A comparative study of FLC and MPC strategies for battery-protective energy management in grid-connected DC microgrids with hybrid renewable sources

The increase in the integration of renewable energy sources such as photovoltaic and wind energy, as a consequence of climate change leading to the need for reduced CO₂ emissions, is pushing towards the development of modern-day power systems. Wind and solar energy are variable and unpredictable sources of energy that pose significant challenges to balancing the power supply with demand, maintaining system stability, and supplying a reliable source of electricity. The inclusion of Battery Energy Storage Systems (BESS) in DC microgrids connected to the grid has the potential to address these challenges by providing more operational flexibility, greater system autonomy, and improved energy use efficiency. However, there are still a number of technical hurdles, such as greater system complexity, effective management of battery charge/discharge cycles, accurate determination of State of Charge (SOC), and ensuring reliable real-time coordination between renewable energy generation, energy storage units, and the electrical grid. Within this framework is the Energy Management System, which is responsible for coordinating power flow; providing protection to the battery; and maximizing the use of renewable energy resources. This paper will present the results of a comparative assessment of two EMS strategies (Fuzzy Logic Control and Model Predictive Control) for better protects lithium-ion batteries while maintaining efficient energy management in a grid-connected DC microgrid. The proposed system has been developed in MATLAB/Simulink and contains Photovoltaic Generation, Wind Generation, Battery Storage System, DC Bus, AC Utility Grid and Variable Local Load. The Fuzzy Logic Control (FLC) method gives low-complexity real-time decisions based on heuristic rules. The Model Predictive Control (MPC) method provides finite-horizon predictive optimization for Socialization and Future Use of Battery Energy Resources (BESR) Usage, anticipatory dispatch, and how to effectively handle constraint management. Both the Fuzzy Logic Controller (FLC) and the Model Predictive Controller (MPC) were simulated under different scenarios of sunny and cloudy weather with various combinations of renewable energy generation sources and energy users. The results show that MPC achieves a more stable delivery of electricity to the grid, faster charging of batteries, better tracking of battery State of Charge (SOC), and better optimization of the microgrid operations compared to FLC. Conversely, FLC has a simpler implementation process, offers greater robustness, and has lower computational costs compared to MPC. Finally, this study provides an understanding of the trade-offs between the performance of the controllers and their computational requirements, which will assist in making decisions on the design of next generation smart DC microgrids.