RAC-GAN-Based Scenario Generation for Newly Built Wind Farm

Jian Tang (), Jianfei Liu, Jinghan Wu, Guofeng Jin, Heran Kang, Zhao Zhang and Nantian Huang
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Jian Tang: Economic and Technological Research Institute of State Grid Inner Mongolia Eastern Power Co., Ltd., Hohhot 010020, China
Jianfei Liu: Economic and Technological Research Institute of State Grid Inner Mongolia Eastern Power Co., Ltd., Hohhot 010020, China
Jinghan Wu: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Guofeng Jin: Economic and Technological Research Institute of State Grid Inner Mongolia Eastern Power Co., Ltd., Hohhot 010020, China
Heran Kang: Economic and Technological Research Institute of State Grid Inner Mongolia Eastern Power Co., Ltd., Hohhot 010020, China
Zhao Zhang: Economic and Technological Research Institute of State Grid Inner Mongolia Eastern Power Co., Ltd., Hohhot 010020, China
Nantian Huang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

Energies, 2023, vol. 16, issue 5, 1-17

Abstract: Due to the lack of historical output data of new wind farms, there are difficulties in the scheduling and planning of power grid and wind power output scenario generation. The randomness and uncertainty of meteorological factors lead to the results of traditional scenario generation methods not having the ability to accurately reflect their uncertainty. This article proposes a RAC-GAN-based scenario generation method for a new wind farm output. First, the Pearson coefficient is adopted in this method to screen the meteorological factors and obtain the ones that have larger impact on wind power output; Second, based on the obtained meteorological factors, the Grey Relation Analysis (GRA) is used to analyze the meteorological correlation between multiple wind farms with sufficient output data and new wind farms (target power stations), so that the wind farm with high meteorological correlation is selected as the source power station. Then, the K-means method is adopted to cluster the meteorological data of the source power station, thus generating the target power station scenario in which the cluster information serves as the label of the robust auxiliary classifier generative adversarial network (RAC-GAN) model and the output data of the source power station is considered as the basis. Finally, the actual wind farm output and meteorological data of a region in northeast China are employed for arithmetic analysis to verify the effectiveness of the proposed method. It is proved that the proposed method can effectively reflect the characteristics of wind power output and solve the problem of insufficient historical data of new wind farm output.

Keywords: RAC-GAN; scenario generation; wind farm; clustering; Grey Relation Analysis (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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