Optimal power flow using recent red-tailed hawk optimization algorithm

The optimal power flow (OPF) solution determines the most efficient and secure operating point by considering certain objective functions and ensuring the system's operational restrictions are met. Optimization can efficiently achieve various objective functions in power systems. The goals may include factors such as overall generating cost, voltage variations, voltage stability, active power transmission losses, and security enhancement. During the optimization phase, modifying the system's control variables is possible. The control variables consist of the active powers produced, the voltage of generating buses, and the transformer tap settings. Various classical and contemporary optimization strategies have addressed the OPF issue. Metaheuristic optimization approaches perform better than classical techniques that rely on sensitivity analysis and gradient-based methods. This is because metaheuristic techniques may avoid becoming stuck in local optima, a limitation of classical techniques. The present work introduces the use of the red-tailed hawk (RTH) optimizer to address the OPF issues, resulting in enhanced performance. The effectiveness of the proposed technique is proven by applying it to the standard IEEE 30-bus and 118-bus systems, considering several objectives that reflect the performance of the power system. The proposed approach will be evaluated using five distinct test scenarios. In addition, the results obtained from the suggested approach have been compared to those achieved by other modern and competitive metaheuristic optimization algorithms, such as particle swarm optimization (PSO), Coot bird's algorithm (COOT), salp swarm algorithm (SSA), Marin predator algorithm (MPA), and the jellyfish search optimizer (JSO). Due to the stochastic nature of the metaheuristic algorithms, each one will be performed several times, and a statistical study will be conducted. Five test cases are considered to evaluate the performance of the proposed strategy. The RTH has reduced the cost to $799.0680 in Case 1, with an enhancement ratio of 0.04 % compared to the PSO. The objective function in Case 2 includes cost reduction and voltage deviation. The RTH provided the best performance of 812.9379, with an enhancement ratio of 0.6 % compared to the PSO. For case 3, which includes the cost and the voltage index, the RTH reduced the resulting cost function to 1.4795 × 103. Cases 4 and 5 include the minimization of active and reactive power transmission loss, respectively. The best results obtained by the RTH algorithm are 2.8506 and -24.2129. These obtained results, along with a comparison with other approaches, demonstrate that the RTH algorithm offers a powerful, robust, and high-quality solution for solving the optimum power flow problem at various levels of complexity.