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Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast

Author

Listed:
  • Andrey I. Vlasov

    (Bauman Moscow State Technical University, Russian Federation.)

  • Boris V. Artemiev

    (Bauman Moscow State Technical University, Russian Federation.)

  • Kirill V. Selivanov

    (Bauman Moscow State Technical University, Russian Federation.)

  • Kirill S. Mironov

    (Bauman Moscow State Technical University, Russian Federation.)

  • Jasur O. Isroilov

    (Bauman Moscow State Technical University, Russian Federation.)

Abstract

Harmonious integration of renewable energy sources into current energy systems has taken on increasing importance amid the scarcity of carbon resources. Among the key problems is the imbalance in power consumption, power generation, and significant peak overloads. To deal with this issue, an intelligent software and hardware system is needed, which will effectively implement predictive control algorithms for various energy sources. The research examines the fundamental provisions of the concept of predictive control over a variable load hybrid power system on the basis of power output forecast. The analysis performed has allowed developing a method of predictive control over the power system in a small locality based on machine learning algorithms. The method was tested using an electric power complex simulation, which included four energy sources (solar panel, wind turbines, small hydrogenerator, and standard carbon-fueled generator). The proposed predictive control method has proved to be productive. The algorithms have allowed diversifying the reliability of power supply by ensuring the sustainability of the power grid.

Suggested Citation

  • Andrey I. Vlasov & Boris V. Artemiev & Kirill V. Selivanov & Kirill S. Mironov & Jasur O. Isroilov, 2022. "Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 1-7, May.
  • Handle: RePEc:eco:journ2:2022-03-1
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    References listed on IDEAS

    as
    1. Andrey I. Vlasov & Pavel V. Grigoriev & Aleksey I. Krivoshein & Vadim A. Shakhnov & Sergey S. Filin & Vladimir S. Migalin, 2018. "Smart management of technologies: predictive maintenance of industrial equipment using wireless sensor networks," Post-Print hal-02342832, HAL.
    2. Natalia Bakhtadze & Evgeny Maximov & Natalia Maximova, 2021. "Digital Identification Algorithms for Primary Frequency Control in Unified Power System," Mathematics, MDPI, vol. 9(22), pages 1-17, November.
    3. Theodoros Anagnostopoulos & Grigorios L. Kyriakopoulos & Stamatios Ntanos & Eleni Gkika & Sofia Asonitou, 2020. "Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources," Sustainability, MDPI, vol. 12(7), pages 1-11, April.
    4. Andrey I. Vlasov & Pavel V. Grigoriev & Aleksey I. Krivoshein & Aleksey I. Krivoshein & Vadim A. Shakhnov & Sergey S. Filin & Sergey S. Filin & Vladimir S. Migalin, 2018. "Smart management of technologies: predictive maintenance of industrial equipment using wireless sensor networks," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 6(2), pages 489-502, December.
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    Cited by:

    1. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.

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    More about this item

    Keywords

    Hybrid power system; Renewable energy sources; Power balance; Decision tree methodology; energy efficiency;
    All these keywords.

    JEL classification:

    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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