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Optimizing the Design of Combined Alkaline and Proton Exchange Membrane Electrolyzers to Fully Utilize Fluctuating Renewable Energy

  • Xi'an Jiaotong University
  • Tsinghua University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

By integrating the economic advantages of alkaline (ALK) water electrolysis with the rapid response capabilities of proton exchange membrane (PEM) electrolysis, there is potential to mitigate power fluctuations and fully utilize renewable energy. However, the optimal design for combining these electrolyzers remains unclear. This paper develops an integrated planning and operation model for ALK and PEM electrolyzers, considering dynamic operational details (i.e., start-up, shutdown, and ramping) and various operating states including idle, underload, variable, and overload states. The problem is to determine the optimal combination of ALK and PEM modules in the combined electrolyzers, which is solved by a commercial solver. The numerical results are conducted based on a photovoltaic site with a 113 MW solar power output. Two typical scenarios, namely peak noon and fluctuating, are selected to analyze the operational details of the combined electrolyzers. It is demonstrated that the combined electrolyzers effectively mitigate wide-range fluctuations and enhance economic efficiency compared to standalone ALK or PEM electrolyzers.

Original languageEnglish
Title of host publication2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024
PublisherIEEE Computer Society
Pages335-340
Number of pages6
ISBN (Electronic)9798350358513
DOIs
StatePublished - 2024
Event20th IEEE International Conference on Automation Science and Engineering, CASE 2024 - Bari, Italy
Duration: 28 Aug 20241 Sep 2024

Publication series

NameIEEE International Conference on Automation Science and Engineering
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference20th IEEE International Conference on Automation Science and Engineering, CASE 2024
Country/TerritoryItaly
CityBari
Period28/08/241/09/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

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