At present, photovoltaic (PV) power plants are growing rapidly to respond to the high demand for energy. In this case, improving the levelized cost of electricity (LCOE) and produced energy for PV power plants is a complicated design tradeoff that involves several parameters, such as meteorological data variation, nonlinear operation of the PV plant components, inverter types, and PV module efficiency. Hence, this study intended to present the application of recent meta-heuristic techniques, namely, salp swarm algorithm (SSA), whale optimization algorithm (WOA), and grey wolf optimization (GWO), for two different cases. The technology of crystalline silicon (c-Si) and thin-film cadmium telluride (CdTe) PV modules is adopted for economic considerations and to determine the suitable PV module for the PV power plant. The optimization process is considered to minimize the LCOE and suggest the optimal sizing of PV modules and inverters on the basis of several candidates and their arrangement within the available area with optimal PV module tilt angle and orientation, as well as the optimal distribution of PV modules among the inverters. The new approaches have been compared with particle swarm optimization (PSO) algorithm. The proposed technique (GWO) shows significant results compared with other methods (PSO) in solving the optimal design of the PV power plant. The PV plant LCOE using thin-film (CdTe) has the lowest value compared to crystalline silicon (c-Si). The PV module efficiency and technology affect the overall dimension of the PV power plant.
