Stochastic techno-economic framework for green hy-drogen projects under uncertainty and risk conditions

Abstract

This thesis presents a comprehensive stochastic techno-economic framework (STEF-H2V) for evaluating green hydrogen projects under uncertainty and risk conditions. Green hydrogen, produced through water electrolysis powered by renewable energy, is a promising solution for decarbonizing various sectors. The economic viability of green hydrogen investments is influenced by uncertainties and risks related to the environment, technological performance, financial parameters, and market dynamics. This research ad-dresses these challenges by developing and applying a robust stochastic techno-economic framework (STEF-H2V) that integrates stochastic modeling techniques and advanced financial risk measures. The core of this thesis is the development of the STEF-H2V, a framework that employs Monte Carlo simula-tions to create a range of outcomes for key financial and operational parameters. This stochastic approach captures the inherent uncertainties in green hydrogen projects, providing a more realistic and robust as-sessment than deterministic methods. The framework incorporates financial risk measures, such as Value-at-Risk (VaR) and Omega ratio, to quantify potential financial losses and support better risk management. The framework structure includes methodological procedures, data collection, and investment analysis techniques. Applying the framework to a distributed green hydrogen generation case study shows its prac-tical utility. The deterministic analysis provides a baseline understanding, while the stochastic analysis reveals the spectrum of outcomes, highlighting the importance of considering variability in financial and operational parameters. The VaR risk analysis shows how potential financial losses can be quantified and mitigated, offering a clearer picture of the project's risk profile. Further exploration of the gap in techno-economic analysis for green hydrogen investments by integrating financial risk management emphasizes the relevance of a holistic approach, including detailed evaluations of CAPEX, OPEX, and revenue streams. Incorporating financial risk measures leads to a more realistic project viability assessment, with sensitivity analysis identifying key drivers, such as electricity prices and technological efficiency. The framework's effectiveness under varying conditions of variability and uncertainty in hydrogen generation is also evaluated. This shows how fluctuations in key variables impact project feasibility, showing that the stochastic framework effectively captures the range of outcomes. The need for dynamic modeling ap-proaches to adjust to changing conditions and provide more robust predictions is reinforced. The main findings of this research indicate that green hydrogen projects can achieve economic viability under favor-able conditions despite high initial costs and technological uncertainties. Supportive policies and market mechanisms are crucial in reducing financial risks and encouraging investment. Integrating advanced risk measures and stochastic modeling techniques provides a comprehensive view of the financial landscape, enabling better risk management and decision-making. This document advances the green hydrogen in-vestment analysis field by providing a detailed and adaptable framework for evaluating projects under uncertainty and risk conditions. By addressing the limitations of traditional approaches and incorporating advanced risk management techniques, STEF-H2V enhances the robustness and reliability of techno-eco-nomic evaluations, supporting the sustainable growth of the green hydrogen sector.