Parametric optimization and sensitivity analysis of photovoltaic modules: seasonal performance evaluation and emission reduction for sustainable environment

Abstract

Solar energy can fulfill global energy demand and improve the environment, economy, and energy security of a country. Solar energy systems are very sensitive to meteorological variables, and they must be optimized for maximum output. This study investigates the effect of WS, T, and RH on the power production of monocrystalline cleaned and uncleaned PV modules during four seasons. Regression analysis determined the importance of parameters using p-value and t-statistic. To maximize power generation, GA optimization and sensitivity analysis were used. T has a p-value below 0.05 and a t-statistic value over |±1.96|, which shows that it is a crucial parameter for optimal module performance. For spring, summer, monsoon, and winter, clean module power production was 41 W, 48.19 W, 40.62 W, and 37.16 W, respectively. The optimal power output for the unclean module was 24.61 W, 35 W, 31.66 W, and 6.43 W for spring, summer, monsoon, and winter, respectively. In the monsoon season, an increase in T by 25 % lowered the power output by 81 % for clean and 87 % for unclean modules, the largest decline of all seasons. The winter season had the lowest power production, 42 % for clean and 13.3 % for unclean modules when a 25 % change in T was considered. CO2 emissions for modules reached the highest value in summer and dropped in winter. Similarly, seasonal revenue is highest in summer and lowest in winter because of more operating hours in summer and vice versa.

Keywords CO2 emissions, Optimization, PV module, Regression analysis, Revenue generation