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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. 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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