Modeling and simulation of photovoltaics help to reduce development costs, design turnaround time and facilitates better techno-economic decisions. However, there is a current need to generate new theories, algorithms, applications and software in order to increase the contribution of solar energy to the global energy supply. For future advancements in the field of photovoltaics, robust techniques for PV modeling, simulation, visualisation and design are required to overcome the limitations of the current approaches. This study proposes the Code-Based Modeling (CBM) approach as a potent approach to facilitate the study of PV technologies. Experimental data were synthesised and used for coding and training of the code-based (CB) model; followed by a validation of the trained model using commercial PV modules. Results clearly show that the model can repeatedly and reliably predict the short circuit current, maximum power point, open circuit voltage with 0%, < 2% and < 10% deviations, respectively. Furthermore, instances of the applicability of the CBM approach in the study of the thermodynamics of PV, solar cell materials characterisation, PV systems design and power monitoring were presented. Above all, CBM approach accepts user-defined functions and therefore presents new opportunities for scientists and engineers to advance model-based investigations of the photovoltaics beyond the current state-of-the-art.

Introduction

Research is actively being carried out on how photovoltaics can be applied as a clean source of energy because it does not emit greenhouse gases during operation (Bukar and Tan, 2019; Ogbonnaya et al., 2019a). The Renewables 2019 Global Status Report (GSR) (REN21, 2018) indicates that solar photovoltaic (PV) constitutes around 100 GW out of 2378 GW global renewable power capacity installed in 2018; which represents 55% of the renewable capacity additions in the year, followed by wind power (28%) and hydropower (11%). Apart from direct power generation with PV systems, PV could also become a primary power subsystem in integrated systems in the future. For instance, PV modules have been studied for integration with electrolysers, fuel cells and batteries for reliable power generation (Lehman and Chamberlin, 1991; Meurer et al., 1999; Özgirgin et al., 2015). Also, PV modules have been integrated with thermal absorbers to create photovoltaic-thermal (PV/T) systems to supply electricity and hot water (Avezov et al., 2011; Michael et al., 2015). The present trends in integrating PV as a power source; or as a subsystem of a hybrid system, suggest that the demand for software including PV models would increase in the future. As an example, the possibility of integrating a maximum power point tracking (MPPT) algorithm with an automotivebased software to optimise solar energy harvesting was designed and verified by (Cheddadi et al., 2018).