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

Svetlana Revinova (), Inna Lazanyuk, Svetlana Ratner and Konstantin Gomonov
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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
Konstantin Gomonov: Department of Economic and Mathematical Modelling, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, Moscow 117198, Russia

Energies, 2023, vol. 16, issue 11, 1-19

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.

Keywords: green hydrogen; electrolysis technology; learning curves; learning rates (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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