Strategies and challenges for reducing green hydrogen cost: Operation mode and revenue streams

H. Sayed-Ahmed, Á.I. Toldy, M. Lappalainen, O. Himanen, C. Bajamundi and A. Santasalo-Aarnio

Renewable and Sustainable Energy Reviews, 2025, vol. 223, issue C

Abstract: The high cost of green hydrogen prevents the introduction of low-carbon hydrogen into the economy. This work aims to improve the profitability of water electrolysis by providing a holistic analysis of PEM electrolysis in steady and dynamic operation modes. In addition, additional streams of revenue are discussed, including the selling of waste heat from electrolysis and participating in electricity reserve markets. First, a data-driven model was developed to simulate the efficiency of PEM electrolysis as a function of loading percentage. Afterwards, the levelized cost of hydrogen (LCOH) was calculated in steady and dynamic operation modes. The sensitivity to capital cost assumptions, electrolyzer degradation rates, the efficiency profile of AC/DC rectifier, and the electricity profile was studied. It was found that dynamic operation affects the LCOH when compared with steady operation under identical electricity price profiles, ranging from decreasing the LCOH by 42 % to increasing the LCOH by 7.6 %. Moreover, each 1 µV/h increase in the electrolyzer degradation rate increases the LCOH by about 1.3–2.2 %. Conversely, utilizing electrolysis waste heat reduces the LCOH by 0.2–0.35 EUR/kg. Finally, the revenue from participating in electricity reserve markets could range from exceeding the total cost of green hydrogen production to not offsetting the increase in capital costs that result from a lower capacity factor. Overall, a holistic optimization of dynamic electrolysis operation is crucial to ensure that the reduction of LCOH from extra streams of revenue and lower electricity prices exceeds the increase in LCOH from a higher degradation rate and lower capacity factor.

Keywords: PEM electrolysis; Dynamic operation; Demand response; Degradation rate; Waste heat; Aspen plus®; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.rser.2025.116065

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