article · Sustainability
Low energy conversion efficiency remains a central challenge in solar power, making accurate modelling and parameter estimation vital for designing, simulating, and controlling photovoltaic panels. Traditional optimisation methods often struggle with this task because they tend to get trapped in local optima. To overcome this limitation, a Growth Optimisation algorithm was introduced, drawing inspiration from human learning and self reflection in social development. The method was simulated and applied to estimate unknown parameters for two photovoltaic systems, RTC France and Kyocera KC200GT, across diverse operating conditions involving varying temperatures and solar irradiance levels. When evaluated against three contemporary methods, including the energy valley optimiser, five phases algorithm, and hazelnut tree search, the growth optimiser consistently delivered superior performance across single and double diode configurations, enhancing the accuracy of electrical characterisation.
Accurate modelling of solar panels is essential for designing efficient solar energy systems and predicting performance under real-world conditions. Standard mathematical optimisation methods often fail to find the best solutions. By using an approach modelled on human learning, engineering simulations can more accurately determine module characteristics, aiding the development and operational control of more reliable photovoltaic installations.
The approach is an algorithmic simulation tool aimed at engineers and researchers involved in photovoltaic module design, control, and system simulation. It enables more accurate parameter extraction across varying operating environments. The technology currently sits at an early computational simulation stage, demonstrated on standard commercial test modules such as RTC France and Kyocera KC200GT, but without explicit reported field deployment or integration into commercial design software.
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One of the most significant barriers to broadening the use of solar energy is low conversion efficiency, which necessitates the development of novel techniques to enhance solar energy conversion equipment design. The correct modeling and estimation of solar cell parameters are critical for the control, design, and simulation of PV panels to achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity to become caught in some local optima. In this study, a Growth Optimization (GO) algorithm is developed and simulated from humans’ learning and reflection capacities in social growing activities. It is based on mimicking two stages. First, learning is a procedure through which people mature by absorbing information from others. Second, reflection is examining one’s weaknesses and altering one’s learning techniques to aid in one’s improvement. It is developed for estimating PV parameters for two different solar PV modules, RTC France and Kyocera KC200GT PV modules, based on manufacturing technology and solar cell modeling. Three present-day techniques are contrasted to GO’s performance which is the energy valley optimizer (EVO), Five Phases Algorithm (FPA), and Hazelnut tree search (HTS) algorithm. The simulation results enhance the electrical properties of PV systems due to the implemented GO technique. Additionally, the developed GO technique can determine unexplained PV parameters by considering diverse operating settings of varying temperatures and irradiances. For the RTC France PV module, GO achieves improvements of 19.51%, 1.6%, and 0.74% compared to the EVO, FPA, and HTS considering the PVSD and 51.92%, 4.06%, and 8.33% considering the PVDD, respectively. For the Kyocera KC200GT PV module, the proposed GO achieves improvements of 94.71%, 12.36%, and 58.02% considering the PVSD and 96.97%, 5.66%, and 61.20% considering the PVDD, respectively.
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DOI: 10.3390/su15107896
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