A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors

Xinghua Wang, Xixian Liu (), Fucheng Zhong, Zilv Li, Kaiguo Xuan and Zhuoli Zhao
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Xinghua Wang: 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
Fucheng Zhong: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Zilv Li: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Kaiguo Xuan: 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

Sustainability, 2023, vol. 15, issue 20, 1-20

Abstract: Under the background of large-scale PV (photovoltaic) integration, generating typical operation scenarios of power systems is of great significance for studying system planning operation and electricity markets. Since the uncertainty of PV output and system load is driven by weather factors to some extent, using PV output, system load, and weather data can allow constructing scenarios more accurately. In this study, we used a TimeGAN (time-series generative adversarial network) based on LSTM (long short-term memory) to generate PV output, system load, and weather data. After classifying the generated data using the k-means algorithm, we associated PV output scenarios and load scenarios using the FP-growth algorithm (an association rule mining algorithm), which effectively generated typical scenarios with weather correlations. In this case study, it can be seen that TimeGAN, unlike other GANs, could capture the temporal features of time-series data and performed better than the other examined GANs. The finally generated typical scenario sets also showed interpretable weather correlations.

Keywords: deep learning; generative adversarial networks (GAN); time series; photovoltaic (PV); scenario generation; k-means; clustering; FP-growth; association rule (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (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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