A new approach for active and reactive power management in renewable based hybrid microgrid considering storage devices

This paper introduces an innovative approach for achieving optimal energy management (OEM) in renewable-based hybrid microgrids (HMGs). The HMGs exhibit enhanced efficiency, minimizing power loss while maximizing generated power, making them advantageous over traditional MGs. This new approach consists of a modified model predictive control based improved Firefly1to3 algorithm (MMPC-IFA1to3). Here, MMPC adeptly regulates converter switching phenomena, ensuring stability during uncertain conditions in HMG. On the other hand, FA1to3 states that for each member movement of a firefly population, occurs towards three members. Here, the improved nature of FA1to3 is achieved by using the chaotic function to fine-tune the regulation parameter. This unified method generates the best power references for both active and reactive powers, making switching operations in voltage source converters simpler. This paper mainly emphasizes active and reactive power management through objective function minimization. The proposed IFA1to3 approach effectively incorporates constraints to minimize costs, ensure power availability, and mitigate voltage deviations in renewable-based HMGs. Moreover, charging/discharging of energy storage devices and power exchange between the utility grid and DC MG are carried out through the MMPC-IFA1to3 algorithm by monitoring active and reactive loads simultaneously. The corresponding converters are modeled based on the bidirectional power flow to cope with active and reactive power management. Further, the robustness of the proposed approach is verified under different operating conditions, and outcomes are compared with benchmark techniques in a MATLAB/Simulink environment to evidence its superior EM in HMG. Finally, the proposed MMPC-IFA1to3 approach is validated in a real-time environment through the dSPACE DS 1104 embedded processor to evidence its industrial applications through its robustness and OEM during various case studies.