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06:04
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
Published on: February 14, 2025
182
A digital twin based forecasting framework for power flow management in DC microgrids
Kerry Sado1, Jarrett Peskar2, Austin Downey2,3
1Department of Electrical Engineering, University of South Carolina, Columbia, SC, USA. ksado@email.sc.edu.
Scientific Reports
|February 21, 2025
Summary
This study introduces a digital twin forecasting framework for DC microgrids, enabling real-time monitoring and decision-making. The system uses sensor data to predict conditions, preventing overloads and improving operational efficiency.
Area of Science:
- Electrical Engineering
- Computer Science
- Systems Engineering
Background:
- Forecasting is crucial for digital twin functionality.
- Real-time feedback for digital twin forecasting in DC microgrids remains under-explored.
- DC microgrids require advanced prediction due to dynamic load variations.
Purpose of the Study:
- Develop a modular forecasting framework for digital twins in DC microgrids.
- Enable real-time monitoring, online forecasting, and in-situ decision-making.
- Facilitate proactive management strategies for DC microgrid stability and efficiency.
Main Methods:
- A modular forecasting framework integrated with a digital twin.
- Leveraging real-time sensor data for system behavior prediction.
- Incorporating an electro-thermal digital twin for power flow management based on thermal constraints.
Main Results:
- The framework provides real-time predictive insights based on dynamic system conditions.
- Demonstrated ability to forecast system behavior under varying load conditions.
- Experimental validation on a three-bus DC microgrid testbed confirmed effectiveness.
Conclusions:
- The digital twin-based forecasting framework enhances DC microgrid management.
- Enables timely adjustments to power flows, preventing thermal overloads.
- Offers a robust solution for proactive control and operational efficiency in DC microgrids.
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