Dynamic power management based on Model Predictive Control and PSO for hybrid microgrid

Hybrid Microgrids (HMGs) are more predominant due to their ability to integrate Distributed Renewable Energy Resources (DREs) with existing utility grids, minimize power conversion losses, enhance energy efficiency, and improve system stability. Due to the fluctuating and unpredictable output of DRES power generation, varying load demands negatively affect HMG’s voltage quality and power-sharing. These fluctuations can be reduced by ensuring a proper balance between the power generation and the load demand. Therefore, this paper presents the robust architecture of HMG comprised of DC microgrid (DCMG) and AC microgrid with Particle Swarm Optimization (PSO) based control algorithm for a Bidirectional Interlinking Converter. The proposed HMG concept consists of a Doubly Fed Induction Generator and Permanent Magnet Synchronous Generator based wind turbines, Photovoltaic (PV) Source (PV), diesel generator, and Battery Energy Storage System (BESS) tied at the AC bus and DC bus. The proposed system employs an Adaptive Neuro-Fuzzy Inference System for a Maximum Power Point Tracking controller in the DC–DC converter of a PV system to achieve maximum output power during various irradiation scenarios due to its accuracy and fast-tracking speed. The Model Predictive Control technique is employed to control the switching of the bidirectional converter of BESS in DCMG due to its non-linear and multi-input multi-output control. It improves DC bus voltage regulation and ensures the smoothing of varying output power generation from RES. The HMG is analyzed for power management under varying source and load conditions. The proposed system is verified in the MATLAB Simulink environment to validate HMG’s grid-connected and islanded operation, and a comparative analysis with and without optimization is presented. The result analysis proves that the PSO-based control algorithm performs better than the conventional controller, significantly enhancing the system’s dynamic conditions. Specifically, the overshoot and undershoot values are reduced from 28.5 to 21.5% and from 17.6 to 9.5% at various transient conditions with less oscillations. These improvements show the effectiveness of the proposed optimization-based control strategy in enhancing transient stability, power sharing, and voltage regulation. These results confirm the effectiveness of the proposed optimization-based control strategy for next-generation hybrid microgrids.