Model predictive control of consensus-based energy management system for DC microgrid.

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy Systems

Background:

• DC microgrids are increasingly integrating distributed renewable energy sources (DRES) and battery energy storage systems (BESS).
• Controllers for DRES and BESS can operate independently as grid-forming (GFM) or grid-feeding (GFE) units, crucial for microgrid stability in standalone and grid-connected modes.

Purpose of the Study:

• To propose a consensus-based energy management system (EMS) using Model Predictive Control (MPC) for DRES and BESS controllers.
• To enable seamless switching between GFM and GFE configurations within DC microgrids.

Main Methods:

• Development of a novel MPC-based EMS that determines power flow modes based on generation, load, environmental factors, and BESS state of charge (SOC).
• Implementation of a single hybrid cost function within MPC for unified GFM/GFE control with auto-tuning weighing factors.
• Validation on an EU low voltage benchmark DC microgrid using MATLAB/SIMULINK.

Main Results:

• The proposed MPC-based EMS successfully enables DRES and BESS controllers to operate in both GFM and GFE modes.
• The MPC technique demonstrated superior performance compared to Proportional Integral (PI) and Sliding Mode Control (SMC) methods.
• Achieved settling time of less than 1μsec and an overshoot of only 5%.

Conclusions:

• The MPC-based EMS offers an effective solution for managing DRES and BESS in DC microgrids, enhancing operational flexibility.
• The auto-tuning hybrid cost function provides robust control for switching between GFM and GFE modes.
• The validated results confirm the efficiency and improved dynamic response of the proposed MPC strategy over traditional control techniques.