This research aims to employ and qualify the bio-inspired algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution Algorithm (DE) in the extraction of the parameters of the circuit equivalent to a photovoltaic module in the models of a diode and five parameters (1D5P) and two diodes and seven parameters (2D7P) in order to simulate the I-V characteristics curves for any irradiation and temperature scenarios. The peculiarity of this study stands in the exclusive use of information present in the module’s datasheet to carry out the full extraction and simulation process without depending on external sources of data or experimental data. To validate the methods, a comparison was made between the data obtained by the simulations with data from the module manufacturer in different scenarios of irradiation and temperature. The algorithm bound to the model with the highest accuracy was DE 1D5P, with a maximum relative error of 0.4% in conditions close to the reference and 3.61% for scenarios far from the reference. On the other hand, the algorithm that obtained the worst result in extracting parameters was the GA in the 2D7P model, which presented a maximum relative error of 9.59% in conditions far from the reference.

The purpose of this study is to employ and improve evolutionary algorithms, namely the genetic algorithm (GA) and the differential evolution algorithm (DE), to extract the parameters of the equivalent circuit model (ECM) of a bifacial photovoltaic module using the representative model of a diode with five parameters (1D5P). The objective is to simulate the characteristics of the I–V curves for various irradiation and temperature scenarios. A distinctive feature of this study is the exclusive use of the information in the technical sheet of the bifacial module to conduct the entire extraction and simulation process, eliminating the need to resort to external sources of data or experimental data. To validate the methods, a comparison was made between the simulation results and the data provided by the bifacial module manufacturer, contemplating different scenarios of irradiation and temperature. The DE was the most accurate algorithm for the 1D5P model, which presented a maximum average error of 1.57%. In comparison, the GA presented a maximum average error of 1.98% in the most distant scenario of STC conditions. Despite the errors inherent to the simulations, none of the algorithms presented relative errors greater than 8%, which represents a satisfactory modeling for the different operational conditions of the bifacial photovoltaic modules.