Optimization of energy management in Malaysian microgrids using fuzzy logic-based EMS scheduling controller.

The microgrid (MG) faces significant security issues due to the two-way power and information flow. Integrating an Energy Management System (EMS) to balance energy supply and demand in Malaysian microgrids, this study designs a Fuzzy Logic Controller (FLC) that considers intermittent renewable sources and fluctuating demand patterns. FLC offers a flexible solution to energy scheduling effectively assessed by MATLAB/Simulink simulations. The microgrid consists of PV, battery, grid, and load. A Maximum Power Point Tracking (MPPT) controller helps to extract the maximum PV output and manages the power storage by providing or absorbing excess power. System analysis is performed by observing the State of Charge (SoC)of the battery and output power for each source. The grid supplies additional power if the battery and PV fail to meet the load demand. Total Harmonic Distortion (THD) analysis compares the performance of the Proportional-Integral Controller (PIC) and FLC. The results show that the PI controller design reduces the THD in the current signal, while FLC does not reduce the THD of the grid current when used in the EMS. However, FLC offers better control over the battery's SOC, effectively preventing overcharging and over-discharging. While PI reduces THD, FLC provides superior SOC control in a system comprising PV, battery, grid, and load. The findings demonstrate that the output current is zero when the SOC is higher than 80% or lower than 20%, signifying that no charging or discharging takes place to avoid overcharging and over-discharging. The third goal was accomplished by comparing and confirming that the grid current's THD for the EMS designed with both the PI Controller and the FLC is maintained below 5%, following the IEEE 519 harmonic standard, using the THD block in MATLAB Simulink. This analysis highlights FLC's potential to address demand-supply mismatches and renewable energy variability, which is crucial for optimizing microgrid performance.