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Forecasting Development of Green Hydrogen Production Technologies Using Component-Based Learning Curves

Author

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  • Svetlana Revinova

    (Department of Economic and Mathematical Modelling, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, Moscow 117198, Russia)

  • Inna Lazanyuk

    (Department of Economic and Mathematical Modelling, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, Moscow 117198, Russia)

  • Svetlana Ratner

    (Department of Economic and Mathematical Modelling, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, Moscow 117198, Russia
    Economic Dynamics and Innovation Management Laboratory, V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow 117997, Russia)

  • Konstantin Gomonov

    (Department of Economic and Mathematical Modelling, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, Moscow 117198, Russia)

Abstract

Hydrogen energy is expected to become one of the most efficient ways to decarbonize global energy and transportation systems. Green hydrogen production costs are currently high but are likely to decline due to the economy of scale and learning-by-doing effects. The purpose of this paper is to forecast future green hydrogen costs based on the multicomponent learning curves approach. The study investigates the learning curves for the main components in hydrogen value chains: electrolyzers and renewable energy. Our findings estimate the learning rates in the production of PEM and AE electrolyzers as 4%, which is quite conservative compared to other studies. The estimations of learning rates in renewable energy electricity generation range from 14.28 to 14.44% for solar-based and 7.35 to 9.63% for wind-based production. The estimation of the learning rate in green hydrogen production ranges from 4% to 10.2% due to uncertainty in data about the cost structure. The study finds that government support is needed to accelerate electrolysis technology development and achieve decarbonization goals by 2050.

Suggested Citation

  • Svetlana Revinova & Inna Lazanyuk & Svetlana Ratner & Konstantin Gomonov, 2023. "Forecasting Development of Green Hydrogen Production Technologies Using Component-Based Learning Curves," Energies, MDPI, vol. 16(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4338-:d:1156148
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    References listed on IDEAS

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