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A Morphing-Based Future Scenario Generation Method for Stochastic Power System Analysis

Author

Listed:
  • Yanna Gao

    (Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510620, China)

  • Hong Dong

    (Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510620, China)

  • Liujun Hu

    (Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510620, China)

  • Zihan Lin

    (Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510620, China)

  • Fanhong Zeng

    (Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510620, China)

  • Cantao Ye

    (Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China)

  • Jixiang Zhang

    (Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China)

Abstract

As multiple wind and solar photovoltaic farms are integrated into power systems, precise scenario generation becomes challenging due to the interdependence of power generation and future climate change. Future climate data derived from obsolete climate models, featuring diminished accuracy, less-refined spatial resolution, and a limited range of climate scenarios compared to more recent models, are still in use. In this paper, a morphing-based approach is proposed for generating future scenarios, incorporating the interdependence of power generation among multiple wind and photovoltaic farms using copula theory. The K-means method was employed for scenario generation. The results of our study indicate that the average annual variations in dry-bulb temperature (DBT), global horizontal irradiance (GHI), and wind speed (WS) are projected to increase by approximately 0.4 to 1.9 °C, 7.5 to 20.4 W/m 2 , and 0.3 to 1.7 m/s, respectively, in the forthcoming scenarios of the four considered Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). It seems that accumulated maximum wind electricity output (WEO) and solar electricity output (SEO) will increase from 0.9% to 7.3% and 1.1% to 6.8%, respectively, in 2050.

Suggested Citation

  • Yanna Gao & Hong Dong & Liujun Hu & Zihan Lin & Fanhong Zeng & Cantao Ye & Jixiang Zhang, 2024. "A Morphing-Based Future Scenario Generation Method for Stochastic Power System Analysis," Sustainability, MDPI, vol. 16(7), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2762-:d:1364680
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    References listed on IDEAS

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    1. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    2. P. Pinson, 2012. "Very-short-term probabilistic forecasting of wind power with generalized logit–normal distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 555-576, August.
    3. Ehsan, Ali & Yang, Qiang, 2018. "Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques," Applied Energy, Elsevier, vol. 210(C), pages 44-59.
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