Multi-objective optimal power flow in a wind–PV integrated system using unified power flow controller for enhanced frequency control and system stability

Abstract The increasing integration of renewable energy sources, particularly wind and photovoltaic system, into power grids introduces significant challenges related to variability, uncertainty, and frequency instability. This study presents a comprehensive study on multi-objective optimal power flow in a hybrid Wind–PV integrated power system incorporating a unified power flow controller to address these challenges. The proposed framework aims to simultaneously minimize generation cost, emission levels, and frequency deviations while maintaining voltage profiles and system security. A modified IEEE-30 bus test system is modeled to simulate the grid-connected Wind–PV environment in a three area system. The UPFC, as a versatile FACTS device, is employed to enhance power flow control, improve voltage stability, and mitigate frequency oscillations caused by intermittent renewable generation. A metaheuristic optimization algorithm—specifically the chaotic african vulture optimization algorithms (CAVOA) is adopted to solve the multi-objective problem efficiently, ensuring convergence toward optimal solutions with a balance between conflicting objectives. Simulation results validate the effectiveness of the proposed approach. The integration of UPFC significantly improves frequency response and system stability, while the optimization algorithm achieves superior trade-offs among the objectives. The study demonstrates that intelligent control of power flow in renewable-rich systems is essential for the future smart grid, ensuring reliability, sustainability, and economic operation. Furthermore, the superiority of the proposed algorithm is also tested through the statistical approach like one-way Analysis of variance (ANOVA) test.