Enhanced voltage regulation and current sharing of radial DC microgrids

Area of Science:

• Electrical Engineering
• Power Systems
• Control Systems

Background:

• Direct current (DC) microgrids offer advantages in efficiency, reliability, and control simplicity over alternating current (AC) systems.
• Effective control is crucial for DC microgrids to maintain stable load voltage and equitable power sharing among distributed generators.
• Existing control strategies often necessitate extensive communication infrastructure, increasing complexity and potential failure points.

Purpose of the Study:

• To develop and validate a communication-less control strategy for radial DC microgrids.
• To simultaneously achieve accurate load voltage regulation and proportional load sharing among distributed generation units (DGUs).
• To enhance the dynamic response and stability of DC microgrids under various operating conditions.

Main Methods:

• A novel control strategy for radial DC microgrids was proposed, requiring only global sensing and minimal signal transmission between DGUs.
• The Tianji Horse Racing Optimization algorithm was employed for precise tuning of the controller parameters.
• System modeling and simulation were performed using MATLAB Simulink to rigorously test the control strategy's performance and stability.

Main Results:

• The proposed control strategy successfully regulated load voltage and shared loads accurately without inter-DG communication.
• Stability analysis confirmed system resilience to load changes, including constant power loads.
• The control method demonstrated a significantly faster dynamic response compared to existing approaches.
• Verification of plug-in/plug-out capability and robustness against communication delays was achieved.

Conclusions:

• The developed control strategy offers an efficient, reliable, and communication-light solution for radial DC microgrid management.
• The method enhances system stability and dynamic performance, making it suitable for modern power systems.
• The optimization algorithm effectively tuned controller parameters, ensuring optimal performance under diverse conditions.