Orthogonal Multi-Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems

Modern power systems encounter significant challenges in optimal power flow (OPF) management due to the unpredictable nature of wind energy integration. Flexible AC Transmission System (FACTS) devices, including Static VAR Compensator (SVC), Thyristor-Controlled Series Compensator (TCSC), and Thyristor-Controlled Phase Shifter (TCPS), enhance system stability, reduce losses, and lower operational costs when optimally placed. Conventional optimization techniques like Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Moth Flame Optimization (MFO), Gray Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) struggle to balance exploration and exploitation in complex OPF problems, leading to suboptimal solutions. This study proposes a novel hybrid metaheuristic approach, the Orthogonal Multi-swarm Greedy Selection Sine Cosine Algorithm (OMGSCA), integrating orthogonal learning, multi-swarm mechanisms, and greedy selection to enhance solution quality. Orthogonal learning explores new search spaces, while the multi-swarm strategy improves exploitation. The greedy selection mechanism prevents premature convergence. OMGSCA optimizes FACTS device placement and sizing in wind-integrated power systems under fixed and uncertain loading conditions. Performance evaluation on the IEEE 30-bus test system with wind energy and FACTS devices demonstrates OMGSCA's superiority over traditional algorithms. Case studies focus on minimizing generation costs, active power losses, and gross costs. Results show OMGSCA achieves a power loss of 5.6209 MW in Case 1, comparable to WOA (5.6121 MW) and outperforming PSO, SCA, and MFO by 0.90%, 0.06%, and 0.57%, respectively. OMGSCA's gross generation cost (1369.3961 $/h) surpasses PSO, SCA, MFO, and GWO by 0.39%, 0.28%, 3.48%, and 0.20%, respectively. The algorithm proves effective in OPF problems, delivering cost-efficient operations, reduced losses, and enhanced stability across varying load conditions.