The rapid integration of renewable energy into the electricity grid, driven by climate goals, has led to an urgent need for greater system flexibility to manage variability in supply and demand. This transition offers new opportunities for hydrogen technologies, particularly in the Netherlands, where large-scale deployment of green hydrogen is central to national energy ambitions. However, despite these ambitions, many investments remain hindered due to ongoing technical, financial and regulatory uncertainties, which hinder progress and threaten to slow down the broader energy transition.

One promising innovation is Battolyser Systems, a dual-function technology that combines battery storage with electrolytic hydrogen production. Its ability to dynamically switch between energy storage and conversion makes it a valuable asset for grid flexibility. Nevertheless, the market uptake is limited by systemic barriers, underscoring the need for robust, uncertainty-based decision-making frameworks to support early-stage investments.

This research addresses this need by developing a simulation model based on a value driver tree (VDT) to evaluate the investment performance of Battolyser Systems under uncertainty in the Dutch green hydrogen market. The central research question is: How can a simulation model based on a value driver tree be designed and applied to the investment performance of Battolyser Systems under uncertainty?

The VDT framework is used as a visual and causal tool to decompose the economic added value (EVA) into its drivers: revenues, costs and capital input. The approach explicitly links technical parameters and policy instruments to investment performance, allowing for a structured analysis under uncertain conditions. After identifying the most important value drivers through literature research and stakeholder analysis, five primary uncertainties were selected for further modelling: electricity price, hydrogen price, unit capital costs, operating hours and system efficiency.

These drivers were formalised in a computational model using Monte Carlo simulation, yielding probabilistic distributions of EVA outcomes. Sensitivity and entropic analyses were performed to assess which parameters most strongly influence investment viability and where vulnerability to uncertainty is greatest. Baseline results indicate a negative EVA under current assumptions, indicating limited financial viability. However, the results show that policy factors, in particular hydrogen price and operating hours, have the greatest influence on shifting outcomes towards profitability.

The findings demonstrate that VDT simulation is a valuable method to capture the techno-economic complexity in energy innovations at an early stage. It allows for transparently tracing causal paths from technical inputs to financial outcomes and supports the exploration of risks and robustness in uncertain futures. Nevertheless, the scope of the model is limited by the availability of empirical data, in particular for new technologies. Moreover, institutional and behavioural dynamics, such as regulatory evolution and stakeholder strategies, have not yet been integrated.

In conclusion, this study provides a structured, simulation-based approach for evaluating investments in emerging hydrogen technologies. The VDT model improves decision-making by linking technical feasibility to financial feasibility under uncertainty. Future extensions should integrate dynamic institutional modelling and broader sustainability metrics to better inform adaptive policy design and systemic innovation in the energy transition.