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Capacity Configuration Optimization for Green Hydrogen Generation by Solar-wind Hybrid Power Based on Comprehensive Performance Criteria


Green hydrogen generation driven by solar-wind hybrid power is a key strategy for obtaining the low-carbon energy, while by considering the fluctuation natures of solar-wind energy resource, the system capacity configuration of power generation, hydrogen production and essential storage devices need to be comprehensively optimized. In this work, a solar-wind hybrid green hydrogen production system is developed by combining the hydrogen storage equipment with the power grid, the coordinated operation strategy of solar-wind hybrid hydrogen production is proposed, furthermore, the NSGA-III algorithm is used to optimize the system capacity configuration with the comprehensive performance criteria of economy, environment and energy efficiency. Through the implemented case study with the hydrogen production capacity of 20,000 tons/year, the abandoned energy power rate will be reduced to 3.32% with the electrolytic cell average load factor of 64.77%, and the system achieves the remarkable carbon emission reduction. In addition, with the advantage of connect to the power grid, the generated surplus solar/wind power can be readily transmitted with addition income, when the sale price of produced hydrogen is suggested to 27.80 CNY/kgH2, the internal rate of return of the system reaches to 8% which present the reasonable economic potential. The research provides technical and methodological suggestions and guidance for the development of solar-wind hybrid hydrogen production schemes with favorable comprehensive performance.

Funding source: The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors appreciate the financial support provided by Shandong Provincial Natural Science Foundation of China (ZR2022YQ58), and the Fundamental Research Funds for the Central Universities (No. 22CX07006A).
Related subjects: Applications & Pathways

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