Intelligent PSO-based MPPT Control for High-Efficiency Bidirectional DC–DC Conversion in Photovoltaic Energy Management Systems

Current trends have concentrated around efficient power conversion, intelligent control and secure integration of energy storage technologies for managing variability as well as meeting grid support specifications. The objective is based on developing and implementing a particle swarm optimization (PSO) based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) integrated bidirectional DC-DC converter to improve the charging of batteries as well as grid interfacing. The implemented method combines the advantages of the traditional methods for maximum power point tracking (MPPT) with fast charging techniques suffering from slow convergence, oscillations around MPP and poor performance in dynamic operating conditions. The main aim is to create an optimal control technique, allowing quick tracking and stabilizing battery operation with improved power quality. A DC-DC bidirectional converter configuration is employed to ensure controlled power flow between PV source, battery bank and AC load. At this work, the whole system was constructed and simulated in MATLAB/Simulink including PV system characteristic, PSO-MPPT controller, battery behaviour and grid interface. The advantage of self-sustain ability to boost the battery voltage, reduces current ripple and contribution in energy extraction from PV under different situations is well documented based on simulation results. The voltage and current waveforms of the grid show that the power quality is satisfactory. The recommended system can be used for renewable energy systems, microgrids, electric vehicle chargers and PV-battery applications.