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Dynamic operation of proton exchange membrane electrolyzers—Critical review

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  • Sayed-Ahmed, H.
  • Toldy, Á.I.
  • Santasalo-Aarnio, A.

Abstract

Green hydrogen is seen as a promising energy storage and balancing solution to complement the ever-increasing share of variable renewable energy sources in the grid. The dynamic operation of polymer electrolyte membrane (PEM) electrolyzers has the potential to simultaneously lower the cost of green hydrogen and improve the flexibility of the grid by taking advantage of the volatility of renewable production. However, dynamic operation affects a wide range of variables related to the degradation of electrolyzer components and the safety and efficiency of the process, often in counterintuitive ways. This, in turn, makes it difficult to predict the levelized cost of the green hydrogen produced when operating on the electricity markets. This critical review examines state-of-the-art literature on the behavior of PEM electrolyzers under dynamic operation, bearing in mind the objective of reducing the levelized cost of green hydrogen. Knowledge gaps, key development directions, and future research needs are identified with respect to PEM electrolysis equipment, operating parameters, degradation, and the role of dynamically operated PEM electrolyzers on the electricity markets. It is found that while the field is developing at a rapid pace, there is a lack of holistic studies that consider all (or even most of) the interconnected variables that affect the levelized cost of green hydrogen during the dynamic operation of PEM electrolyzers. It is postulated that this complex network of interactions will give rise to data-driven approaches (such as Machine Learning) to bridge this gap.

Suggested Citation

  • Sayed-Ahmed, H. & Toldy, Á.I. & Santasalo-Aarnio, A., 2024. "Dynamic operation of proton exchange membrane electrolyzers—Critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
  • Handle: RePEc:eee:rensus:v:189:y:2024:i:pa:s1364032123007414
    DOI: 10.1016/j.rser.2023.113883
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    2. Seyed Mohammad Shojaei & Reihaneh Aghamolaei & Mohammad Reza Ghaani, 2024. "Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review," Sustainability, MDPI, vol. 16(21), pages 1-41, November.

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