In the rapidly evolving energy landscape, accurately predicting the lifetime costs of emerging technologies remains a formidable challenge. Traditional life cycle cost (LCC) analyses often present a single-point estimate that obscures the myriad uncertainties inherent in complex systems. This limitation is especially pronounced in the realm of proton exchange membrane water electrolysis (PEMWE) plants, where capital and operational expenditures fluctuate wildly due to factors extending far beyond immediate control. A team of researchers has now introduced a groundbreaking methodology, leveraging advanced simulation techniques and cutting-edge forecasting algorithms, to capture these dynamic cost ranges with unprecedented clarity.
At the heart of this study lies the recognition that two principal sources of uncertainty dominate PEMWE economics: capital expenditure (CAPEX) and operational expenditure (OPEX). CAPEX uncertainty is primarily rooted in the dependence on scarce and expensive raw materials such as iridium, a precious metal critical for catalyst performance, and high-performance membranes like Nafion. These materials’ prices are subject to global supply constraints and market volatility, driving a significant component of investment risk. On the operational front, OPEX variability is predominantly influenced by long-term electricity prices, which are affected by geopolitical tensions, energy market reforms, and evolving climate policies.
To more faithfully represent these uncertainties, the researchers adopted a bottom-up approach anchored in net present value calculations, fully compliant with the ISO 15686-5 standard for service life planning and costing. This framework integrates Monte Carlo simulation—a statistical technique that performs numerous random sampling trials to model uncertainty—with the Prophet forecasting algorithm, an advanced tool initially developed by Facebook for time series forecasting, adept at accommodating complex seasonal and trend patterns inherent in price data.
Capital expenditure uncertainty was quantified through 350 Sobol-sequence simulations. Sobol sequences are quasi-random low-discrepancy sequences that ensure an evenly distributed sampling of the multidimensional parameter space. This method allowed the authors to explore a comprehensive range of possibilities for material costs, balance-of-plant expenses, recycling rates, and labor costs, drawing on real-world market quotations and official statistics between 2018 and 2023 to ground their models in empirical evidence.
For operational expenditures, the study projected electricity and water prices specifically within the German market, renowned for its fluctuating renewable energy penetration and complex policy environment. The forecasts spanned a 20-year operational life of the PEMWE plant under two contrasting macroeconomic conditions: Scenario 1 presumed extended crisis impacts on energy prices, mirroring real-world disruptions; Scenario 2 envisioned a rapid return to normalized pre-crisis trends. These bifurcated scenarios were vital to gauge how shifts in global and national energy landscapes might influence cost structures.
The resulting cost envelopes are illuminating. CAPEX was projected to vary between €2.14 million and €2.58 million within the 95% probability range. Iridium price volatility alone contributed approximately 35% of the CAPEX variance, underscoring the metal’s critical influence. Nafion membranes accounted for about 25%, while power electronics made up an additional 20% of variability. This granular attribution assists in identifying focal points for cost reduction and risk mitigation strategies in material sourcing and technology development.
Operational expenditures, dominated by energy expenses, spanned between €49.2 million and €80.5 million depending on the scenario, with energy costs constituting over 95% of the variance in both pathways. The breadth of this range underscores the profound impact that electricity market instability can have over the lifespan of a PEMWE installation, emphasizing the need for flexible and adaptive OPEX models in project planning.
When considering total cost of ownership (TCO), combining CAPEX and OPEX uncertainties results in an estimated cost window from €52 million to €82.5 million. This wide span highlights the magnitude of financial risk and opportunity embedded in PEMWE investments and signals to stakeholders that simplistic, deterministic models are insufficient for robust decision-making.
Notably, the levelized cost of hydrogen (LCOH)—a pivotal metric for assessing the economic competitiveness of green hydrogen technology—was found to range from 5.5 to 11.4 €/kg H₂ across the examined scenarios. This range comprehensively captures the nearly two-decade span of LCOH values reported globally (2 to 20 €/kg H₂) since 2012, affirming the method’s effectiveness in reflecting real-world variability and earning the trust of investors and policymakers alike.
An especially promising outcome of this research is the development of an open-source analytical tool, accessible via GitHub. This transparency empowers diverse stakeholders—including investors, governmental bodies, and plant operators—to replace opaque single-value estimates with statistically robust probabilistic envelopes. Moreover, users can modify a broad array of parameters—ranging from commodity prices and recycling efficiencies to discount rates and regional energy tariffs—rendering the framework globally applicable and adaptable to differing technology configurations.
Beyond transparent costing, the ability to model cost-reduction levers such as iridium recycling, membrane reuse, and circular economy leasing arrangements introduces a dimension often overlooked in traditional studies. This capacity promotes both economic and environmental sustainability by incentivizing innovation in resource-efficient manufacturing and operations.
The integration of Monte Carlo methodologies and the Prophet algorithm into the life cycle costing paradigm signifies a paradigm shift. It acknowledges the complex, stochastic reality of energy systems and offers decision-makers a rigorous statistical confidence interval, thereby enhancing the bankability and investment attractiveness of hydrogen projects. This approach transcends mere academic exercise and enters the domain of practical utility, facilitating better-informed policy formulation and capital allocation.
Importantly, the study adopts a rigorous data pipeline, sourcing inputs from market quotations and authoritative statistics over a five-year span from 2018 to 2023. This temporal breadth captures relevant fluctuations and emerging trends, fortifying the model’s relevance and predictive fidelity. Such grounding in empirical data mitigates the risk of overly optimistic or pessimistic forecasts, a common pitfall in emerging technology assessments.
The researchers focused their case study on a mid-scale 5 MW PEMWE plant operating in Germany, planned to produce approximately 17.8 kilotonnes of hydrogen over two decades. This scale reflects current commercial ambitions in green hydrogen deployment and provides a concrete basis for policy and investment discussions at both national and international levels. Given Germany’s leading role in the energy transition, findings from this localized yet representative scenario carry broad implications.
In summary, this innovative research redefines how uncertainty is statistically integrated into life cycle cost assessments for PEMWE technologies, delivering not just numbers but nuanced insights essential for navigating a volatile and complex market landscape. By furnishing stakeholders with dynamic, data-driven cost distributions instead of static point estimates, this work catalyzes more resilient and transparent investment frameworks for sustainable hydrogen production.
Subject of Research: Not applicable
Article Title: Working with uncertainty in life cycle costing: New approach applied to the case study on proton exchange membrane water electrolysis
News Publication Date: 22-Aug-2025
Web References: http://dx.doi.org/10.1007/s11708-025-1033-1
Keywords: Energy
