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

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

Applied Energy, 2025, vol. 377, issue PC, No S030626192402004X

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.

Keywords: Digital twin microgrid control; Energy management; Self-healing; Microgrid optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124621

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