Optimization design of uncertain parameters for improving the stability of photovoltaic system

Abstract

The uncertainty of photovoltaic module material parameters have a great impact on the overall output performance of photovoltaic cells. Ignoring the uncertainty of these parameters results in low reliability in photovoltaic cell applications. In this study, the deterministic and robust optimization design of photovoltaic cells was demonstrated by considering the randomness of the actual working environment of photovoltaic cells and the interval uncertainty of material properties of photovoltaic components, and by using the theoretical efficiency of photovoltaic cells as constraint. The corresponding parameter values were obtained after optimization, and the optimization results are analyzed with Monte Carlo simulations. Making parameters after deterministic optimization obey the normal distribution, and the parameter value obtained after optimization is the mean value, then compared with the robust optimization. It can be concluded that compared with the traditional deterministic design, the mean value of the constraint conditions after robust optimization is far removed from the upper limit, the standard deviation is smaller, and the failure probability is far less than that after setting variables through deterministic optimization. Based on these findings, the influence of the uncertain factors can be reduced, the output is more reliable, and the performance optimization of photovoltaic cells can be provided guidance.