The quick crisscross sine cosine algorithm for optimal FACTS placement in uncertain wind integrated scenario based power systems

• Developed a novel Quick Crisscross Sine Cosine Algorithm (QCSCA) to solve the Optimal Power Flow (OPF) problem in power systems integrating renewable energy sources (RESs) and Flexible AC Transmission Systems (FACTS) devices. • Enhanced the traditional Sine Cosine Algorithm (SCA) by incorporating adaptive parameter control, a Crisscross (CC) selection mechanism, and a Quick Move (QM) mechanism to balance exploration and exploitation effectively. • Demonstrated superior performance of QCSCA on the IEEE 30-bus test system under fixed and dynamic loading conditions, consistently minimizing generation costs, power losses, and gross costs. • Achieved significant cost reductions, such as a gross cost reduction of 515.2580 $/h in Case 4 , outperforming competing algorithms by up to 1.29%. • Realized a maximum power loss reduction of up to 8% compared to other methods, enhancing energy efficiency across multiple scenarios. • Validated QCSCA's effectiveness through top rankings in the Friedman Rank Test, reinforcing its applicability for real-world power flow optimization in renewable energy-integrated grids. • Optimized the placement and sizing of key FACTS devices (TCSC, SVC, and TCPS), improving system stability and efficiency. • Addressed uncertainties associated with renewable energy integration, demonstrating robust performance under different load demand scenarios. • Despite minor trade-offs in voltage deviation, the overall gains in cost and power loss optimization justify the use of QCSCA. • Outperformed several SCA-based algorithms (SCA, ASCA, CCSCA, QMASCA, QMCCSCA) in convergence speed, solution quality, and computational efficiency. The Quick Crisscross Sine Cosine Algorithm (QCSCA) was developed to address the challenges of solving the Optimal Power Flow (OPF) problem in power systems that integrate renewable energy sources and Flexible AC Transmission Systems (FACTS) devices. Traditional optimization methods, such as linear programming, often struggle with the non-linear, multi-dimensional nature of modern power grids, leading to inefficiencies. QCSCA enhances the original Sine Cosine Algorithm (SCA) by incorporating adaptive parameter control, a Crisscross (CC) selection mechanism, and a Quick Move (QM) mechanism, effectively balancing exploration and exploitation. These improvements help avoid local optima and enhance convergence. Evaluated on the IEEE 30-bus test system under fixed and dynamic loading conditions, QCSCA outperformed various SCA variants, consistently minimizing generation costs, power losses, and gross costs. For instance, in Case 4, QCSCA achieved a gross cost reduction of 515.2580 $/h, outperforming competing algorithms by up to 1.29%, while also achieving significant power loss reduction across multiple scenarios. Although its voltage deviation was slightly higher in some cases, the overall performance gains in cost and power loss optimization justified the trade-off. QCSCA superior performance was further validated by its top rankings in the Friedman Rank Test, reinforcing its applicability for real-world power flow optimization in renewable energy-integrated grids.