Working with uncertainty in life cycle costing: New approach applied to the case study on proton exchange membrane water electrolysis

Abstract

Hydrogen, recognized as a critical energy source, requires green production methods, such as proton exchange membrane water electrolysis (PEMWE) powered by renewable energy. This is a key step toward sustainable development, with economic analysis playing an essential role. Life cycle costing (LCC) is commonly used to evaluate economic feasibility, but traditional LCC analyses often provide a single cost outcome, which limits their applicability across diverse regional contexts. To address these challenges, a Python-based tool is developed in this paper, integrating a bottom-up approach with net present value (NPV) calculations and Monte Carlo simulations. The tool allows users to manage uncertainty by intervening in the input data, producing a range of outcomes rather than a single deterministic result, thus offering greater flexibility in decision-making. Applying the tool to a 5 MW PEMWE plant in Germany, the total cost of ownership (TCO) is estimated to range between €52 million and €82.5 million, with hydrogen production costs between 5.5 and 11.4 €/kg H₂. There is a 95% probability that actual costs fall within this range. Sensitivity analysis reveals that energy prices are the key contributors to LCC, accounting for 95% of the variance in LCC, while iridium, membrane materials, and power electronics contribute to 75% of the variation in construction-phase costs. These findings underscore the importance of renewable energy integration and circular economy strategies in reducing LCC.

Graphical abstract

Keywords

life cycle costing (LCC) / green hydrogen / proton exchange membrane (PEM) water electrolysis / uncertainty / Monte Carlo method / Python

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The Author(s). This article is published with open access at link.springer.com and journal.hep.com.cn