To address the growing complexity and stochastic behavior of modern power grids, a comprehensive strategy is proposed in this research to enhance the performance of a large-scale transmission network connected to a hybrid conventional and renewable generator system. While various methods have been applied to the Optimal Power Flow (OPF) problem, few effectively balance economic, technical, and environmental constraints under high-penetration renewable scenarios. This paper introduces a novel application of Circulatory System-Based Optimization (CSBO) on the adjusted IEEE 30 and 57 bus frameworks, with three scenarios taken into consideration for each system, starting with the minimization of the overall generation costs, followed by the reduction of the real power outages, and lastly, the minimization of generation costs including pollution effects. The proposed algorithm is compared alongside Mountain Gazelle Optimization (MGO), Artificial Rabbits Optimization (ARO), and Dwarf Mongoose Optimization Algorithm (DMOA), besides other algorithms highlighted in previous studies. The outcomes demonstrate the efficacy and superiority of the suggested CSBO algorithm over competing methods in terms of quality-feasible solutions and computational time required. For instance, the total generation costs were minimized to 787. 359/h for IEEE 30-bus with a CPU time of 295. 375 s, which translates to an 11% time frame reduction in comparison to MGO. Nevertheless, each metaheuristic technique presents distinct advantages in OPF with the presence of renewables: DMOA excels in broad search capability, MGO balances exploration with exploitation, ARO attains a fast convergence rate, and CSBO gives high precision and offers low computational time, making these algorithms vital for managing the uncertainties of Renewable Energy Resources (RERs).
Fellahi et al. (Wed,) studied this question.