Probabilistic financial modeling and EFAST based sensitivity analysis of industrial rooftop solar photovoltaic systems

This study presents a probabilistic economic assessment of an industrial rooftop solar photovoltaic (PV) system using Monte Carlo simulation (MCS). Conventional deterministic approaches are limited in addressing uncertainties in key parameters. To overcome this, the analysis incorporates variations in system lifespan (modeled with a Weibull distribution), module degradation rate (Gamma distribution), electricity price escalation rate (Uniform distribution), operation and maintenance costs (Triangular distribution), and diesel price escalation (Uniform distribution). A global sensitivity analysis, based on the Extended Fourier Amplitude Sensitivity Test (EFAST), is further employed to quantify the relative influence of these variables on project outcomes. Curve-fitting techniques are used to establish functional relationships between inputs and economic indicators. Results indicate that rooftop PV remains financially viable, achieving a net present value (NPV) of 639 million BDT and a payback period (PBP) of 7.16 years. The system reduces the levelized cost of energy by an average of 2.44 BDT per unit. Sensitivity results identify EPER and lifespan as the most influential factors, with EPER exerting the strongest effect on NPV (0.47), PBP (0.919), and cost savings (0.73). The integration of MCS and EFAST provides a robust framework for evaluating rooftop PV investments, offering actionable insights for investors and policymakers in renewable energy planning.