Numerical Approach to Uncertainty and Sensitivity Analysis in Forecasting the Manufacturing Cost and Performance of PV Modules

Abstract

Forecasting of the manufacturing cost of PV modules is governed by a large number of uncertain factors. Cost estimates are frequently based upon imperfect information and, as a result, may not be perfectly accurate. Existing studies of these uncertainties focus on the sensitivity of the manufacturing cost to individual cost inputs, examining the effects of each input in isolation. Such methods of analysis neglect statistical correlations between inputs, provide no measure of the uncertainty in the projected manufacturing cost, and do not permit the assignment of probability distributions to the inputs in the case that one range of values is thought to be more likely than another. This work describes the development of a stochastic modeling framework that addresses these deficiencies. Furthermore, it demonstrates how sensitivity to particular inputs may be ranked in order to help determine the most effective path to cost reduction. The result is a method with great potential for exploring the link between engineering design, PV module cost, and the manufacturing process.