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Implementation of Digital Twin for Increasing Efficiency of Renewable Energy Sources

Author

Listed:
  • Milan Belik

    (Department of Electrical Power Engineering, Faculty of Electrical Engineering, University of West Bohemia, 30614 Pilsen, Czech Republic)

  • Olena Rubanenko

    (Department of Electrical Power Engineering, Faculty of Electrical Engineering, University of West Bohemia, 30614 Pilsen, Czech Republic
    Research and Innovation Center for Electrical Engineering (RICE), Faculty of Electrical Engineering, University of West Bohemia, 30614 Pilsen, Czech Republic
    Department of Power Plants and System, Vinnitsya National Technical University, 21000 Vinnitsya, Ukraine
    Department of Wind Power, Institute of Renewable Energy, 02094 Kyiv, Ukraine)

Abstract

This paper presents an analysis of the instability of the electricity generation of renewable energy sources (RESs), specifically Digital Twins of RESs. The first part deals with the analysis of RES electricity generation around the world and Ukraine. The following chapter describes features of functioning power grids in modern conditions in Ukraine and ways to ensure the balance reliability in the power system for conditions of high-grade RES integration. The rapid increase in electricity generation RESs causes control problems of distributed power supply in the power grid. A mathematical model of the parameter controls in normal mode electric power systems for conditions with high integration of RESs is proposed in the second part. The study investigates components of the optimality criterion at the control of normal mode parameters of the electric power system with RESs. In general, digital transformation helps decarbonize the energy supply, decrease dependency on fossil fuels, and integrate renewables into power systems. A model Digital Twin (DT) of a photovoltaic system, or an exact 3D visualization, analyzing the accumulator system depending on load and generation, are presented. The problems of Digital Twin are very widely discussed, but many papers and studies are general without any practical implementations. The main part of this paper focuses on research and deals with daily electricity generation from different kinds of RESs, namely mini-hydropower stations, photovoltaic power stations, and wind power stations. Measured data of electricity generation from photovoltaic power plants, wind power plants, and mini-hydropower plants and obtained meteorological factors were used for the calculation of Spearman’s, Kendall’s, and Pearson’s correlation rank coefficients. The main contribution of this research is to determine the main metrological factors for each kind of studied RES. In the future, it will help to decide the task of forecasting power generation more presciently. Additionally, the presented model of DT RESs allows the installation and operation of grids with higher efficiency, because it can help to predict all influences, from shading up to the optimization of the battery storage system.

Suggested Citation

  • Milan Belik & Olena Rubanenko, 2023. "Implementation of Digital Twin for Increasing Efficiency of Renewable Energy Sources," Energies, MDPI, vol. 16(12), pages 1-27, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4787-:d:1173872
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    References listed on IDEAS

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    1. Seungchan Oh & Heewon Shin & Hwanhee Cho & Byongjun Lee, 2018. "Transient Impact Analysis of High Renewable Energy Sources Penetration According to the Future Korean Power Grid Scenario," Sustainability, MDPI, vol. 10(11), pages 1-15, November.
    2. Andrzej Smolarz & Petro Lezhniuk & Stepan Kudrya & Viacheslav Komar & Vladyslav Lysiak & Iryna Hunko & Saltanat Amirgaliyeva & Saule Smailova & Zhassulan Orazbekov, 2023. "Increasing Technical Efficiency of Renewable Energy Sources in Power Systems," Energies, MDPI, vol. 16(6), pages 1-14, March.
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    Cited by:

    1. Vladimir Simankov & Pavel Buchatskiy & Anatoliy Kazak & Semen Teploukhov & Stefan Onishchenko & Kirill Kuzmin & Petr Chetyrbok, 2024. "A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies," Energies, MDPI, vol. 17(2), pages 1-23, January.

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