Enhancing optimal power flow in power systems: A comparative analysis of recent metaheuristic optimization techniques

In this paper, six effective optimization algorithms are proposed to solve the optimal power flow problem: Pelican Optimization Algorithm (POA), Tasmanian devil optimization (TDO), Artificial Rabbits Optimization (ARO), Driving Training-Based Optimization (DTBO), Improved Grey Wolf Optimizer (I-GWO), and Grey Wolf Optimizer (GWO). The standard IEEE 30-bus, and 118-bus test systems have been used to implement the suggested procedures for seven objective functions incorporating single and multi-objective functions. The results produced using these algorithms have also been compared to those acquired using other methods documented in the literature in order to judge the efficacy of the suggested metaheuristic methods. The results show that the IGWO offers an efficient and superior method for resolving the optimal power flow problem when compared to other methods. For example, IGWO reached a fuel generation cost of 799.2420 $/h for case 1, a voltage deviation (VD) of 0.08916 p.u for case 2, and a voltage deviation of 0.10592 p.u while reducing the fuel cost to 803.5009 $/h for case 3, a minimum voltage stability index ( L max ) of 0.100614 for case 4, a minimum voltage stability index of 0.11442 with reducing the fuel generation cost for case 5, minimum active power losses ( P loss ) of 2.9140 MW for case 6, and minimum reactive power losses ( Q loss ) of −23.9183 MVar for case 7. • Investigates six metaheuristics for single and multi-objective OPF, focusing on cost, voltage, and stability. • Tests on IEEE 30-bus and 118-bus systems to validate scalability and accuracy of I-GWO. • I-GWO achieves lower costs, voltage deviations, and losses than other algorithms. • Low standard deviations and rapid convergence confirm I-GWO’s reliability for real-time OPF operations.