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Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques.

Babak Arbab-Zavar¹, Suleiman M Sharkh², Emilio J Palacios-Garcia^3,4

¹AAU Energy, Aalborg University, DK-9220 Aalborg, Denmark.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

Communication failures in microgrids (MGs) can be mitigated by enhancing message content with deep learning (DL) forecasts. This approach improves the coordination of energy management systems (EMS) and energy storage systems (ESS) for reliable operation.

Keywords:

artificial neural networks deep learning machine-to-machine communication microgrid time series forecasting

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Area of Science:

Electrical Engineering
Computer Science
Artificial Intelligence

Background:

Microgrids (MGs) rely on device-to-device (D2D) communication for effective operation.
Communication link failures pose a significant risk to MG control and management systems.

Purpose of the Study:

To investigate the impact of communication failures on MG control.
To propose and evaluate a solution for mitigating the adverse effects of communication link failures.

Main Methods:

Utilizing deep learning (DL) for generation and consumption forecasting.
Developing an integrated architecture for energy management systems (EMS) and energy storage systems (ESS).
Enhancing message content within D2D communication to improve resilience.

Main Results:

Simulations demonstrate the effectiveness of the proposed methods in mitigating the impact of communication failures.
The integrated EMS and ESS architecture shows promising results in coordinated operation.
The combination of MG control, DL forecasting, and D2D communication architectures proves capable of achieving the objective.

Conclusions:

The proposed approach enhances the robustness of microgrid control systems against communication disruptions.
Deep learning-based forecasting integrated with enhanced communication messages offers a viable solution for reliable microgrid operation.
The study highlights the synergy between advanced forecasting techniques and resilient communication architectures for smart grids.