Optimal Device Selection and Placement for Voltage Profile Enhancement in IEEE 9-Bus System Using Multi-Objective Metaheuristic Algorithms

Modern power systems face growing voltage stability challenges due to rising electricity demand, renewable energy integration, and aging transmission infrastructure. Strategic placement of support devices is essential to maintain system performance and comply with grid regulations. This study provides a comparative evaluation of metaheuristic algorithms—Ant Colony Optimisation (ACO), Genetic Algorithm, Whale Optimisation Algorithm (WOA), and a hybrid ACO‑WOA approach—for selecting and placing photovoltaic generators, wind turbines, capacitor banks, and static VAR compensators in IEEE 9‑bus system. A multi‑objective framework minimises voltage deviation and power losses across different loading conditions while ensuring grid code compliance. All algorithms were tested using consistent parameters and evaluated over 30 independent runs, with results validated through ETAP simulation. GA demonstrated the best overall performance, achieving the lowest fitness value (0.006066), converging by generation 5, and identifying an optimal two‑device solution: an SVC at Bus 6 (±10 MVAr) and a 3 MW PV unit at Bus 9. ACO achieved near‑optimal performance but required additional devices, while WOA failed to resolve voltage violations at Bus 6. ETAP validation showed strong correlation with optimisation results, with GA exhibiting only 1.2% average deviation and achieving a 55.5% reduction in voltage deviation index under winter loading.