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

Author

Listed:
  • 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
    Nanjing Institute of Technology, Nanjing 211167, China)

  • Qiuchi Wu

    (Nanjing Institute of Technology, Nanjing 211167, China)

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.

Suggested Citation

  • Xiang Huang & Yapan Qu & Zhentao Zhu & Qiuchi Wu, 2023. "Techno-Economic Analysis of Photovoltaic Hydrogen Production Considering Technological Progress Uncertainty," Sustainability, MDPI, vol. 15(4), pages 1-29, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3580-:d:1069359
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    References listed on IDEAS

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    1. Marshall, A. & Børresen, B. & Hagen, G. & Tsypkin, M. & Tunold, R., 2007. "Hydrogen production by advanced proton exchange membrane (PEM) water electrolysers—Reduced energy consumption by improved electrocatalysis," Energy, Elsevier, vol. 32(4), pages 431-436.
    2. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    3. Peláez-Peláez, Sofía & Colmenar-Santos, Antonio & Pérez-Molina, Clara & Rosales, Ana-Esther & Rosales-Asensio, Enrique, 2021. "Techno-economic analysis of a heat and power combination system based on hybrid photovoltaic-fuel cell systems using hydrogen as an energy vector," Energy, Elsevier, vol. 224(C).
    4. Geissmann, Thomas, 2017. "A probabilistic approach to the computation of the levelized cost of electricity," Energy, Elsevier, vol. 124(C), pages 372-381.
    5. Nikolaos Kouvaritakis & Antonio Soria & Stephane Isoard, 2000. "Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 104-115.
    6. Thomas Longden & Frank Jotzo & Andreas Löschel, 2021. "Conditions for low cost green hydrogen production: mapping cost competitiveness with reduced-form marginal effect relationships," CCEP Working Papers 2108, Centre for Climate & Energy Policy, Crawford School of Public Policy, The Australian National University.
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    1. Elias Roumpakias & Tassos Stamatelos, 2023. "Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks," Sustainability, MDPI, vol. 15(10), pages 1-26, May.

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