuoli Zhao
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Xinghua Wang: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Fucheng Zhong: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Yilin Xu: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Xixian Liu: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Zezhong Li: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Jianan Liu: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Zhuoli Zhao: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Energies, 2023, vol. 16, issue 18, 1-19

Abstract: Regarding the generation and integration of typical scenes of PV and loads in urban photovoltaic distribution networks, as well as the insufficient consideration of the spatiotemporal correlation between PV and loads, this paper proposes a typical scene extraction method based on local linear embedding, kernel density estimation, and a joint PV–load typical scene extraction method based on the FP-growth algorithm. Firstly, the daily operation matrices of PV and load are constructed by using the historical operation data of PV and load. Then, the typical scenes are extracted by the dimensionality reduction of local linear embedding and the kernel density estimation method. Finally, the strong association rules of PV–meteorological conditions and load–meteorological conditions are mined based on the FP-growth algorithm, respectively. The association of PV–load typical daily operation scenarios is completed using meteorological conditions as a link. This experiment involved one year of operation data of a distribution network containing PV in Qingyuan, Guangdong Province. The typical scene extraction joint method, Latin hypercube sampling method, and k-means clustering-based scene generation method proposed in this paper are used for comparison, respectively. The results show that compared to the other two scenario generation methods, the error between the typical scenario obtained by this method and the actual operating scenario of the distribution network is smaller. The extracted typical PV and load scenarios can better fit the actual PV and load operation scenarios, which have more reference value for the operation planning of actual distribution networks containing PV.

Keywords: PV–load scenario association; FP-growth algorithm; local linear embedding; kernel density estimation; meteorological factors (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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