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Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Author

Listed:
  • Hui Wang

    (Qinghai Yellow River Upstream Hydropower Development Co., Ltd., Photovoltaic Industry Technology Branch, Xining 810007, China
    Qinghai Advanced Energy Storage Laboratory Co., Ltd., Xining 810007, China)

  • Xiaowen Chen

    (Qinghai Yellow River Upstream Hydropower Development Co., Ltd., Photovoltaic Industry Technology Branch, Xining 810007, China
    Qinghai Advanced Energy Storage Laboratory Co., Ltd., Xining 810007, China)

  • Qianpeng Yang

    (Qinghai Yellow River Upstream Hydropower Development Co., Ltd., Photovoltaic Industry Technology Branch, Xining 810007, China
    Qinghai Advanced Energy Storage Laboratory Co., Ltd., Xining 810007, China)

  • Bowen Li

    (State Key Laboratory of Combustion Science for Internal Combustion Engines, Tianjin University, Tianjin 300072, China)

  • Zongyu Yue

    (State Key Laboratory of Combustion Science for Internal Combustion Engines, Tianjin University, Tianjin 300072, China)

  • Jeffrey Dankwa Ampah

    (State Key Laboratory of Combustion Science for Internal Combustion Engines, Tianjin University, Tianjin 300072, China)

  • Haifeng Liu

    (State Key Laboratory of Combustion Science for Internal Combustion Engines, Tianjin University, Tianjin 300072, China)

  • Mingfa Yao

    (State Key Laboratory of Combustion Science for Internal Combustion Engines, Tianjin University, Tianjin 300072, China)

Abstract

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

Suggested Citation

  • Hui Wang & Xiaowen Chen & Qianpeng Yang & Bowen Li & Zongyu Yue & Jeffrey Dankwa Ampah & Haifeng Liu & Mingfa Yao, 2024. "Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm," Energies, MDPI, vol. 17(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2384-:d:1395229
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    References listed on IDEAS

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    1. Li, Yangyang & Deng, Xintao & Zhang, Tao & Liu, Shenghui & Song, Lingjun & Yang, Fuyuan & Ouyang, Minggao & Shen, Xiaojun, 2023. "Exploration of the configuration and operation rule of the multi-electrolyzers hybrid system of large-scale alkaline water hydrogen production system," Applied Energy, Elsevier, vol. 331(C).
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