Optimal Power Flow Solution Incorporating Hybrid Conventional and Renewable Resources Using Electric Eel Foraging Optimization Algorithm

In recent years, metaheuristic algorithms have become the main tool in solving the Optimal Power Flow (OPF) problem due to their effectiveness in addressing complicated modern power systems. This complexity is fueled by the rise of Renewable Energy Resources (RERs) and the need to decrease greenhouse emissions. This research presents a comprehensive approach that aims to optimize the performance of power networks in the presence of thermal, wind, and Solar Photovoltaic (SPV) units. The algorithm implemented is named Electrical Eel Foraging Optimization (EEFO). It is carried out using the modified IEEE 30-bus test system. EEFO is compared alongside Kepler Optimization Algorithm (KOA) and Self-adaptive Bonobo Optimizer (SaBO). Two cases were taken into consideration. The first one is minimizing the Total Generation Cost (TGC); the second is minimizing generation cost, including the emission effects. The results show a reduction in TGC at 781.1981 $/h and 792.6531 $/h for the first and second cases, respectively; emissions were also decreased compared with previous studies. The findings obtained in this research show the validity of the proposed EEFO algorithm.