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Modelling and Optimal Utilisation of the Renewable Energy Systems in Microgrid with Improving Reliability and Energy Efficiency

Author

Listed:
  • R. M. Romero

    (Universidad Brazil)

  • Maria Zelinskaya

    (Kuban State Agrarian University Named After I.T. Trubilin)

  • Samariddin Makhmudov

    (Termez University of Economics and Service
    Alfraganus University
    Tashkent State University of Economics)

  • Sindor Sapaev

    (Urgench State University)

  • Jabbarov Umarbek Rustambekovich

    (Mamun University)

Abstract

A microgrid that utilises renewable energy sources is viewed as the most appropriate and cost-effective method to supply electricity. As technology has progressed, energy storage systems have become a viable alternative for stationary power applications, aiding in alleviating the inconsistent characteristics of renewable energy sources. This study investigates the management of energy within a microgrid by incorporating energy storage systems. The innovative hybrid strategy is used in this research merges the Similarity-Navigated Graph Neural Network with Tasmanian Devil Optimisation. The primary aim of this approach is to enhance energy efficiency and improve reliability in the microgrid system. In this framework, Tasmanian Devil Optimisation is employed to fine-tune the parameters of the system while the Similarity-Navigated Graph Neural Network is tasked with predicting these optimised parameters. To implement this proposed methodology, MATLAB software has been utilised, and the current technique is applied to evaluate its performance. The findings indicate that this approach surpasses all existing methods. Notably, the results demonstrate that the proposed method achieves an outstanding efficiency significantly outperforming other contemporary techniques.

Suggested Citation

  • R. M. Romero & Maria Zelinskaya & Samariddin Makhmudov & Sindor Sapaev & Jabbarov Umarbek Rustambekovich, 2025. "Modelling and Optimal Utilisation of the Renewable Energy Systems in Microgrid with Improving Reliability and Energy Efficiency," SN Operations Research Forum, Springer, vol. 6(3), pages 1-20, September.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:3:d:10.1007_s43069-025-00494-1
    DOI: 10.1007/s43069-025-00494-1
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    1. T. X. Du & Kamilia Mehrabi Jorshary & Mirali Seyedrezaei & Valisher Sapayev Odilbek Uglu, 2025. "Optimal Energy Scheduling of Load Demand with Two-Level Multi-objective Functions in Smart Electrical Grid," SN Operations Research Forum, Springer, vol. 6(2), pages 1-23, June.
    2. Wanying Wang & Luyan Li & Victor Shi & Shervin Espahbod, 2024. "Carbon Emission Accounting and Reduction for Buildings Based on a Life Cycle Assessment: A Case Study in China’s Hot-Summer and Warm-Winter Region," Sustainability, MDPI, vol. 16(14), pages 1-18, July.
    3. M. Mohammedi & Majid Naseri & Kamilia Mehrabi Jorshary & Navid Golchin & Shokhjakhon Akhmedov & Valisher Sapayev Odilbek Uglu, 2025. "Economic, Environmental, and Technical Optimal Energy Scheduling of Smart Hybrid Energy System Considering Demand Response Participation," SN Operations Research Forum, Springer, vol. 6(2), pages 1-21, June.
    4. Roula Inglesi-Lotz & Anna Maria Oosthuizen & Sharifa Jumaniyazova & Bekhzod Kuziboev & Jie Liu, 2024. "Exploring the Impact of Women Governance on CO2 Emissions in the European Union and Central Asia," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 639-646, May.
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