Study for Non-uniform Aging Photovoltaic Array Performance Enhancements

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The use of solar energy on a large scale has been socially and politically driven by the negative impact of fossil fuels on the environment. Solar energy is advantageous because it is a renewable energy source and because the supply of sunlight is virtually boundless. On the downside, solar energy is expensive. There are issues of technical control associated with photovoltaic systems (PVs), which capture sunlight via semiconductor materials and convert it to electricity since the current and voltage dynamics of the solar cells lack linearity. Furthermore, although the solar array output is typically equivalent to the sunlight amount, the illumination of various structures (e.g. power plants, solar tents, PV arrays integrated into buildings) may occur in a non-uniform way in the majority of applications. Consequently, PV modules invariably age in a non-uniform manner. This has an adverse impact on the performance of PV plants, especially from the middle till the end of their useful life. The primary reason preventing homogeneous aging is suboptimal environmental conditions, such as temperature, dust, woodland locations, storms, structures or the shadows caused by structures. As different PV modules in a PV array do not age homogeneously, their operating conditions are inconsistent, and the power output of PV arrays varies. The global maximum power point tracking (GMPPT) permits monitoring of the maximum power point (MPP), yet the energy potential of a non-uniform aged PV array has not been evaluated. Given these considerations, the purpose of this study is to make medium- and large-scale aging PV arrays more cost-effective to maintain and operate as well as to enhance their output power efficiency rather than substituting aged PV modules. To that end, a method of offline PV module reconfiguration was devised, taking into account the non-uniform aging of PV arrays to attenuate the impact and preclude the necessity of additional sensors. The proposed method made provision for operational costs and electricity price in order to afford a greater economic advantage. Simulations were conducted with MATLAB and Python software, and an experimental study was performed indoors to assess the method. Four objectives were pursued in the study. First, the method's effectiveness for non-uniformly aging 3 × 4, 5 × 8, and 7 × 8 PV arrays was investigated through repetitive module sorting in a hierarchical order. Secondly, the effectiveness of arbitrarily non-uniformly aged 5 × 5 and 7 × 20 PV arrays was evaluated by applying a gene evolution algorithm (GEA). Thirdly, empirical work was conducted with a 2 × 4 PV array to validate the proposed method. Finally, fourteen countries were chosen as a case study to implement the proposed method to enhance as much as possible the economic advantage of typical 10×10 PV arrays with an output of 18 and 43 kW, taking into account operational costs and the price of electric energy.