A Multi-Objective Optimization Approach for Sizing and Performance Improvement of a Hybrid Grid-Connected PV-Wind System

Grid-connected systems now use renewable energy sources such as solar and wind since they generate power in a clean, green, non-polluting method compared to fossil-based traditional electricity. Nevertheless, due to numerous system design parameters and non-commensurate metrics, size/performance optimization of these hybrid systems is highly challenging. Herein, to handle the complications faced, a multiobjective optimization (MOO) approach has been suggested for the sizing and performance enhancement of a hybrid gridconnected PV-wind system. This combined solution considers the different requirements of minimizing cost, maximizing system efficiency, and reducing the environmental impact. 1 shows that the system components sizing: PV panels, wind turbines, and batteries for power supply are optimized to ensure supplying as much energy needed (annual electricity demand) from renewable resources. By integrating a power controller, the hybrid system's performance is also enhanced, and the power flow between the PV and wind sources is adjusted and controlled. In this regard, a case study of a rural community in India has been presented to show the utility of the proposed approach. The findings show that the MOO solution can significantly improve system cost, efficiency, and environmental impact relative to a classic single-objective optimization approach. Also, designing and operating the hybrid system can ensure a reliable and permanent power supply to the community. It highlights the potential for employing a multiobjective optimization approach for designing and optimizing hybrid grid-connected renewable energy systems.