Parameter identification of solar photovoltaic cell and module models via supply demand optimizer

Abstract

The extraction of photovoltaic (PV) module parameters is regarded as a critical topic for assessing the performance of PV energy systems. The Supply-Demand-Based Optimization Algorithm (SDOA) is employed in this work to extract the unknown parameters of PV models. The SDOA mimics the stability and instability modes between the supply and the demand one in order that the quantity and price of commodities converge to the equilibrium point after a specified number of repetitions. It is frequently used to handle complicated nonlinear problems because of its ease of implementation and powerful optimization capabilities. The Triple-Diode Model (TDM) is extensively adopted in PV module mathematical models. The optimal nine TDM parameters are determined for the PVM 752GaAs PV thin film cell, whereas other solar irradiation and temperature values are used for the SQ 150 and MSX 60 modules. When used on the TDM model, the SDOA was used to verify the fitness values and standard deviation errors. Furthermore, the obtained result achieved by SDOA are contrasted with new techniques established in 2020 which are Backtracking Search Algorithm (BSA), Grey Wolf Optimizer (GWO), Bernstein-Levy Search Differential Evolution Algorithm (BSDE), Crow search Optimizer (CSO), and Manta Ray Foraging Optimizer (MRFO). For accurate and consistent results, thirty runs of the algorithm are executed for the modules and the standard deviations of fitness values are less than 1 × 10–18 for the triple diode model. In addition to this, a practical PV power plant system that lie in the Guizhou Power Grid of China is used to validate the efficiency of SDOA compared to other recent algorithms. Thus, SDOA is considered as a competitive optimizer among other reported techniques in the literature or recent techniques for PV parameter extraction.