Spatial and Capacity Optimization of Grid-Connected Photovoltaic Systems Using Crayfish Algorithm

Integrating renewable energies like photovoltaic (PV) systems into traditional power systems can improve efficiency; however, it introduces technical challenges such as increased costs and power losses if the power flow is not managed correctly. This research employs the Crayfish Optimization Algorithm (COA), a new and unique approach developed in 2024, to determine the optimal way to distribute PV modules in the IEEE 9-bus network. The purpose is to cut expenses, reduce losses, and keep the voltage within acceptable ranges. The MATPOWER tool compares two scenarios: one applies all PV power to a single bus while the other distributes the PV across all buses. Three different load circumstances assess the proposed approach. The results demonstrate that COA outperforms various published optimization approaches, such as the Grey Wolf Optimizer (GWO), the Genetic Algorithm (GA), and the Equilibrium Optimizer (EO), in terms of system performance and cost reduction. This study shows how COA can effectively optimize PV locations and power into power systems.