Optimizing Green Hydrogen Production Through Techno-Economic and Lifecycle Analysis
Keywords:
Green Hydrogen, Techno-Economic Analysis, Lifecycle Assessment, Electrolyze Technology, Renewable Energy, Energy Efficiency, Sustainability, Carbon Neutrality, Production OptimizationAbstract
Green hydrogen, produced via water electrolysis using renewable energy, is a cornerstone of the global transition to low-carbon energy systems. Optimizing green hydrogen production requires a comprehensive assessment of techno-economic performance, energy efficiency, and environmental sustainability. This study investigates the optimization of green hydrogen production through combined techno-economic and lifecycle analysis (TEA-LCA), focusing on cost, energy consumption, carbon emissions, and system design parameters. A quantitative modeling approach was applied, integrating process simulation, cost estimation, and lifecycle assessment. Key parameters analyzed include electrolyzer technology (PEM, alkaline, SOEC), renewable energy input, storage, and distribution. SmartPLS Structural Equation Modeling was used to examine the relationships between techno-economic indicators, environmental performance, operational efficiency, and overall system sustainability. Results indicate that system efficiency, energy source cost, and electrolyzer technology significantly influence both production cost and carbon intensity. Lifecycle analysis reveals that renewable energy mix and electrolyzer durability are critical factors in minimizing environmental impact. TEA-LCA integration identifies optimal operating strategies, highlighting trade-offs between cost reduction and emissions mitigation. The study contributes to energy systems literature by providing empirical insights into the drivers of cost-effective and environmentally sustainable green hydrogen production. Findings inform policymakers, energy developers, and investors on strategies to accelerate the adoption of green hydrogen while achieving carbon neutrality targets. Limitations include modeling assumptions on energy prices and technology performance; future research should incorporate real-time operational data and stochastic modeling to enhance robustness.
