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

Author

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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)

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.

Suggested Citation

  • Xinghua Wang & Xixian Liu & Fucheng Zhong & Zilv Li & Kaiguo Xuan & Zhuoli Zhao, 2023. "A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:15007-:d:1262131
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    References listed on IDEAS

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    3. Zhixin Pan & Jianming Wang & Wenlong Liao & Haiwen Chen & Dong Yuan & Weiping Zhu & Xin Fang & Zhen Zhu, 2019. "Data-Driven EV Load Profiles Generation Using a Variational Auto-Encoder," Energies, MDPI, vol. 12(5), pages 1-15, March.
    4. Chunhui Yuan & Haitao Yang, 2019. "Research on K-Value Selection Method of K-Means Clustering Algorithm," J, MDPI, vol. 2(2), pages 1-10, June.
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