Prognostics and health management of alkaline water electrolyzer: Techno-economic analysis considering replacement moment
Hyunjun Lee,
Jiwon Gu,
Boreum Lee,
Hyun-Seok Cho,
Hankwon Lim
Affiliations
Hyunjun Lee
School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
Jiwon Gu
School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
Boreum Lee
School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
Hyun-Seok Cho
Hydrogen Research Department, Future Energy Research Division, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong, Daejeon, Republic of Korea; Corresponding authors.
Hankwon Lim
School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea; Graduate School of Carbon Neutrality, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea; Carbon Neutrality Demonstration and Research Center, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea; Corresponding authors.
Recently, considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO2 emissions. H2 produced using water electrolysis and renewable energy is regarded as a clean energy carrier, generating electricity without CO2 emissions, called ‘Green H2’. In this paper, a prognostics and health management model for an alkaline water electrolyzer was proposed to predict the load voltage on the electrolyzer to obtain the state of health information. The prognostics and health management model was developed by training historical operating data via machine learning models, support vector machine and gaussian process regression, showing the root mean square error of 1.28 × 10−3 and 8.03 × 10−6. In addition, a techno-economic analysis was performed for a green H2 production system, composed of 1 MW of photovoltaic plant and 1 MW of alkaline water electrolyzer, to provide economic insights and feasibility of the system. A levelized cost of H2 of $ 6.89 kgH2−1 was calculated and the potential to reach the levelized cost of H2 from steam methane reforming with carbon capture and storage was shown by considering the learning rate of the photovoltaic module and electrolyzer. Finally, the replacement of the alkaline water electrolyzer at around 10 years was preferred to increase the net present value from the green H2 production system when capital expenditure and replacement cost are low enough.