Techno-Economic Analysis of Photovoltaic Hydrogen Production Considering Technological Progress Uncertainty

Xiang Huang, Yapan Qu, Zhentao Zhu () and Qiuchi Wu
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Xiang Huang: College of Business, Nanjing University, Nanjing 210093, China
Yapan Qu: School of Public Finance and Taxation, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Zhentao Zhu: International Joint Laboratory of Green and Low Carbon Development, Nanjing 211167, China
Qiuchi Wu: Nanjing Institute of Technology, Nanjing 211167, China

Sustainability, 2023, vol. 15, issue 4, 1-29

Abstract: The application of photovoltaic (PV) power to split water and produce hydrogen not only reduces carbon emissions in the process of hydrogen production but also helps decarbonize the transportation, chemical, and metallurgical industries through P2X technology. A techno-economic model must be established to predict the economics of integrated PV–hydrogen technology at key time points in the future based on the characteristics, variability, and uncertainties of this technology. In this study, we extracted the comprehensive technical factors (including PV tracking system coefficient, PV conversion efficiency, electrolyzer efficiency, and electrolyzer degradation coefficient) of an integrated PV–hydrogen system. Then, we constructed a PV hydrogen production techno-economic (PVH2) model. We used the levelized cost of hydrogen production (LCOH) method to estimate the cost of each major equipment item during the project lifetime. We combined the PVH2 and learning curve models to determine the cost trend of integrated PV–hydrogen technology. We developed a two-dimensional Monte Carlo approach to predict the variation interval of LCOH for PV–hydrogen projects in 2030 and 2050, which described the current technology variability with variable parameters and the uncertainty in the technology advancement with uncertain parameters. The results showed that the most critical factors influencing LCOH are PV conversion efficiency and the capital cost of the electrolyzer. The LCOH of PV to hydrogen in China will drop to CNY 18–32/kg by 2030 and CNY 8–18/kg by 2050. The combination of a learning curve model and a Monte Carlo method is an effective tool to describe the current variability in hydrogen production technologies and the uncertainty in technological progress.

Keywords: PV–hydrogen production; technological progress; LCOH; learning curve; Monte Carlo method; variability; uncertainty (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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