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Main Article Content
A Predictive model for performance analysis of solar PV Systems in Kajiado County, Kenya
Abstract
Solar photovoltaic is one of the emerging technologies widely recognized as a potential solution to energy poverty due to its high reliability, long life, and automatic operation with minimal maintenance requirements. despite its low conversion efficiency and high capital costs. However, solar energy is non-dispatchable, hence not used as a peak load energy source, due to its sporadic nature. Solar PV system design and operation is also affected by intermittency. System design should consider site specific ambient conditions that may affect the output. Such conditions are not often modeled and designers always use standard test conditions. There is need to develop such models that can be used to guide system designers. This study focuses on the development and validation of a predictive numerical system for solar PV systems in Kajiado County. Real-time data on solar irradiance, temperature, and system performance were collected to model and simulate the performance of solar PV systems. The study employed an exploratory and predictive research design, utilizing analytical and numerical techniques to develop and refine the predictive numerical model. The model was validated using an existing solar PV system in Kajiado County, comparing the predicted performance metrics with the actual characteristics of the system. Simulation procedures involved developing the model using module specifications and intermittency variables, and conducting simulations based on site-specific data. The results were analyzed, and the accuracy of the predictive model was assessed. The model demonstrated that solar PV systems perform well at low Nominal Operating Cell Temperature (NOCT) conditions of 30 °C. Based on the simulated data output, the PV module conversion efficiency varies between 20.32% and 16.72%. This differs from the expected efficiency value, which in laboratory circumstances is 21.00%. The simulated power output values had a deviation of between 0.68% and 10.68% in comparison to experimental data from a site specific location in Kajiado County, Kenya. The study concluded that the developed predictive numerical system accurately predicts the performance of solar PV systems, and that the predicted power based on laboratory test conditions is more than the real power based on real site conditions. The findings provide valuable insights for decision-making in the design, operation, and management of solar PV systems. The research recommends further model refinement, expanded validation, real-time monitoring, collaboration, and policy support to enhance the effectiveness and applicability of the predictive numerical system in the renewable energy sector.
