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Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models

Author

Listed:
  • Dhaval Dalal

    (School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA)

  • Muhammad Bilal

    (School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA)

  • Hritik Shah

    (School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA)

  • Anwarul Islam Sifat

    (School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA)

  • Anamitra Pal

    (School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA)

  • Philip Augustin

    (Salt River Project (SRP), 6504 East Thomas Road, Scottsdale, AZ 85251, USA)

Abstract

Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.

Suggested Citation

  • Dhaval Dalal & Muhammad Bilal & Hritik Shah & Anwarul Islam Sifat & Anamitra Pal & Philip Augustin, 2023. "Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models," Energies, MDPI, vol. 16(4), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1636-:d:1060160
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    References listed on IDEAS

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    1. Samuel Matthew G. Dumlao & Keiichi N. Ishihara, 2021. "Weather-Driven Scenario Analysis for Decommissioning Coal Power Plants in High PV Penetration Grids," Energies, MDPI, vol. 14(9), pages 1-23, April.
    2. Yan, Rujing & Wang, Jiangjiang & Huo, Shuojie & Qin, Yanbo & Zhang, Jing & Tang, Saiqiu & Wang, Yuwei & Liu, Yan & Zhou, Lin, 2023. "Flexibility improvement and stochastic multi-scenario hybrid optimization for an integrated energy system with high-proportion renewable energy," Energy, Elsevier, vol. 263(PB).
    3. Geovanny Marulanda & Antonio Bello & Jenny Cifuentes & Javier Reneses, 2020. "Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets," Energies, MDPI, vol. 13(13), pages 1-19, July.
    4. Yilin Xie & Ying Xu, 2022. "Transmission Expansion Planning Considering Wind Power and Load Uncertainties," Energies, MDPI, vol. 15(19), pages 1-18, September.
    5. Hui Hwang Goh & Gumeng Peng & Dongdong Zhang & Wei Dai & Tonni Agustiono Kurniawan & Kai Chen Goh & Chin Leei Cham, 2022. "A New Wind Speed Scenario Generation Method Based on Principal Component and R-Vine Copula Theories," Energies, MDPI, vol. 15(7), pages 1-21, April.
    6. Amedeo Buonanno & Martina Caliano & Marialaura Di Somma & Giorgio Graditi & Maria Valenti, 2022. "A Comprehensive Tool for Scenario Generation of Solar Irradiance Profiles," Energies, MDPI, vol. 15(23), pages 1-18, November.
    7. Wei, Hu & Hongxuan, Zhang & Yu, Dong & Yiting, Wang & Ling, Dong & Ming, Xiao, 2019. "Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks," Applied Energy, Elsevier, vol. 250(C), pages 389-403.
    8. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
    9. Lennard Sund & Saber Talari & Wolfgang Ketter, 2022. "Stochastic Wind Power Generation Planning in Liberalised Electricity Markets within a Heterogeneous Landscape," Energies, MDPI, vol. 15(21), pages 1-21, October.
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    1. Amir Ali Safaei Pirooz & Mohammad J. Sanjari & Young-Jin Kim & Stuart Moore & Richard Turner & Wayne W. Weaver & Dipti Srinivasan & Josep M. Guerrero & Mohammad Shahidehpour, 2023. "Adaptation of High Spatio-Temporal Resolution Weather/Load Forecast in Real-World Distributed Energy-System Operation," Energies, MDPI, vol. 16(8), pages 1-16, April.

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