Optimal power control for wind/solar hybrid energy system based on multi-objective particle swarm optimization

Abstract

The effectiveness of wind and solar energy as electricity generators is significantly impacted by unpredictable and varied environmental circumstances, which affect the output power of the wind-solar hybrid power generation system. So, a control system is required for the optimal power production of hybrid renewable energy systems (HRES). This study delineates optimal power management in wind/solar hybrid energy systems by the application of multi-objective particle swarm optimization (MOPSO) algorithms, inverter controllers, and battery controllers. The MOPSO algorithm enhances power generation by modifying the duty cycle of the direct current (DC)/DC converter based on the output from the wind turbine and photovoltaic (PV) system. The proportional-integral (PI) controller functions as both an inverter and battery controller to ensure the constancy of the DC link voltage and output power. The efficacy of the developed control was evaluated using simulation. A comparison has been conducted between the efficacy of the MOPSO algorithm and the perturb and observe (P&O) approach. The simulation findings indicate that the MOPSO algorithm surpasses the P&O method for performance and output power. The output power produced by HRES with the MOPSO algorithm exceeds that of the P&O approach. Optimal power control utilizing MOPSO can yield optimal power despite fluctuations in wind and solar intensity.