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Novel abstractions and experimental validation for digital twin microgrid design: Lab scale studies and large scale proposals

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  • Sifat, Md. Mhamud Hussen
  • Choudhury, Safwat Mukarrama
  • Das, Sajal K.
  • Pota, Hemanshu
  • Yang, Fuwen

Abstract

Managing the current high penetration of renewable energy sources globally poses a significant challenge due to the distributed and diverse nature of generation components. To maximize real-time power generation, these resources need to perform optimally. Digital twin technology offers a comprehensive framework for managerial support by replicating grid features in a digital environment. This research creates a digital twin of the microgrid to optimize power generation, focusing on computational efficiency and self-healing control. The framework is tested in a laboratory microgrid, with modeling performed using a polynomial regression algorithm. Optimization is achieved through a gradient descent algorithm, and the self-healing model is implemented using a logistic regression algorithm. Real-time data extracted from the microgrid drives this process. The results can be utilized for predictive analysis before deploying a microgrid or to optimize generation in existing systems using the digital twin model. Even though the research focuses on a single microgrid unit, it introduces a framework proposal for extensively distributed microgrids integrating multiple renewable energy sources.

Suggested Citation

  • Sifat, Md. Mhamud Hussen & Choudhury, Safwat Mukarrama & Das, Sajal K. & Pota, Hemanshu & Yang, Fuwen, 2025. "Novel abstractions and experimental validation for digital twin microgrid design: Lab scale studies and large scale proposals," Applied Energy, Elsevier, vol. 377(PC).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pc:s030626192402004x
    DOI: 10.1016/j.apenergy.2024.124621
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    References listed on IDEAS

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