Uncertainty propagation in financial models of photovoltaic systems

Financial analysis has a long history of capturing the stochasticity of real-world phenomena. For informed investment decisions, it is crucial to understand and quantify uncertainty propagation from financial model input to output. Yet to that end, in the photovoltaics sector one has so far relied on coarse-grained approximations or extensive simulations. Here we present a numerically inexpensive approach that exactly traces uncertainty propagation on the level of probability distributions. It leverages analytic shortcuts through switching between different distribution representations, and only assumes independent input variables. With the financial analysis of a typical photovoltaic system as a case study, we use this approach to compute key financial metrics and demonstrate that their values can differ significantly from those obtained by a standard approximation. Moreover, we show with both frameworks that input uncertainty alone can significantly impact the outcome of financial analysis.